Vimeo / LISA’s videoshttps://vimeo.com/lisavt/videosVideos uploaded by LISA on Vimeo.Wed, 24 Aug 2016 16:07:55 -0400Vimeohttps://i.vimeocdn.com/portrait/498169_100x100Vimeo / LISA’s videoshttps://vimeo.com/lisavt/videos
LISA Short Course: Introduction to Web Scraping in RWed, 04 May 2016 09:28:24 -0400https://vimeo.com/165296657<p><iframe src="https://player.vimeo.com/video/165296657" width="640" height="341" frameborder="0" title="LISA Short Course: Introduction to Web Scraping in R" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe></p><p><p class="first">LISA (Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data.</p> <p>Tuesday, April 19, 4:00-6:00 pm;<br> Location: 1100 Torgersen Hall;<br> Instructor: Adam Edwards;<br> Title: Introduction to Web Scraping in R;</p> <p>R is an open source software with many tools for data manipulation and analysis. The basic R packages include several data sets, as well as functions to read data from files on the local drive. This course will go over a more advanced package that will allow users to create their own data sets by compiling information from web pages.</p> <p>In this course, users will learn the functions necessary to collect information from webpages, and how to manipulated compiled information into data frames. This course will provide an example of pulling a table directly from a webpage, as well as a more complex example of compiling a dataset from multiple webpages coherently. All code used in this course will be made available.</p> <p>This is an advanced R course and prior knowledge in R is necessary. It is recommended that participants attend, or watch other LISA short courses in R. For a full list of courses being taught this semester, check <a href="http://www.lisa.stat.vt.edu/?q=short_courses" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/?q=short_courses</a>. Past short courses can be viewed at <a href="http://www.lisa.stat.vt.edu/?q=past_courses" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/?q=past_courses</a>.</p> <p>Topics covered:<br> Reading HTML pages into R<br> Selecting specific HTML nodes<br> Isolating specific attributes of a node<br> Manipulating data into data frames</p> <p>Course files are available on the course webpage (<a href="http://www.lisa.stat.vt.edu/?q=node/10389" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/?q=node/10389</a>).</p></p><p><strong>Cast:</strong> <a href="https://vimeo.com/lisavt">LISA</a></p><p><strong>Tags:</strong> <a href="https://vimeo.com/tag:LISA">LISA</a>, <a href="https://vimeo.com/tag:Statistics">Statistics</a>, <a href="https://vimeo.com/tag:Short+Course">Short Course</a>, <a href="https://vimeo.com/tag:R">R</a>, <a href="https://vimeo.com/tag:Virginia+Tech">Virginia Tech</a> and <a href="https://vimeo.com/tag:Web+Scraping">Web Scraping</a></p>tag:vimeo,2016-05-04:clip165296657LISA Short Course: Introduction to Web Scraping in RLISA Short Course: Statistical Analysis Using RTue, 03 May 2016 10:38:00 -0400https://vimeo.com/165156156<p><iframe src="https://player.vimeo.com/video/165156156" width="640" height="341" frameborder="0" title="LISA Short Course: Statistical Analysis Using R" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe></p><p><p class="first">LISA (Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data.</p> <p>Tuesday, April 5, 4:00-6:00 pm;<br> Location: 1100 Torgersen Hall;<br> Instructor: Jiangeng Huang;<br> Title: Statistical Analysis Using R;</p> <p>R is a free computing and graphical environment for statistical analysis. This short course is designed to provide a basic sense of doing statistical analysis using R. This course starts with a short introduction to R. Then, this course introduces some basics in exploratory data analysis. Two examples of statistical analysis with R is illustrated, one for simple linear regression and the other for ANOVA. Topics covered include Exploratory Data Analysis, Simple Linear Regression, and Analysis of Variance. </p> <p>Course Structure:</p> <p> A Short Introduction to R<br> Some Basics in Exploratory Data Analysis using R<br> One Example of Simple Linear Regression<br> One Example of Analysis of Variance</p> <p>This class is the second in a three-course series that assumes no previous coding experience in R or any other language. Experience using R or attending Part I of this series is suggested but not required for this course. The intended audience for this course includes researchers who want to gain basic exposure to statistical analysis in R with the ultimate goal of incorporating R into their research programs.</p> <p>Course files are available on the course webpage (<a href="http://www.lisa.stat.vt.edu/?q=node/10387" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/?q=node/10387</a>).</p></p><p><strong>Cast:</strong> <a href="https://vimeo.com/lisavt">LISA</a></p><p><strong>Tags:</strong> <a href="https://vimeo.com/tag:LISA">LISA</a>, <a href="https://vimeo.com/tag:Short+Course">Short Course</a>, <a href="https://vimeo.com/tag:Statistics">Statistics</a>, <a href="https://vimeo.com/tag:R">R</a>, <a href="https://vimeo.com/tag:Virginia+Tech">Virginia Tech</a>, <a href="https://vimeo.com/tag:Exploratory+Data+Analysis">Exploratory Data Analysis</a>, <a href="https://vimeo.com/tag:Statistical+Analysis">Statistical Analysis</a>, <a href="https://vimeo.com/tag:Simple+Linear+Regression">Simple Linear Regression</a>, <a href="https://vimeo.com/tag:Analysis+of+Variance">Analysis of Variance</a> and <a href="https://vimeo.com/tag:ANOVA">ANOVA</a></p>tag:vimeo,2016-05-03:clip165156156LISA Short Course: Statistical Analysis Using RLISA Short Course: Introduction to Multivariate Analysis of Variance (MANOVA) in JMPThu, 28 Apr 2016 12:01:57 -0400https://vimeo.com/164588714<p><iframe src="https://player.vimeo.com/video/164588714" width="640" height="341" frameborder="0" title="LISA Short Course: Introduction to Multivariate Analysis of Variance (MANOVA) in JMP" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe></p><p><p class="first">LISA (Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data.</p> <p>Tuesday, April 26, 4:00-6:00 pm;<br> Location: 1100 Torgersen Hall;<br> Instructor: Sumin Shen;<br> Title: Introduction to Multivariate Analysis of Variance (MANOVA) in JMP</p> <p>Multivariate Analysis of Variance (MANOVA) is a way to test hypothesis on two or more dependent variables. For example, you might want to test the hypothesis that four different teaching methods have the same effect on both the students' attendance and acquisition from the class. The primary goal of this short course is to provide a guide to MANOVA for researches who are interested in multivariate method analysis.</p> <p>In univariate analysis of variance (ANOVA), we are looking for the effect of factors (a.k.a .predictors) on a single dependent variable. The question you are interested is whether or not there is difference in the single dependent variable in terms of the factors. MANOVA is an extension of ANOVA in terms of the number of dependent variables. When there are more than one dependent variable, MANOVA is one of the methods to be considered. The JMP software will be used in this course.</p> <p>This course covers:</p> <p> ANOVA and MANOVA<br> Differences between ANOVA and MANOVA<br> When should I use MANOVA?<br> MANOVA In JMP</p> <p>Data sets:</p> <p>1. ANOVA in a study where 33 subjects were administered three different types of analgesics (A, B, and C). The subjects were asked to rate their pain levels on a sliding scale. The study interest is to find out the effect of types of analgesics and gender on the rating scale. The data set is the Analgesics.jmp in Sample Data Library in JMP.</p> <p>2. MANOVA in a multiple response model where the response variables are distances traveled and durability for three bands of golf balls. In this study, a robotic golfer hit a random sample of ten balls of each brand in a random sequence. The data set is the Golf Balls.jmp in Sample Data Library in JMP.</p> <p>3. MANOVA in repeated measures where the response variables are measured at several points over time. In this study, the cholesterol is measured every one month over a 6-month period. a new drug, which is supposed to reduce cholesterol, and a placebo is applied in the study. The research interest is whether or not there is a treatment (drug) effect over time. The data set is in the folder.</p> <p>Below is a result from the repeated measures example by MANOVA in JMP.</p> <p><a href="http://www.lisa.stat.vt.edu/sites/default/files/images/2016-04-26-MANOVA.png" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/sites/default/files/images/2016-04-26-MANOVA.png</a></p> <p>Course files are available on the course webpage (<a href="http://www.lisa.stat.vt.edu/?q=node/10390" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/?q=node/10390</a>).</p></p><p><strong>Cast:</strong> <a href="https://vimeo.com/lisavt">LISA</a></p><p><strong>Tags:</strong> <a href="https://vimeo.com/tag:Virginia+Tech">Virginia Tech</a>, <a href="https://vimeo.com/tag:LISA">LISA</a>, <a href="https://vimeo.com/tag:Statistics">Statistics</a>, <a href="https://vimeo.com/tag:ANOVA">ANOVA</a>, <a href="https://vimeo.com/tag:JMP">JMP</a>, <a href="https://vimeo.com/tag:Short+Course">Short Course</a> and <a href="https://vimeo.com/tag:MANOVA">MANOVA</a></p>tag:vimeo,2016-04-28:clip164588714LISA Short Course: Introduction to Multivariate Analysis of Variance (MANOVA) in JMPLISA Short Course: Better Data Visualization in R Using the ggplot2 PackageTue, 19 Apr 2016 09:02:46 -0400https://vimeo.com/163399560<p><iframe src="https://player.vimeo.com/video/163399560" width="640" height="341" frameborder="0" title="LISA Short Course: Better Data Visualization in R Using the ggplot2 Package" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe></p><p><p class="first">LISA (Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data.</p> <p>Tuesday, April 12, 4:00-6:00 pm;<br> Location: 1100 Torgersen Hall;<br> Instructor: Daniel Berry and Brandi Jones;<br> Title: Better Data Visualization in R Using the ggplot2 Package</p> <p>Data visualization is a key element of scientific communication. The R programming language comes with a variety of built-in options for data visualization and graphing. Fortunately, R is open source and has many excellent packages available to extend the capabilities of base R. The ggplot2 package offers an alternative to R's built-in plotting system and makes it easy to build elegant, custom plots and graphics. ggplot2 is an implementation (Wickham, 2010) of the "Grammar of Graphics" (Wilkinson 2006) system for building plots through an intuitive series of layers.</p> <p>Students will begin by replicating base R plots in ggplot2 and quickly progress to making the complex, multi-layered graphics that ggplot2 excels at. This course is designed to provide both faculty and student researchers with a toolbox for building custom, publication quality graphics. The course cannot cover every feature of ggplot2, however, participants will leave with an understanding of ggplot2 syntax and the ability to create their own plots from scratch. Previous experience using R is assumed but no previous experience using ggplot2 is required. Participants will analyze Virginia demographic data at the county level (provided by the US Census) and be able to produce the following two graphics by the end of the course.</p> <p>Course files are available on the course webpage (<a href="http://www.lisa.stat.vt.edu/?q=node/10388" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/?q=node/10388</a>).</p></p><p><strong>Cast:</strong> <a href="https://vimeo.com/lisavt">LISA</a></p><p><strong>Tags:</strong> <a href="https://vimeo.com/tag:Short+Course">Short Course</a>, <a href="https://vimeo.com/tag:LISA">LISA</a>, <a href="https://vimeo.com/tag:Statistics">Statistics</a>, <a href="https://vimeo.com/tag:R">R</a>, <a href="https://vimeo.com/tag:Virginia+Tech">Virginia Tech</a>, <a href="https://vimeo.com/tag:ggplot2">ggplot2</a>, <a href="https://vimeo.com/tag:plotting">plotting</a>, <a href="https://vimeo.com/tag:graphics">graphics</a> and <a href="https://vimeo.com/tag:data+visualization">data visualization</a></p>tag:vimeo,2016-04-19:clip163399560LISA Short Course: Better Data Visualization in R Using the ggplot2 PackageLISA Short Course: Basics of RTue, 12 Apr 2016 09:09:02 -0400https://vimeo.com/162525524<p><iframe src="https://player.vimeo.com/video/162525524" width="640" height="480" frameborder="0" title="LISA Short Course: Basics of R" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe></p><p><p class="first">LISA (Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data.</p> <p>Tuesday, March 29, 4:00-6:00 pm;<br> Location: 1100 Torgersen Hall;<br> Instructor: Amanda McGough;<br> Title: Basics of R</p> <p>R is a powerful, versatile, and free statistical programming language, which has become increasingly popular among industrial and academic data analysts. This introductory course covers programming basics in R, including the definition and manipulation of data objects, importing/exporting data, simple data summaries, simple graphs and if time permits, simple linear regression. These concepts will be illustrated using both the Home Prices Data and also the National Longitudinal Mortality Survey. The Home Prices Data is a random sample of records of resales of homes from the files maintained by the Albuquerque Board of Realtors and will be used to illustrate the basic principles listed above. The power of R will then be demonstrated by performing similar operations on the National Longitudinal Mortality Survey, which includes nearly a million records with 38 measurements each. The course format includes lecture and computer laboratory components and attendees will have the opportunity to write, modify, and execute R codes for these data.</p> <p>This introductory session is part of a three-course series which assumes no previous coding experience in R or any other language. The intended audience for this course includes researchers who want to gain basic exposure to R with the ultimate goal of incorporating R into their research programs. More experienced users may wish to skip this course and attend subsequent courses on statistical and graphical techniques using R.</p> <p>Course files are available on the course webpage (<a href="http://www.lisa.stat.vt.edu/?q=node/10386" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/?q=node/10386</a>).</p></p><p><strong>Cast:</strong> <a href="https://vimeo.com/lisavt">LISA</a></p><p><strong>Tags:</strong> <a href="https://vimeo.com/tag:importing+data">importing data</a>, <a href="https://vimeo.com/tag:Short+Course">Short Course</a>, <a href="https://vimeo.com/tag:data+objects">data objects</a>, <a href="https://vimeo.com/tag:Basics+of+R">Basics of R</a>, <a href="https://vimeo.com/tag:LISA">LISA</a>, <a href="https://vimeo.com/tag:Statistics">Statistics</a>, <a href="https://vimeo.com/tag:data+summaries">data summaries</a>, <a href="https://vimeo.com/tag:exporting+data">exporting data</a>, <a href="https://vimeo.com/tag:Virginia+Tech">Virginia Tech</a>, <a href="https://vimeo.com/tag:simple+linear+regression">simple linear regression</a> and <a href="https://vimeo.com/tag:graphs">graphs</a></p>tag:vimeo,2016-04-12:clip162525524LISA Short Course: Basics of RLISA Short Course: Data Analytics - Classification, Part IITue, 05 Apr 2016 08:39:33 -0400https://vimeo.com/161616835<p><iframe src="https://player.vimeo.com/video/161616835" width="640" height="341" frameborder="0" title="LISA Short Course: Data Analytics - Classification, Part II" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe></p><p><p class="first">LISA (Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data.</p> <p>Tuesday, March 22, 4:00-6:00 pm;<br> Location: 1100 Torgersen Hall;<br> Instructor: Lin Zhang<br> Title: Data Analytics - Classification</p> <p>Data analytics (DA) is a science that combines data mining, machine learning, and statistics. DA examines raw data with the purpose of discovering useful information, suggesting conclusions, and supporting decision-making (source: <a href="http://en.wikipedia.org/wiki/Data_analysis" target="_blank" rel="nofollow noopener noreferrer">en.wikipedia.org/wiki/Data_analysis</a>). DA has become popular as big data problems have emerged in biological science, engineering, business, and other fields. There are many techniques that have been developed in data analytics. In this short course, we will focus on classification, or supervised learning techniques. These approaches include linear regression (least squares method), Bayes classifier, classification trees, logistic regression and LASSO logistic regression. We will first have a taste of the basic theory behind these techniques, and we will also discuss criteria used to evaluate classification, such as false positive, false negative, precision, and recall. Then we will use both simulated normal mixture data and the email spam data (<a href="http://archive.ics.uci.edu/ml/datasets/Spambase" target="_blank" rel="nofollow noopener noreferrer">archive.ics.uci.edu/ml/datasets/Spambase</a>) to demonstrate how to use these classification techniques (e.g. Figure 1: LS classifier for the normal mixture data). Note: all the class demonstrations will be carried out in R.</p> <p><a href="http://www.lisa.stat.vt.edu/sites/default/files/images/2015-11-17-data-analytics-classification.png" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/sites/default/files/images/2015-11-17-data-analytics-classification.png</a></p> <p>Course files are available on the course webpage (<a href="http://www.lisa.stat.vt.edu/?q=node/10385" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/?q=node/10385</a>).</p></p><p><strong>Cast:</strong> <a href="https://vimeo.com/lisavt">LISA</a></p><p><strong>Tags:</strong> <a href="https://vimeo.com/tag:LISA">LISA</a>, <a href="https://vimeo.com/tag:Short+Course">Short Course</a>, <a href="https://vimeo.com/tag:Virginia+Tech">Virginia Tech</a>, <a href="https://vimeo.com/tag:Data+Analytics">Data Analytics</a>, <a href="https://vimeo.com/tag:Classification">Classification</a>, <a href="https://vimeo.com/tag:Statistics">Statistics</a>, <a href="https://vimeo.com/tag:logistic+regression">logistic regression</a>, <a href="https://vimeo.com/tag:linear+regression">linear regression</a>, <a href="https://vimeo.com/tag:classification+trees">classification trees</a>, <a href="https://vimeo.com/tag:Bayes+classifier">Bayes classifier</a>, <a href="https://vimeo.com/tag:LASSO">LASSO</a>, <a href="https://vimeo.com/tag:R">R</a> and <a href="https://vimeo.com/tag:supervised+learning">supervised learning</a></p>tag:vimeo,2016-04-05:clip161616835LISA Short Course: Data Analytics - Classification, Part IILISA Short Course: Data Analytics - Classification, Part IWed, 30 Mar 2016 10:10:36 -0400https://vimeo.com/160892397<p><iframe src="https://player.vimeo.com/video/160892397" width="640" height="341" frameborder="0" title="LISA Short Course: Data Analytics - Classification, Part I" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe></p><p><p class="first">LISA (Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data.</p> <p>Tuesday, March 22, 4:00-6:00 pm;<br> Location: 1100 Torgersen Hall;<br> Instructor: Lin Zhang<br> Title: Data Analytics - Classification</p> <p>Data analytics (DA) is a science that combines data mining, machine learning, and statistics. DA examines raw data with the purpose of discovering useful information, suggesting conclusions, and supporting decision-making (source: <a href="https://en.wikipedia.org/wiki/Data_analysis" target="_blank" rel="nofollow noopener noreferrer">en.wikipedia.org/wiki/Data_analysis</a>). DA has become popular as big data problems have emerged in biological science, engineering, business, and other fields. There are many techniques that have been developed in data analytics. In this short course, we will focus on classification, or supervised learning techniques. These approaches include linear regression (least squares method), Bayes classifier, classification trees, logistic regression and LASSO logistic regression. We will first have a taste of the basic theory behind these techniques, and we will also discuss criteria used to evaluate classification, such as false positive, false negative, precision, and recall. Then we will use both simulated normal mixture data and the email spam data (<a href="https://archive.ics.uci.edu/ml/datasets/Spambase" target="_blank" rel="nofollow noopener noreferrer">archive.ics.uci.edu/ml/datasets/Spambase</a>) to demonstrate how to use these classification techniques (e.g. Figure 1: LS classifier for the normal mixture data). Note: all the class demonstrations will be carried out in R.</p> <p><a href="http://www.lisa.stat.vt.edu/sites/default/files/images/2015-11-17-data-analytics-classification.png" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/sites/default/files/images/2015-11-17-data-analytics-classification.png</a></p> <p>Course files are available on the course webpage (<a href="http://www.lisa.stat.vt.edu/?q=node/10385" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/?q=node/10385</a>).</p></p><p><strong>Cast:</strong> <a href="https://vimeo.com/lisavt">LISA</a></p><p><strong>Tags:</strong> <a href="https://vimeo.com/tag:Statistics">Statistics</a>, <a href="https://vimeo.com/tag:Virginia+Tech">Virginia Tech</a>, <a href="https://vimeo.com/tag:LISA">LISA</a>, <a href="https://vimeo.com/tag:Data+Analytics">Data Analytics</a>, <a href="https://vimeo.com/tag:Short+Course">Short Course</a>, <a href="https://vimeo.com/tag:Classification">Classification</a>, <a href="https://vimeo.com/tag:classification+trees">classification trees</a>, <a href="https://vimeo.com/tag:supervised+learning">supervised learning</a>, <a href="https://vimeo.com/tag:linear+regression">linear regression</a>, <a href="https://vimeo.com/tag:Bayes+classifier">Bayes classifier</a>, <a href="https://vimeo.com/tag:logistic+regression">logistic regression</a>, <a href="https://vimeo.com/tag:LASSO">LASSO</a> and <a href="https://vimeo.com/tag:R">R</a></p>tag:vimeo,2016-03-30:clip160892397LISA Short Course: Data Analytics - Classification, Part IEffect Size Section of Comparing Means & Other Measures of Location between Two Populations by Significance Tests & Effect SizeTue, 22 Mar 2016 08:56:32 -0400https://vimeo.com/159939815<p><iframe src="https://player.vimeo.com/video/159939815" width="640" height="360" frameborder="0" title="Effect Size Section of Comparing Means & Other Measures of Location between Two Populations by Significance Tests & Effect Size" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe></p><p><p class="first">LISA (Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data.</p> <p>Tuesday, March 15, 4:00-6:00 pm;<br> Location: 1100 Torgersen Hall;<br> Instructor: Ting Guan;<br> Title: Comparing Means and Other Measures of Location between Two Populations by Significance Tests and Effect Size;</p> <p>Motivation: Frequently, comparing samples from two populations is of interest. For example, is the average height in the US different from that in China? If they are different, how different?<br> Goal: In this short course, we will compare averages and other measures of location using significance tests and effect size estimates.</p> <p>In classical hypothesis testing, significance tests assume the null hypothesis is true. Then the probability that data as extreme as or more extreme than those observed is calculated. If this probability is very low, then we reject the null hypothesis. For the two sample case, we will focus on the following methods and questions:</p> <p>Parametric method: two-sample t-test<br> - What are the null hypothesis and alternative hypothesis of a two-sample t-test?<br> - What are the assumptions of two-sample t-test?<br> - How do we check if these assumptions hold?<br> - Should we use pooled or unpooled estimates for standard deviation?<br> - How to run two-sample t-test in R?<br> - What effect size estimate is appropriate in this scenario?</p> <p>Nonparametric method: Mann-Whitney U test<br> - What are the null hypothesis and alternative hypothesis of a Mann-Whitney U test?<br> - What are the assumptions of Mann-Whitney U test?<br> - How to check if these assumptions hold or not?<br> - How to run Mann-Whitney U test in R?<br> - What effect size estimate is appropriate in this scenario?</p> <p>We will use the following datasets:<br> - Data from a lung cancer study at VA (<a href="http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/valung.csv" target="_blank" rel="nofollow noopener noreferrer">biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/valung.csv</a>).<br> - Data from a soil respiration study (<a href="http://stat.purdue.edu/~tqin/system101/method/data/soil.csv" target="_blank" rel="nofollow noopener noreferrer">stat.purdue.edu/~tqin/system101/method/data/soil.csv</a>).<br> - Data from a study on high school students (<a href="http://ats.ucla.edu/stat/r/modules/hsb2.csv" target="_blank" rel="nofollow noopener noreferrer">ats.ucla.edu/stat/r/modules/hsb2.csv</a>).</p> <p>Course files are available on the course webpage (<a href="http://lisa.stat.vt.edu/?q=node/10384" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/?q=node/10384</a>).</p></p><p><strong>Cast:</strong> <a href="https://vimeo.com/lisavt">LISA</a></p><p><strong>Tags:</strong> <a href="https://vimeo.com/tag:Statistics">Statistics</a>, <a href="https://vimeo.com/tag:Virginia+Tech">Virginia Tech</a>, <a href="https://vimeo.com/tag:measures+of+location">measures of location</a>, <a href="https://vimeo.com/tag:LISA">LISA</a>, <a href="https://vimeo.com/tag:Short+Course">Short Course</a>, <a href="https://vimeo.com/tag:significance+tests">significance tests</a>, <a href="https://vimeo.com/tag:Mann-Whitney+U+test">Mann-Whitney U test</a>, <a href="https://vimeo.com/tag:parametric+methods">parametric methods</a>, <a href="https://vimeo.com/tag:effect+size">effect size</a>, <a href="https://vimeo.com/tag:nonparametric+methods">nonparametric methods</a>, <a href="https://vimeo.com/tag:two-sample+t-test">two-sample t-test</a> and <a href="https://vimeo.com/tag:R">R</a></p>tag:vimeo,2016-03-22:clip159939815Effect Size Section of Comparing Means & Other Measures of Location between Two Populations by Significance Tests & Effect SizeLISA Short Course: Comparing Means and Other Measures of Location between Two Populations by Significance Tests and Effect SizeFri, 18 Mar 2016 11:18:50 -0400https://vimeo.com/159512175<p><iframe src="https://player.vimeo.com/video/159512175" width="640" height="341" frameborder="0" title="LISA Short Course: Comparing Means and Other Measures of Location between Two Populations by Significance Tests and Effect Size" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe></p><p><p class="first">LISA (Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data.</p> <p>Tuesday, March 15, 4:00-6:00 pm;<br> Location: 1100 Torgersen Hall;<br> Instructor: Ting Guan;<br> Title: Comparing Means and Other Measures of Location between Two Populations by Significance Tests and Effect Size;</p> <p>Motivation: Frequently, comparing samples from two populations is of interest. For example, is the average height in the US different from that in China? If they are different, how different?<br> Goal: In this short course, we will compare averages and other measures of location using significance tests and effect size estimates.</p> <p>In classical hypothesis testing, significance tests assume the null hypothesis is true. Then the probability that data as extreme as or more extreme than those observed is calculated. If this probability is very low, then we reject the null hypothesis. For the two sample case, we will focus on the following methods and questions:</p> <p>Parametric method: two-sample t-test<br> - What are the null hypothesis and alternative hypothesis of a two-sample t-test?<br> - What are the assumptions of two-sample t-test?<br> - How do we check if these assumptions hold?<br> - Should we use pooled or unpooled estimates for standard deviation?<br> - How to run two-sample t-test in R?<br> - What effect size estimate is appropriate in this scenario?</p> <p>Nonparametric method: Mann-Whitney U test<br> - What are the null hypothesis and alternative hypothesis of a Mann-Whitney U test?<br> - What are the assumptions of Mann-Whitney U test?<br> - How to check if these assumptions hold or not?<br> - How to run Mann-Whitney U test in R?<br> - What effect size estimate is appropriate in this scenario?</p> <p>We will use the following datasets:<br> - Data from a lung cancer study at VA (<a href="http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/valung.csv" target="_blank" rel="nofollow noopener noreferrer">biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/valung.csv</a>).<br> - Data from a soil respiration study (<a href="http://www.stat.purdue.edu/~tqin/system101/method/data/soil.csv" target="_blank" rel="nofollow noopener noreferrer">stat.purdue.edu/~tqin/system101/method/data/soil.csv</a>).<br> - Data from a study on high school students (<a href="http://www.ats.ucla.edu/stat/r/modules/hsb2.csv" target="_blank" rel="nofollow noopener noreferrer">ats.ucla.edu/stat/r/modules/hsb2.csv</a>).</p> <p>Course files are available on the course webpage (<a href="http://www.lisa.stat.vt.edu/?q=node/10384" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/?q=node/10384</a>).</p></p><p><strong>Cast:</strong> <a href="https://vimeo.com/lisavt">LISA</a></p><p><strong>Tags:</strong> <a href="https://vimeo.com/tag:LISA">LISA</a>, <a href="https://vimeo.com/tag:Short+Course">Short Course</a>, <a href="https://vimeo.com/tag:Virginia+Tech">Virginia Tech</a>, <a href="https://vimeo.com/tag:Statistics">Statistics</a>, <a href="https://vimeo.com/tag:parametric+methods">parametric methods</a>, <a href="https://vimeo.com/tag:significance+tests">significance tests</a>, <a href="https://vimeo.com/tag:effect+size">effect size</a>, <a href="https://vimeo.com/tag:measures+of+location">measures of location</a>, <a href="https://vimeo.com/tag:two-sample+t-test">two-sample t-test</a>, <a href="https://vimeo.com/tag:nonparametric+methods">nonparametric methods</a>, <a href="https://vimeo.com/tag:R">R</a> and <a href="https://vimeo.com/tag:Mann-Whitney+U+test">Mann-Whitney U test</a></p>tag:vimeo,2016-03-18:clip159512175LISA Short Course: Comparing Means and Other Measures of Location between Two Populations by Significance Tests and Effect SizeLISA Short Course: Visualizing and Analyzing Spatial Data with R, Part IITue, 12 Jan 2016 10:00:07 -0500https://vimeo.com/151523912<p><iframe src="https://player.vimeo.com/video/151523912" width="640" height="401" frameborder="0" title="LISA Short Course: Visualizing and Analyzing Spatial Data with R, Part II" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe></p><p><p class="first">LISA (Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data.</p> <p>Tuesday, December 1, 2015, 4:30-6:30 pm;<br> Location: 1080 Torgersen Hall;<br> Instructor: Matthew Keefe;<br> Title: Visualizing and Analyzing Spatial Data with R ;</p> <p>Researchers often work with data corresponding to geographic locations. In these situations, it may be of interest to visualize data and take full advantage of spatial statistical methods in order to model and incorporate relationships among locations. In general, spatially oriented data sets fall into one of three categories: point-referenced data, areal data, or point pattern data. This short course will focus on areal data, where a fixed region of interest is divided into a finite number of units, such as counties or states. Methods for exploratory data analysis and visualization of areal data will be discussed, as well as estimation of spatial models for areal data.</p> <p>This is an advanced short course that assumes prerequisite knowledge of linear models and R. This short course includes lecture and computer laboratory components. The lecture component will briefly motivate the mathematical concepts of areal data models. SAT scores from 1999 in the 48 contiguous United States (Wall, 2004) and sudden-infant-death-syndrome (SIDS) data in North Carolina counties (Cressie and Read, 1985) will be explored during the computer laboratory component using R to demonstrate techniques for visualization and analysis of areal data.</p> <p><a href="http://www.lisa.stat.vt.edu/sites/default/files/images/2015-12-01-spatial-data.png" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/sites/default/files/images/2015-12-01-spatial-data.png</a></p> <p>Course files are available on the course webpage (<a href="http://www.lisa.stat.vt.edu/?q=node/9618" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/?q=node/9618</a>).</p></p><p><strong>Cast:</strong> <a href="https://vimeo.com/lisavt">LISA</a></p><p><strong>Tags:</strong> <a href="https://vimeo.com/tag:Virginia+Tech">Virginia Tech</a>, <a href="https://vimeo.com/tag:Short+Course">Short Course</a>, <a href="https://vimeo.com/tag:Spatial+Data">Spatial Data</a>, <a href="https://vimeo.com/tag:LISA">LISA</a>, <a href="https://vimeo.com/tag:R">R</a>, <a href="https://vimeo.com/tag:Statistics">Statistics</a>, <a href="https://vimeo.com/tag:areal+data">areal data</a>, <a href="https://vimeo.com/tag:analysis">analysis</a> and <a href="https://vimeo.com/tag:visualization">visualization</a></p>tag:vimeo,2016-01-12:clip151523912LISA Short Course: Visualizing and Analyzing Spatial Data with R, Part IILISA Short Course: Visualizing and Analyzing Spatial Data with R, Part IWed, 06 Jan 2016 14:39:26 -0500https://vimeo.com/150938633<p><iframe src="https://player.vimeo.com/video/150938633" width="640" height="401" frameborder="0" title="LISA Short Course: Visualizing and Analyzing Spatial Data with R, Part I" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe></p><p><p class="first">LISA (Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data.</p> <p>Tuesday, December 1, 2015, 4:30-6:30 pm;<br> Location: 1080 Torgersen Hall;<br> Instructor: Matthew Keefe;<br> Title: Visualizing and Analyzing Spatial Data with R ;</p> <p>Researchers often work with data corresponding to geographic locations. In these situations, it may be of interest to visualize data and take full advantage of spatial statistical methods in order to model and incorporate relationships among locations. In general, spatially oriented data sets fall into one of three categories: point-referenced data, areal data, or point pattern data. This short course will focus on areal data, where a fixed region of interest is divided into a finite number of units, such as counties or states. Methods for exploratory data analysis and visualization of areal data will be discussed, as well as estimation of spatial models for areal data.</p> <p>This is an advanced short course that assumes prerequisite knowledge of linear models and R. This short course includes lecture and computer laboratory components. The lecture component will briefly motivate the mathematical concepts of areal data models. SAT scores from 1999 in the 48 contiguous United States (Wall, 2004) and sudden-infant-death-syndrome (SIDS) data in North Carolina counties (Cressie and Read, 1985) will be explored during the computer laboratory component using R to demonstrate techniques for visualization and analysis of areal data.</p> <p><a href="http://www.lisa.stat.vt.edu/sites/default/files/images/2015-12-01-spatial-data.png" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/sites/default/files/images/2015-12-01-spatial-data.png</a></p> <p>Course files are available on the course webpage (<a href="http://www.lisa.stat.vt.edu/?q=node/9618" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/?q=node/9618</a>).</p></p><p><strong>Cast:</strong> <a href="https://vimeo.com/lisavt">LISA</a></p><p><strong>Tags:</strong> <a href="https://vimeo.com/tag:Virginia+Tech">Virginia Tech</a>, <a href="https://vimeo.com/tag:R">R</a>, <a href="https://vimeo.com/tag:LISA">LISA</a>, <a href="https://vimeo.com/tag:visualization">visualization</a>, <a href="https://vimeo.com/tag:Short+Course">Short Course</a>, <a href="https://vimeo.com/tag:Spatial+Data">Spatial Data</a>, <a href="https://vimeo.com/tag:areal+data">areal data</a>, <a href="https://vimeo.com/tag:analysis">analysis</a> and <a href="https://vimeo.com/tag:Statistics">Statistics</a></p>tag:vimeo,2016-01-06:clip150938633LISA Short Course: Visualizing and Analyzing Spatial Data with R, Part ILISA Short Course: Data Analytics - Classification, Part IIMon, 04 Jan 2016 10:03:38 -0500https://vimeo.com/150678925<p><iframe src="https://player.vimeo.com/video/150678925" width="640" height="401" frameborder="0" title="LISA Short Course: Data Analytics - Classification, Part II" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe></p><p><p class="first">LISA (Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data.</p> <p>Tuesday, November 17, 2015, 4:30-6:30 pm;<br> Location: 1080 Torgersen Hall;<br> Instructor: Lin Zhang;<br> Title: Data Analytics - Classification;</p> <p>Data analytics (DA) is a science that combines data mining, machine learning, and statistics. DA examines raw data with the purpose of discovering useful information, suggesting conclusions, and supporting decision-making (source: <a href="https://en.wikipedia.org/wiki/Data_analysis" target="_blank" rel="nofollow noopener noreferrer">en.wikipedia.org/wiki/Data_analysis</a>). DA has become popular as big data problems have emerged in biological science, engineering, business, and other fields. There are many techniques that have been developed in data analytics. In this short course, we will focus on classification, or supervised learning techniques. These approaches include linear regression (least squares method), Bayes classifier, classification trees, logistic regression and LASSO logistic regression. We will first have a taste of the basic theory behind these techniques, and we will also discuss criteria used to evaluate classification, such as false positive, false negative, precision, and recall. Then we will use both simulated normal mixture data and the email spam data (<a href="https://archive.ics.uci.edu/ml/datasets/Spambase" target="_blank" rel="nofollow noopener noreferrer">archive.ics.uci.edu/ml/datasets/Spambase</a>) to demonstrate how to use these classification techniques (e.g. Figure 1: LS classifier for the normal mixture data). Note: all the class demonstrations will be carried out in R.</p> <p><a href="http://www.lisa.stat.vt.edu/sites/default/files/images/2015-11-17-data-analytics-classification.png" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/sites/default/files/images/2015-11-17-data-analytics-classification.png</a></p> <p>Course files are available on the course webpage (<a href="http://www.lisa.stat.vt.edu/?q=node/9617" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/?q=node/9617</a>).</p></p><p><strong>Cast:</strong> <a href="https://vimeo.com/lisavt">LISA</a></p><p><strong>Tags:</strong> <a href="https://vimeo.com/tag:Statistics">Statistics</a>, <a href="https://vimeo.com/tag:LISA">LISA</a>, <a href="https://vimeo.com/tag:Data+Analytics">Data Analytics</a>, <a href="https://vimeo.com/tag:Virginia+Tech">Virginia Tech</a>, <a href="https://vimeo.com/tag:Short+Course">Short Course</a>, <a href="https://vimeo.com/tag:machine+learning">machine learning</a>, <a href="https://vimeo.com/tag:Classification">Classification</a>, <a href="https://vimeo.com/tag:Bayes+classifier">Bayes classifier</a>, <a href="https://vimeo.com/tag:supervised+learning">supervised learning</a>, <a href="https://vimeo.com/tag:data+mining">data mining</a>, <a href="https://vimeo.com/tag:linear+regression">linear regression</a>, <a href="https://vimeo.com/tag:logistic+regression">logistic regression</a>, <a href="https://vimeo.com/tag:LASSO">LASSO</a> and <a href="https://vimeo.com/tag:classification+trees">classification trees</a></p>tag:vimeo,2016-01-04:clip150678925LISA Short Course: Data Analytics - Classification, Part IILISA Short Course: Data Analytics - Classification, Part ITue, 22 Dec 2015 15:55:21 -0500https://vimeo.com/149804184<p><iframe src="https://player.vimeo.com/video/149804184" width="640" height="401" frameborder="0" title="LISA Short Course: Data Analytics - Classification, Part I" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe></p><p><p class="first">LISA (Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data.</p> <p>Tuesday, November 17, 2015, 4:30-6:30 pm;<br> Location: 1080 Torgersen Hall;<br> Instructor: Lin Zhang;<br> Title: Data Analytics - Classification;</p> <p>Data analytics (DA) is a science that combines data mining, machine learning, and statistics. DA examines raw data with the purpose of discovering useful information, suggesting conclusions, and supporting decision-making (source: <a href="https://en.wikipedia.org/wiki/Data_analysis" target="_blank" rel="nofollow noopener noreferrer">en.wikipedia.org/wiki/Data_analysis</a>). DA has become popular as big data problems have emerged in biological science, engineering, business, and other fields. There are many techniques that have been developed in data analytics. In this short course, we will focus on classification, or supervised learning techniques. These approaches include linear regression (least squares method), Bayes classifier, classification trees, logistic regression and LASSO logistic regression. We will first have a taste of the basic theory behind these techniques, and we will also discuss criteria used to evaluate classification, such as false positive, false negative, precision, and recall. Then we will use both simulated normal mixture data and the email spam data (<a href="https://archive.ics.uci.edu/ml/datasets/Spambase" target="_blank" rel="nofollow noopener noreferrer">archive.ics.uci.edu/ml/datasets/Spambase</a>) to demonstrate how to use these classification techniques (e.g. Figure 1: LS classifier for the normal mixture data). Note: all the class demonstrations will be carried out in R.</p> <p><a href="http://www.lisa.stat.vt.edu/sites/default/files/images/2015-11-17-data-analytics-classification.png" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/sites/default/files/images/2015-11-17-data-analytics-classification.png</a></p> <p>Course files are available on the course webpage (<a href="http://www.lisa.stat.vt.edu/?q=node/9617" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/?q=node/9617</a>).</p></p><p><strong>Cast:</strong> <a href="https://vimeo.com/lisavt">LISA</a></p><p><strong>Tags:</strong> <a href="https://vimeo.com/tag:Statistics">Statistics</a>, <a href="https://vimeo.com/tag:Data+Analytics">Data Analytics</a>, <a href="https://vimeo.com/tag:LISA">LISA</a>, <a href="https://vimeo.com/tag:Short+Course">Short Course</a>, <a href="https://vimeo.com/tag:Classification">Classification</a>, <a href="https://vimeo.com/tag:Virginia+Tech">Virginia Tech</a>, <a href="https://vimeo.com/tag:lineaer+regression">lineaer regression</a>, <a href="https://vimeo.com/tag:data+mining">data mining</a>, <a href="https://vimeo.com/tag:supervised+learning">supervised learning</a>, <a href="https://vimeo.com/tag:Bayes+classifier">Bayes classifier</a>, <a href="https://vimeo.com/tag:machine+learning">machine learning</a>, <a href="https://vimeo.com/tag:LASSO">LASSO</a>, <a href="https://vimeo.com/tag:classification+trees">classification trees</a> and <a href="https://vimeo.com/tag:logistic+regression">logistic regression</a></p>tag:vimeo,2015-12-22:clip149804184LISA Short Course: Data Analytics - Classification, Part ILISA Short Course: Comparing Means and Other Measures of Location between Two Populations by Significance Tests and Effect SizeThu, 17 Dec 2015 16:33:01 -0500https://vimeo.com/149334916<p><iframe src="https://player.vimeo.com/video/149334916" width="640" height="401" frameborder="0" title="LISA Short Course: Comparing Means and Other Measures of Location between Two Populations by Significance Tests and Effect Size" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe></p><p><p class="first">LISA (Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data.</p> <p>Tuesday, November 10, 2015, 4:30-6:30 pm;<br> Location: 1080 Torgersen Hall;<br> Instructor: Ting Guan;<br> Title: Comparing Means and Other Measures of Location between Two Populations by Significance Tests and Effect Size ;</p> <p>Motivation: Frequently, comparing samples from two populations is of interest. For example, is the average height in the US different from that in China? If they are different, how different?</p> <p>Goal: In this short course, we will compare averages and other measures of location using significance tests and effect size estimates.</p> <p>In classical hypothesis testing, significance tests assume the null hypothesis is true. Then the probability that data as extreme as or more extreme than those observed is calculated. If this probability is very low, then we reject the null hypothesis. For the two sample case, we will focus on the following methods and questions:</p> <p>Parametric method: two-sample t-test<br> - What are the null hypothesis and alternative hypothesis of a two-sample t-test?<br> - What are the assumptions of two-sample t-test?<br> - How do we check if these assumptions hold?<br> - Should we use pooled or unpooled estimates for standard deviation?<br> - How to run two-sample t-test in R?<br> - What effect size estimate is appropriate in this scenario?</p> <p>Nonparametric method: Mann-Whitney U test<br> - What are the null hypothesis and alternative hypothesis of a Mann-Whitney U test?<br> - What are the assumptions of Mann-Whitney U test?<br> - How to check if these assumptions hold or not?<br> - How to run Mann-Whitney U test in R?<br> - What effect size estimate is appropriate in this scenario?</p> <p>We will use the following datasets:</p> <p>- Data from a lung cancer study at VA (<a href="http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/valung.csv" target="_blank" rel="nofollow noopener noreferrer">biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/valung.csv</a>).<br> - Data from a monkey calls study (link).<br> - Data from a study on high school students (<a href="http://www.ats.ucla.edu/stat/r/modules/hsb2.csv" target="_blank" rel="nofollow noopener noreferrer">ats.ucla.edu/stat/r/modules/hsb2.csv</a>).</p> <p>Course files are available on the course webpage (<a href="http://www.lisa.stat.vt.edu/?q=node/9616" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/?q=node/9616</a>).</p></p><p><strong>Cast:</strong> <a href="https://vimeo.com/lisavt">LISA</a></p><p><strong>Tags:</strong> <a href="https://vimeo.com/tag:LISA">LISA</a>, <a href="https://vimeo.com/tag:Statistics">Statistics</a>, <a href="https://vimeo.com/tag:measures+of+location">measures of location</a>, <a href="https://vimeo.com/tag:Virginia+Tech">Virginia Tech</a>, <a href="https://vimeo.com/tag:significance+tests">significance tests</a>, <a href="https://vimeo.com/tag:effect+size">effect size</a>, <a href="https://vimeo.com/tag:Short+Course">Short Course</a>, <a href="https://vimeo.com/tag:two-sample+t-test">two-sample t-test</a>, <a href="https://vimeo.com/tag:parametric+methods">parametric methods</a>, <a href="https://vimeo.com/tag:Mann-Whitney+U+test">Mann-Whitney U test</a>, <a href="https://vimeo.com/tag:nonparametric+methods">nonparametric methods</a> and <a href="https://vimeo.com/tag:R">R</a></p>tag:vimeo,2015-12-17:clip149334916LISA Short Course: Comparing Means and Other Measures of Location between Two Populations by Significance Tests and Effect SizeLISA Short Course: Multivariate Clustering Analysis, Part IITue, 08 Dec 2015 16:09:51 -0500https://vimeo.com/148275568<p><iframe src="https://player.vimeo.com/video/148275568" width="640" height="401" frameborder="0" title="LISA Short Course: Multivariate Clustering Analysis, Part II" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe></p><p><p class="first">LISA (Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data.</p> <p>Tuesday, November 3, 2015, 4:30-6:30 pm;<br> Location: 1080 Torgersen Hall;<br> Instructor: Yuhyun Song;<br> Title: Multivariate Clustering Analysis;</p> <p>Multivariate analysis in statistics is a set of useful methods for analyzing data when there are more than one variable under consideration. Multivariate analysis techniques may be used for several purposes, such as dimension reduction, clustering, or classification. The primary goal of this short course is to help researchers who want to understand multivariate data and explore multivariate analysis tools.</p> <p>This short course concentrates on clustering techniques. The goal of clustering analysis is to establish a set of meaningful groups of similar objects by investigating relationships between objects. For example, if you have data from customers, you may segment customers into clusters based on their buying habits and their demographical characteristics. Then, you can use clustering results to custom tailor your marketing efforts. </p> <p>This short course includes lecture and computer laboratory components. In the lecture component, distance measures measuring distance between clusters are briefly reviewed. Then, several popular clustering techniques such as Agglomerative hierarchical clustering, K-means clustering algorithm, Partitioning around medoids, and Density-based clustering will be introduced. Also, how to visualize the clustering solutions and how to evaluate the quality of clustering results will be discussed. In the laboratory component, various clustering algorithm will be implemented using R on tweets data, which containing 340 tweets (<a href="http://www.rdatamining.com" target="_blank" rel="nofollow noopener noreferrer">rdatamining.com</a>). This short course assumes basic R coding ability and familiarity with a normal distribution.</p> <p>Course files are available on the course webpage (<a href="http://www.lisa.stat.vt.edu/?q=node/9614" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/?q=node/9614</a>).</p></p><p><strong>Cast:</strong> <a href="https://vimeo.com/lisavt">LISA</a></p><p><strong>Tags:</strong> <a href="https://vimeo.com/tag:Multivariate+Clustering+Analysis">Multivariate Clustering Analysis</a>, <a href="https://vimeo.com/tag:LISA">LISA</a>, <a href="https://vimeo.com/tag:Statistics">Statistics</a>, <a href="https://vimeo.com/tag:Short+Course">Short Course</a>, <a href="https://vimeo.com/tag:density-based+clustering">density-based clustering</a>, <a href="https://vimeo.com/tag:k-means+clustering">k-means clustering</a>, <a href="https://vimeo.com/tag:R">R</a> and <a href="https://vimeo.com/tag:partitioning+around+medoids">partitioning around medoids</a></p>tag:vimeo,2015-12-08:clip148275568LISA Short Course: Multivariate Clustering Analysis, Part IILISA Short Course: Multivariate Clustering Analysis, Part ITue, 01 Dec 2015 09:57:18 -0500https://vimeo.com/147466097<p><iframe src="https://player.vimeo.com/video/147466097" width="640" height="400" frameborder="0" title="LISA Short Course: Multivariate Clustering Analysis, Part I" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe></p><p><p class="first">LISA (Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data.</p> <p>Tuesday, November 3, 2015, 4:30-6:30 pm;<br> Location: 1080 Torgersen Hall;<br> Instructor: Yuhyun Song;<br> Title: Multivariate Clustering Analysis;</p> <p>Multivariate analysis in statistics is a set of useful methods for analyzing data when there are more than one variable under consideration. Multivariate analysis techniques may be used for several purposes, such as dimension reduction, clustering, or classification. The primary goal of this short course is to help researchers who want to understand multivariate data and explore multivariate analysis tools.</p> <p>This short course concentrates on clustering techniques. The goal of clustering analysis is to establish a set of meaningful groups of similar objects by investigating relationships between objects. For example, if you have data from customers, you may segment customers into clusters based on their buying habits and their demographical characteristics. Then, you can use clustering results to custom tailor your marketing efforts. </p> <p>This short course includes lecture and computer laboratory components. In the lecture component, distance measures measuring distance between clusters are briefly reviewed. Then, several popular clustering techniques such as Agglomerative hierarchical clustering, K-means clustering algorithm, Partitioning around medoids, and Density-based clustering will be introduced. Also, how to visualize the clustering solutions and how to evaluate the quality of clustering results will be discussed. In the laboratory component, various clustering algorithm will be implemented using R on tweets data, which containing 340 tweets (<a href="http://www.rdatamining.com" target="_blank" rel="nofollow noopener noreferrer">rdatamining.com</a>). This short course assumes basic R coding ability and familiarity with a normal distribution.</p> <p>Course files are available on the course webpage (<a href="http://www.lisa.stat.vt.edu/?q=node/9614" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/?q=node/9614</a>).</p></p><p><strong>Cast:</strong> <a href="https://vimeo.com/lisavt">LISA</a></p><p><strong>Tags:</strong> <a href="https://vimeo.com/tag:Virginia+Tech">Virginia Tech</a>, <a href="https://vimeo.com/tag:Short+Course">Short Course</a>, <a href="https://vimeo.com/tag:Multivariate+Clustering+Analysis">Multivariate Clustering Analysis</a>, <a href="https://vimeo.com/tag:LISA">LISA</a>, <a href="https://vimeo.com/tag:Statistics">Statistics</a>, <a href="https://vimeo.com/tag:R">R</a>, <a href="https://vimeo.com/tag:K-means+clustering">K-means clustering</a>, <a href="https://vimeo.com/tag:Clustering">Clustering</a>, <a href="https://vimeo.com/tag:medoids">medoids</a>, <a href="https://vimeo.com/tag:partitioning">partitioning</a> and <a href="https://vimeo.com/tag:density-based+clustering">density-based clustering</a></p>tag:vimeo,2015-12-01:clip147466097LISA Short Course: Multivariate Clustering Analysis, Part ILISA Short Course: Intro to SAS University Edition, Part IITue, 17 Nov 2015 09:31:09 -0500https://vimeo.com/145995488<p><iframe src="https://player.vimeo.com/video/145995488" width="640" height="400" frameborder="0" title="LISA Short Course: Intro to SAS University Edition, Part II" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe></p><p><p class="first">LISA (Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data.</p> <p>Tuesday, October 27, 2015, 4:30-6:30 pm;<br> Location: 1080 Torgersen Hall;<br> Instructor: Will DeShong;<br> Title: Intro to SAS University Edition;</p> <p>The Intro to SAS University short course provides an introduction to SAS statistical software and is intended for people that have little or no experience with programming languages. SAS University is free statistical programming software (compatible with Mac and Windows) offered by SAS for Virginia Tech personnel. The course will provide an introduction to the SAS University environment including a look into the user interface, output delivery system, and permanent libraries. The course will spend approximately 40% of the time on data management (DATA step) and 60% of the time on basic procedures used in SAS (PROC statements). The DATA step will focus on importing and merging data sets. The PROC statements covered will range from summarizing the data to basic analyses such as simple linear regression. The main data sets that will be used are 1986 Baseball Data and Airline Employee Test Data. This short course will be conducted in a computer lab and attendees will program the SAS code along with the instructor.</p> <p>SAS University Edition is available (<a href="http://www.sas.com/en_us/software/university-edition/download-software.html" target="_blank" rel="nofollow noopener noreferrer">sas.com/en_us/software/university-edition/download-software.html</a>).</p> <p>Course files are available on the course webpage (<a href="http://www.lisa.stat.vt.edu/?q=node/9615" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/?q=node/9615</a>).</p></p><p><strong>Cast:</strong> <a href="https://vimeo.com/lisavt">LISA</a></p><p><strong>Tags:</strong> <a href="https://vimeo.com/tag:Statistics">Statistics</a>, <a href="https://vimeo.com/tag:Short+Course">Short Course</a>, <a href="https://vimeo.com/tag:Virginia+Tech">Virginia Tech</a>, <a href="https://vimeo.com/tag:SAS+University+Edition">SAS University Edition</a>, <a href="https://vimeo.com/tag:LISA">LISA</a>, <a href="https://vimeo.com/tag:installation">installation</a>, <a href="https://vimeo.com/tag:DATA+statement">DATA statement</a> and <a href="https://vimeo.com/tag:PROC+statement">PROC statement</a></p>tag:vimeo,2015-11-17:clip145995488LISA Short Course: Intro to SAS University Edition, Part IILISA Short Course: Intro to SAS University Edition, Part IFri, 13 Nov 2015 08:16:45 -0500https://vimeo.com/145628845<p><iframe src="https://player.vimeo.com/video/145628845" width="640" height="400" frameborder="0" title="LISA Short Course: Intro to SAS University Edition, Part I" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe></p><p><p class="first">LISA (Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data.</p> <p>Tuesday, October 27, 2015, 4:30-6:30 pm;<br> Location: 1080 Torgersen Hall;<br> Instructor: Will DeShong;<br> Title: Intro to SAS University Edition;</p> <p>The Intro to SAS University short course provides an introduction to SAS statistical software and is intended for people that have little or no experience with programming languages. SAS University is free statistical programming software (compatible with Mac and Windows) offered by SAS for Virginia Tech personnel. The course will provide an introduction to the SAS University environment including a look into the user interface, output delivery system, and permanent libraries. The course will spend approximately 40% of the time on data management (DATA step) and 60% of the time on basic procedures used in SAS (PROC statements). The DATA step will focus on importing and merging data sets. The PROC statements covered will range from summarizing the data to basic analyses such as simple linear regression. The main data sets that will be used are 1986 Baseball Data and Airline Employee Test Data. This short course will be conducted in a computer lab and attendees will program the SAS code along with the instructor.</p> <p>SAS University Edition is available here (<a href="http://www.sas.com/en_us/software/university-edition/download-software.html" target="_blank" rel="nofollow noopener noreferrer">sas.com/en_us/software/university-edition/download-software.html</a>).</p> <p>Course files are available on the course webpage (<a href="http://www.lisa.stat.vt.edu/?q=node/9615" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/?q=node/9615</a>).</p></p><p><strong>Cast:</strong> <a href="https://vimeo.com/lisavt">LISA</a></p><p><strong>Tags:</strong> <a href="https://vimeo.com/tag:Short+Course">Short Course</a>, <a href="https://vimeo.com/tag:LISA">LISA</a>, <a href="https://vimeo.com/tag:Virginia+Tech">Virginia Tech</a>, <a href="https://vimeo.com/tag:SAS+University+Edition">SAS University Edition</a>, <a href="https://vimeo.com/tag:Statistics">Statistics</a>, <a href="https://vimeo.com/tag:installation">installation</a>, <a href="https://vimeo.com/tag:DATA+statement">DATA statement</a> and <a href="https://vimeo.com/tag:PROC+statement">PROC statement</a></p>tag:vimeo,2015-11-13:clip145628845LISA Short Course: Intro to SAS University Edition, Part ILISA Short Course: Calculating Sample Sizes and Power for ResearchThu, 05 Nov 2015 14:00:19 -0500https://vimeo.com/144786953<p><iframe src="https://player.vimeo.com/video/144786953" width="640" height="400" frameborder="0" title="LISA Short Course: Calculating Sample Sizes and Power for Research" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe></p><p><p class="first">LISA (Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data.</p> <p>Tuesday, October 13, 2015, 4:30-6:30 pm;<br> Location: 1080 Torgersen Hall;<br> Instructor:Celia Rose Eddy;<br> Title: Calculating Sample Sizes and Power for Research;</p> <p>Power analyses and sample size calculations are important parts of many research projects. Often, before data are even collected, it is necessary to calculate and justify the required sample size for a study. Choosing an appropriate sample size is important since it allows us to detect anticipated treatment effects and associations with appropriately high probability, or synonymously, high statistical power. If we have too few samples, the chance of overlooking existing effects is high. Drawing too many samples leads to wasted time and resources.</p> <p>Sample size and power calculations rely on a few important quantities: effect size, desired power, sample size, and type I error (i.e., false positive) rate. For a given analysis plan, specification of any three of these quantities enables calculation of the fourth. For example, if a researcher wants a 90% chance to detect a certain correlation with a 5% type I error rate, the sample size that achieves these conditions can be obtained from a variety of widely available software packages.</p> <p>In this short course we will review pertinent concepts regarding hypothesis testing and choice of statistical analysis plan. Furthermore, we will define each component of a power analysis and discover how to find the information necessary to complete a sample size calculation. Then, we will explore how to apply this information within the scope of several common experimental setups (one- and two- sample proportions and means, correlations, one-way ANOVA, etc.). Finally, we will use G*Power (a free software download) to practice obtaining required sample sizes based on different effect sizes and desired levels of power.</p> <p>Becoming comfortable with sample size calculations can be a major asset for anyone who participates in research. After this short course, attendees will be able to explore power, effect size, and sample size within the context of their own projects in order to become more effective researcher.</p> <p>Course files are available on the course webpage (<a href="http://www.lisa.stat.vt.edu/?q=node/9613" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/?q=node/9613</a>).</p></p><p><strong>Cast:</strong> <a href="https://vimeo.com/lisavt">LISA</a></p><p><strong>Tags:</strong> <a href="https://vimeo.com/tag:LISA">LISA</a>, <a href="https://vimeo.com/tag:Statistics">Statistics</a>, <a href="https://vimeo.com/tag:Sample+Size">Sample Size</a>, <a href="https://vimeo.com/tag:Virginia+Tech">Virginia Tech</a>, <a href="https://vimeo.com/tag:Short+Course">Short Course</a>, <a href="https://vimeo.com/tag:effect+size">effect size</a>, <a href="https://vimeo.com/tag:G%2APower">G*Power</a>, <a href="https://vimeo.com/tag:Research">Research</a>, <a href="https://vimeo.com/tag:Power">Power</a> and <a href="https://vimeo.com/tag:type+I+error">type I error</a></p>tag:vimeo,2015-11-05:clip144786953LISA Short Course: Calculating Sample Sizes and Power for ResearchLISA Short Course: T-tests & ANOVA, Part IITue, 27 Oct 2015 14:32:35 -0400https://vimeo.com/143778315<p><iframe src="https://player.vimeo.com/video/143778315" width="640" height="400" frameborder="0" title="LISA Short Course: T-tests & ANOVA, Part II" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe></p><p><p class="first">LISA (Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data.</p> <p>Tuesday, October 6, 2015, 4:30-6:30 pm;<br> Location: 1080 Torgersen Hall;<br> Instructor: Kristopher Patton;<br> Title: T-tests & ANOVA;</p> <p>Often in science it is important to address the question of whether mean responses differ from one another between groups. For example, average anthropometric measurements may differ between organisms that come from two differing environmental backgrounds. When one is interested in the difference between means of strictly two groups, the most common statistical procedure in this situation is known as the t-test. When there are two or more groups the typical approach is to employ the analysis of variance (ANOVA).</p> <p>This course will review the concepts behind two-sample and paired t tests and one and two-way ANOVA models. Course attendees will learn the basic motivation and assumptions of each method. Hypothesis tests, confidence intervals, and measures of effect size will also be described for each approach.</p> <p>To exemplify the t-test and one-way ANOVA, the Egyptian Skulls data set (<a href="http://www.dm.unibo.it/~simoncin/EgyptianSkulls.html" target="_blank" rel="nofollow noopener noreferrer">dm.unibo.it/~simoncin/EgyptianSkulls.html</a>) will be utilized, which consists of measurements of male Egyptian skulls from five different time periods.</p> <p>For a better understanding of the two-way ANOVA, we will incorporate analysis of a data set from a lung cancer study at the VA (<a href="http://www.biostat.mc.vanderbilt.edu/wiki/Main/DataSets" target="_blank" rel="nofollow noopener noreferrer">biostat.mc.vanderbilt.edu/wiki/Main/DataSets</a>).</p> <p>The software JMP (<a href="http://www.jmp.com/en_us/home.html" target="_blank" rel="nofollow noopener noreferrer">jmp.com/en_us/home.html</a>) will be used to illustrate topics in this course.</p> <p>Course files are available on the course webpage (<a href="http://www.lisa.stat.vt.edu/?q=node/9612" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/?q=node/9612</a>).</p></p><p><strong>Cast:</strong> <a href="https://vimeo.com/lisavt">LISA</a></p><p><strong>Tags:</strong> <a href="https://vimeo.com/tag:LISA">LISA</a>, <a href="https://vimeo.com/tag:Statistics">Statistics</a>, <a href="https://vimeo.com/tag:T-tests">T-tests</a>, <a href="https://vimeo.com/tag:ANOVA">ANOVA</a>, <a href="https://vimeo.com/tag:Short+Course">Short Course</a>, <a href="https://vimeo.com/tag:JMP">JMP</a> and <a href="https://vimeo.com/tag:Virginia+Tech">Virginia Tech</a></p>tag:vimeo,2015-10-27:clip143778315LISA Short Course: T-tests & ANOVA, Part IILISA Short Course: T-tests & ANOVA, Part IFri, 23 Oct 2015 11:10:40 -0400https://vimeo.com/143386639<p><iframe src="https://player.vimeo.com/video/143386639" width="640" height="400" frameborder="0" title="LISA Short Course: T-tests & ANOVA, Part I" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe></p><p><p class="first">LISA (Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data.</p> <p>Tuesday, October 6, 2015, 4:30-6:30 pm;<br> Location: 1080 Torgersen Hall;<br> Instructor: Kristopher Patton;<br> Title: T-tests & ANOVA;</p> <p>Often in science it is important to address the question of whether mean responses differ from one another between groups. For example, average anthropometric measurements may differ between organisms that come from two differing environmental backgrounds. When one is interested in the difference between means of strictly two groups, the most common statistical procedure in this situation is known as the t-test. When there are two or more groups the typical approach is to employ the analysis of variance (ANOVA). </p> <p>This course will review the concepts behind two-sample and paired t tests and one and two-way ANOVA models. Course attendees will learn the basic motivation and assumptions of each method. Hypothesis tests, confidence intervals, and measures of effect size will also be described for each approach.</p> <p>To exemplify the t-test and one-way ANOVA, the Egyptian Skulls data set (<a href="http://www.dm.unibo.it/~simoncin/EgyptianSkulls.html" target="_blank" rel="nofollow noopener noreferrer">dm.unibo.it/~simoncin/EgyptianSkulls.html</a>) will be utilized, which consists of measurements of male Egyptian skulls from five different time periods.</p> <p>For a better understanding of the two-way ANOVA, we will incorporate analysis of a data set from a lung cancer study at the VA (<a href="http://biostat.mc.vanderbilt.edu/wiki/Main/DataSets" target="_blank" rel="nofollow noopener noreferrer">biostat.mc.vanderbilt.edu/wiki/Main/DataSets</a>).</p> <p>The software JMP (<a href="http://www.jmp.com/en_us/home.html" target="_blank" rel="nofollow noopener noreferrer">jmp.com/en_us/home.html</a>) will be used to illustrate topics in this course.</p> <p>Course files are available on the course webpage (<a href="http://www.lisa.stat.vt.edu/?q=node/9612" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/?q=node/9612</a>).</p></p><p><strong>Cast:</strong> <a href="https://vimeo.com/lisavt">LISA</a></p><p><strong>Tags:</strong> <a href="https://vimeo.com/tag:Short+Course">Short Course</a>, <a href="https://vimeo.com/tag:Statistics">Statistics</a>, <a href="https://vimeo.com/tag:LISA">LISA</a>, <a href="https://vimeo.com/tag:Virginia+Tech">Virginia Tech</a>, <a href="https://vimeo.com/tag:T-tests">T-tests</a>, <a href="https://vimeo.com/tag:ANOVA">ANOVA</a> and <a href="https://vimeo.com/tag:JMP">JMP</a></p>tag:vimeo,2015-10-23:clip143386639LISA Short Course: T-tests & ANOVA, Part ILISA Short Course: Basics of R, Part IIThu, 15 Oct 2015 16:32:05 -0400https://vimeo.com/142557219<p><iframe src="https://player.vimeo.com/video/142557219" width="640" height="400" frameborder="0" title="LISA Short Course: Basics of R, Part II" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe></p><p><p class="first">LISA (Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data.</p> <p>Tuesday, September 29, 2015, 5:15-6:45 pm;<br> Location: 1080 Torgersen Hall;<br> Instructor: Ana Maria Ortega Villa;<br> Title: Basics of R;</p> <p>R is a powerful, versatile, and free statistical programming language, which has become increasingly popular among industrial and academic data analysts. This introductory course covers programming basics in R, including the definition and manipulation of data objects, importing/exporting data, simple data summaries, simple graphs and if time permits, simple linear regression. These concepts will be illustrated using both the Home Prices Data and also the National Longitudinal Mortality Survey. The Home Prices Data is a random sample of records of resales of homes from the files maintained by the Albuquerque Board of Realtors and will be used to illustrate the basic principles listed above. The power of R will then be demonstrated by performing similar operations on the National Longitudinal Mortality Survey, which includes nearly a million records with 38 measurements each. The course format includes lecture and computer laboratory components and attendees will have the opportunity to write, modify, and execute R codes for these data.</p> <p>This introductory session is part of a three-course series which assumes no previous coding experience in R or any other language. The intended audience for this course includes researchers who want to gain basic exposure to R with the ultimate goal of incorporating R into their research programs. More experienced users may wish to skip this course and attend subsequent courses on statistical and graphical techniques using R.</p> <p>Course files are available on the course webpage (<a href="http://www.lisa.stat.vt.edu/?q=node/9611" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/?q=node/9611</a>).</p></p><p><strong>Cast:</strong> <a href="https://vimeo.com/lisavt">LISA</a></p><p><strong>Tags:</strong> <a href="https://vimeo.com/tag:Virginia+Tech">Virginia Tech</a>, <a href="https://vimeo.com/tag:LISA">LISA</a>, <a href="https://vimeo.com/tag:Basics+of+R">Basics of R</a>, <a href="https://vimeo.com/tag:importing+data">importing data</a>, <a href="https://vimeo.com/tag:Short+Course">Short Course</a>, <a href="https://vimeo.com/tag:data+summaries">data summaries</a>, <a href="https://vimeo.com/tag:exporting+data">exporting data</a>, <a href="https://vimeo.com/tag:data+objects">data objects</a>, <a href="https://vimeo.com/tag:graphs">graphs</a>, <a href="https://vimeo.com/tag:simple+linear+regression">simple linear regression</a> and <a href="https://vimeo.com/tag:Statistics">Statistics</a></p>tag:vimeo,2015-10-15:clip142557219LISA Short Course: Basics of R, Part IILISA Short Course: Basics of R, Part IWed, 07 Oct 2015 10:25:02 -0400https://vimeo.com/141671644<p><iframe src="https://player.vimeo.com/video/141671644" width="640" height="400" frameborder="0" title="LISA Short Course: Basics of R, Part I" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe></p><p><p class="first">LISA (Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data.</p> <p>Tuesday, September 29, 2015, 5:15-6:45 pm;<br> Location: 1080 Torgersen Hall;<br> Instructor: Ana Maria Ortega Villa;<br> Title: Basics of R;</p> <p>R is a powerful, versatile, and free statistical programming language, which has become increasingly popular among industrial and academic data analysts. This introductory course covers programming basics in R, including the definition and manipulation of data objects, importing/exporting data, simple data summaries, simple graphs and if time permits, simple linear regression. These concepts will be illustrated using both the Home Prices Data and also the National Longitudinal Mortality Survey. The Home Prices Data is a random sample of records of resales of homes from the files maintained by the Albuquerque Board of Realtors and will be used to illustrate the basic principles listed above. The power of R will then be demonstrated by performing similar operations on the National Longitudinal Mortality Survey, which includes nearly a million records with 38 measurements each. The course format includes lecture and computer laboratory components and attendees will have the opportunity to write, modify, and execute R codes for these data.</p> <p>This introductory session is part of a three-course series which assumes no previous coding experience in R or any other language. The intended audience for this course includes researchers who want to gain basic exposure to R with the ultimate goal of incorporating R into their research programs. More experienced users may wish to skip this course and attend subsequent courses on statistical and graphical techniques using R.</p> <p>Course files are available on the course webpage (<a href="http://www.lisa.stat.vt.edu/?q=node/9611" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/?q=node/9611</a>).</p></p><p><strong>Cast:</strong> <a href="https://vimeo.com/lisavt">LISA</a></p><p><strong>Tags:</strong> <a href="https://vimeo.com/tag:Virginia+Tech">Virginia Tech</a>, <a href="https://vimeo.com/tag:LISA">LISA</a>, <a href="https://vimeo.com/tag:data+objects">data objects</a>, <a href="https://vimeo.com/tag:data+summaries">data summaries</a>, <a href="https://vimeo.com/tag:Statistics">Statistics</a>, <a href="https://vimeo.com/tag:Short+Course">Short Course</a>, <a href="https://vimeo.com/tag:importing+data">importing data</a>, <a href="https://vimeo.com/tag:Basics+of+R">Basics of R</a>, <a href="https://vimeo.com/tag:exporting+data">exporting data</a>, <a href="https://vimeo.com/tag:graphs">graphs</a> and <a href="https://vimeo.com/tag:simple+linear+regression">simple linear regression</a></p>tag:vimeo,2015-10-07:clip141671644LISA Short Course: Basics of R, Part ILISA Short Course: Multivariate Clustering Analysis in RTue, 04 Aug 2015 13:49:18 -0400https://vimeo.com/135381791<p><iframe src="https://player.vimeo.com/video/135381791" width="640" height="480" frameborder="0" title="LISA Short Course: Multivariate Clustering Analysis in R" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe></p><p><p class="first">LISA (Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data.</p> <p>Wednesday, July 22, 2015, 4:00-6:00 pm;<br> Location: 1080 Torgersen Hall;<br> Instructor: Yuhyun Song;<br> Title: Multivariate Clustering Analysis in R;</p> <p>Multivariate analysis in statistics is a set of useful methods for analyzing data when there are more than one variables under consideration. Multivariate analysis techniques may be used for several purposes, such as dimension reduction, clustering, or classification. The primary goal of this short course is to help researchers who want to understand multivariate data and explore multivariate analysis tools.</p> <p>In this course, we briefly talk about general multivariate analysis, then concentrate on clustering techniques. The goal of clustering analysis is to establish a set of meaningful groups of similar objects by investigating relationships between objects. For example, if you have data from customers, you may segment customers into clusters based on their buying habits and their demographical characteristics. Then, you can use clustering results to custom tailor your marketing efforts. In this course, we will explore two popular clustering techniques: Agglomerative hierarchical clustering and K-means clustering algorithm. Also, we discuss how to choose the number of clusters and how to visualize the clustering solutions. R software will be used in this course.</p> <p>This course covers:<br> - What is clustering analysis? Why is clustering analysis important?<br> - Agglomerative hierarchical clustering algorithm<br> - K-means clustering algorithm<br> - How do we choose the number of clusters?<br> - How to visualize the clustering solutions</p> <p>Data Set:</p> <p>The data set can be downloaded at <a href="http://archive.ics.uci.edu/ml/datasets/Wine" target="_blank" rel="nofollow noopener noreferrer">archive.ics.uci.edu/ml/datasets/Wine</a>. The data set includes 178 wines grown in the same region in Italy. 13 attributes which are chemical analysis results of wines were measured from each wine. We will use this data set for exploring the clustering algorithms.</p> <p>The graph below shows the clustering results by the K-means clustering method (<a href="http://www.lisa.stat.vt.edu/sites/default/files/images/K-means-clustering.png" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/sites/default/files/images/K-means-clustering.png</a>).</p> <p>Course files are available on the course webpage (<a href="http://www.lisa.stat.vt.edu/?q=node/9082" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/?q=node/9082</a>).</p></p><p><strong>Cast:</strong> <a href="https://vimeo.com/lisavt">LISA</a></p>tag:vimeo,2015-08-04:clip135381791LISA Short Course: Multivariate Clustering Analysis in RLISA Short Course: Graphics in R, Part IITue, 28 Jul 2015 11:27:20 -0400https://vimeo.com/134740277<p><iframe src="https://player.vimeo.com/video/134740277" width="640" height="480" frameborder="0" title="LISA Short Course: Graphics in R, Part II" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe></p><p><p class="first">LISA (Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data.</p> <p>Wednesday, July 15, 2015, 4:00-6:00 pm;<br> Location: 1080 Torgersen Hall;<br> Instructor: Will DeShong;<br> Title: Graphics in R;</p> <p>R is a powerful, versatile, and free statistical programming language, which has become increasingly popular among industrial and academic data analysts. This introductory course covers programming basics in R, including the definition and manipulation of data objects, importing/exporting data, simple data summaries, simple graphs and if time permits, simple linear regression. These concepts will be illustrated using both the Home Prices Data and also the National Longitudinal Mortality Survey. The Home Prices Data is a random sample of records of resales of homes from the files maintained by the Albuquerque Board of Realtors and will be used to illustrate the basic principles listed above. The power of R will then be demonstrated by performing similar operations on the National Longitudinal Mortality Survey, which includes nearly a million records with 38 measurements each. The course format includes lecture and computer laboratory components and attendees will have the opportunity to write, modify, and execute R codes for these data.</p> <p>This introductory session is part of a three-course series which assumes no previous coding experience in R or any other language. The intended audience for this course includes researchers who want to gain basic exposure to R with the ultimate goal of incorporating R into their research programs. More experienced users may wish to skip this course and attend subsequent courses on statistical and graphical techniques using R.</p> <p>Course files are available on the course webpage (<a href="http://www.lisa.stat.vt.edu/?q=node/9079" target="_blank" rel="nofollow noopener noreferrer">lisa.stat.vt.edu/?q=node/9079</a>).</p></p><p><strong>Cast:</strong> <a href="https://vimeo.com/lisavt">LISA</a></p>tag:vimeo,2015-07-28:clip134740277LISA Short Course: Graphics in R, Part II