LISA: Laboratory for Interdisciplinary Statistical Analysis - Short Courses

R is a free computing and graphical software/environment for statistical analysis. Part III of this short course consists of 3 sections: Section 5 introduces the concept of generalized linear models. R will be used for performing logistic regression and Poisson regression. Section 6 introduces the concept of categorical data analysis. Topics to be covered include: graphical displays of categorical data, measures of association, and contingency tables analyses. Section 7 will cover writing functions in R. Users can write functions in R to carry out operations and return one or more values. Examples of functions will be given and participants will also be given exercises to help with writing their own functions. Note: experience using R or attending Part I and Part II of this series is suggested but not required for Part III. R can be downloaded here: http://www.r-project.org/ RStudio can be downloaded here: http://rstudio.org/download/desktop Course files available here:www.lisa.stat.vt.edu/?q=node/5039.

R is a free computing and graphical software/environment for statistical analysis. Part III of this short course consists of 3 sections: Section 5 introduces the concept of generalized linear models. R will be used for performing logistic regression and Poisson regression. Section 6 introduces the concept of categorical data analysis. Topics to be covered include: graphical displays of categorical data, measures of association, and contingency tables analyses. Section 7 will cover writing functions in R. Users can write functions in R to carry out operations and return one or more values. Examples of functions will be given and participants will also be given exercises to help with writing their own functions. Note: experience using R or attending Part I and Part II of this series is suggested but not required for Part III. R can be downloaded here: http://www.r-project.org/ RStudio can be downloaded here: http://rstudio.org/download/desktop Course files available here: www.lisa.stat.vt.edu/?q=node/5039.

Structural equation modeling (SEM) encompasses such diverse statistical techniques as path analysis, confirmatory factor analysis, causal modeling with latent variables, and even analysis of variance and multiple linear regression models. This short course features an introduction to the logic of SEM, the assumptions and required input for SEM analysis, and how to perform SEM analyses using the AMOS (Analysis of Moment Structures) software. We also will cover time–related latent variables, the use of modification indices and critical ratio in exploratory analyses, computation of implied moments, factor score weights, total effects, and indirect effects. Course files available here: www.lisa.stat.vt.edu/?q=node/4875.

Visualizing your data is an essential first step for any statistical analysis and often times the graph itself provides the best statistical interpretation of the data. This course will present principles of good graphics and provide examples to help you answer your research question graphically using R and JMP. Course files available here: www.lisa.stat.vt.edu/?q=node/4694.

R is a free computing and graphical software/environment for statistical analysis. Part II of this short course consists of 2 sections: Section 3 introduces statistical analysis in R, including t-tests, ANOVA, linear regression, nonparametric tests, and logistic regression. Section 4 contains advanced R programming concepts such as writing functions, data simulation, and the statistical bootstrap. Note: experience using R or attending Part I of this series is suggested but not required for Part II. R can be downloaded here: http://www.r-project.org/ RStudio can be downloaded here: http://rstudio.org/download/desktop Course files available here: www.lisa.stat.vt.edu/?q=node/4693.

This course will follow the Designing Surveys course focusing on analyzing the data collected from surveys. Different statistical approaches will be discussed as well as graphing and presentation of results. Course files available here: www.lisa.stat.vt.edu/?q=node/4691.

In this short course, learn how to design a survey to strengthen your research by applying statistical thinking to designing your survey and learning the five steps necessary for collecting data that will answer your research questions. We will cover how to select your sample (including sample size calculations), how to design a questionnaire to minimize errors and biases inherent in surveys, how to pilot test and re-test your survey, and how to implement your survey. You will have opportunities to apply what you’ve learned to your own problems and, at the end, to ask LISA statistical collaborators any questions you might still have about designing surveys. This course is broken up into 6 parts: Section 1 is Introductions Section 2 is Survey Fundamentals Section 3 is Questionnaire Design (is broken down into Part 1 and 2) Section 4 is Survey Implementation Section 5 is Preparing for Data Analysis Section 6 is Questions and Answers (not available for download) Course files are available here: www.lisa.stat.vt.edu/?q=node/4690.

In this short course, learn how to design a survey to strengthen your research by applying statistical thinking to designing your survey and learning the five steps necessary for collecting data that will answer your research questions. We will cover how to select your sample (including sample size calculations), how to design a questionnaire to minimize errors and biases inherent in surveys, how to pilot test and re-test your survey, and how to implement your survey. You will have opportunities to apply what you’ve learned to your own problems and, at the end, to ask LISA statistical collaborators any questions you might still have about designing surveys. This course is broken up into 6 parts: Section 1 is Introductions Section 2 is Survey Fundamentals Section 3 is Questionnaire Design (is broken down into Part 1 and 2) Section 4 is Survey Implementation Section 5 is Preparing for Data Analysis Section 6 is Questions and Answers (not available for download) Course files are available here: www.lisa.stat.vt.edu/?q=node/4690.

In this short course, learn how to design a survey to strengthen your research by applying statistical thinking to designing your survey and learning the five steps necessary for collecting data that will answer your research questions. We will cover how to select your sample (including sample size calculations), how to design a questionnaire to minimize errors and biases inherent in surveys, how to pilot test and re-test your survey, and how to implement your survey. You will have opportunities to apply what you’ve learned to your own problems and, at the end, to ask LISA statistical collaborators any questions you might still have about designing surveys. This course is broken up into 6 parts: Section 1 is Introductions Section 2 is Survey Fundamentals Section 3 is Questionnaire Design (is broken down into Part 1 and 2) Section 4 is Survey Implementation Section 5 is Preparing for Data Analysis Section 6 is Questions and Answers (not available for download) Course files are available here: www.lisa.stat.vt.edu/?q=node/4690.

In this short course, learn how to design a survey to strengthen your research by applying statistical thinking to designing your survey and learning the five steps necessary for collecting data that will answer your research questions. We will cover how to select your sample (including sample size calculations), how to design a questionnaire to minimize errors and biases inherent in surveys, how to pilot test and re-test your survey, and how to implement your survey. You will have opportunities to apply what you’ve learned to your own problems and, at the end, to ask LISA statistical collaborators any questions you might still have about designing surveys. This course is broken up into 6 parts: Section 1 is Introductions Section 2 is Survey Fundamentals Section 3 is Questionnaire Design (is broken down into Part 1 and 2) Section 4 is Survey Implementation Section 5 is Preparing for Data Analysis Section 6 is Questions and Answers (not available for download) Course files are available here: www.lisa.stat.vt.edu/?q=node/4690.

In this short course, learn how to design a survey to strengthen your research by applying statistical thinking to designing your survey and learning the five steps necessary for collecting data that will answer your research questions. We will cover how to select your sample (including sample size calculations), how to design a questionnaire to minimize errors and biases inherent in surveys, how to pilot test and re-test your survey, and how to implement your survey. You will have opportunities to apply what you’ve learned to your own problems and, at the end, to ask LISA statistical collaborators any questions you might still have about designing surveys. This course is broken up into 6 parts: Section 1 is Introductions Section 2 is Survey Fundamentals Section 3 is Questionnaire Design (is broken down into Part 1 and 2) Section 4 is Survey Implementation Section 5 is Preparing for Data Analysis Section 6 is Questions and Answers (not available for download) Course files are available here: www.lisa.stat.vt.edu/?q=node/4690.

In this short course, learn how to design a survey to strengthen your research by applying statistical thinking to designing your survey and learning the five steps necessary for collecting data that will answer your research questions. We will cover how to select your sample (including sample size calculations), how to design a questionnaire to minimize errors and biases inherent in surveys, how to pilot test and re-test your survey, and how to implement your survey. You will have opportunities to apply what you’ve learned to your own problems and, at the end, to ask LISA statistical collaborators any questions you might still have about designing surveys. This course is broken up into 6 parts: Section 1 is Introductions Section 2 is Survey Fundamentals Section 3 is Questionnaire Design (is broken down into Part 1 and 2) Section 4 is Survey Implementation Section 5 is Preparing for Data Analysis Section 6 is Questions and Answers (not available for download) Course files are available here: www.lisa.stat.vt.edu/?q=node/4690.

Across all disciplines, the ability to test theories by experimentation is vital for validation and discovery. When designing an experiment, the researcher hopes to maximize the obtained information by reducing wasted resources and allocating treatments in such a way as to minimize variances. Ideally, a design will account for major sources of variation so that the researcher can be confident the effects of treatments are not confounded with extraneous factors. In this course, the basic principles of experimental design will be given and specific designs discussed. The first designs introduced will be completely randomized designs, the most straightforward design when a researcher wants to test for differences among multiple treatments. Optimal blocking strategies will then be presented as a variance-reducing technique, e.g. perhaps the researcher feels a subject's gender may significantly affect observations. For each design we will discuss implementation, appropriate analysis and provide examples in SAS. If time permits we may also introduce more complicated designs tailored specifically to the researchers attending the course. This 2 hour course is broken up into 18 parts. 1 - Introduction 2 - Obs Study vs Designed Experiment 3 - Design Example 4 - Design Fundamentals 5 - Lady Tasting Tea Example (Assignment) 6 - Intro to completely Randomized Design 7 - Analysis - Linear Model 8 - CRD Linear Model; ANOVA; and Estimability 9 - CRD Analysis Example in JMP 10 - Factorial Treatment; Linear Model; ANOVA 11 - Factorial Analysis Example in JMP 12 - Exercises 13 - ANCOVA; Linear Model 14 - ANCOVA Example in JMP 15 - Blocking 16 - Randomized Complete Block Design 17 - RCBD Analysis Example in JMP 18 - Blocking vs ANCOVA Course files available here: www.lisa.stat.vt.edu/?q=node/4687.

Across all disciplines, the ability to test theories by experimentation is vital for validation and discovery. When designing an experiment, the researcher hopes to maximize the obtained information by reducing wasted resources and allocating treatments in such a way as to minimize variances. Ideally, a design will account for major sources of variation so that the researcher can be confident the effects of treatments are not confounded with extraneous factors. In this course, the basic principles of experimental design will be given and specific designs discussed. The first designs introduced will be completely randomized designs, the most straightforward design when a researcher wants to test for differences among multiple treatments. Optimal blocking strategies will then be presented as a variance-reducing technique, e.g. perhaps the researcher feels a subject's gender may significantly affect observations. For each design we will discuss implementation, appropriate analysis and provide examples in SAS. If time permits we may also introduce more complicated designs tailored specifically to the researchers attending the course. This 2 hour course is broken up into 18 parts. 1 - Introduction 2 - Obs Study vs Designed Experiment 3 - Design Example 4 - Design Fundamentals 5 - Lady Tasting Tea Example (Assignment) 6 - Intro to completely Randomized Design 7 - Analysis - Linear Model 8 - CRD Linear Model; ANOVA; and Estimability 9 - CRD Analysis Example in JMP 10 - Factorial Treatment; Linear Model; ANOVA 11 - Factorial Analysis Example in JMP 12 - Exercises 13 - ANCOVA; Linear Model 14 - ANCOVA Example in JMP 15 - Blocking 16 - Randomized Complete Block Design 17 - RCBD Analysis Example in JMP 18 - Blocking vs ANCOVA Course files available here: www.lisa.stat.vt.edu/?q=node/4687.

Across all disciplines, the ability to test theories by experimentation is vital for validation and discovery. When designing an experiment, the researcher hopes to maximize the obtained information by reducing wasted resources and allocating treatments in such a way as to minimize variances. Ideally, a design will account for major sources of variation so that the researcher can be confident the effects of treatments are not confounded with extraneous factors. In this course, the basic principles of experimental design will be given and specific designs discussed. The first designs introduced will be completely randomized designs, the most straightforward design when a researcher wants to test for differences among multiple treatments. Optimal blocking strategies will then be presented as a variance-reducing technique, e.g. perhaps the researcher feels a subject's gender may significantly affect observations. For each design we will discuss implementation, appropriate analysis and provide examples in SAS. If time permits we may also introduce more complicated designs tailored specifically to the researchers attending the course. This 2 hour course is broken up into 18 parts. 1 - Introduction 2 - Obs Study vs Designed Experiment 3 - Design Example 4 - Design Fundamentals 5 - Lady Tasting Tea Example (Assignment) 6 - Intro to completely Randomized Design 7 - Analysis - Linear Model 8 - CRD Linear Model; ANOVA; and Estimability 9 - CRD Analysis Example in JMP 10 - Factorial Treatment; Linear Model; ANOVA 11 - Factorial Analysis Example in JMP 12 - Exercises 13 - ANCOVA; Linear Model 14 - ANCOVA Example in JMP 15 - Blocking 16 - Randomized Complete Block Design 17 - RCBD Analysis Example in JMP 18 - Blocking vs ANCOVA Course files available here: www.lisa.stat.vt.edu/?q=node/4687.

Across all disciplines, the ability to test theories by experimentation is vital for validation and discovery. When designing an experiment, the researcher hopes to maximize the obtained information by reducing wasted resources and allocating treatments in such a way as to minimize variances. Ideally, a design will account for major sources of variation so that the researcher can be confident the effects of treatments are not confounded with extraneous factors. In this course, the basic principles of experimental design will be given and specific designs discussed. The first designs introduced will be completely randomized designs, the most straightforward design when a researcher wants to test for differences among multiple treatments. Optimal blocking strategies will then be presented as a variance-reducing technique, e.g. perhaps the researcher feels a subject's gender may significantly affect observations. For each design we will discuss implementation, appropriate analysis and provide examples in SAS. If time permits we may also introduce more complicated designs tailored specifically to the researchers attending the course. This 2 hour course is broken up into 18 parts. 1 - Introduction 2 - Obs Study vs Designed Experiment 3 - Design Example 4 - Design Fundamentals 5 - Lady Tasting Tea Example (Assignment) 6 - Intro to completely Randomized Design 7 - Analysis - Linear Model 8 - CRD Linear Model; ANOVA; and Estimability 9 - CRD Analysis Example in JMP 10 - Factorial Treatment; Linear Model; ANOVA 11 - Factorial Analysis Example in JMP 12 - Exercises 13 - ANCOVA; Linear Model 14 - ANCOVA Example in JMP 15 - Blocking 16 - Randomized Complete Block Design 17 - RCBD Analysis Example in JMP 18 - Blocking vs ANCOVA Course files available here: www.lisa.stat.vt.edu/?q=node/4687.

Across all disciplines, the ability to test theories by experimentation is vital for validation and discovery. When designing an experiment, the researcher hopes to maximize the obtained information by reducing wasted resources and allocating treatments in such a way as to minimize variances. Ideally, a design will account for major sources of variation so that the researcher can be confident the effects of treatments are not confounded with extraneous factors. In this course, the basic principles of experimental design will be given and specific designs discussed. The first designs introduced will be completely randomized designs, the most straightforward design when a researcher wants to test for differences among multiple treatments. Optimal blocking strategies will then be presented as a variance-reducing technique, e.g. perhaps the researcher feels a subject's gender may significantly affect observations. For each design we will discuss implementation, appropriate analysis and provide examples in SAS. If time permits we may also introduce more complicated designs tailored specifically to the researchers attending the course. This 2 hour course is broken up into 18 parts. 1 - Introduction 2 - Obs Study vs Designed Experiment 3 - Design Example 4 - Design Fundamentals 5 - Lady Tasting Tea Example (Assignment) 6 - Intro to completely Randomized Design 7 - Analysis - Linear Model 8 - CRD Linear Model; ANOVA; and Estimability 9 - CRD Analysis Example in JMP 10 - Factorial Treatment; Linear Model; ANOVA 11 - Factorial Analysis Example in JMP 12 - Exercises 13 - ANCOVA; Linear Model 14 - ANCOVA Example in JMP 15 - Blocking 16 - Randomized Complete Block Design 17 - RCBD Analysis Example in JMP 18 - Blocking vs ANCOVA Course files available here: www.lisa.stat.vt.edu/?q=node/4687.

Across all disciplines, the ability to test theories by experimentation is vital for validation and discovery. When designing an experiment, the researcher hopes to maximize the obtained information by reducing wasted resources and allocating treatments in such a way as to minimize variances. Ideally, a design will account for major sources of variation so that the researcher can be confident the effects of treatments are not confounded with extraneous factors. In this course, the basic principles of experimental design will be given and specific designs discussed. The first designs introduced will be completely randomized designs, the most straightforward design when a researcher wants to test for differences among multiple treatments. Optimal blocking strategies will then be presented as a variance-reducing technique, e.g. perhaps the researcher feels a subject's gender may significantly affect observations. For each design we will discuss implementation, appropriate analysis and provide examples in SAS. If time permits we may also introduce more complicated designs tailored specifically to the researchers attending the course. This 2 hour course is broken up into 18 parts. 1 - Introduction 2 - Obs Study vs Designed Experiment 3 - Design Example 4 - Design Fundamentals 5 - Lady Tasting Tea Example (Assignment) 6 - Intro to completely Randomized Design 7 - Analysis - Linear Model 8 - CRD Linear Model; ANOVA; and Estimability 9 - CRD Analysis Example in JMP 10 - Factorial Treatment; Linear Model; ANOVA 11 - Factorial Analysis Example in JMP 12 - Exercises 13 - ANCOVA; Linear Model 14 - ANCOVA Example in JMP 15 - Blocking 16 - Randomized Complete Block Design 17 - RCBD Analysis Example in JMP 18 - Blocking vs ANCOVA Course files available here: www.lisa.stat.vt.edu/?q=node/4687.

Across all disciplines, the ability to test theories by experimentation is vital for validation and discovery. When designing an experiment, the researcher hopes to maximize the obtained information by reducing wasted resources and allocating treatments in such a way as to minimize variances. Ideally, a design will account for major sources of variation so that the researcher can be confident the effects of treatments are not confounded with extraneous factors. In this course, the basic principles of experimental design will be given and specific designs discussed. The first designs introduced will be completely randomized designs, the most straightforward design when a researcher wants to test for differences among multiple treatments. Optimal blocking strategies will then be presented as a variance-reducing technique, e.g. perhaps the researcher feels a subject's gender may significantly affect observations. For each design we will discuss implementation, appropriate analysis and provide examples in SAS. If time permits we may also introduce more complicated designs tailored specifically to the researchers attending the course. This 2 hour course is broken up into 18 parts. 1 - Introduction 2 - Obs Study vs Designed Experiment 3 - Design Example 4 - Design Fundamentals 5 - Lady Tasting Tea Example (Assignment) 6 - Intro to completely Randomized Design 7 - Analysis - Linear Model 8 - CRD Linear Model; ANOVA; and Estimability 9 - CRD Analysis Example in JMP 10 - Factorial Treatment; Linear Model; ANOVA 11 - Factorial Analysis Example in JMP 12 - Exercises 13 - ANCOVA; Linear Model 14 - ANCOVA Example in JMP 15 - Blocking 16 - Randomized Complete Block Design 17 - RCBD Analysis Example in JMP 18 - Blocking vs ANCOVA Course files available here: www.lisa.stat.vt.edu/?q=node/4687.

Across all disciplines, the ability to test theories by experimentation is vital for validation and discovery. When designing an experiment, the researcher hopes to maximize the obtained information by reducing wasted resources and allocating treatments in such a way as to minimize variances. Ideally, a design will account for major sources of variation so that the researcher can be confident the effects of treatments are not confounded with extraneous factors. In this course, the basic principles of experimental design will be given and specific designs discussed. The first designs introduced will be completely randomized designs, the most straightforward design when a researcher wants to test for differences among multiple treatments. Optimal blocking strategies will then be presented as a variance-reducing technique, e.g. perhaps the researcher feels a subject's gender may significantly affect observations. For each design we will discuss implementation, appropriate analysis and provide examples in SAS. If time permits we may also introduce more complicated designs tailored specifically to the researchers attending the course. This 2 hour course is broken up into 18 parts. 1 - Introduction 2 - Obs Study vs Designed Experiment 3 - Design Example 4 - Design Fundamentals 5 - Lady Tasting Tea Example (Assignment) 6 - Intro to completely Randomized Design 7 - Analysis - Linear Model 8 - CRD Linear Model; ANOVA; and Estimability 9 - CRD Analysis Example in JMP 10 - Factorial Treatment; Linear Model; ANOVA 11 - Factorial Analysis Example in JMP 12 - Exercises 13 - ANCOVA; Linear Model 14 - ANCOVA Example in JMP 15 - Blocking 16 - Randomized Complete Block Design 17 - RCBD Analysis Example in JMP 18 - Blocking vs ANCOVA Course files available here: www.lisa.stat.vt.edu/?q=node/4687.

Across all disciplines, the ability to test theories by experimentation is vital for validation and discovery. When designing an experiment, the researcher hopes to maximize the obtained information by reducing wasted resources and allocating treatments in such a way as to minimize variances. Ideally, a design will account for major sources of variation so that the researcher can be confident the effects of treatments are not confounded with extraneous factors. In this course, the basic principles of experimental design will be given and specific designs discussed. The first designs introduced will be completely randomized designs, the most straightforward design when a researcher wants to test for differences among multiple treatments. Optimal blocking strategies will then be presented as a variance-reducing technique, e.g. perhaps the researcher feels a subject's gender may significantly affect observations. For each design we will discuss implementation, appropriate analysis and provide examples in SAS. If time permits we may also introduce more complicated designs tailored specifically to the researchers attending the course. This 2 hour course is broken up into 18 parts. 1 - Introduction 2 - Obs Study vs Designed Experiment 3 - Design Example 4 - Design Fundamentals 5 - Lady Tasting Tea Example (Assignment) 6 - Intro to completely Randomized Design 7 - Analysis - Linear Model 8 - CRD Linear Model; ANOVA; and Estimability 9 - CRD Analysis Example in JMP 10 - Factorial Treatment; Linear Model; ANOVA 11 - Factorial Analysis Example in JMP 12 - Exercises 13 - ANCOVA; Linear Model 14 - ANCOVA Example in JMP 15 - Blocking 16 - Randomized Complete Block Design 17 - RCBD Analysis Example in JMP 18 - Blocking vs ANCOVA Course files available here: www.lisa.stat.vt.edu/?q=node/4687.

Across all disciplines, the ability to test theories by experimentation is vital for validation and discovery. When designing an experiment, the researcher hopes to maximize the obtained information by reducing wasted resources and allocating treatments in such a way as to minimize variances. Ideally, a design will account for major sources of variation so that the researcher can be confident the effects of treatments are not confounded with extraneous factors. In this course, the basic principles of experimental design will be given and specific designs discussed. The first designs introduced will be completely randomized designs, the most straightforward design when a researcher wants to test for differences among multiple treatments. Optimal blocking strategies will then be presented as a variance-reducing technique, e.g. perhaps the researcher feels a subject's gender may significantly affect observations. For each design we will discuss implementation, appropriate analysis and provide examples in SAS. If time permits we may also introduce more complicated designs tailored specifically to the researchers attending the course. This 2 hour course is broken up into 18 parts. 1 - Introduction 2 - Obs Study vs Designed Experiment 3 - Design Example 4 - Design Fundamentals 5 - Lady Tasting Tea Example (Assignment) 6 - Intro to completely Randomized Design 7 - Analysis - Linear Model 8 - CRD Linear Model; ANOVA; and Estimability 9 - CRD Analysis Example in JMP 10 - Factorial Treatment; Linear Model; ANOVA 11 - Factorial Analysis Example in JMP 12 - Exercises 13 - ANCOVA; Linear Model 14 - ANCOVA Example in JMP 15 - Blocking 16 - Randomized Complete Block Design 17 - RCBD Analysis Example in JMP 18 - Blocking vs ANCOVA Course files available here: www.lisa.stat.vt.edu/?q=node/4687.

Across all disciplines, the ability to test theories by experimentation is vital for validation and discovery. When designing an experiment, the researcher hopes to maximize the obtained information by reducing wasted resources and allocating treatments in such a way as to minimize variances. Ideally, a design will account for major sources of variation so that the researcher can be confident the effects of treatments are not confounded with extraneous factors. In this course, the basic principles of experimental design will be given and specific designs discussed. The first designs introduced will be completely randomized designs, the most straightforward design when a researcher wants to test for differences among multiple treatments. Optimal blocking strategies will then be presented as a variance-reducing technique, e.g. perhaps the researcher feels a subject's gender may significantly affect observations. For each design we will discuss implementation, appropriate analysis and provide examples in SAS. If time permits we may also introduce more complicated designs tailored specifically to the researchers attending the course. This 2 hour course is broken up into 18 parts. 1 - Introduction 2 - Obs Study vs Designed Experiment 3 - Design Example 4 - Design Fundamentals 5 - Lady Tasting Tea Example (Assignment) 6 - Intro to completely Randomized Design 7 - Analysis - Linear Model 8 - CRD Linear Model; ANOVA; and Estimability 9 - CRD Analysis Example in JMP 10 - Factorial Treatment; Linear Model; ANOVA 11 - Factorial Analysis Example in JMP 12 - Exercises 13 - ANCOVA; Linear Model 14 - ANCOVA Example in JMP 15 - Blocking 16 - Randomized Complete Block Design 17 - RCBD Analysis Example in JMP 18 - Blocking vs ANCOVA Course files available here: www.lisa.stat.vt.edu/?q=node/4687.

Across all disciplines, the ability to test theories by experimentation is vital for validation and discovery. When designing an experiment, the researcher hopes to maximize the obtained information by reducing wasted resources and allocating treatments in such a way as to minimize variances. Ideally, a design will account for major sources of variation so that the researcher can be confident the effects of treatments are not confounded with extraneous factors. In this course, the basic principles of experimental design will be given and specific designs discussed. The first designs introduced will be completely randomized designs, the most straightforward design when a researcher wants to test for differences among multiple treatments. Optimal blocking strategies will then be presented as a variance-reducing technique, e.g. perhaps the researcher feels a subject's gender may significantly affect observations. For each design we will discuss implementation, appropriate analysis and provide examples in SAS. If time permits we may also introduce more complicated designs tailored specifically to the researchers attending the course. This 2 hour course is broken up into 18 parts. 1 - Introduction 2 - Obs Study vs Designed Experiment 3 - Design Example 4 - Design Fundamentals 5 - Lady Tasting Tea Example (Assignment) 6 - Intro to completely Randomized Design 7 - Analysis - Linear Model 8 - CRD Linear Model; ANOVA; and Estimability 9 - CRD Analysis Example in JMP 10 - Factorial Treatment; Linear Model; ANOVA 11 - Factorial Analysis Example in JMP 12 - Exercises 13 - ANCOVA; Linear Model 14 - ANCOVA Example in JMP 15 - Blocking 16 - Randomized Complete Block Design 17 - RCBD Analysis Example in JMP 18 - Blocking vs ANCOVA Course files available here: www.lisa.stat.vt.edu/?q=node/4687.

Across all disciplines, the ability to test theories by experimentation is vital for validation and discovery. When designing an experiment, the researcher hopes to maximize the obtained information by reducing wasted resources and allocating treatments in such a way as to minimize variances. Ideally, a design will account for major sources of variation so that the researcher can be confident the effects of treatments are not confounded with extraneous factors. In this course, the basic principles of experimental design will be given and specific designs discussed. The first designs introduced will be completely randomized designs, the most straightforward design when a researcher wants to test for differences among multiple treatments. Optimal blocking strategies will then be presented as a variance-reducing technique, e.g. perhaps the researcher feels a subject's gender may significantly affect observations. For each design we will discuss implementation, appropriate analysis and provide examples in SAS. If time permits we may also introduce more complicated designs tailored specifically to the researchers attending the course. This 2 hour course is broken up into 18 parts. 1 - Introduction 2 - Obs Study vs Designed Experiment 3 - Design Example 4 - Design Fundamentals 5 - Lady Tasting Tea Example (Assignment) 6 - Intro to completely Randomized Design 7 - Analysis - Linear Model 8 - CRD Linear Model; ANOVA; and Estimability 9 - CRD Analysis Example in JMP 10 - Factorial Treatment; Linear Model; ANOVA 11 - Factorial Analysis Example in JMP 12 - Exercises 13 - ANCOVA; Linear Model 14 - ANCOVA Example in JMP 15 - Blocking 16 - Randomized Complete Block Design 17 - RCBD Analysis Example in JMP 18 - Blocking vs ANCOVA Course files available here: www.lisa.stat.vt.edu/?q=node/4687.

Across all disciplines, the ability to test theories by experimentation is vital for validation and discovery. When designing an experiment, the researcher hopes to maximize the obtained information by reducing wasted resources and allocating treatments in such a way as to minimize variances. Ideally, a design will account for major sources of variation so that the researcher can be confident the effects of treatments are not confounded with extraneous factors. In this course, the basic principles of experimental design will be given and specific designs discussed. The first designs introduced will be completely randomized designs, the most straightforward design when a researcher wants to test for differences among multiple treatments. Optimal blocking strategies will then be presented as a variance-reducing technique, e.g. perhaps the researcher feels a subject's gender may significantly affect observations. For each design we will discuss implementation, appropriate analysis and provide examples in SAS. If time permits we may also introduce more complicated designs tailored specifically to the researchers attending the course. This 2 hour course is broken up into 18 parts. 1 - Introduction 2 - Obs Study vs Designed Experiment 3 - Design Example 4 - Design Fundamentals 5 - Lady Tasting Tea Example (Assignment) 6 - Intro to completely Randomized Design 7 - Analysis - Linear Model 8 - CRD Linear Model; ANOVA; and Estimability 9 - CRD Analysis Example in JMP 10 - Factorial Treatment; Linear Model; ANOVA 11 - Factorial Analysis Example in JMP 12 - Exercises 13 - ANCOVA; Linear Model 14 - ANCOVA Example in JMP 15 - Blocking 16 - Randomized Complete Block Design 17 - RCBD Analysis Example in JMP 18 - Blocking vs ANCOVA Course files available here: www.lisa.stat.vt.edu/?q=node/4687.

Across all disciplines, the ability to test theories by experimentation is vital for validation and discovery. When designing an experiment, the researcher hopes to maximize the obtained information by reducing wasted resources and allocating treatments in such a way as to minimize variances. Ideally, a design will account for major sources of variation so that the researcher can be confident the effects of treatments are not confounded with extraneous factors. In this course, the basic principles of experimental design will be given and specific designs discussed. The first designs introduced will be completely randomized designs, the most straightforward design when a researcher wants to test for differences among multiple treatments. Optimal blocking strategies will then be presented as a variance-reducing technique, e.g. perhaps the researcher feels a subject's gender may significantly affect observations. For each design we will discuss implementation, appropriate analysis and provide examples in SAS. If time permits we may also introduce more complicated designs tailored specifically to the researchers attending the course. This 2 hour course is broken up into 18 parts. 1 - Introduction 2 - Obs Study vs Designed Experiment 3 - Design Example 4 - Design Fundamentals 5 - Lady Tasting Tea Example (Assignment) 6 - Intro to completely Randomized Design 7 - Analysis - Linear Model 8 - CRD Linear Model; ANOVA; and Estimability 9 - CRD Analysis Example in JMP 10 - Factorial Treatment; Linear Model; ANOVA 11 - Factorial Analysis Example in JMP 12 - Exercises 13 - ANCOVA; Linear Model 14 - ANCOVA Example in JMP 15 - Blocking 16 - Randomized Complete Block Design 17 - RCBD Analysis Example in JMP 18 - Blocking vs ANCOVA Course files available here: www.lisa.stat.vt.edu/?q=node/4687.

Across all disciplines, the ability to test theories by experimentation is vital for validation and discovery. When designing an experiment, the researcher hopes to maximize the obtained information by reducing wasted resources and allocating treatments in such a way as to minimize variances. Ideally, a design will account for major sources of variation so that the researcher can be confident the effects of treatments are not confounded with extraneous factors. In this course, the basic principles of experimental design will be given and specific designs discussed. The first designs introduced will be completely randomized designs, the most straightforward design when a researcher wants to test for differences among multiple treatments. Optimal blocking strategies will then be presented as a variance-reducing technique, e.g. perhaps the researcher feels a subject's gender may significantly affect observations. For each design we will discuss implementation, appropriate analysis and provide examples in SAS. If time permits we may also introduce more complicated designs tailored specifically to the researchers attending the course. This 2 hour course is broken up into 18 parts. 1 - Introduction 2 - Obs Study vs Designed Experiment 3 - Design Example 4 - Design Fundamentals 5 - Lady Tasting Tea Example (Assignment) 6 - Intro to completely Randomized Design 7 - Analysis - Linear Model 8 - CRD Linear Model; ANOVA; and Estimability 9 - CRD Analysis Example in JMP 10 - Factorial Treatment; Linear Model; ANOVA 11 - Factorial Analysis Example in JMP 12 - Exercises 13 - ANCOVA; Linear Model 14 - ANCOVA Example in JMP 15 - Blocking 16 - Randomized Complete Block Design 17 - RCBD Analysis Example in JMP 18 - Blocking vs ANCOVA Course files available here: www.lisa.stat.vt.edu/?q=node/4687.

Across all disciplines, the ability to test theories by experimentation is vital for validation and discovery. When designing an experiment, the researcher hopes to maximize the obtained information by reducing wasted resources and allocating treatments in such a way as to minimize variances. Ideally, a design will account for major sources of variation so that the researcher can be confident the effects of treatments are not confounded with extraneous factors. In this course, the basic principles of experimental design will be given and specific designs discussed. The first designs introduced will be completely randomized designs, the most straightforward design when a researcher wants to test for differences among multiple treatments. Optimal blocking strategies will then be presented as a variance-reducing technique, e.g. perhaps the researcher feels a subject's gender may significantly affect observations. For each design we will discuss implementation, appropriate analysis and provide examples in SAS. If time permits we may also introduce more complicated designs tailored specifically to the researchers attending the course. This 2 hour course is broken up into 18 parts. 1 - Introduction 2 - Obs Study vs Designed Experiment 3 - Design Example 4 - Design Fundamentals 5 - Lady Tasting Tea Example (Assignment) 6 - Intro to completely Randomized Design 7 - Analysis - Linear Model 8 - CRD Linear Model; ANOVA; and Estimability 9 - CRD Analysis Example in JMP 10 - Factorial Treatment; Linear Model; ANOVA 11 - Factorial Analysis Example in JMP 12 - Exercises 13 - ANCOVA; Linear Model 14 - ANCOVA Example in JMP 15 - Blocking 16 - Randomized Complete Block Design 17 - RCBD Analysis Example in JMP 18 - Blocking vs ANCOVA Course files available here: www.lisa.stat.vt.edu/?q=node/4687.

Across all disciplines, the ability to test theories by experimentation is vital for validation and discovery. When designing an experiment, the researcher hopes to maximize the obtained information by reducing wasted resources and allocating treatments in such a way as to minimize variances. Ideally, a design will account for major sources of variation so that the researcher can be confident the effects of treatments are not confounded with extraneous factors. In this course, the basic principles of experimental design will be given and specific designs discussed. The first designs introduced will be completely randomized designs, the most straightforward design when a researcher wants to test for differences among multiple treatments. Optimal blocking strategies will then be presented as a variance-reducing technique, e.g. perhaps the researcher feels a subject's gender may significantly affect observations. For each design we will discuss implementation, appropriate analysis and provide examples in SAS. If time permits we may also introduce more complicated designs tailored specifically to the researchers attending the course. This 2 hour course is broken up into 18 parts. 1 - Introduction 2 - Obs Study vs Designed Experiment 3 - Design Example 4 - Design Fundamentals 5 - Lady Tasting Tea Example (Assignment) 6 - Intro to completely Randomized Design 7 - Analysis - Linear Model 8 - CRD Linear Model; ANOVA; and Estimability 9 - CRD Analysis Example in JMP 10 - Factorial Treatment; Linear Model; ANOVA 11 - Factorial Analysis Example in JMP 12 - Exercises 13 - ANCOVA; Linear Model 14 - ANCOVA Example in JMP 15 - Blocking 16 - Randomized Complete Block Design 17 - RCBD Analysis Example in JMP 18 - Blocking vs ANCOVA Course files available here: www.lisa.stat.vt.edu/?q=node/4687.

The main focus of this course will be on linear mixed models. That is, linear models with fixed effects and random effects. Some topics we’ll discuss are: When would I want to use a random effect? How does estimation change for random versus fixed effects? How does inference change for random versus fixed effects? How are random effects related to correlated data? Examples in SAS will be provided. Course files available here: www.lisa.stat.vt.edu/?q=node/4190.

R is a free computing and graphical software/environment for statistical analysis. Part II of this short course consists of 2 sections: Section 3 introduces statistical analysis and data visualization in R, including t-tests, ANOVA, linear regression, nonparametric tests, and logistic regression. Section 4 focuses on creating presentation or paper quality graphics. Note: experience using R or attending Part I of this series is suggested but not required for Part II. R can be downloaded here: http://www.r-project.org/ RStudio can be downloaded here: http://rstudio.org/download/desktop Course files available here: www.lisa.stat.vt.edu/?q=node/4189.

In this short course, learn how to use surveys and statistical analysis to strengthen your research. Surveys are a popular research tool that come with easily-overlooked pitfalls. However, the thoughtfully and properly designed survey can elicit very useful data and information. In this course, the principles of error minimization and survey bias will be discussed. Topics covered will include sampling methods, sample size calculations, questionnaire design, exploratory data plotting, and some descriptive statistical analyses. Real-world examples will be presented. Course files available here: www.lisa.stat.vt.edu/?q=node/4187.

Structural equation modeling (SEM) encompasses such diverse statistical techniques as path analysis, confirmatory factor analysis, causal modeling with latent variables, and even analysis of variance and multiple linear regression models. This short course features an introduction to the logic of SEM, the assumptions and required input for SEM analysis, and how to perform SEM analyses using the AMOS (Analysis of Moment Structures) software. We also will cover time–related latent variables, the use of modification indices and critical ratio in exploratory analyses, computation of implied moments, factor score weights, total effects, and indirect effects. Course files available here: www.lisa.stat.vt.edu/?q=node/2319

The classical design (factorial design, response surface design, etc.) may fail to meet our requirements due to an irregular design space, involving categorical variables, a nonstandard model, or unusual requirements of sample size. We will introduce how to use JMP to tackle these design challenges within a unified framework and make best use of your design budget. The topics covered in this course include: computer generated design, augmenting an existing design, and design from a candidate set. Note: The prerequisite for this course is that the audience has taken Statistics in Research (STAT 5616) or has attended or watched a LISA short course on design of experiments: http://www.lisa.stat.vt.edu/?q=node/3374 Course files available here: www.lisa.stat.vt.edu/?q=node/4185.

This short course provides an introduction to JMP statistical software and is designed for people who are not familiar with JMP. It starts with basic data manipulation and moves to some advanced features. Topics covered in this course include importing data, graphing data, descriptive statistics such as numerical summaries, and inferential statistics such as t-tests, ANOVA, and regression. Course files available here: www.lisa.stat.vt.edu/?q=node/4170.

An outline for questions I hope to answer: What is Bayes’ Rule? (lecture portion) ► What is the likelihood? ► What is the prior distribution? ► How should I choose it? ► Why use a conjugate prior? ► What is a subjective versus objective prior? ► What is the posterior distribution? ► How do I use it to make statistical inference? ► How is this inference different from frequentist/classical inference? ► What computational tools do I need in order to make inference? How can I use R to do regression in a Bayesian paradigm? (computer portion) ► What libraries in R support Bayesian analysis? ► How do I use some of these libraries? ► How do I interpret the output? ► How do I produce diagnostic plots? ► What common topics do these libraries not support? ► How can I do them myself? ► How can LISA help me? ► What resources are available to help me Bayesian methods in R? Course files available here: www.lisa.stat.vt.edu/?q=node/3382.

Excel is the most prevalent software used for data storage and analysis. There are a lot of built in statistical functions in Excel along with other more savy features from a free add-in called “Analysis ToolPak.” On the other hand, R is a free and open source program, and one of the most powerful and the fastest-growing statistics packages. Combing the power of Excel and R together into a seamless interface will provide you all the tools and efficiency you need to analyze your data for your research or business endeavors. Course files available here: www.lisa.stat.vt.edu/?q=node/3378.

R is a free computing and graphical software/environment for statistical analysis. Part 2 of this short course consists of 2 sections: Section 3 introduces statistical analysis in R, including t-tests, ANOVA, linear regression, nonparametric tests, and logistic regression. Section 4 contains advanced R programming concepts such as functions, data simulation, the statistical bootstrap, and scraping data from the web using FTP. Note: experience using R or attending Part I of this series is suggested but not required for Part II. R can be downloaded here: http://www.r-project.org/ RStudio can be downloaded here: http://rstudio.org/download/desktop Course files available here: www.lisa.stat.vt.edu/?q=node/3377.

R is a free computing and graphical software/environment for statistical analysis. Part 1 of this short course consists of 2 sections: Section 1 introduces R programming basics, including data objects, loops, and importing/exporting datasets, then these concepts are reinforced using examples of data manipulation/cleaning. Section 2 discusses the R graphics environment and the creation basic statistical plots as well as paper/presentation quality images. This course has a Part 2. It is not required to attend both courses. R can be downloaded here: http://www.r-project.org/ RStudio can be downloaded here: http://rstudio.org/download/desktop Course files available here: www.lisa.stat.vt.edu/?q=node/3376.

Researchers sometimes ask, “What is the best way to know [something about] my subjects?” Often, the answer is simply to “Just ask them!” Surveys are a popular research tool that come with easily-overlooked pitfalls. However, the thoughtfully and properly designed survey can ellicit very useful data and information. Once the data are collected, data visualization methods can provide the researcher with a next step in the analysis. In this course, the principles of error minimization and survey bias will be discussed. Topics covered will include sampling methods, sample size calculations, questionnaire design, and exploratory data plotting. Real-world examples will be presented. Course files available here: www.lisa.stat.vt.edu/?q=node/3375.

Across all disciplines, the ability to test theories by experimentation is vital for validation and discovery. When designing an experiment, the researcher hopes to maximize the obtained information by reducing wasted resources and allocating treatments in such a way as to minimize variances. Ideally, a design will account for major sources of variation so that the researcher can be confident the effects of treatments are not confounded with extraneous factors. In this course, the basic principles of experimental design will be given and specific designs discussed. The first designs introduced will be completely randomized designs, the most straightforward design when a researcher wants to test for differences among multiple treatments. Optimal blocking strategies will then be presented as a variance-reducing technique, e.g. perhaps the researcher feels a subject's gender may significantly affect observations. For each design we will discuss implementation, appropriate analysis and provide examples in SAS. If time permits we may also introduce more complicated designs tailored specifically to the researchers attending the course. Course files available here: www.lisa.stat.vt.edu/?q=node/3374.

This short course is designed for beginners who are not yet familiar with JMP. It will begin with basic data manipulation, demonstrate JMP's summary and analysis capabilities, and move to some advanced features. Topics covered in this course include importing data, graphing data, descriptive statistics, simple inferential tests such as t-tests, ANOVA, and regression. Course files available here: www.lisa.stat.vt.edu/?q=node/2802.

The primary purpose of this short course is to illustrate the steps and the thought process statistical collaborators often go through when given a set of data analyze, particularly data that needs more complicated statistical methods. Heavy emphasis will be placed on graphical methods for exploratory data analysis and diagnosing model fit. The statistical modeling techniques illustrated might be considered advanced to many attendees. However, for the purpose of this course it is more important to understand why the advanced methods are chosen, and not necessarily how to actually implement them. Course files available here: www.lisa.stat.vt.edu/?q=node/2807.

Across all disciplines, the ability to test theories by experimentation is vital for validation and discovery. When designing an experiment, the researcher hopes to maximize the obtained information by reducing wasted resources and allocating treatments in such a way as to minimize variances. Ideally, a design will account for major sources of variation so that the researcher can be confident the effects of treatments are not confounded with some extraneous factor. In this course, the basic principles of experimental design will be given and specific designs discussed. The first designs introduced will be completely randomized designs, the most straightforward design when a researcher wants to test for differences amongst multiple treatments. Optimal blocking strategies will then be presented as a variance-reducing technique, e.g. perhaps the researcher feels a subject's gender may significantly affect observations. For each design we will discuss implementation, appropriate analysis and provide examples in SAS. If time permits we may also introduce more complicated designs tailored specifically to the researchers attending the course. Course files available here: www.lisa.stat.vt.edu/?q=node/2804.

The goal of this short course is to first explain the services provided by the Laboratory for Interdisciplinary Statistical Analysis (LISA) and second to offer t-test and ANOVA (Analysis of Variance) training for researchers. The discussion of LISA services will also motivate the importance of the remaining short courses offered in the fall of 2011. The statistical training will include the discussion of the one sample t-test, the two sample t-test, matched-pairs t-test, and ANOVA. We will cover data analysis examples using JMP statistical software. Course files available here: www.lisa.stat.vt.edu/?q=node/2801.

Many response variables are handled poorly by regression models when the errors are assumed to be normally distributed. For example, modeling the state damaged/not damaged of cells after treated with a certain chemical; and modeling the number of insects caught by a certain kind of trap. These types of situations can often be modeled well by a large class of regression models called generalized linear models (GLM). We will go over some of the basic statistical concepts of GLM and how it is relates to regression using normal errors. We will also go through some data analysis examples of GLMs in popular software such as R and SAS (possibly JMP if time allows) and explain how we interpret some of the output from each software. If time allows, the Bayesian approach to GLM will also be discussed. Course files available here: www.lisa.stat.vt.edu/?q=node/2321.

This course will discuss the concept of random effects, why they are called random effects and how they are incorporated in the framework of mixed models. The primary focus of the course will be to identify scenarios where a mixed model approach will be appropriate. We will discuss several examples with various types of response and experimental designs. The course will also talk briefly about what is a hierarchical model and why they are the obvious choice of modelers in most cases. This will be followed by an example that explicitly defines a hierarchical structure. The concepts will be explained almost wholly through examples in SAS or in R. Course files available here: www.lisa.stat.vt.edu/?q=node/2323.

R is a free computing and graphical software/environment for statistical analysis. This short course consists of 3 sections: Section 1 provides introduction to R programming basics, such as data objects in R, loops, import/export datasets, along with examples of data manipulation/cleaning. Section 2 discusses the R graphing environment and how to create some basic statistical plots. Section 3 introduces how to perform basic statistical analysis in R, such as t-tests, ANOVA and linear regression. Course files available here: www.lisa.stat.vt.edu/?q=node/2317.

We’ll discuss some basic concepts and vocabulary in Bayesian statistics such as the likelihood, prior and posterior distributions, and how they relate to Bayes’ Rule. R statistical software will be used to discuss how parameter estimation and inference changes in a Bayesian paradigm versus in a classical paradigm, with a particular focus on applications using regression. Course files available here: www.lisa.stat.vt.edu/?q=node/1784.

R is a free computing and graphical software/environment for statistical analysis. This short course consists of 3 sections: Section 1 provides introduction to R programming basics, such as data objects in R, loops, import/export datasets, along with an example of data manipulation/cleaning. Section 2 discusses the R graphing environment and how to create some basic statistical plots. Section 3 introduces how to perform basic statistical analysis in R, such as t-test, ANOVA and linear regression. Course files available here: www.lisa.stat.vt.edu/?q=node/1947.