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# R Statistics Essential Training

### Course Summary

Use R to model statistical relationships using its graphs, calculations, tests, and other analysis tools.

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### Course Syllabus

•  Introduction
• Welcome
• Using the exercise files
• Using the challenges
• Getting Started
• Installing R on your computer
• Using RStudio
• Taking a first look at the interface
• Installing and managing packages
• Using built-in datasets in R
• Entering data manually
• Importing data
• Converting tabular data to row data
• Working with color in R
• Exploring color with Colorbrewer
• Challenge: Creating color palettes in R
• Solution: Creating color palettes in R
• Charts for One Variable
• Creating bar charts for categorical variables
• Creating pie charts for categorical variables
• Creating histograms for quantitative variables
• Creating box plots for quantitative variables
• Overlaying plots
• Saving images
• Challenge: Layering plots
• Solution: Layering plots
• Statistics for One Variable
• Calculating frequencies
• Calculating descriptives
• Using a single proportion: Hypothesis test and confidence interval
• Using a single mean: Hypothesis test and confidence interval
• Using a single categorical variable: One sample chi-square test
• Examining robust statistics for univariate analyses
• Challenge: Calculating descriptive statistics
• Solution: Calculating descriptive statistics
• Modifying Data
• Examining outliers
• Transforming variables
• Computing composite variables
• Coding missing data
• Challenge: Transforming skewed data to pull in outliers
• Solution: Transforming skewed data to pull in outliers
• Working with the Data File
• Selecting cases
• Analyzing by subgroup
• Merging files
• Challenge: Analyzing guinea pig data subgroups
• Solution: Analyzing guinea pig data subgroups
• Charts for Associations
• Creating bar charts of group means
• Creating grouped box plots
• Creating scatter plots
• Challenge: Creating your own grouped box plots
• Solution: Creating your own grouped box plots
• Statistics for Associations
• Calculating correlation
• Computing a bivariate regression
• Comparing means with the t-test
• Comparing paired means: Paired t-test
• Comparing means with a one-factor analysis of variance (ANOVA)
• Comparing proportions
• Creating cross tabs for categorical variables
• Computing robust statistics for bivariate associations
• Challenge: Comparing proportions across several different groups
• Solution: Comparing proportions across several different groups
• Charts for Three or More Variables
• Creating clustered bar charts for means
• Creating scatter plots for grouped data
• Creating scatter plot matrices
• Creating 3D scatter plots
• Challenge: Creating your own scatter plot matrix
• Solution: Creating your own scatter plot matrix
• Statistics for Three or More Variables
• Computing a multiple regression
• Comparing means with a two-factor ANOVA
• Conducting a cluster analysis
• Conducting a principal components/factor analysis
• Challenge: Creating a cluster analysis of states in the US
• Solution: Creating a cluster analysis of states in the US
• Conclusion
• Next steps

Course Fee:
USD 25

Self-Study

### Course Status:

Active

1 - 5 hours / week

This course is listed under Development & Implementations and Data & Information Management Community

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