Lynda - R Statistics Essential Training

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  • Lynda - R Statistics Essential Training
    • 1. Getting Started
      • 06. Entering data manually.mp4 (13.06 Mb)
      • 05. Using built-in datasets in R.mp4 (17.77 Mb)
      • 11. Challenge Creating color palettes in R.mp4 (3.91 Mb)
      • 07. Importing data.mp4 (24.25 Mb)
      • 09. Working with color in R.mp4 (31.66 Mb)
      • 03. Taking a first look at the interface.mp4 (29.22 Mb)
      • 04. Installing and managing packages.mp4 (38.41 Mb)
      • 12. Solution Creating color palettes in R.mp4 (10.37 Mb)
      • 02. Using RStudio.mp4 (12.01 Mb)
      • 10. Exploring color with Colorbrewer.mp4 (21.10 Mb)
      • 01. Installing R on your computer.mp4 (10.99 Mb)
      • 08. Converting tabular data to row data.mp4 (39.55 Mb)
    • 7. Statistics for Associations
      • 06. Comparing proportions.mp4 (8.69 Mb)
      • 02. Computing a bivariate regression.mp4 (19.24 Mb)
      • 010 .Solution Comparing proportions across several different groups.mp4 (11.91 Mb)
      • 04. Comparing paired means Paired t-test.mp4 (18.80 Mb)
      • 07. Creating cross tabs for categorical variables.mp4 (16.43 Mb)
      • 05. Comparing means with a one-factor analysis of variance (ANOVA).mp4 (26.12 Mb)
      • 08. Computing robust statistics for bivariate associations.mp4 (27.50 Mb)
      • 01. Calculating correlation.mp4 (12.50 Mb)
      • 09. Challenge Comparing proportions across several different groups.mp4 (2.21 Mb)
      • 03. Comparing means with the t-test.mp4 (18.87 Mb)
    • 4. Modifying Data
      • 02. Transforming variables.mp4 (28.82 Mb)
      • 06. Solution Transforming skewed data to pull in outliers.mp4 (6.64 Mb)
      • 01. Examining outliers.mp4 (16.94 Mb)
      • 05. Challenge Transforming skewed data to pull in outliers.mp4 (1.49 Mb)
      • 04. Coding missing data.mp4 (15.18 Mb)
      • 03. Computing composite variables.mp4 (17.55 Mb)
    • Conclusion
      • 01. Next steps.mp4 (12.14 Mb)
    • 2. Charts for One Variable
      • 01. Creating bar charts for categorical variables.mp4 (25.39 Mb)
      • 04. Creating box plots for quantitative variables.mp4 (22.87 Mb)
      • 06. Saving images.mp4 (17.26 Mb)
      • 07. Challenge Layering plots.mp4 (1.22 Mb)
      • 05. Overlaying plots.mp4 (21.48 Mb)
      • 03. Creating histograms for quantitative variables.mp4 (17.72 Mb)
      • 02. Creating pie charts for categorical variables.mp4 (21.56 Mb)
      • 08. Solution Layering plots.mp4 (5.74 Mb)
    • Ex_Files_RStats_EssT
    • 9. Statistics for Three or More Variables
      • 05. Challenge Creating a cluster analysis of states in the US.mp4 (1.39 Mb)
      • 01. Computing a multiple regression.mp4 (30.65 Mb)
      • 02. Comparing means with a two-factor ANOVA.mp4 (15.83 Mb)
      • 06. Solution Creating a cluster analysis of states in the US.mp4 (14.25 Mb)
      • 04. Conducting a principal componentsfactor analysis.mp4 (34.12 Mb)
      • 03. Conducting a cluster analysis.mp4 (46.59 Mb)
    • 5. Working with the Data File
      • 02. Analyzing by subgroup.mp4 (10.26 Mb)
      • 03. Merging files.mp4 (16.65 Mb)
      • 05. Solution Analyzing guinea pig data subgroups.mp4 (4.12 Mb)
      • 04. Challenge Analyzing guinea pig data subgroups.mp4 (1.17 Mb)
      • 01. Selecting cases.mp4 (17.69 Mb)
    • 0. Introduction
      • 02. Using the exercise files.mp4 (1.07 Mb)
      • 01. Welcome.mp4 (9.47 Mb)
      • 03. Using the challenges.mp4 (5.41 Mb)
    • 3. Statistics for One Variable
      • 03. Using a single proportion Hypothesis test and confidence interval.mp4 (9.93 Mb)
      • 07. Challenge Calculating descriptive statistics.mp4 (1.18 Mb)
      • 06. Examining robust statistics for univariate analyses.mp4 (23.13 Mb)
      • 01. Calculating frequencies.mp4 (9.72 Mb)
      • 02. Calculating descriptives.mp4 (17.18 Mb)
      • 05. Using a single categorical variable One sample chi-square test.mp4 (15.59 Mb)
      • 04. Using a single mean Hypothesis test and confidence interval.mp4 (11.75 Mb)
      • 08. Solution Calculating descriptive statistics.mp4 (6.35 Mb)
    • 8. Charts for Three or More Variables
      • 04. Creating 3D scatter plots.mp4 (16.50 Mb)
      • 05. Challenge Creating your own scatter plot matrix.mp4 (1.54 Mb)
      • 02. Creating scatter plots for grouped data.mp4 (7.77 Mb)
      • 06. Solution Creating your own scatter plot matrix.mp4 (15.02 Mb)
      • 01. Creating clustered bar charts for means.mp4 (10.39 Mb)
      • 03. Creating scatter plot matrices.mp4 (19.42 Mb)
    • 6. Charts for Associations
      • 05. Solution Creating your own grouped box plots.mp4 (12.42 Mb)
      • 04. Challenge Creating your own grouped box plots.mp4 (1.59 Mb)
      • 03. Creating scatter plots.mp4 (13.76 Mb)
      • 02. Creating grouped box plots.mp4 (16.07 Mb)
      • 01. Creating bar charts of group means.mp4 (12.17 Mb)

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Description

R is the language of big data—a statistical programming language that helps describe, mine, and test relationships between large amounts of data. Author Barton Poulson shows how to use R to model statistical relationships using graphs, calculations, tests, and other analysis tools. Learn how to enter and modify data; create charts, scatter plots, and histograms; examine outliers; calculate correlations; and compute regressions, bivariate associations, and statistics for three or more variables. Challenge exercises with step-by-step solutions allow you to test your skills as you progress.