**R Essential Training Part 2: Modeling Data**

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 3h 59m | 662 MB

Trying to locate meaning and direction in big data is difficult. R can help you find your way. R is a statistical programming language to analyze and visualize the relationships between large amounts of data. It’s one of the most important tools available for data analysis, machine learning, and data science. This training series provides a thorough introduction to R, with detailed instruction for working with R and RStudio and hands-on examples, from exploratory graphics to neural networks. In part two, Modeling Data, instructor Barton Poulson shows how to compute statistics, analyze data, predict outcomes, and group and classify cases. These are the fundamental techniques you need to generate meaningful insights for your organization.

Topics include:

- Computing frequencies and correlations
- Computing descriptive statistics
- Conducting an item analysis
- Comparing proportions
- Comparing paired means
- Comparing multiple means
- Predicting outcomes with linear and logistic regression
- Grouping cases with k-means clustering
- Classifying cases with k-nearest neighbors
- Creating ensemble models

**Table of Contents**

1 Model data with R

2 Using the exercise files

3 Data science with R A case study

4 Computing frequencies

5 Computing descriptive statistics

6 Computing correlations

7 Creating contingency tables

8 Conducting a principal component analysis

9 Conducting an item analysis

10 Conducting a confirmatory factor analysis

11 Comparing proportions

12 Comparing one mean to a population One-sample t-test

13 Comparing paired means Paired samples t-test

14 Comparing two means Independent samples t-test

15 Comparing multiple means One-factor analysis of variance

16 Comparing means with multiple categorical predictors Factorial analysis of variance

17 Predicting outcomes with linear regression

18 Predicting outcomes with lasso regression

19 Predicting outcomes with quantile regression

20 Predicting outcomes with logistic regression

21 Predicting outcomes with Poisson or log-linear regression

22 Assessing predictions with blocked-entry models

23 Grouping cases with hierarchical clustering

24 Grouping cases with k-means clustering

25 Classifying cases with k-nearest neighbors

26 Classifying cases with decision tree analysis

27 Creating ensemble models with random forest classification

28 Next steps

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