# Computer Statistics with R

## Tutorials

These tutorials aim to illustrate the use of the R environment in an introductory course on mathematical and applied statistics. They are not a substitute of a typical, standalone academic lecture on the subject. The student is assumed to have some background theoretical knowledge on each presented topic.

Polish speaking students will be more interested in our book on statistical inference with R:
Wnioskowanie statystyczne z wykorzystaniem środowiska R. These tutorials are merely its abridged version. Hence, some chapters are not available. Moreover, they have not been updated for a while – apologies for any bugs, typos, and errors! These tutorials are provided under the terms of the Creative Commons Attribution 3.0 Unported License.

### 1. Introduction to R

Contents:

1. Preliminaries
2. Built-in data types
1. Atomic vectors
2. Factors
3. Lists
4. Data frames
5. Matrices
3. Functions
4. Data handling
1. Importing tabular data into R
2. Organizing data
3. Other functions
4. Exporting data
5. Examples

### 2. Exploratory Data Analysis (Descriptive Statistics)

Contents:

1. Preliminaries
2. Analysis of qualitative data
3. Analysis of quantitative data
4. Time series plots
5. Kernel density estimators (*)
6. Commonly used graphical parameters (*)
1. Plot description
2. Colors
3. Symbols
4. Line types

### 3. Probability Distributions and Simulation Basics

Contents:

1. Preliminaries
1. Basic probability distributions
2. Sampling with and without replacement
3. Special functions (*)
2. Examples
3. Conditional statements
1. if...else
2. ifelse() function
4. Loops
1. for loop
2. while loop
3. repeat loop
4. A note on efficiency
5. replicate() function

### 4. Sufficient Statistics

Exercises on Sufficient Statistics will not be exposed

### 5. Point Estimation

Exercises on Point Estimation will not be exposed

### 6. Interval Estimation

Contents:

1. Preliminaries
1. Confidence intervals for mean
2. Confidence intervals for variance
3. Confidence intervals for proportion
4. Calculating confidence intervals in R
2. Statistical tables
1. Cumulative standardized normal distribution function
2. Standardized normal quantiles
3. Student's t quantiles
4. Chi-squared quantiles
3. Examples

### 7. Parametric Tests

Contents:

1. Preliminaries
1. Theory of hypothesis tests
2. Tests for mean
3. Tests for variance
4. Tests for proportion
2. Examples

### 8. Nonparametric Tests

Contents:

1. Goodness-of-fit tests
1. Tests for normality
2. Pearson chi-squared test
3. Kolmogorov test
2. Test for independence
1. Pearson chi-squared test
3. R two-sample tests decision tree
1. Wilcoxon rank-sum test
2. Wilcoxon signed-ranks test
4. Examples