# Chapter 14 Introduction

## 14.1 Statistics and other quantitative methods: courses and text books

### 14.1.1 Statistics in R

Andrew Bruce and Peter Bruce, Practical Statistics for Data Scientists

Christoph Hanck, Martin Arnold, Alexander Gerber and Martin Schmelzer, Introduction to Econometrics with R

Chester Ismay and Albert Y. Kim, An Introduction to Statistical and Data Sciences via R

Danielle Navarro, Learning Statistics with R

Christopher Prenner, Sociospatial Data Science, 2018

### 14.1.2 Statistics: general and other tools

Minitab Express Support, Help and How-To

• essentially an introduction to descriptive and inferential statistics

## 14.2 Distributions

### 14.2.1 Beta distribution

What is the intuition behind beta distribtution? (CrossValidated)

### 14.2.2 R

#### 14.2.2.1`{distributions3}`

“Tools to create and manipulate probability distributions using S3. Generics random(), pdf(), cdf() and quantile() provide replacements for base R’s r/d/p/q style functions. Functions and arguments have been named carefully to minimize confusion for students in intro stats courses. The documentation for each distribution contains detailed mathematical notes.”

## 14.3 Randomization and random number generation

### 14.3.1 Introduction

Randomization and random number generation has a variety of applications in data science, from creating mock or test data to pulling survey samples from lists.

### 14.3.3 R

Martin Monkman, 2013-12-01, “A few random things”

correlated random variables: a gist

#### 14.3.3.1 Random() `{base R}`

package

CRAN page: Random {base}: Random number generation

package

#### 14.3.3.3`{sampling}`

package

CRAN page: sampling: Survey Sampling

articles

Examples of how the package can be applied

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