Chapter 13 Introduction

13.1 Statistics and other quantitative methods: courses and text books

13.1.1 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

Danielle Navarro, Learning Statistics with R: A tutorial for psychology students and other beginners

Christopher Prenner, Sociospatial Data Science, 2018

13.1.2 Statistics: general and other tools

Minitab Express Support, Help and How-To

  • essentially an introduction to descriptive and inferential statistics

13.2 Distributions

13.2.2 R

13.2.2.1 {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.”


13.3 Randomization and random number generation

13.3.1 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.

13.3.2 Theory and methods

13.3.3 R

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

correlated random variables: a gist

13.3.3.1 Random() {base R}

package

CRAN page: Random {base}: Random number generation

13.3.3.2 {random}

package

CRAN page: random: True Random Numbers using RANDOM.ORG

13.3.3.3 {sampling}

package

CRAN page: sampling: Survey Sampling

articles

Examples of how the package can be applied

-30-