# Introduction to Statistics and Data Science for Management

More video lectures will be posted soon.

**preliminary – subject to changes**

## Course Description

This course introduces to the theory and practice of describing and analysing data, testing hypotheses and how to use the free statistical computing software R for your data analyses.

Topics discussed include, but are not limited to, combinatorics, probability, random variables, density and distribution functions, sampling from a population, sample properties and descriptive summary statistics, and inferring population characteristics from a random sample.

## Course Objectives

The course will enable students to describe and to analyse data using simple statistical methods and to interpret and to report their data analysis results. Students will learn how to use the statistical computing software R, a software widely used in academia and industry.

## Course Materials

### Required:

The course does not follow a single textbook, there will be weekly, mandatory reading assignments from different sources as indicated under the topics below.

### Recommended Reading list

#### Data Analysis

- Agresti, A., 2019. An Introduction to Categorical Data Analysis, 3rd ed. Wiley.
- Bilder, C.R. and Loughin, T.M., 2015. Analysis of Categorical Data with R. Wiley.
- Çetinkaya-Rundel, M. and Hardin, J, 2021. Introduction to Modern Statistics. https://openintro-ims.netlify.app/
- Chihara. L.M. and Hesterberg, T.C., 2019. Mathematical Statistics with Resampling and R, 2nd ed. Wiley.
- Cumming, G. and Calin-Jageman, R., 2016. Introduction to the New Statistics. Routledge.
- Devlin, T.D. et al., 2018. Seeing Theory. https://seeing-theory.brown.edu/index.html
- Downey, A., 2016. There is still only one test. http://allendowney.blogspot.com/2016/06/there-is-still-only-one-test.html
- Gandrud, Ch., 2020. Reproducible Research with R and RStudio, 3rd edition. CRC Press.
- Good, P.I., 2013. Introduction to Statistics Through Resampling Methods and R, 2nd ed. Wiley.
- Good, P.I. and Hardin, J.W., 2012. Common Errors in Statistics (and How to Avoid Them). Wiley.
- Heiss, A., 2019. Half a dozen frequentist and Bayesian ways to measure the difference in means in two groups. https://github.com/andrewheiss/diff-means-half-dozen-ways
- Ismay, C. and Kennedy, P. C., 2019. Getting used to R, RStudio, and R Markdown. https://ismayc.github.io/rbasics-book/index.html

- Keller, G., 2018. Statistics for Management and Economics, 11th ed. Cengage.
- Levine, D.M. and Stephan, D.F., 2010. Even You Can Learn Statistics, 2nd ed. Pearson.
- Reinhart, A., 2015. Statistics Done Wrong. No Starch Press. https://www.statisticsdonewrong.com/
- Selvamuthu, D. and Das, D., 2018. Introduction to Statistical Methods, Design of Experiments and Statistical Quality Control. Springer.
- Stanton, J.M., 2017. Reasoning with Data: An Introduction to Traditional and Bayesian Statistics Using R. Guilford Publications.
- Tukey, J.W., 1977. Exploratory data analysis. Addison-Wesley.
- Upton, G.J.G., 2017. Categorical Data Analysis by Example. Wiley.
- Urdan, T.C., 2011. Statistics in plain English. Routledge.
- De Veaux, R.D., Velleman, P.F. and Bock, D.E., 2018. Intro stats, 5th ed. Boston: Pearson.
- Wickham, H. and Grolemund, G., 2016. R for data science: import, tidy, transform, visualize, and model data. O’Reilly Media, Inc. https://r4ds.had.co.nz/

#### Data Visualization & Communication

- Chang, W., 2021. R Graphics Cookbook, 2nd edition. O’Reilly Media, Inc. https://r-graphics.org/
- Cleveland, W.S., 1993. Visualizing Data. Hobart Press.
- Cleveland, W.S., 1994. The elements of graphing data. Hobart Press.
- Few, S., 2009. Now you see it. Analytics Press.
- Few, S., 2012. Show me the numbers, 2nd ed. Analytics Press.
- Few, S., 2015. Signal – Understanding What Matters in a World of Noise. Analytics Press.
- Harris, R.L., 1999. Information Graphics. Oxford University Press.
- Healy, K., 2018. Data Visualization: A Practical Introduction. Princeton University Press. http://socviz.co/
- Healy, K., 2018. The Plain Person’s Guide to Plain Text Social Science. http://plain-text.co
- Kirk, A., 2019. Data Visualisation, 2nd edition. SAGE.
- Knaflic, C.N., 2015. Storytelling with Data. Wiley.
- Knaflic, C.N., 2020. Storytelling with Data - let’s practice! Wiley.
- Miller, J.E., 2015. The Chicago Guide to Writing about Numbers, 2nd ed. Chicago University Press.
- Robbins, N.B., 2005. Creating More Effective Graphs. Wiley.
- Schwabish, J., 2021. Better Data Visualizations. Columbia University Press.
- Turabian, K.L., 2018. A Manual for Writers of Research Papers, Theses, and Dissertations, 9th ed. University of Chicago Press.
- Wainer, H., 2005. Graphic Discovery – A Trout in the Milk and Other Visual Adventures. Princeton University Press.
- Wainer, H., 2009. Picturing the Uncertain World. Princeton University Press.
- Wickham, H., 2016. ggplot2: Elegant Graphics for Data Analysis. Springer. https://ggplot2.tidyverse.org/
- Wilke, C.O., 2019. Fundamentals of Data Visualization. O’Reilly Media, Inc. https://clauswilke.com/dataviz/ https://github.com/clauswilke/dataviz
- Xie, Y., 2015. Dynamic Documents with R and knitr. CRC Press. https://github.com/yihui/knitr-book/tree/master/markdown
- Xie, Y., Allaire, J.J. and Grolemund, G., 2018. R markdown: The definitive guide. CRC Press. https://bookdown.org/yihui/rmarkdown/
- Yau, N., 2013. Data Points – Visualization that means something. Wiley.

## Course Requirements:

Students must read the weekly reading assignments *before* each session.
Students need access to a computer with the statistical computing
software R. To get this access, students should create an account at
https://rstudio.cloud for an in-browser version of RStudio and R.
Alternatively, R is available free of charge from
www.r-project.org. The graphical frontend
RStudio is
available in a free version, too. R and Rstudio are installed in the
college’s computer lab.

Homework needs to be submitted electronically before class. See below for problem sets and deadlines.

## Instructor Information:

Prof. Dr. Dennis A. V. Dittrich

dennis.dittrich@touroberlin.de

https://economicscience.net

You can always reach me via email. For meetings in my office, appointments can be arranged through my webpage at: https://economicscience.net/appointments/

Updated information, links to the literature, additional materials, etc. can be found on my webpage.

## Grading Guidelines:

Grading Component | Weight |
---|---|

Problem Sets | 70% |

Data Analysis Project: Report | 30% |

## Workload

A typical 3 US credits / 5 ECTS course requires 150 hours of your time. The table below identifies how I expect those 150 hours will be allocated. While you do not receive direct marks for reading, reading will affect your ability to participate in class discussions and activities and your ability to succeed in the assessments and therefore your final grade.

Activity | Time |
---|---|

Class Time (3 hours / week) | 45 hours |

Reading (4 hours / week) | 60 hours |

Problem Sets (2 hours / week) | 30 hours |

Preparation and Review (1 hour / week) | 15 hours |

## Weekly Topics and Reading Assignments

### Session 1:

Introduction to course and to data science and statistics

Required reading:

Wickham, H. and Grolemund, G., 2016. R for data science: import, tidy, transform, visualize, and model data. Chapter 2.

Ismay, C. and Kennedy, P. C., 2018. Getting used to R, RStudio, and R Markdown. Chapters 2 & 3.

Join the TCB-stats workspace on RStudio.cloud.

### Session 2

Reproducible Research with R, RStudio, and R Markdown

Required reading:

Tierney, N, 2020. RMarkdown for Scientists

Ismay, C. and Kennedy, P. C., 2018. Getting used to R, RStudio, and R Markdown. Chapter 4.Further recommendation:

Healy, K., 2018. Data Visualization: A Practical Introduction. Princeton University Press. Chapter 2.

Healy, K., 2018. The Plain Person’s Guide to Plain Text Social Science.

RStudio. RMarkdownAs a reference:

Gandrud, Ch., 2020. Reproducible Research with R and RStudio, 3rd edition. CRC Press.

Xie, Y., 2015. Dynamic Documents with R and knitr. CRC Press.

Xie, Y., Allaire, J.J. and Grolemund, G., 2018. R markdown: The definitive guide. CRC Press.

### Session 3:

Exploratory Data Analysis and Data Visualization

Required reading:

Wickham, H. and Grolemund, G., 2016. R for data science: import, tidy, transform, visualize, and model data. Chapter 3.

Çetinkaya-Rundel, M. and Hardin, J, 2021. Introduction to Modern Statistics. Chapter 2: Summarizing and visualizing dataslides dataviz 1 & slides dataviz 2 & video

Further recommendations:

Healy, K., 2018. Data Visualization: A Practical Introduction. Princeton University Press. Chapters 1 and 3.

De Veaux, R.D., Velleman, P.F. and Bock, D.E., 2018. Intro stats. Boston: Pearson. Chapters 2-4.

Chihara. L.M. and Hesterberg, T.C., 2019. Mathematical Statistics with Resampling and R, 2nd ed. Wiley. Chapter 2.

Michonneau, F. and Fournier, A, 2019. Data Analysis and Visualization in R for Ecologists. Section “Visualizing data”.

and the other sources from the recommended reading list section Data Visualization & Communication.

### Session 4:

Exploratory Data Analysis and Data Visualization

Required reading:

De Veaux, R.D., Velleman, P.F. and Bock, D.E., 2018. Intro stats. Boston: Pearson. Chapters 2-4.Further recommendations:

Videos: One Chart at a Time

Schwabish, J., 2021. Better Data Visualizations. Columbia University Press.

Healy, K., 2018. Data Visualization: A Practical Introduction. Princeton University Press. Chapters 4, 5, and 8.

Chihara. L.M. and Hesterberg, T.C., 2019. Mathematical Statistics with Resampling and R, 2nd ed. Wiley. Chapter 2.As a reference:

Chang, W., 2021. R Graphics Cookbook, 2nd edition. O’Reilly.

Wickham, H., 2016. ggplot2: Elegant Graphics for Data Analysis. Springer.

Wickham, H. and Grolemund, G., 2016. R for data science: import, tidy, transform, visualize, and model data. Part I Explore.

Wilke, C.O., 2019. Fundamentals of Data Visualization. O’Reilly Media, Inc.

Miller, J.E., 2015. The Chicago Guide to Writing about Numbers, 2nd ed. Chicago University Press.

### Session 5:

Working with Data, R, and RStudio

Required reading:

Wickham, H. and Grolemund, G., 2016. R for data science: import, tidy, transform, visualize, and model data. Chapters 4, 5, 11, and 15.Further recommendations:

Michonneau, F. and & Fournier, A, 2020. Data Analysis and Visualization in R for Ecologists. Sections “Before we start”, “Intro to R”, “Starting with data”, and “Manipulating data”.

### Session 6:

Introduction to Probability

Required reading:

Keller, G., 2018. Statistics for Management and Economics, 11th ed. Cengage. Chapter 6.

https://seeing-theory.brown.edu/basic-probability/index.html

https://seeing-theory.brown.edu/compound-probability/index.htmlFurther recommendations:

Levine, D.M. and Stephan, D.F., 2010. Even You Can Learn Statistics, 2nd ed. Pearson. Chapter 4.

Good, P.I., 2013. Introduction to Statistics Through Resampling Methods and R, 2nd ed. Wiley. Chapter 2.

### Session 7:

Probability Distributions and the Central Limit Theorem

Required reading:

Keller, G., 2018. Statistics for Management and Economics, 11th ed. Cengage. Chapter 7-8.

https://seeing-theory.brown.edu/probability-distributions/index.htmlFurther recommendations:

Urdan, T.C., 2011. Statistics in plain English. Routledge. Chapter 4.

Levine, D.M. and Stephan, D.F., 2010. Even You Can Learn Statistics, 2nd ed. Pearson. Chapter 5.

Good, P.I., 2013. Introduction to Statistics Through Resampling Methods and R, 2nd ed. Wiley. Chapter 3.

Chihara. L.M. and Hesterberg, T.C., 2019. Mathematical Statistics with Resampling and R, 2nd ed. Wiley. Chapter 4.

### Session 8:

- Probability Distributions and the Central Limit Theorem

Required reading:

Keller, G., 2018. Statistics for Management and Economics, 11th ed. Cengage. Chapter 8-9.

### Session 9:

Inference: Estimation of Population Parameters and their Margin of Error - Continuous Data

Required reading:

Cumming, G. and Calin-Jageman, R., 2016. Introduction to the New Statistics. Routledge. Chapter 5.Further recommendations:

Keller, G., 2018. Statistics for Management and Economics, 11th ed. Cengage. Chapter 10.

Good, P.I., 2013. Introduction to Statistics Through Resampling Methods and R, 2nd ed. Wiley. Chapter 4.

Levine, D.M. and Stephan, D.F., 2010. Even You Can Learn Statistics, 2nd ed. Pearson. Chapter 6.

Urdan, T.C., 2011. Statistics in plain English. Routledge. Chapter 7.

### Session 10:

Inference: Null Hypothesis Tests and Significance

Required reading:

Downey, A., 2016. There is still only one test.

https://www.rstudio.com/resources/videos/infer-a-package-for-tidy-statistical-inference/

Levine, D.M. and Stephan, D.F., 2010. Even You Can Learn Statistics, 2nd ed. Pearson. Chapter 7.Andrew B., et. al., 2020. infer: Tidy Statistical Inference. R package version 0.5.4.

Further recommendations:

Cumming, G. and Calin-Jageman, R., 2016. Introduction to the New Statistics. Routledge. Chapter 6.

Stanton, J.M., 2017. Reasoning with Data: An Introduction to Traditional and Bayesian Statistics Using R. Guilford Publications. Chapter 5.

Chihara. L.M. and Hesterberg, T.C., 2019. Mathematical Statistics with Resampling and R, 2nd ed. Wiley. Chapter 3.

Good, P.I., 2013. Introduction to Statistics Through Resampling Methods and R, 2nd ed. Wiley. Chapter 5.

Urdan, T.C., 2011. Statistics in plain English. Routledge. Chapter 7.

Keller, G., 2018. Statistics for Management and Economics, 11th ed. Cengage. Chapter 11.

### Session 11:

Inference: Null Hypothesis Tests and Significance - Continuous Data

Required reading:

Urdan, T.C., 2011. Statistics in plain English. Routledge. Chapter 9-11.

Heiss, A., 2019. Half a dozen frequentist and Bayesian ways to measure the difference in means in two groups.Further recommendations:

Stanton, J.M., 2017. Reasoning with Data: An Introduction to Traditional and Bayesian Statistics Using R. Guilford Publications. Chapter 5 & 6.

Levine, D.M. and Stephan, D.F., 2010. Even You Can Learn Statistics, 2nd ed. Pearson. Chapter 8 & 9.

Good, P.I., 2013. Introduction to Statistics Through Resampling Methods and R, 2nd ed. Wiley. Chapter 5.

Chihara. L.M. and Hesterberg, T.C., 2019. Mathematical Statistics with Resampling and R, 2nd ed. Wiley. Chapter 8 & 12.

Keller, G., 2018. Statistics for Management and Economics, 11th ed. Cengage. Chapter 12-14.

### Session 12:

Inference: Estimation of Population Parameters and their Margin of Error - Count Data

Required reading:

Cumming, G. and Calin-Jageman, R., 2016. Introduction to the New Statistics. Routledge. Chapter 13.Further recommendations:

Upton, G.J.G., 2017. Categorical Data Analysis by Example. Wiley. Chapter 2-4.

Agresti, A., 2019. An Introduction to Categorical Data Analysis, 3rd ed. Wiley. Chapter 2, Section 8.1.

### Session 13:

Inference: Null Hypothesis Tests and Significance - Count Data

Required reading:

Agresti, A., 2019. An Introduction to Categorical Data Analysis, 3rd ed. Wiley. Chapter 2 and Section 8.1.Further recommendations:

Upton, G.J.G., 2017. Categorical Data Analysis by Example. Wiley. Chapter 2-4.

Chihara. L.M. and Hesterberg, T.C., 2019. Mathematical Statistics with Resampling and R, 2nd ed. Wiley. Chapter 8 & 10.

Bilder, C.R. and Loughin, T.M., 2015. Analysis of Categorical Data with R. Wiley. Chapter 1.

### Session 14:

Designing Experiments

Required reading:

Good, P.I., 2013. Introduction to Statistics Through Resampling Methods and R, 2nd ed. Wiley. Chapter 6.

Ford, C., 2018. Getting started with the pwr packageFurther recommendation:

Boddy, R. and Smith, G., 2010. Effective Experimentation. Wiley.

### Session 15:

Review: Statistics Done Wrong

Further recommendation:

Reinhart, A., 2015. Statistics Done Wrong. No Starch Press.

Good, P.I. and Hardin, J.W., 2012. Common Errors in Statistics (and How to Avoid Them). Wiley.

Topics and reading assignments are subject to changes.

## Homework Problems

You will find the homework problems and other material for download in this pCloud folder. [Click Here!]

Upload you homework solutions to this pCloud folder [Click Here!]

### RMarkdown for homework problems

You have to use RMarkdown with PDF output for your homework solutions. See the RStudio lessons on RMarkdown and R Markdown: The Definitive Guide for an introduction and reference to RMarkdown, respectively. See also Xie, Y., 2015. Dynamic Documents with R and knitr.

If you do not have a pCloud account yet: You can get one for free! You do not require an account to access to above folders.