# Introduction to Statistics and Data Science for Management

## 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.
- Chihara. L.M. and Hesterberg, T.C., 2019. Mathematical Statistics with Resampling and R, 2nd ed. Wiley.
- Davis, T., 2016. The Book of R. No Starch Press.
- 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
- 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., 2018. 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/
- Render, B. et al., 2018. Quantitative Analysis for Management, 13th ed. Pearson.
- 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.
- Teetor, P., 2011. R Cookbook. O’Reilly Media, Inc.
- Torfs, P. and Brauer, C, 2018. A (very) short introduction to R. https://github.com/ClaudiaBrauer/A-very-short-introduction-to-R/blob/master/documents/A%20(very)%20short%20introduction%20to%20R.pdf
- 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.
- 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

- 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
- Knaflic, C.N., 2015. Storytelling with Data. 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.
- Tufte, E.R., 1990. Envisioning Information, 2nd ed. Cheshire, CT: Graphics Press.
- Tufte, E.R., 2001. The Visual Display of Quantitative Information, 2nd ed. Cheshire, CT: Graphics Press.
- Tufte, E.R., 1997. Visual Explanations. Cheshire, CT: Graphics Press.
- Tufte, E.R., 2006. Beautiful evidence. Cheshire, CT: Graphics 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 Doscovery – 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/
- 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. The software is available free of charge from www.r-project.org. As a graphical frontend RStudio is recommended. Alternatively, students can create an account at https://rstudio.cloud to get access to an in-browser version of RStudio.

## Instructor Information:

Prof. Dr. Dennis A. V. Dittrich

dennis.dittrich@touroberlin.de

http://economicscience.net

You can always reach me via email. For meetings in my office appointments can be arranged through my webpage at: http://economicscience.net/content/book-appointment.

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

## Grading Guidelines:

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

Problem Sets | 40% |

Data Analysis Project: Report | 50% |

Data Analysis Project: Presentation | 10% |

## 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: 13.02.

- Introduction to course and to data science and statistics

### Session 2: 20.02.

Working with Data, R, and RStudio

Ismay, C. and Kennedy, P. C., 2018. Getting used to R, RStudio, and R Markdown. Chapters 2, 3, 5.

Healy, K., 2018. Data Visualization: A Practical Introduction. Princeton University Press. Chapters 1 & 2.Further recommendation:

Torfs, P. and Brauer, C, 2018. A (very) short introduction to R.

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

### Session 3: 27.02.

Working with Data, continued

https://datacarpentry.org/R-ecology-lesson/03-dplyr.htmlExploratory Data Analysis and Data Visualization

https://datacarpentry.org/R-ecology-lesson/04-visualization-ggplot2.html

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

Healy, K., 2018. Data Visualization: A Practical Introduction. Princeton University Press. Chapters 3-5.

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

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

Tukey, J.W., 1977. Exploratory data analysis. Addison-Wesley.

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

### Session 4: 06.03.

Exploratory Data Analysis and Data Visualization

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

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

### Session 5: 13.03.

Introduction to Combinatorics and Probability

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.

Teetor, P., 2011. R Cookbook. O’Reilly Media, Inc.. Chapter 8.

### Session 6: 20.03.

Probability Distributions and the Central Limit Theorem

Keller, G., 2018. Statistics for Management and Economics, 11th ed. Cengage. Chapter 7-9.

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 & 6.

Good, P.I., 2013. Introduction to Statistics Through Resampling Methods and R, 2nd ed. Wiley. Chapter 3 & 4.

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

Davis, T, 2016. The Book of R. No Starch Press. Chapter 16.

### Session 7: 27.03.

- Probability Distributions and the Central Limit Theorem

### Session 8: 03.04.

Inference and Significance

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.Further recommendations:

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.

Davis, T., 2016. The Book of R. No Starch Press. Chapter 17 & 18.

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

### Session 9: 10.04.

Inference for Comparing Means

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

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

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

Teetor, P., 2011. R Cookbook. O’Reilly Media, Inc.. Chapter 9.

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.

Davis, T., 2016. The Book of R. No Starch Press. Chapter 18 & 19.

### Session 10: 17.04.

Inference for Proportions

Agresti, A., 2019. An Introduction to Categorical Data Analysis, 3rd ed. Wiley. Chaper 2 & 8.Further recommendations:

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

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

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

### Session 11: 29.04. (Monday)

Designing Experiments

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

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

### Session 12: 08.05.

Statistical Quality Control

Render, B. et al., 2018. Quantitative Analysis for Management, 13th ed. Pearson. Chapter 15.Further recommendation:

Selvamuthu, D. and Das, D., 2018. Introduction to Statistical Methods, Design of Experiments and Statistical Quality Control. Springer. Chapter 10.**Deadline**for the approval of your data analysis project.

### Session 13: 15.05.

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.

### Session 14: 22.05.

Writing about Numbers

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

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

### Session 15: 29.05.

- Data Analysis Project: Presentations

https://toolbox.google.com/datasetsearch

Topics and reading assignments are subject to changes.

## Homework Problems

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