Introduction to Statistics and Data Science for Management
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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.
 Ã‡etinkayaRundel, M. and Hardin, J, 2021. Introduction to Modern Statistics. https://openintroims.netlify.app/
 Chihara. L.M. and Hesterberg, T.C., 2019. Mathematical Statistics with Resampling and R, 2nd ed. Wiley.
 Cumming, G. and CalinJageman, R., 2016. Introduction to the New Statistics. Routledge.
 Devlin, T.D. et al., 2018. Seeing Theory. https://seeingtheory.brown.edu/index.html
 Downey, A., 2016. There is still only one test. http://allendowney.blogspot.com/2016/06/thereisstillonlyonetest.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/diffmeanshalfdozenways
 Ismay, C. and Kennedy, P. C., 2019. Getting used to R, RStudio, and R Markdown. https://ismayc.github.io/rbasicsbook/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.
 Tukey, J.W., 1977. Exploratory data analysis. AddisonWesley.
 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://rgraphics.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://plaintext.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/knitrbook/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 inbrowser version of RStudio and R. Alternatively, R is available free of charge from www.rproject.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 TCBstats workspace on RStudio.cloud.
Session 2

Reproducible Research with R, RStudio, and R Markdown
Required reading and recorded lecture:
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 and recorded lecture:
Wickham, H. and Grolemund, G., 2016. R for data science: import, tidy, transform, visualize, and model data. Chapter 3.
Ã‡etinkayaRundel, M. and Hardin, J, 2021. Introduction to Modern Statistics. Chapter 2: Summarizing and visualizing dataFurther 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 24.
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:

Working with Data, R, and RStudio
Required reading and recorded lecture:
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 5:

Working with Data & Exploratory Data Analysis
Required reading and recorded lecture:
De Veaux, R.D., Velleman, P.F. and Bock, D.E., 2018. Intro stats. Boston: Pearson. Chapters 24.Further recommendations:
Videos: One Chart at a Time
Schwabish, J., 2021. Better Data Visualizations. Columbia University Press.
Rost, L.C., 2020. How to pick more beautiful colors for your data visualizations. Datawrapper Blog
Rost, L.C., 2021. Which color scale to use when visualizing data. Datawrapper Blog
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 6:

Introduction to Probability
Required reading:
Keller, G., 2018. Statistics for Management and Economics, 11th ed. Cengage. Chapter 6.
https://seeingtheory.brown.edu/basicprobability/index.html
https://seeingtheory.brown.edu/compoundprobability/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 78.
https://seeingtheory.brown.edu/probabilitydistributions/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 89.
Session 9:

Inference: Estimation of Population Parameters and their Margin of Error  Continuous Data
Required reading:
Cumming, G. and CalinJageman, 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.
Chihara. L.M. and Hesterberg, T.C., 2019. Mathematical Statistics with Resampling and R, 2nd ed. Wiley. Chapter 5 and 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/rstudioconf2018/inferapackagefortidystatisticalinference/
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 CalinJageman, R., 2016. Introduction to the New Statistics. Routledge. Chapter 6.
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  One Sample
Required reading:
Urdan, T.C., 2011. Statistics in plain English. Routledge. Chapter 9.Further recommendations:
Levine, D.M. and Stephan, D.F., 2010. Even You Can Learn Statistics, 2nd ed. Pearson. Chapter 8.
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.
Keller, G., 2018. Statistics for Management and Economics, 11th ed. Cengage. Chapter 12.
Session 12:

Inference: Null Hypothesis Tests and Significance  Continuous Data  Multiple Samples
Required reading:
Urdan, T.C., 2011. Statistics in plain English. Routledge. Chapter 10 and 11.
Heiss, A., 2019. Half a dozen frequentist and Bayesian ways to measure the difference in means in two groups.Further recommendations:
Levine, D.M. and Stephan, D.F., 2010. Even You Can Learn Statistics, 2nd ed. Pearson. Chapter 8 and 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 12.
Keller, G., 2018. Statistics for Management and Economics, 11th ed. Cengage. Chapter 13 and 14.
Session 13:

Inference: Categorical Data  Estimation of Population Parameters and their Margin of Error, NHST
Required reading:
Cumming, G. and CalinJageman, R., 2016. Introduction to the New Statistics. Routledge. Chapter 13.
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 24.
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:

Experiment and Survey Design: Randomization and Measurement 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:
Cumming, G. and CalinJageman, R., 2016. Introduction to the New Statistics. Routledge. Chapter 2 and 10.
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.