SURV699M: Review of Statistical Concepts

Data Analysis

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Instructor: Anna-Carolina Haensch & Mirjan Kummerow

By the end of this course, students should be up to speed on statistical reasoning, hypothesis testing, and regression analysis. First, the course covers basic statistical thinking, reviewing the difference between a sample and a population as well as methods in sampling and experiments. Students will also be expected to recognize numerical and categorical data and apply the proper methods throughout the course based on the type of the data. This includes using the proper summaries and visualizations of the data, but also applies later on in hypothesis testing and confidence intervals.

We will cover the basics of the Central Limit Theorem, applying it in the form of confidence intervals and hypothesis tests. Students should know what conditions must be met in order to perform certain hypothesis tests, as well as be able to interpret the confidence intervals and hypothesis tests appropriately. This course will cover various types of tests, including one- and two-sample paired and unpaired t-tests, chi-squared tests, ANOVA, and more.

This course also covers simple linear regression, multiple regression, and logistic regression. Students should know how a least-squares regression line is fit, as well as understand the assumptions that go along with it. In addition, students should be comfortable with evaluating the linear regression models and using diagnostics to determine whether it is appropriate to use a linear regression. For logistic regression, students should also know how to properly interpret the coefficients.

Course objectives: 

By the end of the course, students will…

  • Understand sample and population and know how to apply statistical methods appropriately
  • Be able to apply basic probability
  • Know basic probability distributions and how to apply them
  • Perform hypothesis tests and construct confidence intervals
  • Regression analysis, including multiple regression and logistic regression.

Grading will be based on:

  • 11 homework assignments (60% of grade total, lowest homework dropped)
  • Participation in online meetings and submission of questions demonstrating understanding of readings (10% of grade)
  • Online Final Exam (30% of grade)

Students must get a 70% or higher in order to pass the class.


No Prerequisites


Diez, D. M., Barr, C. D., & Çetinkaya-Rundel, M. (2012). Open Intro Statistics.

Weekly online meetings & assignments:

  • Week 1: Introduction (Assignment 1)
  • Week 2: Descriptive Statistics (Assignment 2)
  • Week 3: Probability (Assignment 3)
  • Week 4: The Normal Distribution and Z- Scores (Assignment 4)
  • Week 5: Other Probability Distributions (Assignment 5)
  • Week 6: Confidence Intervals (Assignment 6) 
  • Week 7: Central Limit Theorem and Hypothesis Testing (Assignment 7) 
  • Week 8: Inference for Numerical Data (Assignment 8)
  • Week 9: Inference for Categorical Data (Assignment 9)
  • Week 10: Linear Regression (Assignment 10)
  • Week 11: Regression Assumptions, Multiple- and Logistic Regression (Assignment 11)
  • Final exam 

Course Dates


Summer Term (June – August)


Summer Term (June – August)