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Introductory Statistics for Credit

Introductory Statistics for Credit

This course will teach you the equivalent of a semester course in introductory statistics.


This course is designed to teach sometimes tricky statistical concepts in an easy-to-understand business and real-world context. It relies on the innovative text “Introductory Statistics and Analytics: A Resampling Perspective” and intentionally references the growing field of Data Science.

The course is divided into two four-week sections, Part 1 – Probability and Study Design, and Part 2 – Inference and Association.

The course is approved for academic credit recommendation (3 credits) by the American Council on Education.

  • Introductory
  • 8 Weeks
  • Expert Instructor
  • Tuiton-Back Guarantee
  • 100% Online
  • TA Support

Learning Outcomes

Students who complete this course will understand fundamentals of probability and study design including statistical significance, categorical data and contingency tables, random sampling, the Bootstrap, confidence intervals and more. You will also learn basics of inference and association including confidence intervals for proportions, correlation and simple regression, multiple regression, and using regression models to make predictions.

  • Specify the design of a basic randomized controlled study
  • Conduct computer resampling simulations, including the bootstrap and permutation test, to model the effects of chance
  • Conduct A-B tests (2-sample comparisons) and test the results for statistical significance
  • Measure correlation
  • Use regression for prediction and explanation, and assess the model
  • Explain the use of k-nearest-neighbor methods for predicting a binary outcome

Who Should Take This Course

Anyone who needs a basic statistics course for refreshing their memory of a previous course taken, or who need a university-level statistics course for academic credit.

Our Instructors

Mrs. Meena Badade

Mrs. Meena Badade

Mrs. Meena Badade has over 23 years teaching experience, leading courses in statistics at various levels of education and at different institutions nationally and internationally. She also has a number of research papers published in respected journals. In addition to academic practice, she has considerable corporate experience at Metric Consultancy, where she worked as a statistical consultant and data analyst for international clients, applying various statistical techniques to projects in several industries.
Ms. Anuja Kulkarni

Ms. Anuja Kulkarni

Ms. Anuja Kulkarni has managed and taught over 125 online course sessions and more than 1000 students as an Assistant Teacher at The Institute for Statistics Education. She holds a Masters’ degree in Statistics from Kolhapur University, India, where she also taught undergraduate statistics. Ms. Kulkarni teaches Statistics, Optimization Methods and Predictive Analytics and assists in several other course topics here for over six years. In all, her passion is leading new students into the fascinating and practical world of statistics through the introductory statistics course series at The Institute.


Mr. Peter Bruce

Mr. Peter Bruce

Mr. Peter Bruce is Founder and President of The Institute for Statistics Education at He is the developer of Resampling Stats software (originated by Julian Simon in the 1970’s), and has also taught resampling statistics at the University of Maryland and in a variety of short courses. He is the author of Responsible Data Science, with Grant Fleming (Wiley, 2021), Machine Learning for Business Analytics, with Galit Shmueli, Peter Gedeck, Inbal Yahav and Nitin R. Patel (prior title Data Mining for Business Analytics, Wiley, 3rd ed. 2016; JMP version 2017, R version 2018, Python version 2019), Introductory Statistics and Analytics (Wiley, 2015), and Practical Statistics for Data Scientists, with Andrew Bruce and Peter Gedeck, (O’Reilly 2016). His books have been translated into Japanese, Chinese, Korean, German, Polish and Spanish.

Course Syllabus

Week 1

Study Design, Statistical Significance

  • Intro, Study Design
  • Measures of Central Location and Variability
  • Distance
  • Data Format
  • Variables
  • Graphs
  • Null Hypothesis
  • Resampling
  • Normal Distribution
  • Significance

Week 2

Categorical Data, Contingency Tables

  • Categorical Data
  • Graphical Exploration
  • Indexing
  • Simple Probability
  • Distributions
  • Normal Distribution again
  • 2-Way (Contingency) Tables
  • Conditional Probability

Week 3

More Probability, Random Sampling, The Bootstrap

  • Bayes Rule
  • Independence
  • Surveys
  • Random Sampling
  • Bootstrap

Week 4

Confidence Intervals

  • Point Estimates
  • Confidence Intervals
  • Formula Counterparts
  • Standard Error
  • Beyond Random Sampling

Week 5

Confidence Intervals for Proportions; 2-Sample Comparisons

  • CI for a proportion
  • The language of hypothesis testing
  • A-B tests (2-group comparisons)
  • Bandit Algorithms (briefly)

Week 6

Correlation and Simple (1-variable) Regression

  • Correlation coefficient
  • Significance testing for correlation
  • Fitting a regression line by hand
  • Least squares fit
  • Using the regression equation

Week 7

Multiple Regression

  • Explain or predict?
  • Multiple predictor variables
  • Assessing the regression model
  • Goodness-of-fit (R-squared)
  • Interpreting the coefficients
  • RMSE (root mean squared error)

Week 8

Prediction; K-Nearest Neighbors

  • Using the regression model to make predictions
  • Using a hold-out sample
  • Assessing model performance
  • K-nearest neighbors

Class Dates


05/05/2023 to 06/30/2023
06/02/2023 to 07/28/2023
07/07/2023 to 09/01/2023
08/04/2023 to 09/29/2023
09/01/2023 to 11/03/2023
10/06/2023 to 12/01/2023
11/03/2023 to 12/29/2023
12/01/2023 to 01/26/2024


01/05/2024 to 03/01/2024
02/02/2024 to 03/29/2024
03/01/2024 to 04/26/2024
04/05/2024 to 05/31/2024
05/03/2024 to 06/28/2024
06/07/2024 to 08/02/2024
07/05/2024 to 08/30/2024
08/02/2024 to 09/27/2024
09/06/2024 to 11/01/2024


The only mathematics you need is arithmetic.

This course requires the use of software. Please read the “Software” section below under “Additional Course Information.”

Karolis Urbonas
Susan Kamp
Stephen McAllister
Amir Aminimanizani
Elena Rose
Leonardo Nagata
Richard Jackson

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Introductory Statistics for Credit

Additional Information

Organization of Course

The course is comprised of two parts:

Part 1: Statistics 1 – Probability and Study Design (4 weeks)

Part 2: Statistics 2 – Inference and Association (4 weeks)

This course takes place online at the Institute for 8 weeks. During each course week, you participate at times of your own choosing – there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirements

This is an 8-week course requiring 10-15 hours per week of review and study, at times of your choosing.


Homework in this course consists of short response exercises; the use of software is required for some exercises.

Course Text

All needed course material will also be provided electronically as part of the course.  The course is based on the book Introductory Statistics and Analytics: A Resampling Perspective by Peter Bruce, (2014, Wiley). It does not synchronize exactly with the course material, which has updates, but is a useful parallel reference if you want to have a printed book.


In this course, software is needed for statistical analysis and simple resampling/simulation operations. We recommend one of these three options:

  1. Regular Excel (not Excel Starter) and Resampling Stats for Excel (must have Windows)
  2. StatCrunch (Windows or Mac OS)
  3. R
  4. Python

Excel: you will need to have some facility with using formulas in Excel.

Resampling Stats for Excel: this is a commercial add-in for Excel, designed as a practitioner’s tool for doing resampling simulations. A free license is available to all course participants, while they are enrolled in the sequence of introductory statistics courses. Runs only on Windows. Enrolled students will be given access to a free 1-year trial of Resampling Stats through the software download link on the main Stats course webpage.  You can also visit the Resampling Stats website and download the 1-year trial here.

StatCrunch: this is a very affordable web-based statistical software program, which also has simulation and resampling capabilities. Runs over the web, so can be used with both Windows and Mac. Resampling is not as intuitive as with Box Sampler and Resampling Stats for Excel.  Learn more at

NOTE for StatCrunch Users:  On all platforms, we recommend that you use the New version of StatCrunch.  All examples in the textbook supplement are based on the New version of StatCrunch.

R: R is a powerful opensource statistical scripting language that is widely recognized as an industry standard.  You will need to have familiarity with R and RStudio prior to taking the Statistics 1, 2 or 3 courses if you choose to use R as your software package.  Comprehensive supplemental materials are available for R users.  You can learn more about R here and RStudio here.

Python: Python is a language long used in computer science that has recently become quite popular in data science.  You will need to have familiarity with Python prior to taking the Statistics 1, 2 or 3 courses if you choose to use Python as your software package.  Comprehensive supplemental materials and support are available for Python users.  We recommend the use of Jupyter notebooks and the Anaconda installation package.

Options for Credit and Recognition

ACE CREDIT | Academic Credit
This course has been evaluated by the American Council on Education (ACE) and is recommended for the lower division baccalaureate degree, 3 semester hours in statistics. Please note that the decision to accept specific credit recommendations is up to the academic institution accepting the credit.

Required Exams for Academic Credit via ACE CREDIT
Those seeking ACE Credit, and certificate candidates needing to satisfy their introductory statistics requirement, must pass an online exam to receive credit in either instance.

ACE has evaluated and recommended academic credit of 3.00 Semester Hours in Statistics or Mathematics for Introduction to Statistics by taking this course 2. ACE credit recommendation requires marks of 70% or better on the two courses combined, plus passing an online proctored final online exam scheduled at the end of the course.

While each institution makes its own decisions about whether to grant credit and how much to grant, most U.S. higher education institutions participate in the American Council on Education’s (ACE) credit recommendation service.

Supplemental Information

Literacy, Accessibility, and Dyslexia

At, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:







  • Navidys (for colorblindness, dyslexia, and reading difficulties)
  • HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)

Register For This Course

Introductory Statistics for Credit