Introductory Statistics for College Credit

Introductory Statistics for College Credit

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

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

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Overview

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.
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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 college-level statistics course for college credit.

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.

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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.

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Mr. Peter Bruce

Mr. Peter Bruce

Mr. Peter Bruce is Founder and President of The Institute for Statistics Education at Statistics.com. 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 Data Mining for Business Analytics, with Galit Shmueli, Peter Gedeck, Inbal Yahav and Nitin R. Patel (Wiley, 3r...

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Course Syllabus - Part 1

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

Course Syllabus - Part 2

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

2021

Aug 6, 2021 to Oct 1, 2021

Sep 3, 2021 to Oct 29, 2021

Oct 1, 2021 to Nov 26, 2021

Nov 5, 2021 to Feb 26, 2021

2022

Jan 7, 2022 to Mar 3, 2022

Feb 4, 2022 to Apr 1, 2022

Mar 4, 2022 to Apr 29, 2022

Apr 1, 2022 to May 27, 2022

May 6, 2022 to Jul 1, 2022

2023

No classes scheduled at this time.

Prerequisites

There are no prerequisites for this course.

 The only mathematics you need is arithmetic.

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

What Our Students Say

I very much enjoyed the Stats 1 and 2 classes, and I think your book and approach really are excellent.  I have taken a variety of stats-type classes, including calculus-based probability, but the re-sampling gave me a much more intuitive understanding than any other approach.

Lily Gadamus
Program Evaluator, Southcentral Foundation

I have to say that I learned more about statistics in one month with Statistics.com than I learned in a full quarter of engineering statistics as an undergrad 20 years ago. The concept makes all the difference in the world. I now more fully understand what is behind the mathematical concepts they tried to teach me in college.

Jeff Cox
City of Columbus

Frequently Asked Questions

What is your satisfaction guarantee and how does it work?

We offer a “Student Satisfaction Guarantee” that includes a tuition-back guarantee, so go ahead and take our courses risk free. That’s our commitment to student satisfaction. Students may cancel, transfer, or withdraw from a course under certain conditions. If you’re not satisfied with a course, you may withdraw from the course and receive a tuition refund.

Please see our knowledge center for more information.

Who are the instructors at the Institute?

The Institute has more than 60 instructors who are recruited based on their expertise in various areas in statistics. Our faculty members are:

  • Authors of well-regarded texts in their area;
  • Advisory board members;
  • Senior faculty; and
  • Educators who have made important contributions to the field of statistics or online education in statistics.

The majority of our instructors have more than five years of teaching experience online at the Institute.

Please visit our faculty page for more information on each instructor at The Institute for Statistics Education.

Please see our knowledge center for more information.

What type of courses does the Institute offer?

The Institute offers approximately 80 courses each year. Topics include basic survey courses for novices, a full sequence of introductory statistics courses, bridge courses to more advanced topics. Our courses cover a range of topics including biostatistics, research statistics, data mining, business analytics, survey statistics, and environmental statistics.

Please see our course search or knowledge center for more information.

Do your courses have for-credit options?

Our courses have several for-credit options:

  • Continuing education units (CEU)
  • College credit through The American Council on Education (ACE CREDIT)
  • Course credits that are transferable to the INFORMS Certified Analytics Professional (CAP®)

Please see our knowledge center for more information.

Is the Institute for Statistics Education certified?

The Institute for Statistics Education is certified to operate by the State Council of Higher Education for Virginia (SCHEV). For more information visit: https://www.schev.edu/

Please see our knowledge center for more information.

Visit our knowledge base and learn more.

FAQs + Knowledge Base

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Additional Course Information

Organization of Course

The course is comprised of two separate courses, taken together with a week off in-between:

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

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

Note:  Parts 1 and 2 can be taken separately.

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

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.

Software

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 statistics.com 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 www.statcrunch.com.

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.

Software Uses and Descriptions | Available Free Versions
To learn more about the software used in this course, or how to obtain free versions of software used in our courses, please read our knowledge base article “What software is used in courses?” 

Course Fee & Information

Enrollment
Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date unless you specify otherwise.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates
Contact us to get information on group rates.

Discounts
Academic affiliation?  In most courses you are eligible for a discount at checkout.

New to Statistics.com?  Click here for a special introductory discount code.  

Invoice or Purchase Order
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.

Options for Credit and Recognition

This course is eligible for the following credit and recognition options:

No Credit
You may take this course without pursuing credit or a record of completion.

Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.

CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.

ACE CREDIT | College 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 College 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.

Click here for details about the examination process.

ACE has evaluated and recommended college credit of 3.00 Semester Hours in Statistics or Mathematics for Introduction to Statistics by taking both Statistics 1 and Statistics 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 Statistics 2 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

There is no supplemental content for this course.

Miscellaneous

There is no additional information for this course.

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Introductory Statistics for College Credit
599