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.

 

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
659