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Introduction to Statistics

Purpose of Course  showclose

In this course, we will look at the properties behind the basic concepts of probability and statistics and focus on applications of statistical knowledge.  We will learn how statistics and probability work together.  The subject of statistics involves the study of methods for collecting, summarizing, and interpreting data.  Statistics formalizes the process of making decisions—and this course is designed to help you cultivate statistic literacy so that you can use this knowledge to make better decisions.  Note that this course has applications in sciences, economics, computer science, finance, psychology, sociology, criminology, and many other fields.

Every day, we read articles and reports in print or online.  After finishing this course, you should be comfortable asking yourself whether the articles make sense.  You will be able to extract information from the articles and display that information effectively.  You will also be able to understand the basics of how to draw statistical conclusions.

This course will begin with a brief overview of the discipline of statistics and will then quickly focus on descriptive statistics, introducing graphical methods of describing data. You will learn about combinatorial probability and random distributions, the latter of which serves as the foundation for statistical inference.  On the side of inference, we will focus on both estimation and hypothesis testing issues. We will also examine the techniques to study the relationship between two or more variables; this is known as regression.

By the end of this course, you should gain a sound understanding about what statistics represent, how to use statistics to organize and display data, and how to draw valid inferences based on data by using appropriate statistical tools.

Course Information  showclose

Welcome to PSCHY201/MA121: Introduction to Statistics. General information about this course and its requirements can be found below.
Course Designer: Ou Zhao, Ph.D. University of Michigan
Primary Resources: This course comprises a range of different free, online materials. However, the course makes primary use of the following materials:
Requirements for Completion: In order to complete this course, you will need to work through each unit and all of its assigned materials. You will also need to complete:
  • The Final Exam
Note that you will only receive an official grade on your final exam. However, in order to adequately prepare for this exam, you will need to work through all of the lectures, interactive labs, reading material, and associated questions in the course.
In order to pass this course, you will need to earn a 70% or higher on the final exam. Your score on the exam will be tabulated as soon as you complete it. If you do not pass the exam, you may take it again.
Time Commitment: This course should take you a total of 93 hours to complete. Each unit includes a time advisory that lists the amount of time you are expected to spend on each subunit. These should help you plan your time accordingly. It may be useful to take a look at these time advisories, to determine how much time you have over the next few weeks to complete each unit, and then to set goals for yourself. For example, unit 1 should take you 21.75 hours. Perhaps you can sit down with your calendar and decide to complete subunit 1.1 (a total of 4.75 hours) on Monday and Tuesday nights; subunit 1.2.1 (a total of 4.75 hours) on Wednesday and Thursday nights; etc.
Tips/Suggestions: It will be helpful to have a calculator for this course. If you do not own one or have access to one, consider using this freeware version.
As you read and watch the lectures, take careful notes on a separate sheet of paper. Mark down any important equations, formulas, and definitions that stand out to you. These notes will be useful to review as you study for your final exam. 

Learning Outcomes  showclose

Upon successful completion of this course, you will be able to:
  • define and apply the meaning of descriptive statistics and statistical inference, describe the importance of statistics, and interpret examples of statistics in a professional context;
  • distinguish between a population and a sample;
  • calculate and explain the purpose of measures of location, variability, and skewness;
  • apply simple principles of probability;
  • compute probabilities related to both discrete and continuous random variables;
  • identify and analyze sampling distributions for statistical inferences;
  • identify and analyze confidence intervals for means and proportions;
  • compare and analyze data sets using descriptive statistics, parameter estimation, hypothesis testing;
  • explain how the central limit theorem applies in inference, and use the theorem to construct confidence intervals;
  • calculate and interpret confidence intervals for one population average and one population proportion;
  • differentiate between type I and type II errors;
  • conduct and interpret hypothesis tests;
  • identify and evaluate relationships between two variables using simple linear regression; and
  • discuss concepts pertaining to linear regression, and use regression equations to make predictions.

Course Requirements  showclose

In order to take this course, you must:

√    Have access to a computer

√    Have continuous broadband internet access

√    Have the ability/permission to install plug-ins or software (e.g. Adobe Reader of Flash)

√    Have the ability to download and save files and documents to a computer

√    Have the ability to open Microsoft files and documents (.doc, .ppt, .xls, etc.)

√    Be competent in the English language

√    Access to a calculator.

√    Have read the Saylor Student Handbook.

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