Welcome to MA121: Introduction to Statistics

Specific information about this course and its requirements can be found below. For more general information about taking Saylor Academy courses, including information about Community and Academic Codes of Conduct, please read the Student Handbook.

Course Description

Examine the properties behind the concepts of probability and statistics by learning how to investigate the relationships between various characteristics of data.

Course Introduction

If you invest in financial markets, you may want to predict the price of a stock in six months from now based on company performance measures and other economic factors. As a college student, you may be interested in knowing the dependence of the mean starting salary of a college graduate, based on your GPA. These are just some examples that highlight how statistics are used in our modern society. To figure out the desired information for each example, you need data to analyze.

The purpose of this course is to introduce you to the subject of statistics as a science of data. Data abounds in this information age; extracting useful knowledge and gaining a sound understanding of complex data sets has been more of a challenge. In this course, we will focus on the fundamentals of statistics, broadly described as the techniques to collect, clarify, summarize, organize, analyze, and interpret numerical information.

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, which are the foundation for statistical inference. With inference, we will focus on estimation and hypothesis testing issues. We will also examine the techniques to study the relationship between two or more variables, known as regression.

By the end of this course, you should understand 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.

The purpose of this course is to introduce you to the subject of statistics as a science of data. Data abounds in this information age; how to extract useful knowledge and gain a sound understanding of complex data sets has been more of a challenge. In this course, we will focus on the fundamentals of statistics, which may be broadly described as the techniques to collect, clarify, summarize, organize, analyze, and interpret numerical information.

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 of 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.

This course includes the following units:

  • Unit 1: Statistics and Data
  • Unit 2: Elements of Probability and Random Variables
  • Unit 3: Sampling Distributions
  • Unit 4: Estimation with Confidence Intervals
  • Unit 5: Hypothesis Test
  • Unit 6: Linear Regression

Course Learning Outcomes

Upon successful completion of this course, you will be able to:

  • Describe the meaning and importance of descriptive and inferential statistics;
  • Distinguish between a population and a sample;
  • Calculate measures of location, variability, and skewness;
  • Apply simple principles of probability;
  • Compute probabilities related to both discrete and continuous random variables;
  • Analyze sampling distributions for statistical inferences;
  • Analyze confidence intervals for means and proportions;
  • Analyze data sets using descriptive statistics, parameter estimation, and hypothesis testing;
  • Explain how the central limit theorem applies in inference;
  • Interpret the results of hypothesis tests;
  • Analyze the relationships between two variables using simple linear regression; and
  • Use regression equations to make predictions.

Throughout this course, you will also see learning outcomes in each unit. You can use those learning outcomes to help organize your studies and gauge your progress.

Course Materials

This course's primary learning materials are articles, lectures, and videos.

All course materials are free to access and can be found in each unit of the course. Pay close attention to the notes that accompany these course materials, as they will tell you what to focus on in each resource and will help you understand how the learning materials fit into the course as a whole. You can also see a list of all the learning materials in this course by clicking on Resources in the navigation bar.

Evaluation and Minimum Passing Score

Only the final examination is considered when awarding you a grade for this course. To pass this course, you will need to earn 70% or higher on the final exam.

Your score on the exam will be calculated as soon as you complete it. There is a 14-day waiting period between each attempt. You may only attempt the final exam a maximum of three times. Be sure to study in between each attempt! If you do not pass the exam after three attempts, you will not complete this course.

There is also a practice exam that you may take as many times as you want to help you prepare for the final exam. The course also contains end-of-unit assessments in this course. The end-of-unit assessments are designed to help you study and do not factor into your final course grade. You can take these as many times as you want to until you understand the concepts and material covered. You can see all of these assessments by clicking on Quizzes in the course's navigation bar.

Continuing Education Credits

The certificate earned by passing this self-paced course displays not only the program hours you completed, but also continuing education credits (CEUs) for documenting successful completion of courses that are designed to improve the knowledge and skills of working adults. Many industries value CEUs, and now your certificate reflects them clearly, and they may be used to support career advancement or to meet professional licensing standards. This course contains 3.5 CEUs.

Tips for Success

MA121: Introduction to Statistics is a self-paced course, meaning you can decide when to start and complete the course. We estimate the "average" student will take hours to complete. We recommend studying at a comfortable pace and scheduling your study time in advance.

Learning new material can be challenging, so here are a few study strategies to help you succeed:

  • Take notes on terms, practices, and theories. This helps you understand each concept in context and provides a refresher for later study.
  • Test yourself on what you remember and how well you understand the concepts. Reflecting on what you've learned improves long-term memory retention.

Technical Requirements

This course is delivered entirely online. You will need access to a computer or web-capable mobile device and consistent internet access to view or download resources and complete auto-graded assessments and the final exam.

To access the full course, including assessments and the final exam, log into your Saylor Academy account and enroll in the course. If you don’t have an account, you can create one for free here. Note that tracking progress and taking assessments require login.

For more details and guidance, please review our complete Technical Requirements and our student Help Center.

Optional Saylor Academy Mobile App

You can access all course features directly from your mobile browser, but if you have limited internet connectivity, the Saylor Academy mobile app provides an option to download course content for offline use. The app is available for iOS and Android devices.

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Fees

This course is entirely free to enroll in and access. All course materials, including textbooks, videos, webpages, and activities, are available at no charge. This course also contains a free final exam and course completion certificate.