Introduction to Statistics

Course Content from Acrobatiq
Course Number: MAT202
Download Course Syllabus
In this course, students will look at the properties behind the basic concepts of probability and statistics and focus on applications of statistical knowledge. Students will learn about how statistics and probability work together. The subject of statistics involves the study of methods for collecting, summarizing, and interpreting data. After finishing this course, students should be comfortable evaluating an author’s use of data and be able to extract information from articles and display that information effectively. Students will also be able to understand the basics of how to draw statistical conclusions. This course will begin with descriptive statistics and the foundation of statistics, move onto probability and random distributions, the latter of which enables statisticians to work with several aspects of random events and their applications. Finally, students will examine a number of ways to investigate the relationships between various characteristics of data.

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Self Paced
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Mathematics
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Course Objectives

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

  • define 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;
  • 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;
  • 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
  • use regression equations to make predictions.
Topic Topic Title Module
1
Learning Strategies and Big Picture
  • Introduction
  • Module 1: Learning Strategies
  • The Big Picture
2 Exploratory Data Analysis
  • Exploratory Data Analysis Introduction
  • Module 2: Examining Distributions
  • Module 3: Examining Relationships
  • Exploratory Data Analysis Summary
3 Producing Data
  • Producing Data Introduction
  • Module 4: Sampling
  • Module 5: Designing Studies
  • Producing Data Summary
4 Probability
  • Probability Introduction
  • Module 6: Introduction (Probability)
  • Module 7: Finding Probability of Events
  • Module 8: Conditional Probability and Independence
  • Module 9: Random Variables
  • Module 10: Sampling Distributions
  • Probability Summary
5 Inference
  • Inference Introduction
  • Module 11: Introduction (Inference)
  • Module 12: Inference For One Variable
  • Module 13: Estimation
  • Module 14: Hypothesis Testing
  • Module 15: Inference for Relationships
  • Module 16: Inference for Relationships
  • Module 17: The Chi-Square Goodness-of-Fit Test
Final Exam
  • Study Guide Practice Examination Final Examination

Successful completion of College Algebra is recommended before taking Introduction to Statistics.  

Important Terms

In this course, different terms are used to designate tasks:

  • ADAPTivities: Adaptive exercises designed to help you retain important content and information. There are generally two types identified by name:
  • 1. Learn By Doing activities: Learn By Doing activities give you the chance to practice the concept that you are learning, with hints and feedback to guide you if you struggle.
  • 2. Did I Get This? activities: Did I Get This? activities are your chance to do a quick "self-check" and assess your own understanding of the material before doing a graded activity.
  • StatTutor: StatTutor is an interactive learning tool that provides you with a data analysis problem, supports you as you attempt to solve it, and gives you hints and feedback along the way.
  • Checkpoint: A graded online assessment or “Quiz” covering a full module
  • Final Exam: A comprehensive proctored final examination
This course does not require a text.

StraighterLine provides a percentage score and letter grade for each course. A passing percentage is 70% or higher. If you have chosen a Partner College to award credit for this course, your final grade will be based upon that college's grading scale. Only passing scores will be considered by Partner Colleges for an award of credit.

There is a total of 100% in the course:

Unit Assessment Points Available
  • Examining Distributions Checkpoint 1
  • Examining Distributions Checkpoint 2
  • Examining Relationships Checkpoint 1
  • Examining Relationships Checkpoint 2
  • Sampling Checkpoint
  • Designing Studies Checkpoint 1
  • Introduction to Probability
  • Probability Checkpoint 1
  • Probability Checkpoint 2
  • Conditional Probability and Independence Checkpoint 1
  • Conditional Probability and Independence Checkpoint 2
  • Random Variables Checkpoint 1
  • Random Variables Checkpoint 2
  • Random Variables Checkpoint 3
  • Random Variables Checkpoint 4
  • Sampling Distributions Checkpoint 1
  • Sampling Distributions Checkpoint 2
  • Estimation Checkpoint
  • Overview Checkpoint
  • Hypothesis Testing for a Population Proportion Checkpoint
  • Hypothesis Testing for a Population Mean Checkpoint
  • Hypothesis Testing Checkpoint
  • Type I and Type II Checkpoint
  • Case C→C and Q→Q Checkpoint
  • Inference for Relationships Checkpoint
  • Levels of Measure Checkpoint
  • Goodness of Fit Test Checkpoint
70% of your course grade
Final Graded Exam 30% of your course grade
Total 100%

Final Proctored Exam

The final exam is developed to assess the knowledge you learned taking this course. All students are required to take an online proctored final exam in order complete the course and be eligible for transfer credit.

Learn more about Proctored Exams

In this course, students will look at the properties behind the basic concepts of probability and statistics and focus on applications of statistical knowledge. Students will learn about how statistics and probability work together. The subject of statistics involves the study of methods for collecting, summarizing, and interpreting data. After finishing this course, students should be comfortable evaluating an author’s use of data and be able to extract information from articles and display that information effectively. Students will also be able to understand the basics of how to draw statistical conclusions. This course will begin with descriptive statistics and the foundation of statistics, move onto probability and random distributions, the latter of which enables statisticians to work with several aspects of random events and their applications. Finally, students will examine a number of ways to investigate the relationships between various characteristics of data.

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