Course Syllabus

 

Course Description

Statistics are used in a variety of contexts ranging from scientific experiments to political advertisements and beyond. Because statistics can be used to mislead, an understanding of the topic can be helpful for more than just the mathematical skills that it imparts: knowledge of statistics theory can also be a strong defense against attempts by the unscrupulous to mislead others. 

This statistics course introduces the basic concepts of statistical analysis, with a focus on both univariate (single-variable) and bivariate (two-variable) data. The course starts with an introduction to statistics terms and then moves on to organization and display of data. Analysis of univariate data by way of measures of central tendency (such as the mean or average), dispersion (such as the variance), and asymmetry ("skewness") is presented next, followed by an introduction to probability theory.

The relationship of probability to statistics is also discussed, providing students with the tools they need to understand how "chance" plays a role in statistical analysis. Statistical distributions, with a focus on the normal distribution and its uses, are also considered, along with a discussion of bivariate data and linear (least-squares) regression. Finally, the course culminates with a low-level introduction to hypothesis testing. Although this last topic could be a course of its own, the student is provided with enough theory and sufficient practice to conduct analyses of simple statistical hypotheses.

The course assumes a minimal understanding of and proficiency in algebra, but many of the concepts can be understood, and even many of the calculations performed, without an extensive mathematical background. This course is designed for anyone who wants a little more than just a cursory overview of statistics, but who doesn't want to get bogged down in the mathematical theory that underlies it.

Statistics is a subject that has earned a certain amount of notoriety because of its misuse in various contexts. Nevertheless, statistics is a tool that, if used properly, can be of tremendous help in math, science, engineering, history, politics, and numerous other fields. As you study this subject, always keep in mind that statistics is more than just math: it is not simply manipulation of numbers through addition, subtraction, multiplication, division, and other mathematical operations. Statistics also involves language and units: when a statistician (or layman) provides a statistic, it involves a number and a label of some sort. For instance, the number 5.3 is not in and of itself a statistical value; "an average age of 5.3 years," however, is a statistical value. This linguistic aspect of statistics sometimes allows a certain amount of ambiguity that can be misleading. By studying statistics, you will equip yourself to identify and understand both uses and abuses of this tool.

What is Statistics?

Statistics is used for quantifying sets of data such as attributes of a group of people and measurements taken in a laboratory. Consider, for instance, the population of a particular country. The people who reside in that country have varying heights: some are short, some are tall, some are in between. If we wanted to compare the height of this population with that of some other population in a convenient manner, we would not want to compare individual people. Such a task would be burdensome (the number of people in a country might be in the millions or billions) and would not necessarily be particularly helpful as a means of comparing populations as a whole. Instead, we can use an average or median height as the basis for our comparison. These statistical values are single numbers that quantify the data (the heights of a country's population) and that provide a convenient way to express and compare certain characteristics of those data. Part of the goal of this course is to teach you how to select and use statistical tools like averages and medians, as well as a host of others, in assessing and comparing data.

Simply defined, statistics (sometimes colloquially termed "stats") is the study of collecting, analyzing, interpreting, and representing of sets of numerical data. Thus, virtually any field of study that uses numbers can, at least occasionally, involve statistics. Statistics, because it makes extensive use of numbers, is math-intensive, and a decent grasp of basic arithmetic and algebra is required to study this field.

  • Completely Online
  • Self-Paced
  • 6 Months to Complete
  • 24/7 Availability
  • Start Anytime
  • PC & Mac Compatible
  • Android & iOS Friendly
  • Accredited CEUs
Universal Class is an IACET Accredited Provider
 

Learning Outcomes

By successfully completing this course, students will be able to:
  • Define statistics terms.
  • Display statistical data.
  • Define measures of central tendency.
  • Select an appropriate measure of central tendency.
  • Define measures of dispersion.
  • Describe measures of asymmetry.
  • Describe Probability I problems
  • Describe and solve Probability II problems.
  • Summarize statistical distributions.
  • Describe the normal distribution.
  • Describe bivariate data.
  • Perform regression analysis.
  • Describe Hypothesis Testing I and II.
  • Demonstrate mastery of lesson content at levels of 70% or higher.
 
 

Assessment Guide

Assessment Points
Introduction 1 points
Lesson 1 Statistics Terms and Motivation 10 points
Lesson 2 Displaying Statistical Data 9 points
Lesson 3 Measures of Central Tendency 7 points
Lesson 3 Exercises 6 points
Lesson 4 Selecting an Appropriate Measure of Central Tendency 11 points
Lesson 5 Measures of Dispersion 10 points
Lesson 5 Exercises 4 points
Lesson 6 Measures of Asymmetry 9 points
Lesson 7 Other Statistical Measures 10 points
Lesson 7 Exercises 4 points
Lesson 8 Introduction to Probability I 10 points
Lesson 8 Exercises 7 points
Lesson 9 Introduction to Probability II 10 points
Lesson 9 Exercises 4 points
Lesson 10 Statistical Distributions 11 points
Lesson 10 Exercises 3 points
Lesson 11 The Normal Distribution 10 points
Lesson 11 Exercises 6 points
Lesson 12 Bivariate Data 9 points
Lesson 13 Regression Analysis 10 points
Lesson 14 Introduction to Hypothesis Testing I 10 points
Lesson 14 Exercises 3 points
Lesson 15 Introduction to Hypothesis Testing II 10 points
The Final Exam 146 points
Lesson 15 Exercises 2 points
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