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Course Syllabus: Applied Statistics 101

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Course Description

Large sets of numbers can be daunting, and characterizing them in a few words or numbers can be even more daunting. This course considers how to take data sets--whether large or small--and describe them using a few numbers (descriptive statistics). This, however, is only a small portion of the course.

The majority of the course is dedicated to reaching statistically justified conclusions on the basis of these descriptive statistics. For instance, does the average value of one data set deviate from another in what might be called a "statistically significant" manner? To this end, the course covers cross tabulation of data (including the chi-square test), correlation, linear regression, Student's t-tests, analysis of variance (ANOVA), repeated measures analysis, and factor analysis. 

Although the course does not always provide rigorous mathematical justifications for every aspect of the statistical tools and theory discussed therein, it does provide common-sense explanations of many of these aspects, and it includes numerous real-world examples to illustrate the use of these tools.

Thus, this course teaches students to take sets of data, describe them using a few numbers (including the mean, variance, and skewness), and then reach statistically justifiable conclusions about those data sets. Students should come away from the course with confidence in their ability to tackle basic applied statistics problems and with the fundamental knowledge needed to learn more in-depth statistical theory.

Course Requirements

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.

Course Goals and Objectives


Course Goals:

Learn other types of means, including geometric and power means associated in descriptive statistics

Learn how to represent measures of dispersion and asymmetry

Calculate the variance, standard deviation, and skewness of data sets

Create frequency tables to represent data sets

Represent multivariate data using tables and scatterplots

 Create cross tabulations for bivariate data sets

Understand how variance can be used to define a statistic that measures the linear relationship between variables

 Understand the fundamental concepts associated with stepwise linear regression

Calculate data values for standardized variables

 Calculate standardized regression coefficients using matrix math

 Recognize and construct path diagrams

 Learn and apply hypothesis testing procedure to the one-sample Student's t-test

 Apply the paired two-sample Student's t-test to determine if two samples have statistically different means

Identify the test statistic for one-way ANOVA

Use one-way ANOVA to compare the means of multiple sample groups

Recognize repeated measures designs

Understand the overall purpose and procedures of exploratory and confirmatory factor analysis

Lessons will cover these topics:

Descriptive Statistics I

Descriptive Statistics II


Multivariate Data

Cross Tabulation I

Cross Tabulation II


Linear Regression I

Linear Regression II

Student's t-Tests I

Student's t-Tests II


Repeated Measures

Factor Analysis

Course Materials

No additional course materials required to complete this course.

Grading Policy

Each lesson includes exercises and review exams.

Students will successfully complete this course with an overall grade of 70% or higher.

Learning Outcomes

By successfully completing this course, students will be able to:
  • Define descriptive statistics.
  • Demonstrate statistics and the use frequencies and how to solve these problems.
  • Identify multivariate data.
  • Demonstration Cross Tabulation problems and solutions
  • Demonstrate understanding of correlation in statistics.
  • Demonstrate problem and solution use of t-Tests.
  • Demonstrate One-Way ANOVA statistical problems and solutions
  • Demonstrate Repeated Measures.
  • Demonstrate Factor Analysis.
  • Describe basic usage of SPSS for graphing and solving applied statistics problems, and
  • Demonstrate mastery of lesson content at levels of 70% or higher.
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Assessment Guide

Lesson 1 Descriptive Statistics I10
Feedback about the exam.0
Lesson 2 Descriptive Statistics II10
Lesson 3 Frequencies9
Lesson 4 Multivariate Data10
Lesson 5 Cross Tabulation I10
Lesson 6 Cross Tabulation II10
Lesson 7 Correlation10
Lesson 8 Students t-Tests I10
Lesson 9 Students t-Tests II10
Lesson 10 One-Way ANOVA10
Lesson 11 Repeated Measures10
Lesson 12 Factor Analysis10
Lesson 13 Linear Regression I10
Lesson 14 Linear Regression II6
The Final Exam90
Total Points:226
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