Online Class: Applied Statistics 101

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.

Self-Paced, Online Class

Library Subscription

14

Lessons
17

Exams &
Assignments
21

Hours
average time
2.1

CEUs
Video Audit
Available

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.

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Areas of Study

Course Lessons

Average Lesson Rating:

"Extraordinarily Helpful"

(446 votes)

Lesson 1 - Descriptive Statistics I
o Review arithmetic means and some associated concepts in descriptive statistics
o Consider other types of means, including geometric and power means
Lesson 2 - Descriptive Statistics II
o Use moments about the mean to represent measures of dispersion and asymmetry
o Calculate the variance, standard deviation, and skewness of data sets
Lesson 3 - Frequencies
o Create frequency tables to represent data sets
o Calculate relative, cumulative, and cumulative relative frequencies
o Create and interpret bar graphs and histograms
Lesson 4 - Multivariate Data
o Recognize multivariate data versus univariate data
o Represent multivariate data using tables and scatterplots
o Calculate descriptive statistics for multivariate data, including covariances
Lesson 5 - Cross Tabulation I
o Create cross tabulations for bivariate data sets
o Review the basic terminology and procedure associated with statistical hypothesis testing
Lesson 6 - Cross Tabulation II
o Understand how to calculate the chi-square statistic for a cross tabulation
o Use the chi-square statistic to test hypotheses regarding cross tabulations
Lesson 7 - Correlation
o Understand how variance can be used to define a statistic that measures the linear relationship between variables
o Use the correlation coefficient to quantify the correlation between two variables
Lesson 8 - Student's t-Tests I
o Learn and apply hypothesis testing procedure to the one-sample Student's t-test
Lesson 9 - Student's t-Tests II
o Apply the paired two-sample Student's t-test to determine if two samples have statistically different means
Lesson 10 - One-Way ANOVA
o Identify the test statistic for one-way ANOVA
o Use one-way ANOVA to compare the means of multiple sample groups
Lesson 11 - Repeated Measures
Recognize repeated measures designs
Determine an alternate method of evaluating means for repeated measures designs (repeated measures ANOVA)
Lesson 12 - Factor Analysis
o Recognize some of the key terms associated with factor analysis
o Understand the overall purpose and procedures of exploratory and confirmatory factor analysis
Lesson 13 - Linear Regression I
Previously, we considered scatterplots as a way to display bivariate data sets. Scatterplots lead us to ask whether we can statistically construct a function that approximates the data.
Lesson 14 - Linear Regression II
We now extend our look at linear regression by considering path diagrams, which can be a useful tool for visually representing the relationships among variables in a data set.

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.
Demonstrate mastery of lesson content at levels of 70% or higher.

Additional Course Information

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Course Title: Applied Statistics 101

Course Number: 9770454

Lessons Rating: 4.8 / 5 Stars (446 votes)

Languages: English - United States, Canada and other English speaking countries

Category:

UniversalClass.com > Mathematics > Algebra and Math Studies

Availability: This course is online and available in all 50 states including: California, Florida, Georgia, Illinois, New York, Pennsylvania, Ohio, Texas, and Washington.

Last Updated: August 2023

Course Type: Self-Paced, Online Class

CEU Value: 2.1 IACET CEUs (Continuing Education Units)

CE Accreditation: Universal Class, Inc. has been accredited as an Authorized Provider by the International Association for Continuing Education and Training (IACET).

Grading Policy: Earn a final grade of 70% or higher to receive an online/downloadable CEU Certification documenting CEUs earned.

Assessment Method: Lesson assignments and review exams

Course Author: Dr. Jeffrey Clark, Ph.D.

Instructor: April Cordry-Moore

Syllabus: View Syllabus

Student Testimonials

"The instructor always got back to me quickly when I had a question or comment. He also graded the assignments and exams in a very timely manner." -- Cindy C.