Online Class: Statistics 101

Name: Statistics 101
Price: 95.00 USD

Learn to navigate the world of statistics, where data organization meets probability theory, and explore univariate measures like mean and variance alongside bivariate regression analysis. The course empowers you with knowledge to discern statistical truths from deception, applying insights across diverse disciplines without getting tangled in complex mathematics.

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15

Lessons
26

Exams &
Assignments
4,783

Students
have taken this course
35

Hours
average time
3.5

CEUs

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.

Courses Frequently Bought Together:

Algebra 101: Beginner to Intermediate Level

Math All-In-One (Arithmetic, Algebra, and Geometry Review)

Geometry 101: Beginner to Intermediate Level

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

Course Lessons

Average Lesson Rating:

"Extraordinarily Helpful"

(1,307 votes)

Lesson 1. Unveiling the World of Statistics: A Gateway to Understanding and Application

Statistics is a versatile tool that aids in compiling, interpreting, and applying numerical data across multiple disciplines. This overview establishes the groundwork by introducing key statistical concepts and the importance of differentiating between population and sample data. 11 Total Points

Lesson 1 Video A
Lesson 1 Video B : How to Take an Exam
Lesson discussions: Reasons for Taking this Course
Complete Assignment: Introduction
Assessment: Lesson 1 Statistics Terms and Motivation

Lesson 2. Data Organization and Visualization: Building Tables and Graphs

Organizing data for statistical analysis is crucial for efficient calculations and inference; this lesson covers constructing tables and understanding graphical data presentations. Emphasizing terms like frequency, cumulative frequency, and relative frequency, it guides their application through examples. Additional lesson topics: Concept of Graphical representation of Data 9 Total Points

Lesson 2 Video
Assessment: Lesson 2 Displaying Statistical Data

Lesson 3. Measuring the Center: Understanding Mean, Median, and Mode

Measures of central tendency, including the mean, median, and mode, describe the 'center' of a dataset, each uniquely suited to different data distributions. This lesson emphasizes that while the mean is popular, skewed distributions may require the median or mode for accurate representations. Additional lesson topics: Measures of Central Tendency; Mean and Medain 13 Total Points

Lesson 3 Video
Assessment: Lesson 3 Measures of Central Tendency
Assessment: Lesson 3 Exercises

Lesson 4. Choosing Your Measures: Understanding Central Tendency

This lesson teaches how data set nuances can make selecting the right measure of central tendency a non-trivial task. By illustrating with examples, it discusses the balance between the mean, median, and mode, and the importance of context to avoid misleading interpretations. 11 Total Points

Lesson 4 Video
Assessment: Lesson 4 Selecting an Appropriate Measure of Central Tendency

Lesson 5. Characterizing the Spread: A Guide to Variance and Standard Deviation

This lesson on measures of dispersion explains variance and standard deviation as tools to gauge data spread around a mean. It distinguishes between broad population analyses and specific sample estimates, with adjustments for freedom degrees to improve accuracy. Additional lesson topics: Standard Deviation and Variance; Why n-1 for standard deviation 14 Total Points

Lesson 5 Video
Assessment: Lesson 5 Measures of Dispersion
Assessment: Lesson 5 Exercises

Lesson 6. The Role of Skewness in Statistical Analysis

This lesson delves into skewness, a statistical measure that explains the asymmetry of a data set, enriching our understanding beyond just central tendency and dispersion. By quantifying skewness, we can determine both the degree and direction of data asymmetry relative to the mean. Additional lesson topics: What is a moment 9 Total Points

Lesson 6 Video
Assessment: Lesson 6 Measures of Asymmetry

Lesson 7. Understand Percentiles and Quartiles

The lesson explores statistical measures like percentiles and quartiles, which help describe data set positions. Understanding these concepts enhances data interpretation beyond just central tendency and dispersion. 14 Total Points

Lesson 7 Video
Assessment: Lesson 7 Other Statistical Measures
Assessment: Lesson 7 Exercises

Lesson 8. Understanding Probability: The Backbone of Statistical Analysis

The connection between chance events in experiments and statistics highlights the importance of terms like outcome, event, sample space, and random variable in probability theory. Using statistical measures like mean and standard deviation helps in comprehending and describing these random variations. 17 Total Points

Lesson 8 Video
Assessment: Lesson 8 Introduction to Probability I
Assessment: Lesson 8 Exercises

Lesson 9. Counting Outcomes for Probability

This lesson delves into solving probability problems by counting the outcomes of random experiments, utilizing key concepts such as permutations and combinations. By understanding the influence of replacement and ordering, one can effectively calculate probabilities by determining relative frequencies. 14 Total Points

Lesson 9 Video
Assessment: Lesson 9 Introduction to Probability II
Assessment: Lesson 9 Exercises

Lesson 10. Fundamentals of Discrete and Continuous Distributions

This lesson blends statistics and probability to explore statistical distributions, highlighting the distinction between discrete and continuous types, and showing how to calculate mean and standard deviation. With practice problems, it demonstrates practical applications like rolling a die or estimating measurement expectations. 14 Total Points

Lesson 10 Video
Assessment: Lesson 10 Statistical Distributions
Assessment: Lesson 10 Exercises

Lesson 11. Normal Distribution and Z-Scores

This lesson deciphers the normal distribution and its statistical applications, emphasizing how to use Z-scores for standardizing data to estimate probabilities with ease. Probabilities are often computed using tables, which provide a user-friendly method to navigate complex probability calculations. 16 Total Points

Lesson 11 Video
Assessment: Lesson 11 The Normal Distribution
Assessment: Lesson 11 Exercises

Lesson 12. Exploring Bivariate Data Distributions: A Foundation for Advanced Statistical Concepts

By transitioning from univariate to bivariate data analyses, the lesson underscores the importance of scatterplots and marginal distributions, equipping learners to evaluate statistical dependencies. It serves as groundwork for exploring how random variable interactions inform advanced statistical methods like regression. 9 Total Points

Lesson 12 Video
Assessment: Lesson 12 Bivariate Data

Lesson 13. Introduction to Least-Squares Regression

This lesson delves into least-squares linear regression, discussing its formulation and the correlation measure for bivariate data. It offers a robust algebraic approach to fitting linear trends to datasets while addressing the 'goodness of fit.' 10 Total Points

Lesson 13 Video
Assessment: Lesson 13 Regression Analysis

Lesson 14. Crunch Time: Hypothesis Testing in Practice

Through understanding hypothesis testing, students will grasp concepts like null and alternative hypotheses, significance levels, and test statistics. The lesson applies these principles to real-world scenarios, enhancing analytical skills and decision-making capabilities. 13 Total Points

Lesson 14 Video
Assessment: Lesson 14 Introduction to Hypothesis Testing I
Assessment: Lesson 14 Exercises

Lesson 15. Significance Levels and Error Analysis in Hypothesis Testing

By focusing on error types in hypothesis testing, the lesson underscores the critical nature of choosing an appropriate significance level, influencing the probability of errors like Type I or Type II. Practice questions enable students to apply these concepts and strengthen their analytical skills. 158 Total Points

Lesson 15 Video
Lesson discussions: How would you rate this course?; Program Evaluation Follow-up Survey (End of Course); Course Comments
Assessment: Lesson 15 Introduction to Hypothesis Testing II
Assessment: The Final Exam
Assessment: Lesson 15 Exercises

332

Total Course Points

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.

Additional Course Information

Document Your Lifelong Learning Achievements
Earn an Official Certificate Documenting Course Hours and CEUs
Verify Your Certificate with a Unique Serial Number Online

View and Share Your Certificate Online or Download/Print as PDF
Display Your Certificate on Your Resume and Promote Your Achievements Using Social Media

Course Title: Statistics 101

Course Number: 9770453

Lessons Rating: 4.5 / 5 Stars (1,307 votes)

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

Category:

UniversalClass.com > Mathematics > Foundational Math Skills

Availability: This course is offered online and is accessible in every state across the U.S., including California, Texas, Florida, New York, Pennsylvania, Illinois, Ohio, and Georgia. Learners in English-speaking countries like Canada, Australia, the United Kingdom, and South Africa can also enroll.

Last Updated: February 2024

CEU Value: 3.5 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.

Course Type: Self-Paced, Online Classes

Assessment Method: Lesson assignments and review exams

Instructor: April Cordry-Moore

Syllabus: View Syllabus

Course Fee: $120.00 U.S. dollars

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Student Testimonials

"Great timely feedback!" -- Audrey K.
"I found that the course resources helpful. I needed it especially for the lesson on normal distriuubtion. I needed to refer to it for the z-scores." -- Tiffany L.
"I found the course very interesting and the instructor was very helpful. Well organized chapters and the practice problems." -- Gayatri M.
"I found all the parts to be helpful." -- Nancy G.
"My course instructor was very prompt in providing feedback and responding to emails and tried to clarify concepts that I had questions on." -- Kimberlly S.
"I like that the course was organized well." -- Kimberlly S.
"Very knowledgeable and helpful instructor. This course set-up is much more user friendly than other statistics refreshers I have tried." -- Julie R.
"The lessons are laid out very nicely. They tell you what you need to now for the most part. My instructor answered questions promptly and with detail." -- Julie R.
"The instructor is wonderful. I would have him as an instructor again." -- Cynthia M.
"The Instructor was very pleasant, offering words of encouragement when I did poorly on the second exam. He was fairly prompt with grading assignments and answering emails. I would recommend him for this class in the future." -- Jessica S.