Online Class: Applied Statistics 101

Unlock the potential of data-driven decision-making with this comprehensive Applied Statistics course, where you'll seamlessly transition from summarizing data using descriptive statistics to applying powerful inferential techniques like regression and chi-square tests. By focusing on intuitive insights and real-life applications, the course demystifies statistics and prepares you for numerous professional avenues.
$95.00
no certificate
- OR -
$120.00
with CEU Certificate*
Start Right Now!
$120.00 (with CEU Certificate)
Taking multiple courses? Save with our platinum program.
 
  • 14
    Lessons
  • 22
    Exams &
    Assignments
  • 2,102
    Students
    have taken this course
  • 21
    Hours
    average time
  • 2.1
    CEUs
 
 
 

Course Description

Welcome to an empowering journey through the world of Applied Statistics, where numbers unlock the secrets of data, and informed decisions are born. This course is your gateway to transforming intimidating data sets into clear, actionable insights using the power of statistics.

Begin your statistical adventure by mastering the art of Descriptive Statistics. Learn how to succinctly summarize vast amounts of data with key metrics like mean, variance, and skewness. This foundational skill set is just the beginning, as these summaries pave the way for deeper analysis.

Dive into the core of the course where the real magic happens—Inferential Statistics. Here, you will explore how to make compelling arguments and draw robust conclusions from your data. Questions like "Is the average value of one dataset significantly different from another?" will not only be answered but demonstrated through practical, real-world applications. You'll delve into advanced techniques such as cross-tabulation and chi-square tests, correlation studies, linear regression, Student's t-tests, analysis of variance (ANOVA), repeated measures analysis, and even factor analysis.

While the mathematical underpinnings of these tools are vast, this course emphasizes intuitive understanding over complex equations. Through a blend of common-sense explanations and a wealth of real-life examples, you will see these statistical methods come to life, demystifying data and revealing patterns and truths hidden within.

By the end of this course, you will not only possess the skills to describe and analyze data sets effectively but also the confidence to apply these skills in real-world scenarios. Whether you're tackling basic applied statistics problems or gearing up for more advanced study, this course will equip you with a solid foundation in statistical reasoning that is crucial for a multitude of careers and research paths.

Embark on this statistical journey with us and transform the way you view data forever. Whether for academic pursuits, professional advancement, or personal curiosity, mastering applied statistics opens a world of possibilities and gives you the tools to navigate the data-driven decisions of tomorrow.

Courses Frequently Bought Together:
  • Completely Online
  • Self-Paced
  • Printable Lessons
  • Full HD Video  
  • 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:
4.8 / 5 Stars (Average Rating)
"Extraordinarily Helpful"
(474 votes)

  • Lesson 1. Descriptive Statistics Primer: Unpacking the Concept of Means

    The lesson introduces the course's approach to descriptive statistics, focusing on different types of means to measure central tendency, including arithmetic, geometric, and power means. It aims to equip students with advanced mathematical tools for analyzing discrete data sets while assuming familiarity with fundamental statistics and probability concepts.

  • Lesson 2. Beyond the Mean: Measuring Data Spread and Skewness

    The lesson emphasizes variance and standard deviation as critical measures of how widely data points disperse around the mean, identifying whether data is tightly clustered or spread out. By utilizing moments, one can assess both dispersion and asymmetry, offering a richer understanding of data irregularities through skewness calculations.

  • Lesson 3. Mastering Frequencies: A Guide to Data Representation

    Frequencies, an essential statistical tool, offer a method to simplify data representation by replacing repeated values with summarized tables. This lesson explores fundamental frequency concepts, teaching students to construct tables and visualize data distribution using bar graphs and histograms.

  • Lesson 4. Multivariate Data 101

    Lesson Summary 3: This lesson introduces multivariate data, primarily bivariate, allowing analysis of relationships between data points. Through representations like tables and scatterplots, students learn to calculate descriptive statistics and use matrices for organizing data.

  • Lesson 5. Exploring Cross Tabulations: Unveiling the Relationship Patterns in Multivariate Data

    Understanding cross tabulations is essential for simplifying multivariate data, offering a clear view of relationships such as those between variables in bivariate data. Statistical hypothesis testing complements this by exploring the validity of the relationships through a structured analysis of the data.

  • Lesson 6. Cross Tabulations and Hypothesis Testing: Chi-Square Methods

    In analyzing cross tabulations through chi-square, this lesson emphasizes formulating hypotheses and calculating expected frequences where critical values help confirm statistical significance of variable relationships. Interpretation of chi-square tests provides insight into data beyond initial conjectures.

  • Lesson 7. Exploring the Depths of Correlation in Statistics

    In this lesson, we examine a method for expressing how two statistical variables relate linearly, using Pearson's correlation coefficient. It covers the importance of covariance and the calculation of correlation coefficients, underscoring that correlation does not imply causation.

  • Lesson 8. Understanding One-Sample Student's t-Tests: A Deep Dive into Hypothesis Testing

    The one-sample Student's t-test is a statistical method for evaluating if a sample mean differs significantly from a known population mean, crucial for quality control processes, such as checking machine output. Following steps like hypothesis formulation, critical value identification, and statistical calculation, this lesson guides application under assumptions of normal distribution and random sampling.

  • Lesson 9. Exploring the Significance of Differences: The Paired t-Test Method

    This lesson delves into the paired two-sample Student's t-test, scrutinizing whether two correlated data sets have distinct means through calculations of differences and hypotheses tests. Adopting similar principles to one-sample tests, students learn to determine statistical significance in experiments with examples from medical studies.

  • Lesson 10. One-Way ANOVA: A Leap Beyond Traditional t-Tests

    In this lesson, we learn how one-way ANOVA is used to compare the means of multiple sample groups by analyzing variances between and within these groups, utilizing the F-test for hypothesis testing. Unlike Student’s t-tests, ANOVA allows broader comparisons while requiring assumptions of normality and population variance equivalence within independent samples.

  • Lesson 11. Understanding Repeated Measures Design

    This lesson explored repeated measures design as an alternate within-subjects method to analyze dependent datasets, typically generated when a group is tested across multiple scenarios. By modifying the F statistic calculation, repeated measures ANOVA offers a tailored approach for determining significant differences in means under these conditions.

  • Lesson 12. Understanding Factor Analysis: Techniques and Applications

    Factor analysis provides a qualitative approach to understanding how different variables impact measurements, divided into exploratory and confirmatory methods. While exploratory factor analysis identifies influential factors, confirmatory factor analysis tests predefined factor models against observed data.

  • Lesson 13. Stepwise Linear Regression: A Broader Overview

    Lesson Summary 3: Scatterplots depict bivariate data and lead to the application of least-squares regression to derive a line minimizing squared distances from data points. The lesson then examines the fundamentals of stepwise regression, useful when dealing with numerous independent variables.

  • Lesson 14. Exploring Path Diagrams in Linear Regression

    A detailed methodology to calculate standardized coefficients is presented, enhancing the analysis of multivariate data through matrix operations. Using path diagrams, this lesson elucidates the processes of visualizing and quantifying effect relationships among variables.
 

Learning Outcomes

By successfully completing this course, students will be able to:
  • Calculate the geometric mean of a given data set and compare it with the arithmetic mean to demonstrate differences in data characterization.
  • Define and calculate the arithmetic mean of a given data set to understand the central tendency of the data.
  • Calculate the variance and standard deviation of a given data set to assess its dispersion around the mean.
  • Demonstrate the ability to compute skewness to evaluate the asymmetry of a data distribution.
  • Create a frequency table to represent a given data set, calculating relative, cumulative, and cumulative relative frequencies accurately.
  • Demonstrate the ability to interpret bar graphs and histograms by analyzing visual representations of frequency data efficiently.
  • Demonstrate the ability to create and interpret scatterplots, calculate mean vectors, and covariance matrices for bivariate data sets.
  • Recognize and differentiate between univariate and multivariate data by describing specific examples and key characteristics.
  • Demonstrate the ability to create cross tabulations for bivariate data sets by organizing gender and handedness into a contingency table.
  • Identify and define the null and alternative hypotheses in a statistical hypothesis test for analyzing cross tabulations related to gender and handedness.
  • Demonstrate the ability to interpret chi-square test results and determine hypotheses significance based on critical values
  • Understand how to calculate expected frequencies in a cross tabulation using row, column, and grand totals
  • Demonstrate the calculation of Pearson's correlation coefficient for a given data set to quantify the linear relationship between two variables.
  • Demonstrate mastery of lesson content at levels of 70% or higher.
 

Additional Course Information

Online CEU Certificate
  • 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
Document Your CEUs on Your Resume
 
Course Title: Applied Statistics 101
Course Number: 9770454
Lessons Rating: 4.8 / 5 Stars (474 votes)
Languages: English - United States, Canada and other English speaking countries
Availability: This course is accessible online throughout all states, including California, Texas, Florida, New York, Pennsylvania, Illinois, Ohio, and Georgia. It's also available in top English-speaking countries like the United Kingdom, Canada, Australia, and New Zealand.
Last Updated: February 2024
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.
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

Choose Your Subscription Plan

Course Only
One Course
No Certificate / No CEUs
$95
for 6 months
 
Add to Cart
Billed once
This course only
Includes certificate X
Includes CEUs X
Self-paced Yes
Instructor support Yes
Time to complete 6 months
No. of courses 1 course
Certificate Course
One Course
Certificate & CEUs
$120
for 6 months
 
Add to Cart
Billed once
This course only
Includes certificate Yes
Includes CEUs Yes
Self-paced Yes
Instructor support Yes
Time to complete 6 months
No. of courses 1 course
Platinum Yearly
ALL COURSES
Certificates & CEUs
$189
per year
 
Start Now
Billed once
Includes all 600+ courses
Includes certificate Yes
Includes CEUs Yes
Self-paced Yes
Instructor support Yes
Time to complete 12 Months
No. of courses 600+
Platinum 2 Years
ALL COURSES
Certificates & CEUs
$299
for 2 years
You save $79.00!
Start Now
Billed once
Includes all 600+ courses
Includes certificate Yes
Includes CEUs Yes
Self-paced Yes
Instructor support Yes
Time to complete 24 Months
No. of courses 600+
 

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.

Related Courses