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Ch 24: Data Distribution & Graphs

About This Chapter

In this chapter, you'll find data distribution and graphs covered in a mobile-friendly format. The chapter is made up of short video lessons and self-assessment quizzes you can use to prepare for an upcoming exam.

Data Distribution & Graphs - Chapter Summary

Take as much time as you need to study with this self-paced chapter on data distribution and graphs. Our engaging video lessons outline the measures of central tendency and different ways to represent data, including pie charts, frequency tables and more. After completing this chapter, you should know how to:

  • Discuss skewness, symmetry and shape in a visual representation of a data set
  • Define the measures of dispersion and skewness
  • Read bar graphs and pie charts
  • Describe the process used to create histograms
  • Interpret a box plot and give examples of this type of data set
  • Outline the different types of frequency distributions
  • Identify the uses for cumulative frequency tables
  • Detail the use of ogive graphs in statistics

Even the toughest data distribution topics are easy to understand when you use this professionally-designed chapter. Our instructors make these topics accessible with short, easy-to-follow videos featuring clear narration and plenty of examples. Before moving on, take the short quiz provided with each lesson to ensure you're ready. If you need to go back and review just one part of a video, use the timeline feature for easy navigation.

9 Lessons in Chapter 24: Data Distribution & Graphs
Test your knowledge with a 30-question chapter practice test
Central Tendency: Measures, Definition & Examples

1. Central Tendency: Measures, Definition & Examples

Explore the measures of central tendency. Learn more about mean, median, and mode and how they are used in the field of psychology. At the end, test your knowledge with a short quiz.

Visual Representations of a Data Set: Shape, Symmetry & Skewness

2. Visual Representations of a Data Set: Shape, Symmetry & Skewness

Visual representations are a fantastic way of understanding and analyzing your data. Use this lesson to understand the characteristics of visual representations of data.

Measures of Dispersion and Skewness

3. Measures of Dispersion and Skewness

Watch this video lesson to learn how you can describe your data using two different statistical characteristics. Learn what it means for your graph to have variability and what it means for your graph to be skewed.

Understanding Bar Graphs and Pie Charts

4. Understanding Bar Graphs and Pie Charts

In this lesson, we will examine two of the most widely used types of graphs: bar graphs and pie charts. These two graphs can provide the reader with a comparison of the different data that is displayed.

Creating & Interpreting Histograms: Process & Examples

5. Creating & Interpreting Histograms: Process & Examples

Creating histograms can help you easily identify and interpret data. This lesson will give you several examples to better understand histograms and how to create them.

Creating & Interpreting Box Plots: Process & Examples

6. Creating & Interpreting Box Plots: Process & Examples

Box plots are an essential tool in statistical analysis. This lesson will help you create a box plot and understand its meaning. When you are finished, test your understanding with a short quiz!

Frequency Distributions: Definition & Types

7. Frequency Distributions: Definition & Types

This lesson explores the process of creating frequency distributions and histograms to give readers of your future scientific articles a numerical or visual way to understand the data you have presented.

Cumulative Frequency Tables: Definition, Uses & Examples

8. Cumulative Frequency Tables: Definition, Uses & Examples

Cumulative frequency tables can help you analyze and understand large amounts of information. In this lesson, you practice creating and interpreting cumulative frequency tables.

Definition of an Ogive Graph in Statistics

9. Definition of an Ogive Graph in Statistics

An ogive plot is useful in statistics when we want to know how many observations to expect for a range of data. It allows us to quickly see how many observations were measured for all ranges less than a particular number.

Chapter Practice Exam
Test your knowledge of this chapter with a 30 question practice chapter exam.
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Practice Final Exam
Test your knowledge of the entire course with a 50 question practice final exam.
Not Taken

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