Ch 6: Understanding Data Using Statistics

About This Chapter

Take a closer look at this chapter to recall how to use statistics to understand data. Review how data can be represented visually, then memorize all the important key terms included in each lesson that describe how to gather and measure data.

Understanding Data Using Statistics - Chapter Summary

This chapter will help you refresh your memory concerning the intricacies of understanding data and using statistics. Each lesson has been designed to walk you through the different aspects of statistical data analysis, including visual representations, related terminology, and data interpretation methodologies. After you complete this chapter, you should possess the skills to do the following:

  • Describe dot plots, histograms, scatterplots, and box plots
  • Identify central tendency measurements
  • Analyze interquartile and quartile ranges
  • Check out how to calculate the standard deviation
  • Define major vocab words related to data sets
  • Compare relative, conditional, marginal, and joint frequencies
  • Explain correlation, curve fitting, and how to analyze residuals

Our materials make studying this topic easier, since we divide all the major concepts into individual lessons. You can go directly to the chapter menu to see a list of all available lessons. As each lesson is clearly labeled by topic, you should have no problems finding the exact information you need to review. You have the option to focus on a few key lessons, or you can go through every lesson we offer. Each one of our lessons can stand on its own, so you do not have to watch them in order. We keep our materials available to you online, so you can always come back to this chapter and go through the lessons as many times as you need.

14 Lessons in Chapter 6: Understanding Data Using Statistics
Test your knowledge with a 30-question chapter practice test
Creating & Interpreting Dot Plots: Process & Examples

1. Creating & Interpreting Dot Plots: Process & Examples

Dot plots are a visual way to display the frequency distribution in a data set. In this lesson, you will learn how to construct a dot plot and understand its uses.

Creating & Interpreting Histograms: Process & Examples

2. 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

3. 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!

Mean, Median & Mode: Measures of Central Tendency

4. Mean, Median & Mode: Measures of Central Tendency

By describing the data using central tendency, a researcher and reader can understand what the typical score looks like. In this lesson, we will explore in more detail these measures of central tendency and how they relate to samples and populations.

Quartiles & the Interquartile Range: Definition, Formulate & Examples

5. Quartiles & the Interquartile Range: Definition, Formulate & Examples

Quartiles and the interquartile range can be used to group and analyze data sets. In this lesson, learn the definition and steps for finding the quartiles and interquartile range for a given data set.

Calculating the Standard Deviation

6. Calculating the Standard Deviation

In this lesson, we will examine the meaning and process of calculating the standard deviation of a data set. Standard deviation can help to determine if the data set is a normal distribution.

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

7. 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.

Spread in Data Sets: Definition & Example

8. Spread in Data Sets: Definition & Example

Identifying the spread in data sets is a very important part of statistics. You can do this several ways, but the most common methods are through range, interquartile range, and variance.

Maximums, Minimums & Outliers in a Data Set

9. Maximums, Minimums & Outliers in a Data Set

When analyzing data sets, the first thing to identify is the maximums, minimums, and outliers. This lesson will help you learn how to identify these important items.

Frequency & Relative Frequency Tables: Definition & Examples

10. Frequency & Relative Frequency Tables: Definition & Examples

Frequency and relative frequency tables are a good way to visualize information. This is especially useful for information that is grouped into categories where you are looking for popularity or mode.

Joint, Marginal & Conditional Frequencies: Definitions, Differences & Examples

11. Joint, Marginal & Conditional Frequencies: Definitions, Differences & Examples

Joint, marginal, and conditional frequencies are all part of analyzing categorical data and two-way tables. This lesson will help you learn the definitions and differences between each concept.

Creating & Interpreting Scatterplots: Process & Examples

12. Creating & Interpreting Scatterplots: Process & Examples

Scatterplots are a great visual representation of two sets of data. In this lesson, you will learn how to interpret bivariate data to create scatterplots and understand the relationship between the two variables.

Correlation: Definition, Analysis & Examples

13. Correlation: Definition, Analysis & Examples

Correlation describes the relationship between two sets of data. In this lesson, we'll delve into what correlation is and the different types of correlation that can be encountered.

Analyzing Residuals: Process & Examples

14. Analyzing Residuals: Process & Examples

Can you tell what's normal or independent and what's not? Sometimes, we need to figure this out in the world of statistics. This lesson shows you how as it explains residuals and regression assumptions in the context of linear regression analysis.

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

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