Ch 6: Understanding Data Using Statistics

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.

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.

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!

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.