About This Chapter
Descriptive Statistics of Data Sets - Chapter Summary and Learning Objectives
Descriptive statistics can offer a clear overview of data. In this chapter, you'll meet the various types of descriptive statistics, find out how they're used and discover handy formulas to employ down the road. Complete the multiple-choice quizzes to gauge your learning, and feel free to ask the instructors if you're left with any questions. All of the topics below are addressed:
- Measures of central tendency
- Comparison of median and mean
- Standard deviation
- Data set spread
- Significance of outliers
|What is the Center in a Data Set? - Definition, Lesson & Quiz||Identify how to find the number that summarizes the whole data set.|
|Mean, Median & Mode: Measures of Central Tendency||Find out how the measures of central tendency relate to populations and samples.|
|How to Calculate Mean, Median, Mode & Range||Demonstrate how to calculate each of the measures of central tendency.|
|Calculating the Mean, Median, Mode & Range: Practice Problems, Lesson & Quiz||Practice the strategies for coming up with these measurements.|
|Unimodal & Bimodal Distributions: Definition, Examples & Quiz||Explain when a bimodal or unimodal distribution is most appropriate.|
|The Mean vs the Median: Differences, Uses, Lesson & Quiz||Compare when the median or the mean is best to describe information.|
|Spread in Data Sets: Definition, Example, Lesson & Quiz||Define spread and determine which calculation should be used in specific circumstances.|
|Maximums, Minimums & Outliers in a Data Set: Lesson & Quiz||Illustrate how to identify these essentials in data sets.|
|Quartiles & the Interquartile Range: Definition, Formulate & Examples||Find out how quartiles and interquartile range are put into play to group and analyze given sets of data.|
|Finding Percentiles in a Data Set: Formula, Examples & Quiz||Take a look at how finding percentiles in data sets can help organize and compare numbers.|
|Calculating the Standard Deviation||Find out what standard deviation can divulge about your data.|
|The Effect of Linear Transformations on Measures of Center & Spread: Lesson & Quiz||Discover how to manipulate your data with these transformations.|
|Population & Sample Variance: Definition, Formula & Examples||Differentiate between sample and population variance.|
|Ordering & Ranking Data: Process, Example, Lesson & Quiz||Examine how these procedures might be used in competitions and research analysis.|
1. What is the Center in a Data Set? - Definition & Options
Finding the center in a data set can sometimes be a little confusing. This lesson will help you determine the correct method for finding the center in a data set, and when you are finished, test your knowledge with a short quiz!
2. 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.
3. How to Calculate Mean, Median, Mode & Range
Measures of central tendency can provide valuable information about a set of data. In this lesson, explore how to calculate the mean, median, mode and range of any given data set.
4. Calculating the Mean, Median, Mode & Range: Practice Problems
Calculating the mean, median, mode, and range of a data set is a fundamental part of learning statistics. Use this video to practice your skills and then test your knowledge with a short quiz.
5. Unimodal & Bimodal Distributions: Definition & Examples
Sometimes a single mode does not accurately describe a data set. In this lesson, learn the differences between and the uses of unimodal and bimodal distribution. When you are finished, test your knowledge with a quiz!
6. The Mean vs the Median: Differences & Uses
Most people can find the mean and the median of a data set, but do you know when to use the mean and when to use the median to describe the information?
7. 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.
8. 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.
9. 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.
10. Finding Percentiles in a Data Set: Formula & Examples
Percentiles are often used in academics to compare student scores. Finding percentiles in a data set can be a useful way to organize and compare numbers in a data set.
11. 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.
12. The Effect of Linear Transformations on Measures of Center & Spread
Linear transformations can be a great way to manipulate and analyze data. This lesson will show you how those transformations affect the center and spread of data.
13. Population & Sample Variance: Definition, Formula & Examples
Population and sample variance can help you describe and analyze data beyond the mean of the data set. In this lesson, learn the differences between population and sample variance.
14. Ordering & Ranking Data: Process & Example
Ordering and ranking data can often be more important than you might think. In addition to being an important part of competitions, ranking data can be another way of analyzing and evaluating research.
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Other chapters within the DSST Principles of Statistics: Study Guide & Test Prep course
- Data Types & Measurements in Statistics
- Sampling Methods in Statistics
- Visual Representations in Statistics
- Probability: Rules for Events
- Probability Combinations, Permutations & Expected Values
- Probability: Discrete & Continuous Distributions
- Correlation & Regression in Statistics
- Sampling Distributions in Statistics
- Hypothesis Testing in Inferential Statistics