# Ch 24: Honors Algebra 1: Descriptive Statistics

### About This Chapter

## Honors Algebra 1: Descriptive Statistics - Chapter Summary and Learning Objectives

Having large amounts of data can be overwhelming, so it's helpful to have the skills to organize and describe it. By watching the video lessons in this chapter, you'll learn how to visually represent and explain your data using descriptive statistics. Take a self-assessment quiz after each lesson to apply what you've learned. If you have any additional questions, reach out to your instructor. By the end of the chapter, you'll have explored:

- Ways to visually represent and compare your data
- Bimodal and unimodal distributions
- Methods to calculate spread, standard deviation and measures of central tendency
- Ways to find percentiles and identify outliers
- Quartiles and interquartile range

Video | Objective |
---|---|

What is the Center in a Data Set? - Definition & Options | Find the center in a data set. |

Mean, Median & Mode: Measures of Central Tendency | Learn to calculate measures of central tendency. |

Calculating the Mean, Median, Mode & Range: Practice Problems | Practice finding the measures of central tendency. |

Visual Representations of a Data Set: Shape, Symmetry & Skewness | Explore characteristics of visual representations of a data set. |

Understanding Bar Graphs and Pie Charts | Discover how to compare data using bar graphs and pie charts. |

Unimodal & Bimodal Distributions: Definition & Examples | Compare unimodal and bimodal distributions. |

The Mean vs the Median: Differences & Uses | Analyze differences between the mean and the median. |

Spread in Data Sets: Definition & Example | Determine the spread of a data set. |

Maximums, Minimums & Outliers in a Data Set | Learn to identify maximums, minimums and outlies in a data set. |

Quartiles & the Interquartile Range: Definition, Formulate & Examples | Find the quartiles and calculate the interquartile range. |

Finding Percentiles in a Data Set: Formula & Examples | Measure percentiles in a data set to organize and compare information. |

The Effect of Linear Transformations on Measures of Center & Spread | Analyze data using linear transformations. |

Population & Sample Variance: Definition, Formula & Examples | Discover the differences between population and sample variance. |

Ordering & Ranking Data: Process & Example | Evaluate data by ordering and ranking the information. |

### 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. 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.

### 4. 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.

### 5. 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.

### 6. 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!

### 7. 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?

### 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. 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.

### 11. 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.

### 12. 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.

### 13. 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.

### 14. 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.

### 15. 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

Other chapters within the Honors Algebra 1 Textbook course

- Honors Algebra 1: Basic Arithmetic Review
- Honors Algebra 1: Fractions & Decimal Review
- Honors Algebra 1: Calculations, Ratios, Percent & Proportions
- Honors Algebra 1: Properties of Real Numbers
- Honors Algebra 1: Exponents & Scientific Notation
- High School Algebra: Radical Expressions
- Honors Algebra 1: Composing Algebraic Equations & Expressions
- Honors Algebra 1: Solving Algebraic Expressions & Equations
- Honors Algebra 1: Properties of Functions
- Honors Algebra 1: Absolute Value Expressions & Equations
- Honors Algebra 1: Matrices in Linear Equations
- High School Algebra: Working With Inequalities
- High School Algebra: Properties of Exponents
- High School Algebra: Properties of Polynomial Functions
- Honors Algebra 1: Vectors in Linear Algebra
- High School Algebra: Complex and Imaginary Numbers
- High School Algebra: Algebraic Distribution
- Honors Algebra 1: Linear Equations
- High School Algebra: Factoring
- Honors Algebra 1: Factoring with FOIL, Graphing Parabolas & Solving Quadratics
- Honors Algebra 1: Graphing & Factoring Quadratic Equations
- High School Algebra: Rational Expressions
- High School Algebra: Cubic Equations
- Honors Algebra 1: Data Analysis
- Honors Algebra 1: Probability & Statistics
- Honors Algebra 1: Units of Measurement in Geometry
- Honors Algebra 1: Shapes of Geometry
- Honors Algebra 1: Similar Polygons
- Honors Algebra 1: Pre-Calculus Geometry
- High School Algebra: Well-Known Equations
- Honors Algebra 1: Maximum & Minimum Value of Functions
- Honors Algebra 1: Patterns in Math