About This Chapter
Exploring Bivariate Data - Chapter Summary
In this chapter on bivariate data, you'll have the opportunity to explore the relationships between variables and their significance in data analysis. Each lesson breaks down an important aspect of these types of statistics and allows you to review the underlying concepts in a way that is straightforward and concise. By the end of this chapter you will be able to:
- Define and analyze correlation of data points
- Solve equations using least-squares regression
- Give examples of residual plots
- Explain transformations of the 1/x function
- Use the appropriate steps to solve linear regression problems
Our expert instructors have created the lessons in this chapter to help you solidify your knowledge of bivariate data. You'll be able to choose video or text lessons, and you can test your knowledge with brief self-assessment quizzes. All of your progress is tracked on your Dashboard so you can evaluate your readiness for an exam.
1. 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.
2. Least-Squares Regression: Definition, Equations & Examples
In this lesson, we will explore least-squares regression and show how this method relates to fitting an equation to some data. Using examples, we will learn how to predict a future value using the least-squares regression method.
3. Residual Plots: Definition & Example
This lesson will look at the definition of a residual, how to make a residual plot, and how to use a residual plot to evaluate the fit of a prediction equation. You will also compare the sum of the squared residuals.
4. Transformations of the 1/x Function
In this lesson, you'll learn about the function f(x) = 1/x. You'll also learn about the different transformations that can be applied to the equation to change the graph of this function.
5. Problem Solving Using Linear Regression: Steps & Examples
Linear regression can be a powerful tool for predicting and interpreting information. Learn to use two common formulas for linear regression in this lesson.
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Other chapters within the AP Statistics: Exam Prep course
- Types of Data
- Graphical Displays of Data
- Data Summaries in Statistics
- Data Collection Methods
- Sample Types in Statistics
- Planning & Conducting Surveys
- Planning & Conducting Experiments
- Generalization of Results & Conclusions
- Evaluating Probabilities
- Discrete Probability Distributions in Statistics
- Continuous Probability Distributions in Statistics
- Sampling Distribution
- Statistical Estimation & Confidence Intervals
- Tests of Significance
- AP Statistics Flashcards