About This Chapter
Who's it for?
Anyone who needs help learning or mastering college statistics material will benefit from taking this course. There is no faster or easier way to learn college statistics. Among those who would benefit are:
- Students who have fallen behind in understanding simple linear regression or working with scatterplots
- Students who struggle with learning disabilities or learning differences, including autism and ADHD
- Students who prefer multiple ways of learning math (visual or auditory)
- Students who have missed class time and need to catch up
- Students who need an efficient way to learn about regression and correlation
- Students who struggle to understand their teachers
- Students who attend schools without extra math learning resources
How it works:
- Find videos in our course that cover what you need to learn or review.
- Press play and watch the video lesson.
- Refer to the video transcripts to reinforce your learning.
- Test your understanding of each lesson with short quizzes.
- Verify you're ready by completing the Regression and Correlation chapter exam.
Why it works:
- Study Efficiently: Skip what you know; review what you don't.
- Retain What You Learn: Engaging animations and real-life examples make topics easy to grasp.
- Be Ready on Test Day: Use the Regression and Correlation chapter exam to be prepared.
- Get Extra Support: Ask our subject-matter experts any regression and correlation question. They're here to help!
- Study With Flexibility: Watch videos on any web-ready device.
Students will review:
This chapter helps students review the concepts in a Regression and Correlation unit of a standard college statistics course. Topics covered include:
- Creating and interpreting scatter plots
- Simple linear regression
- Interpreting the slope and intercept of a linear model
- How to interpret correlations in research results
- Correlation vs. causation
1. Creating & Interpreting Scatterplots: Process & Examples
Creating and interpreting scatterplots is a great depiction of a correlation between two sets of data. Learn more about scatterplots, including the process of creating them and some examples.
2. Simple Linear Regression: Definition, Formula & Examples
Simple linear regression refers to the relationship between two variables. Learn the definition of simple linear regression, understand how to use the scatterplot and formula to find the regression line by hand or graphing calculator, and review the examples.
3. Problem Solving Using Linear Regression: Steps & Examples
Linear regression is a process used to model and evaluate the relationship between dependent and independent variables. Learn about problem solving using linear regression by exploring the steps in the process and working through examples. Review a linear regression scenario, identify key terms in the process, and practice using linear regression to solve problems.
4. Analyzing Residuals: Process & Examples
In a linear regression analysis, residuals can be used to find out if the assumptions are valid. Learn the statistical process of regression analysis, define terms like linearity, and show how a scatter plot can help illustrate whether assumptions are violated through examples.
5. Interpreting the Slope & Intercept of a Linear Model
In statistics, the slope and intercept can be useful tools for understanding the relationships in a set of data. Learn about interpreting the slope and intercept of a linear model. Explore how to identify the slope and intercept, interpret the slope, interpret the intercept, and check your understanding with a practice problem.
6. The Correlation Coefficient: Definition, Formula & Example
In statistics, the correlation coefficient helps determine the strength of two variables' association. Explore the definition, formula, and examples of the correlation coefficient to understand how to decode as well as how to use a correlation coefficient. Review the numerator and denominator for the equation, and understand how to interpret it.
7. How to Interpret Correlations in Research Results
Correlations acknowledge some relationship between two variables. In this lesson, learn how to graphically represent and statistically interpret correlational data.
8. Correlation vs. Causation: Differences & Definition
Correlation is when two sets of variables appear to have a relationship, which may look similar to Causation where there is an active influence of one variable on another. Learn the nuances of each, and learn to identify them through examples provided.
9. Interpreting Linear Relationships Using Data: Practice Problems
Linear relationships in algebra are useful in analyzing data with consistent relationships between variables. Practice creating and solving linear equations from the data sets provided.
10. Transforming Nonlinear Data: Steps & Examples
Nonlinear data is found where there is not a consistent value pattern or proportion between two variables. Learn the steps of transformations involved in making nonlinear data more manageable, and practice different types of transformations through example problems.
11. Coefficient of Determination: Definition, Formula & Example
The coefficient of determination is a measure of how well a model fits a data set. Learn the definition of the coefficient of determination, understand how the formula is derived from linear regression analysis, and practice computing with examples.
12. Scatter Diagram: Definition & Examples
A scatter diagram or scatter plot is a graphic tool that uses dots to represent data points in order to explore relationships between two variables. Discover how a visual map of data makes it easier to find these relationships, learn how to create a scatter diagram, and check out some examples.
13. Correlation: Definition, Analysis & Examples
Correlation is a measure of the strength of association between two variables. Learn about the definition, types, analysis, and examples of correlation, and understand the meaning of the correlation coefficient.
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Other chapters within the Introduction to Statistics: Help and Review course
- Overview of Statistics: Help and Review
- Summarizing Data: Help and Review
- Tables and Plots: Help and Review
- Probability: Help and Review
- Discrete Probability Distributions: Help and Review
- Continuous Probability Distributions: Help and Review
- Sampling: Help and Review
- Hypothesis Testing in Statistics