Regression Analysis: Statistics Quiz Questions – Week 4, Week 4

Summary

Regression analysis is a statistical method used to determine relationships between variables. Students learn key concepts and test their understanding with multiple-choice questions.

Simple Linear Regression

Simple linear regression is explained. The relationship between variables is shown with a scatter diagram. The standard error of the estimate is defined.

Variable Types

Qualitative and quantitative variables are distinguished. The role of explanatory and response variables is described. Another name for an explanatory variable is given.

Confidence and Prediction Intervals

Confidence intervals and prediction intervals are compared. The width of these intervals is discussed. The standard error of the estimate is explained.

Assumptions of Regression Analysis

Assumptions of regression analysis are listed. Violations of these assumptions are identified. Serial correlation is defined.

Key terms

Explanatory variable

A variable that is used to predict another variable.

Response variable

The variable that is being predicted.

Standard error of the estimate

Measures the variability of the observed y-values around the predicted y-values.

Serial correlation

A situation where the error term is correlated with an explanatory variable.

Confidence interval

A range of values that is likely to include an unknown population parameter.

Prediction interval

A range of values that is likely to include a future observation.

Questions this document helps with

• What is the difference between an explanatory variable and a response variable?
• How do you interpret the standard error of the estimate?
• What are the assumptions of regression analysis?

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