Linear Regression & Residuals

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What is Linear Regression?

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Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data.

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What is a Residual in Linear Regression?

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A residual is the difference between the observed value and the predicted value of the dependent variable in a regression model.

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What does a Positive Residual indicate?

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A positive residual indicates that the observed value is higher than the predicted value, meaning the model underestimated the actual outcome.

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What does a Negative Residual indicate?

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A negative residual indicates that the observed value is lower than the predicted value, meaning the model overestimated the actual outcome.

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What is the Explanatory Variable?

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The explanatory variable (or independent variable) is the variable that is manipulated or controlled in an experiment to observe its effect on the response variable.

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What is the Response Variable?

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The response variable (or dependent variable) is the outcome that is measured in an experiment and is affected by the explanatory variable.

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How do you write a linear equation for a cost model?

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What is the purpose of a Scatter Plot in Linear Regression?

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A scatter plot visually represents the relationship between two quantitative variables, helping to identify patterns, trends, and potential correlations.

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What does it mean if the correlation coefficient is close to 1?

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If the correlation coefficient is close to 1, it indicates a strong positive linear relationship between the two variables.

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What does it mean if the correlation coefficient is close to -1?

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If the correlation coefficient is close to -1, it indicates a strong negative linear relationship between the two variables.