Linear Regression and Residuals - Using Desmos

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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 the context of 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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How do you calculate a residual?

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Residual = Observed value - Predicted value.

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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.

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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.

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What is the purpose of using Desmos for linear regression?

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Desmos is a graphing calculator that allows users to visualize data, create regression models, and analyze the relationships between variables.

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What is the equation of a linear regression line?

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The equation of a linear regression line is typically expressed in the form y = mx + b, where m is the slope and b is the y-intercept.

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What does the slope of a linear regression line represent?

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The slope represents the change in the dependent variable for a one-unit change in the independent variable.

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What does the y-intercept of a linear regression line represent?

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The y-intercept represents the predicted value of the dependent variable when the independent variable is zero.

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How can you interpret the results of a linear regression analysis?

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The results can be interpreted by examining the slope, y-intercept, and residuals to understand the strength and direction of the relationship between variables.