Linear Regression Review

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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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CCSS.8.SP.A.2

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What does the line of best fit represent?

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The line of best fit represents the predicted values of the dependent variable based on the independent variable, minimizing the distance between the line and all data points.

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CCSS.8.EE.B.5

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What is the formula for a linear equation?

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The formula for a linear equation is typically written as y = mx + b, where m is the slope and b is the y-intercept.

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CCSS.8.EE.B.6

CCSS.8.F.A.3

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How do you interpret the slope (m) in a linear regression equation?

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The slope (m) indicates the change in the dependent variable (y) for a one-unit increase in the independent variable (x).

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What does the y-intercept (b) represent in a linear equation?

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The y-intercept (b) represents the value of the dependent variable (y) when the independent variable (x) is zero.

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CCSS.8.EE.B.6

CCSS.8.F.A.3

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What is a scatter plot?

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A scatter plot is a graphical representation of two variables, showing the relationship between them using points on a Cartesian plane.

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CCSS.8.SP.A.1

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What does it mean if a scatter plot shows no relationship?

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If a scatter plot shows no relationship, the points are scattered randomly, indicating that changes in one variable do not affect the other.

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CCSS.HSF-LE.A.1B

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How do you calculate the predicted value of y using a linear regression equation?

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To calculate the predicted value of y, substitute the value of x into the linear regression equation and solve for y.

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CCSS.HSF.IF.A.2

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What is the significance of rounding coefficients in linear regression?

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Rounding coefficients in linear regression helps simplify the equation for easier interpretation and application.

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CCSS.8.SP.A.3

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What is the purpose of using linear regression in real-world scenarios?

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Linear regression is used to predict outcomes, identify trends, and understand relationships between variables in various fields such as economics, biology, and social sciences.