Interpret Linear Regression Practice M1

Front

What is linear regression?

Back

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.

Tags

CCSS.8.SP.A.2

Front

What does the slope in a linear regression equation represent?

Back

The slope represents the rate of change of the dependent variable for each unit increase in the independent variable.

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CCSS.HSF.LE.B.5

Front

What does the y-intercept in a linear regression equation indicate?

Back

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

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CCSS.HSF.LE.B.5

Front

How do you interpret the correlation coefficient (r)?

Back

The correlation coefficient (r) measures the strength and direction of a linear relationship between two variables, ranging from -1 to 1.

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CCSS.HSS.ID.C.8

Front

What does a correlation coefficient of 0.89 indicate?

Back

A correlation coefficient of 0.89 indicates a strong positive linear relationship between the two variables.

Tags

CCSS.HSS.ID.B.5

Front

How do you predict a value using a linear regression equation?

Back

To predict a value, substitute the independent variable's value into the linear regression equation and solve for the dependent variable.

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CCSS.HSF.IF.A.2

Front

What is the formula for a linear regression equation?

Back

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

Tags

CCSS.8.EE.B.5

Front

If the linear regression equation is y = 2x + 3, what is the predicted value of y when x = 5?

Back

y = 2(5) + 3 = 10 + 3 = 13.

Tags

CCSS.8.EE.B.5

Front

What does it mean if the slope of a linear regression line is negative?

Back

A negative slope indicates that as the independent variable increases, the dependent variable decreases.

Tags

CCSS.8.EE.B.5

Front

What is the significance of the residuals in linear regression?

Back

Residuals are the differences between observed and predicted values; they help assess the fit of the regression model.

Tags

CCSS.HSS.ID.B.6B