Linear Regression

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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.

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What does the slope in a linear regression equation 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 is the formula for a simple linear regression equation?

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The formula is y = mx + b, where m is the slope and b is the y-intercept.

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What does a positive correlation indicate in linear regression?

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A positive correlation indicates that as one variable increases, the other variable also tends to increase.

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What does a negative correlation indicate in linear regression?

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A negative correlation indicates that as one variable increases, the other variable tends to decrease.

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What is the purpose of calculating the linear regression equation?

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The purpose is to predict the value of the dependent variable based on the values of the independent variable(s).

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What is the significance of the y-intercept in a linear regression equation?

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

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How can you interpret a weak negative correlation?

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A weak negative correlation suggests that there is a slight tendency for one variable to decrease as the other variable increases, but the relationship is not strong.

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What is the role of residuals in linear regression?

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Residuals are the differences between observed values and the values predicted by the regression model; they help assess the model's accuracy.

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What does it mean if the linear regression model has a high R-squared value?

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A high R-squared value indicates that a large proportion of the variance in the dependent variable is explained by the independent variable(s).