Linear Regression

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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 does the slope in a linear regression equation represent?

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

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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 from a regression model.

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How do you interpret a positive residual?

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A positive residual indicates that the observed value is higher than the predicted value.

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What is the purpose of a residual plot?

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A residual plot is used to assess the fit of a regression model by showing the residuals on the vertical axis and the independent variable on the horizontal axis.

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What does it mean if a residual plot shows a random pattern?

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A random pattern in a residual plot suggests that the linear regression model is a good fit for the data.

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What is the equation of a line in slope-intercept form?

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

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How can you use a trend line to make predictions?

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A trend line can be used to predict values by extending the line and finding the corresponding value on the dependent variable axis.

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

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