Linear and Quadratic 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 is Quadratic Regression?

Back

Quadratic regression is a type of polynomial regression that models the relationship between a dependent variable and an independent variable using a quadratic equation (y = ax² + bx + c).

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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 change in the independent variable.

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What is the domain of a function?

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The domain of a function is the set of all possible input values (x-values) for which the function is defined.

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What is the range of a function?

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The range of a function is the set of all possible output values (y-values) that the function can produce.

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How do you determine the curve of best fit?

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The curve of best fit can be determined using methods such as least squares regression, which minimizes the sum of the squares of the residuals between observed and predicted values.

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What is the equation of a linear function?

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

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

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The y-intercept is the value of the dependent variable when the independent variable is zero; it represents the starting point of the linear relationship.

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What does a positive slope indicate in a linear regression?

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A positive slope indicates that as the independent variable increases, the dependent variable also increases.

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What does a negative slope indicate in a linear regression?

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