Linear/Exponential/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 Exponential Regression?

Back

Exponential regression is a type of regression analysis used to model data that grows or decays at a constant percentage rate, represented by an equation of the form y = ab^x.

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What is Quadratic Regression?

Back

Quadratic regression is a type of regression analysis used to model the relationship between a dependent variable and an independent variable by fitting a quadratic equation (y = ax^2 + bx + c) to the data.

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What does the term 'line of best fit' mean?

Back

The line of best fit is a straight line that best represents the data on a scatter plot, minimizing the distance between the line and all the data points.

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How do you identify a quadratic function from a set of data?

Back

A quadratic function can be identified by its parabolic shape when graphed, and it can be represented in the form y = ax^2 + bx + c.

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What is the general form of a quadratic equation?

Back

The general form of a quadratic equation is y = ax^2 + bx + c, where a, b, and c are constants and a ≠ 0.

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What is the significance of the coefficient 'a' in a quadratic equation?

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The coefficient 'a' determines the direction of the parabola (upward if a > 0, downward if a < 0) and affects the width of the parabola.

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What is the difference between linear and exponential growth?

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Linear growth increases by a constant amount, while exponential growth increases by a constant percentage, leading to faster growth over time.

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What is the purpose of regression analysis?

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Regression analysis is used to determine the strength and character of the relationship between variables, allowing for predictions based on data.

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How do you interpret the slope of a linear regression line?

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The slope of a linear regression line indicates the change in the dependent variable for each one-unit increase in the independent variable.