Regression and Residuals - Using Desmos

Regression and Residuals - Using Desmos

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Flashcard

Mathematics

11th Grade

Hard

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15 questions

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1.

FLASHCARD QUESTION

Front

What is a linear regression model?

Back

A linear regression model 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.

2.

FLASHCARD QUESTION

Front

What does the slope in a linear equation represent?

Back

The slope represents the rate of change of the dependent variable with respect to the independent variable. It indicates how much the dependent variable is expected to increase (or decrease) when the independent variable increases by one unit.

3.

FLASHCARD QUESTION

Front

What is the y-intercept in a linear equation?

Back

The y-intercept is the value of the dependent variable when the independent variable is zero. It represents the point where the line crosses the y-axis.

4.

FLASHCARD QUESTION

Front

What does the r-value (correlation coefficient) indicate?

Back

The r-value indicates the strength and direction of a linear relationship between two variables. It ranges from -1 to 1, where 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no correlation.

5.

FLASHCARD QUESTION

Front

What is a residual in regression analysis?

Back

A residual is the difference between the observed value and the predicted value of the dependent variable. It measures how well the regression model fits the data.

6.

FLASHCARD QUESTION

Front

What is interpolation in the context of regression?

Back

Interpolation is the process of estimating unknown values within the range of known data points. For example, predicting a value for x=8 when x-values in the data set include values around 8.

7.

FLASHCARD QUESTION

Front

What is extrapolation in regression analysis?

Back

Extrapolation is the process of estimating unknown values outside the range of known data points. For example, predicting a value for x=15 when the data set only includes values up to 10.

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