Linear Regression Flashcards

1
Q

When do we use linear regression?

A

Predicting the outcome of 1 numeric variable based on the values of another numeric variable

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2
Q

What is the equation for linear regression?

A

y^ = a + bx

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3
Q

Interpretation for linear regression

A

__% of the variability in ____ (y^) is accounted for by the variability in ____(x)

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4
Q

Assumptions for linear regression

A

Relationship between x and y is linear (use residual plots to find no curvature), Errors are normally distributed (histogram and box plot for symmetry, outliers), Errors have constant variance for all x-values (look at residual plot of vertical scatter being the same)

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5
Q

Hypotheses

A

H0: ____ is not a linear predictor of ____

Ha: ____ is a linear predictor of _____

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6
Q

What values do we list?

A

Test statistic (t or F), p-value

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7
Q

Conclusion

A

There is/isn’t sufficient evidence of a linear relationship between _____ and _____

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8
Q

CI

A

We are 95% confident that as ____ increases by 1 ___(units) ______ increases/decreases between _____ and ____ on average

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9
Q

How do you find residual values

A

y - y^

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