Topic 7: Multiple Linear Regression Flashcards Preview

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Flashcards in Topic 7: Multiple Linear Regression Deck (9)
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1

What extra assumptions are introduced in multiple variable regression?

- No exact collinearity between X variables

- No specification bias

2

What does an estimate in multiple linear regression mean?

The change in Y caused by a change in x, holding all other variables constant

3

How does the correlation between regressors affect the error of the estimates

Greater the correlation, higher the error

4

Why do we use Adjusted R^2?

Because normal R^2 can be increased just by adding junk regressors. Adjusted R^2 compensates for the number of variables

5

When can we compare R^2 values?

-When sample size is the same

-When dependant variables are the same

6

Give the formula for adjusted R^2

7

Does R^2 have any intrinsic properties that might favour its use over other calculations?

Nope, pretty arbitrary

8

What is the Gross/Simple correlation coefficient?

Shown as r1-2, where 1 = Y and i > 1 = Xi. Shows the correlation between two variables

9

What is the partial correlation coefficient?

The correlation between two variables, eliminating the correlation effect from some other variables. Shown as r12.34, where the effects from 3 and 4 are eliminated