# Inference in regression: F-testJan 25 2019

## Homework #1

Solutions on canvas

I graded the initial data analysis.

• Everyone was looking at the right things!
• But, the writeups could use some improvement
• HW #3 gets you to repeat this process on a different data set

## Homework #3

I’ve posted an example with some guidelines as 01-initial-data-analysis-report, but I started from 01-initial-data-analysis-draft.

Key things I’ll be looking for in HW #3:

• < 2 pages (notice my draft is 10 pages, but report is only 1.5 pages)
• you control what output/code is in the final version
• plots are labelled and sized appropriately

## Today

• The F-test
• Practice with F-tests

## Motivation

t-tests on individual parameters only allow us to ask a limited number of questions.

To ask questions about more than one coefficient we need something more complicted.

F-tests do this by comparing nested models. In practice, the hard part is translating a scientific question in a comparison of two models.

## F-test

Let $$\Omega$$ denote a larger model of interest with $$p$$ parameters
and $$\omega$$ a smaller model that represents some simplification of $$\Omega$$ with $$q$$ parameters.

Intuition: If both models “fit” as well as each other, we should prefer the simpler model, $$\omega$$. If $$\Omega$$ shows substantially better fit than $$\omega$$, that suggests the simplification is not justified.

How do we measure fit? What is substantially better fit?

## F-statistic

$F = \frac{(\RSS{\omega} - \RSS{\Omega})/(p - q)}{\RSS{\Omega}/(n - p)}$

Null hypothesis: the simplification to $$\Omega$$ implied by the simpler model, $$\omega$$.

Under the null hypothesis, the F-statistic has an F-distribution with $$p-q$$ and $$n-p$$ degrees of freedom.

Leads to tests of the form: reject $$H_0$$ for $$F > F_{p-q, n-p}^{(\alpha)}$$.

Deriving this fact is beyond this class (take Linear Models).

## Example: Overall regression F-test

The overall regression F-test asks if any predictors are related to the response.

Full model: $$Y = X\beta + \epsilon, \quad \epsilon \sim N(0, \sigma^2 I)$$
Reduced model: $$Y = \beta_0 + \epsilon$$

Null hypothesis: $$H_0: \beta_1 = \beta_2 = \ldots = \beta_{p-1} = 0$$
All the parameters (other than the intercept) are zero.

Alternative hypothesis: At least one parameter is non-zero.

Exercise: question #1 on handout

If there is evidence against the null hypothesis:

• The null is not true, or
• the null is true but we got unlucky, or
• the full model isn’t true and the F-test is meaningless.

If there is no evidence against the null hypothesis:

• The null is true, or
• the null is false but we didn’t gather enough evidence to reject it, or
• the full model isn’t true and the F-test is meaningless.

## Example: One predictor

Null hypothesis: $$\beta_j = 0$$

Equivalent to the t-test, reject null if $|t_j| = \left|\frac{\hat{\beta_j}}{\SE{\hat{\beta_j}}}\right| > t_{n-p}^{\alpha/2}$

In fact, in this case, $$F = t_j^2$$.

Exercise: questions #2 & #3 on handout

## Other examples

• More than one parameter
• A subspace of the parameter space

Exercise: questions #4 & #5 on handout

## We can’t do F-tests when

• we want to test non-linear hypotheses, e.g. $$H_0: \beta_j\beta_k = 1$$ (we might be able to make use of the Delta method, though)
• we want to compare non-nested models (find an example on the handout)
• the models fit use different data (most often comes up when a variable of interest has some missing values)
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