The midterm will cover the following topics:

The two uses of regression models

The assumptions underlying the Guass-Markov Theorem

The Guass-Markov Theorem and what BLUE means

Reasons for an error in a regression model

How t-tests and z-tests differ (when to use each one)

Single hypothesis tests: z-tests and t-tests

Multiple hypothesis tests: F-tests and Chi-Square tests

[Should be able to do hypothesis tests with these four distributions]

Functional form: changes in units

Functional form: log-linear, semi-log, reciprocal, and log reciprocal models (see page 190 for a summary).

Use of dummy variables in regression models

Dummy variable trap

Chow tests and dummy variables

Piecewise linear regression

Model selection criteria

Types of specification errors

Consequences of omitting a relevant variable

Consequences of including an irrelevant variable

LM test for adding variables to a regression model

How to do an AIC test

Consequences of errors in measuring y

Consequences of errors in measuring the x variables.

Perfect and imperfect multicollinearity

Consequences of multicollinearity

Detection of multicollinearity

What to do about multicollinearity

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