Chapter 3

Question 1

the study of the relationships between a dependent variable (Y) and one or more independent or explanatory variables (X1, X2,..).

Answer 1

Regression

Question 2

Conditional Mean (or Expectation):

Answer 2

E(Y|X=Xi)

Question 3

E(Y|X=Xi) = f(Xi) =

Answer 3

ß1 + ß2Xi

Question 4

E(Y|X=Xi) = f(Xi) is

Answer 4

Population Regression Function (PRF) or Population Regression (PR).

Question 5

E(Y|X=Xi) = f(Xi) = ß1 + ß2Xi

ß1 and ß2 are WHAT coefficients,
ß1 is WHAT and
ß2 is WHAT coefficient

Answer 5

ß1 and ß2 are regression coefficients,
ß1 is intercept and
ß2 is slope coefficient

Question 6

• X appears with a power of 1 only (no X2 or √X)
• X is not multiplied or divided by another variable

Answer 6

Linearity in the variables

Question 7

A function is linear in the parameter β1, if β2 appears with a power of 1 only.

Answer 7

Linearity in the Parameters

Question 8

Stochastic Specification of prf

Answer 8

Ui =Y - E(Y|X=Xi)
or
Yi = E(Y|X=Xi) + Ui

Question 9

WHAT IS Ui

Answer 9

Stochastic disturbance or stochastic error term. It is nonsystematic component.

Question 10

Stochastic disturbance or stochastic error term.

Answer 10

Ui

Question 11

It is nonsystematic component.

Answer 11

Ui

Question 12

A component that is systematic or deterministic.

Answer 12

E(Y|X=Xi)

Question 13

The assumption that the regression line passes through the conditional means of Y implies that E(Ui|Xi ) =

Answer 13

0

Question 14

IT is a surrogate for all variables that are omitted from the model but they collectively affect

Answer 14

Ui = Stochastic Disturbance

Question 15

7 reasons for using Ui

Answer 15

1. Vagueness of theory
2. Unavailability of Data
3. Core Variables vs. Peripheral Variables
4. Intrinsic randomness in human behavior
5. Poor proxy variables
6. Principle of parsimony
7. Wrong functional form

Question 16

A particular numerical value obtained by the estimator in an application.

Answer 16

Estimate