GenerL

Question 1

If the independent variable is divided or multiplied by some nonzero constant, c

Answer 1

then the OLS slope coefficient is multiplied or divided by c, respectively.

Question 2

In general, changing the units of measurement of only the independent variable

Answer 2

does not affect the intercept.

Question 3

the goodness of fit of the model should not depend on the

Answer 3

units of measurement of our variables.

Question 4

more reasonable to increase/decrease by

Answer 4

constant percentage
log(wage)=b0 +b1(edu)+u
%delta(wage)=(100*b1) delta education

Question 5

why using the log on wage?

Answer 5

Is to impose a constant percentage effect of education on wage

Question 6

Another important use of the natural log is

Answer 6

in obtaining a constant elasticity model

Question 7

log(salary)=4.822+0.257log(sales), The coefficient of log(sales) is the estimated

Answer 7

the elasticity of salary with respect to sales. It implies that a 1% increase in firm sales increases CEO salary by about 0.257%

Question 8

the sum of the logs is equal

Answer 8

therefore, the slope is still b1, but the intercept still increases

Question 9

if independent variable is log(x) and we change the units of measurement of x before taking the log

Answer 9

the slope remains the same, but the intercept changes.

Question 10

linear regression: The key is that this equation is linear in

Answer 10

the parameters b0 and b1. There are no restrictions on how y and x relate to the original explained and explanatory variables of interest.

Question 11

not linear in their

Answer 11

PARAMETERS

Question 12

Linear regression estimates the

Answer 12

conditional mean of the response variable. This means that, for a given value of the predictor variable X, linear regression will give you the mean value of the response variable Y.

Question 13

In (simple) linear regression, we are looking for

Answer 13

a line of best fit to model the relationship between our predictor, X and our response variable Y.

Question 14

To find the intercept and slope coefficients of the line of best fit, linear regression uses

Answer 14

the least squares method, which seeks to minimise the sum of squared deviations between the n observed data points y1…yn and the predicted values, which we’ll call y^.

Question 15

the OLS slope and intercept estimates are not defined unless

Answer 15

we have sample variation in the explanatory variable.

Question 16

Random sampling

Answer 16

where individuals are chosen randomly and their wage and education are both recorded.

Question 17

unbiasedness is a feature of

Answer 17

the sampling distributions of b^ 1 and b^ 0, which says nothing about the estimate that we obtain for a given sample. We hope that, if the sample we obtain is somehow “typical,” then our estimate should be “near” the population value.

Question 18

the error term u in equation is correlated with lnchprg

Answer 18

In fact, u contains factors such as the poverty rate of children attending school, which affects student performance and is highly correlated with eligibility in the lunch program

Question 19

correlation + anova

Answer 19

linear regression

Question 20

regression=

Answer 20

the value of one variable is a function of another variable

Question 21

linear:

Answer 21

NOT radical and not power

Question 22

m= OR beta or slope

Answer 22

Y2-Y1/X2-X1, Rise/Run

Question 23

f(x)=

Answer 23

Y

Question 24

E(y)=

Answer 24

is expected value or average value of y for the given value of x, is where we expect to intersect in the graph.

Question 25

the y value is not a POINT in regression

Answer 25

its distribution of y for given x because the regression value is not perfected, so it is best to use approximation. so the, y value is the mean of the distribution of y value for each x given value. the main value of y.

Question 26

when the slope amount is zero the independent variable

Answer 26

does not exist.

Question 27

goal:

Answer 27

line reduce residuals sum of squares

Question 28

y hat:

Answer 28

when we know the parameter of the sample NOT population. E(y) is for the population.