general 2

Question 1

regression is very sensitive to

Answer 1

rounding

Question 2

dependent variable mean

Answer 2

line, the regression line

Question 3

SSR=

Answer 3

SST-SSE

Question 4

when SST is equal to SSE

Answer 4

when we don’t have an independent variable or we have just intercept amount.

Question 5

Is related to the time that we estimate the line just with the average amount of dependent variable so

Answer 5

we have a horizontal line without slope.

Question 6

SSE is related to

Answer 6

regression model, diffrence between obsereved value and predicted value

Question 7

SST IS

Answer 7

DIFFERENCE between the observed value and the mean of the independent variable ( when we have a just horizontal line)

Question 8

SSR is

Answer 8

the difference between the two lines, the horizontal line that is the mean of the dependent variable without an independent variable and regression line with the independent variable.

Question 9

SSR is

Answer 9

the total residual of the regression line

Question 10

r square =

Answer 10

SSR/ SST

Question 11

degree of freedom in simple regression always is

Answer 11

two, because we just estimated the slope and intercept.

Question 12

MSE=

Answer 12

SSE/DEGREE OF FREEDOM

Question 13

MSE or mean some square is the

Answer 13

the variance of the error shows how spreads of data point around the regression line.

Question 14

why the degree of freedom divided by n-2 and not divided just on n?

Answer 14

because we dealing with the sample, not the population, and divided on n give us the average.

Question 15

the standard error is

Answer 15

the standard deviation of the error term is the average distance of observation that falls from the regression line in its units of the dependent variable.

Question 16

the standard error formula is

Answer 16

the square root of the MSE

Question 17

Priority

Answer 17

first: SSE second: MSE third: s or standard error.

Question 18

when the beta equals zero then

Answer 18

the null hypothesis will be rejected.

Question 19

adding more independent variables will lead to

Answer 19

overfitting and some problem so it is not always a good procedure. second part: multicollinearity is one of these problems that means independent variables are correlated together.

Question 20

when two independent variables is correlated to each other

Answer 20

we cannot sure which of them explaining the variation in the dependent variable.

Question 21

regression types

Answer 21

regression model and regression equation and third estimated multiple regression.

Question 22

in multiple regression, each coefficient interpreted as

Answer 22

an estimate of the change in y variable corresponding to one unit change in one independent variable when all other variables remain constant

Question 23

the primary purpose of log in regression:

Answer 23

is the way of scale skewed data, get the form of the normal distribution to skewed data,

Question 24

price = 1079ln(x), interpret:

Answer 24

1 percent increase in x will to 1079/100 increase in price

Question 25

ln price = 0.197ln(x), interpret:

Answer 25

1 percent increase in x will lead to a 0.197 percent increase in y. when to side have ln no dividing and multiple!

Question 26

polynomial regression adds

Answer 26

extra independent variables that are the power of the original variable.

Question 27

quadratic model:

Answer 27

it is squared of independent variable its allow our model to capture curvature. in original scatter plot

Question 28

nonlinear quadratic model:

Answer 28

explained more variance, the tighter fit of observation around the regression line, reduce the model error.

Question 29

Thus, the change in y is simply

Answer 29

b1 multiplied by the change in x. This means that b1 is the slope parameter in the relationship between y and x, holding the other factors in u fixed.

Question 30

How can we hope to learn in general about the ceteris paribus effect of x on y, holding other factors fixed, when we are ignoring all those other factors?

Answer 30

As long as the intercept b0 is included in the equation, nothing is lost by assuming that the average value of u in the population is zero