Regression Analysis Test - Machine Learning

Question 1

All explanatory variables are significant at 90% confidence level

Answer

true

Question 2

The most important variable in explaining the target is Years of education

Answer

true

Question 3

Living in the South decreases the average wage

Answer

true

Question 4

Living in a Metropolitan Statistical Area increases the average wage

Answer

true

Question 5

A stepwise method was used to produce the above output

Answer

true

Question 6

If we enter a new variable most likely our R Square will increase

Answer

true

Question 7

If sample size were 20 observations, we still may rely on the results above

Answer

true

Question 8

R Square is the proportion of variance of the target variable explained by the predictors

Answer

true

Question 9

Individual significance of variables in Linear Regression is checked with t-student test

Answer

true

Question 10

Autocorrelation is produced if we have high correlation between explanatory variables

Answer

true

Question 11

Main uses of Linear Regression are simulation, structural analysis and prediction

Answer

true

Question 12

A telco company wants to predict a flag field named churn. Linear Regression is the appropriate technique to use

Answer

true

Question 13

Heteroscedasticity is having unequal variances of explanatory variables

Answer

true

Question 14

If Beta(x2) = 5 * Beta (x1), then x2 is five times more important than x1 in predicting the dependent

Answer

true

Question 15

Average wage “penalty” for Afro-American (AA) workers is on average approx. 18%

In this regression, the LOGARITHM of the yearly wage of workers (log of WAGE) is explained by the AGE of worker in years, father’s education in years and being Afro-American (AA) or not (1=yes / 0= no). (***) Be aware that COMMA “,” is used as decimal separator.

Elasticity between Wage and Age is approximately 0.027.

Answer

true

Question 16

An additional year of worker father’s education yields an average wage increase of 0.027% on average

In this regression, the LOGARITHM of the yearly wage of workers (log of WAGE) is explained by the AGE of worker in years, father’s education in years and being Afro-American (AA) or not (1=yes / 0= no). (***) Be aware that COMMA “,” is used as decimal separator.

Elasticity between Wage and Age is approximately 0.027.

Answer

true

Question 17

We can REJECT the null hypothesis of individual significance for all variables with a 99% confidence

In this regression, the LOGARITHM of the yearly wage of workers (log of WAGE) is explained by the AGE of worker in years, father’s education in years and being Afro-American (AA) or not (1=yes / 0= no). (***) Be aware that COMMA “,” is used as decimal separator.

Elasticity between Wage and Age is approximately 0.027.

Answer

true

Question 18

In this regression, the LOGARITHM of the yearly wage of workers (log of WAGE) is explained by the AGE of worker in years, father’s education in years and being Afro-American (AA) or not (1=yes / 0= no). (***) Be aware that COMMA “,” is used as decimal separator.

Elasticity between Wage and Age is approximately 0.027.

Elasticity between Wage and Age is approximately 0.027.

Answer

true

Question 19

Comparing coefficients size, AA is clearly the most influential variable

In this regression, the LOGARITHM of the yearly wage of workers (log of WAGE) is explained by the AGE of worker in years, father’s education in years and being Afro-American (AA) or not (1=yes / 0= no). (***) Be aware that COMMA “,” is used as decimal separator.

Elasticity between Wage and Age is approximately 0.027.

Answer

true

Question 20

1 unit increase in Age yields an average 2.7% increase in Wage

In this regression, the LOGARITHM of the yearly wage of workers (log of WAGE) is explained by the AGE of worker in years, father’s education in years and being Afro-American (AA) or not (1=yes / 0= no). (***) Be aware that COMMA “,” is used as decimal separator.

Elasticity between Wage and Age is approximately 0.027.

Answer

true

Question 21

Confidence interval for AA variable includes the value -0.1

In this regression, the LOGARITHM of the yearly wage of workers (log of WAGE) is explained by the AGE of worker in years, father’s education in years and being Afro-American (AA) or not (1=yes / 0= no). (***) Be aware that COMMA “,” is used as decimal separator.

Elasticity between Wage and Age is approximately 0.027.

Answer

true

Question 22

Father’s education Beta real value in population might be 20

Answer

true

Regression Analysis Test - Machine Learning

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