Make sure to include the unit of the values whenever appropriate.

Q1 Build a regression model to predict wages using the following predictors: 1) years of education, 2) years of experience, and 3) sex.

Hint: The variables are available in the CPS85 data set from the mosaicData package.

## 
## Call:
## lm(formula = wage ~ exper + sex + educ, data = CPS85)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.571 -2.746 -0.653  1.893 37.724 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -6.50451    1.20985  -5.376 1.14e-07 ***
## exper        0.11330    0.01671   6.781 3.19e-11 ***
## sexM         2.33763    0.38806   6.024 3.19e-09 ***
## educ         0.94051    0.07886  11.926  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.454 on 530 degrees of freedom
## Multiple R-squared:  0.2532, Adjusted R-squared:  0.2489 
## F-statistic: 59.88 on 3 and 530 DF,  p-value: < 2.2e-16

Q2 Is the coefficient of education statistically significant at 5%?

Education is very significant at 5% because of the 3 star rating.

Q3 Interpret the coefficient of education.

Hint: Discuss both its sign and magnitude. The total value being 11.926 and education’s significant rating being 3 stars.

Q4 Is there evidence for gender discrimination in wages? Make your argument using the relevant test results.

Hint: Discuss all three aspects of the relevant predictor: 1) statistical significance, 2) sign, and 3) magnitude. There is no evidence for gender discriminstion in wages because of the 3 star rating and its magnitude

Q5 Predict wage for a woman who has 15 years of education, 5 years of experience.

I’d perdict that the average wage for a woman who has 15 years of education and 5 years of experience would be paid around 10-11 dollars per hour.

Q6 Interpret the Intercept.

Hint: Provide a technical interpretation.

Q7 Build another model by adding a predictor to the model above. The additional predictor is whether the person is a union member. Which of the two models is better?

Hint: Discuss in terms of both residual standard error and reported adjusted R squared.

Q8 Hide the messages, but display the code and its results on the webpage.

Hint: Use message, echo and results in the chunk options. Refer to the RMarkdown Reference Guide.

Q9 Display the title and your name correctly at the top of the webpage.

Q10 Use the correct slug.