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 ~ educ + exper + sex, 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      0.0000001141795 ***
## educ         0.94051    0.07886  11.926 < 0.0000000000000002 ***
## exper        0.11330    0.01671   6.781      0.0000000000319 ***
## sexM         2.33763    0.38806   6.024      0.0000000031877 ***
## ---
## 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: < 0.00000000000000022

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

Yes, it is statistically siginficant because the P value is less than .05.

Q3 Interpret the coefficient of education.

Hint: Discuss both its sign and magnitude.

For every year that you are recieveing education, your rate will increase by $0.94 per hour.

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 evidence for gender dicriminiation in wages, with males having higher significance. A male in this particular work area would be making $2.01 more than females.

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

A women with 15 years of education and 5 years of experience would be making $8.15.

Q6 Interpret the Intercept.

Hint: Provide a technical interpretation.

If none of the factors were not connected to the wages, then the intercept would be $-6.50.

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.

## 
## Call:
## lm(formula = wage ~ educ + exper + sex + union, data = CPS85)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.496 -2.708 -0.712  1.909 37.784 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept) -6.48023    1.20159  -5.393        0.00000010459 ***
## educ         0.93495    0.07835  11.934 < 0.0000000000000002 ***
## exper        0.10692    0.01674   6.387        0.00000000037 ***
## sexM         2.14765    0.39097   5.493        0.00000006145 ***
## unionUnion   1.47111    0.50932   2.888              0.00403 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.423 on 529 degrees of freedom
## Multiple R-squared:  0.2648, Adjusted R-squared:  0.2592 
## F-statistic: 47.62 on 4 and 529 DF,  p-value: < 0.00000000000000022

The second one, is has a .1 more accuracy because the first one was .24 and the second one was .25.

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.