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.

library(tidyverse)

data(CPS85, package="mosaicData")
houses_lm <- lm(wage ~ educ + exper + sex,
                data = CPS85)

# View summary of model 1
summary(houses_lm)
## 
## 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 1.14e-07 ***
## educ         0.94051    0.07886  11.926  < 2e-16 ***
## exper        0.11330    0.01671   6.781 3.19e-11 ***
## sexM         2.33763    0.38806   6.024 3.19e-09 ***
## ---
## 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
data(CPS85, package="mosaicData")

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

It is not significant because the T value is more than five percent.

Q3 Interpret the coefficient of education.

Hint: Discuss both its sign and magnitude.

Because the higher unit of education increased by 1 year it creates 94 more cents and hour for that professors wage.

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 gender discrimination because the males having higher significance. For instance a male in the same workplace as a woman would be making about 2.01 more than the female.

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

A woman with 15 years of educations and 5 years of experience in the workplace would make 8.15.

Q6 Interpret the Intercept.

Hint: Provide a technical interpretation.

The intercept would be -6.50 without any of the factors applying to the wage.

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.

library(tidyverse)

data(CPS85, package="mosaicData")
houses_lm <- lm(wage ~ educ + exper + sex + 
                  union, 
                data = CPS85)

# View summary of model 1
summary(houses_lm)
## 
## 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 1.05e-07 ***
## educ         0.93495    0.07835  11.934  < 2e-16 ***
## exper        0.10692    0.01674   6.387 3.70e-10 ***
## sexM         2.14765    0.39097   5.493 6.14e-08 ***
## 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: < 2.2e-16
data(CPS85, package="mosaicData")

The 2nd model has a lower standard error.

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.