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.

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

# View summary of model 1
summary(wages_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

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

Because the p value is at 5% the cofficient of edicatuion is staistically significant

Q3 Interpret the coefficient of education.

Hint: Discuss both its sign and magnitude.

The professor will get 94 more cents per hour on there wage for every year of edication taught

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.

In this case there is evidence of gender discrimination in wages between men and women. males make roughly $2.34 per hour in their slalry more than women for every year of additional edication they have recived

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

For a women with 15 years of experiance in edication the perdicted wage would be $8.20.

Q6 Interpret the Intercept.

Hint: Provide a technical interpretation.

On the chart it shows $-6.50 and that is not possible because you cant make negative dollars an hour

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.

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

# View summary of model 1
summary(wages_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

I think this modle is alot better due to the fact that the reported adjusted r squared is higher and the residual standars error is smaller

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.