1 Report the descriptive statistics of the annual salary, experience, and age of the employees 2 Run and display a scatterplot Matrix for Salary X Gender X Experience X TrainingLevel Interpret the graph Basically explain to me enough that I know you understand what you are looking at 3 Run the linear regression model You dont have to report anything yet Your model should have salary as the dependent variable and gender, experience, and training level as the independent variables 4 Run through the four linear regression assumptions and report the status of each assumption If you violated any of the assumptions can you still run the model 5 Examine the results of the linear regression This is the summary(model) line of code in R First report the F stat What does it mean and is it significant Basically do we have a good model to move forward 6 Next report the coefficient of each independent variable Explain what it means in terms of the independent and dependent variable Report and explain the p value and what it means For example Table 1 Estimated Coefficients Variable Estimated Coefficient Intercept 550 Experience 250 p Given the results above, for every one year increase in experience the average salary increases by $250 00 given the same blah blah blah This is very highly significant Repeat this for every variable 7 Report the R2 and Adjusted R2 values (remember Adjusted R2 adjust for bigger datasets) Tell me what this means 8 Last, tell me if the employee bringing the lawsuit has a valid case Does this company exhibit pay discrimination based off sex Here is the table Employee Number Annual salary Min 1 0 1st Qu 18 5 1st Qu 72292 Median 36 0 Median 84033 Mean 36 0 Mean 80142 3rd Qu 53 5 Max 71 0 Max 97530 Training Level A 38 B 24 C 9 Gender Experience 58975 F 28 Min 1st Age 30 00 Min Qu 40 50 0 000 1st Qu 3 000 Median 46 00 Median 5 000 Mean 45 54 Mean 5 746 3rd Qu 51 00 3rd Qu 8 500 Max 10 000 Min M 43 3rd Qu 88174 Max 61 00 Employee Number Annual salary Min 1 0 1st Qu 18 5 1st Qu 72292 Median 36 0 Median 84033 Mean 36 0 Mean 80142 3rd Qu 53 5 Max 71 0 Max 97530 Training Level A 38 B 24 C 9 Gender Experience 58975 F 28 Min 1st Age 30 00 Min Qu 40 50 0 000 1st Qu 3 000 Median 46 00 Median 5 000 Mean 45 54 Mean 5 746 3rd Qu 51 00 3rd Qu 8 500 Max 10 000 Min M 43 3rd Qu 88174 Max 61 00

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1. Report the descriptive statistics of the annual salary, experience, and age of the employees 2. Run and display a scatterplot Matrix for Salary X

1. Report the descriptive statistics of the annual salary, experience, and age of the employees

2. Run and display a scatterplot Matrix for Salary X Gender X Experience X TrainingLevel. Interpret the graph. Basically explain to me enough that I know you understand what you are looking at.

3. Run the linear regression model. You dont have to report anything yet. Your model should have salary as the dependent variable and gender, experience, and training level as the independent variables.

4. Run through the four linear regression assumptions and report the status of each assumption. If you violated any of the assumptions can you still run the model?

5. Examine the results of the linear regression. This is the summary(model) line of code in R. First report the F-stat. What does it mean and is it significant? Basically do we have a good model to move forward.

6. Next report the coefficient of each independent variable. Explain what it means in terms of the independent and dependent variable. Report and explain the p-value and what it means. For example:

Table 1: Estimated Coefficients

Variable	Estimated Coefficient
Intercept	550***
Experience	250***

*p<.05>

Given the results above, for every one year increase in experience the average salary increases by $250.00 given the same blah blah blah . This is very highly significant.

Repeat this for every variable.

7. Report the R2 and Adjusted R2 values (remember Adjusted R2 adjust for bigger datasets). Tell me what this means.

8. Last, tell me if the employee bringing the lawsuit has a valid case. Does this company exhibit pay discrimination based off sex?

Here is the table:

Employee. Number Annual.salary Min. 1.0 1st Qu:18.5 1st Qu. :72292 Median 36.0 Median:84033 Mean 36.0 Mean:80142 3rd Qu. :53.5 Max. :71.0 Max. :97530 Training. Level A:38 B:24 C:9 Gender Experience 58975 F:28 Min 1st Age 30.00 Min. Qu .:40.50 :0. 000 1st Qu.: 3.000 Median :46.00 Median 5.000 Mean 45. 54 Mean: 5.746 3rd Qu. :51.00 3rd Qu.: 8.500 Max. :10.000 Min. M:43 3rd Qu. :88174 Max. :61.00 Employee. Number Annual.salary Min. 1.0 1st Qu:18.5 1st Qu. :72292 Median 36.0 Median:84033 Mean 36.0 Mean:80142 3rd Qu. :53.5 Max. :71.0 Max. :97530 Training. Level A:38 B:24 C:9 Gender Experience 58975 F:28 Min 1st Age 30.00 Min. Qu .:40.50 :0. 000 1st Qu.: 3.000 Median :46.00 Median 5.000 Mean 45. 54 Mean: 5.746 3rd Qu. :51.00 3rd Qu.: 8.500 Max. :10.000 Min. M:43 3rd Qu. :88174 Max. :61.00