1) Using Excel, create a simple linear regression to predict the Salary Expectat

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Question

1) Using Excel, create a simple linear regression to predict the Salary Expectation (column I) of undergraduate students using their Age (column C) as the independent variable. What is the numeric value of the slope coefficient (b1) of this model? (Round your answer to 3 decimals).

2) Using the regression model from the previous problem, what is the interpretation of the slope coefficient b1? Assume Salary is measured in $1,000's.

3)Using the previous regression model, what salary would you expect a student who is 24 years old to expect? Round your answer to 1 decimal and express in $1,000's (same format the Salary variable has in the Excel dataset).

4)Using the previous regression model, what is the p-value associated with the hypothesis test of significance between Age and Salary Expectation? Round your answer to 3 decimal places.

5)Using the previous regression model and your answer to problem #4, is there a statistically significant relationship between Age and Salary Expectation at the 5% significance level? Enter the word "relationship" if a relationship exists and "none" if there is not enough evidence to establish a relationship.

ID Gender Age Class Major Grad Intention GPA Employment Salary Social Networking Satisfaction Spending Computer Text Messages Wealth 1 Female 20 Junior Other Yes 2.9 Full-Time 50.0 1 3 350 Laptop 200 2.00 2 Male 23 Senior Management Yes 3.6 Part-Time 25.0 1 4 360 Laptop 50 10.00 3 Male 21 Junior Other Yes 2.5 Part-Time 45.0 2 4 600 Laptop 200 70.00 4 Male 21 Junior CIS Yes 2.5 Full-Time 40.0 4 6 600 Laptop 250 100.00 5 Male 23 Senior Other Undecided 2.8 Unemployed 40.0 2 4 500 Laptop 100 1.00 6 Female 22 Senior Economics/Finance Undecided 2.3 Unemployed 78.0 3 2 700 Laptop 30 5.00 7 Female 21 Junior Other Undecided 3.0 Part-Time 50.0 1 3 500 Laptop 50 0.60 8 Female 22 Senior Other Undecided 3.1 Full-Time 80.0 1 2 200 Tablet 300 1.00 9 Female 20 Junior Management Yes 3.6 Unemployed 30.0 0 4 500 Laptop 400 0.60 10 Female 21 Senior Economics/Finance Undecided 3.3 Part-Time 37.5 1 4 200 Laptop 100 1.00 11 Female 23 Senior Economics/Finance Yes 2.8 Full-Time 50.0 2 5 400 Laptop 200 30.00 12 Male 21 Senior Undecided No 3.5 Full-Time 37.0 2 3 500 Laptop 100 4.00 13 Male 22 Senior International Business Undecided 3.4 Part-Time 40.0 2 3 400 Desktop 45 0.70 14 Male 22 Senior International Business Undecided 3.1 Part-Time 40.0 1 3 400 Laptop 150 2.00 15 Male 21 Senior Management Yes 3.2 Part-Time 54.0 3 4 600 Laptop 400 6.00 16 Male 24 Senior Management Undecided 3.4 Part-Time 45.0 4 4 500 Laptop 175 1.50 17 Female 19 Junior CIS Undecided 3.7 Part-Time 55.0 1 4 450 Laptop 150 0.50 18 Male 21 Junior Economics/Finance Undecided 3.1 Part-Time 55.0 2 3 600 Laptop 300 10.00 19 Male 19 Junior Economics/Finance Yes 3.5 Part-Time 52.0 2 5 500 Laptop 300 2.00 20 Female 20 Junior Management Undecided 3.2 Unemployed 60.0 2 6 300 Laptop 350 0.40 21 Female 22 Junior Retailing/Marketing Undecided 3.2 Part-Time 55.0 1 3 690 Laptop 50 0.35 22 Male 18 Sophomore Accounting Undecided 3.0 Unemployed 60.0 1 4 600 Laptop 500 10.00 23 Female 22 Senior Retailing/Marketing Undecided 3.0 Part-Time 55.0 0 4 300 Laptop 35 0.15 24 Male 22 Senior Undecided Yes 2.6 Full-Time 45.0 1 5 400 Laptop 600 1.50 25 Female 20 Junior Economics/Finance Yes 3.0 Part-Time 55.0 1 3 600 Laptop 300 0.10 26 Male 24 Senior Management Yes 3.3 Full-Time 60.0 0 1 300 Laptop 40 10.00 27 Male 20 Junior Economics/Finance Yes 3.1 Full-Time 65.0 1 5 375 Laptop 300 2.00 28 Female 20 Junior International Business Yes 2.9 Part-Time 50.0 3 1 900 Laptop 100 1.00 29 Male 22 Senior Retailing/Marketing Yes 3.3 Part-Time 55.0 1 6 1100 Laptop 60 100.00 30 Male 20 Sophomore Retailing/Marketing Undecided 3.1 Part-Time 45.0 1 4 400 Laptop 140 7.00 31 Male 20 Junior Accounting Undecided 3.4 Part-Time 55.0 2 3 500 Laptop 750 5.00 32 Male 20 Junior Other Yes 2.9 Part-Time 47.0 3 1 300 Laptop 300 1.25 33 Male 20 Junior Accounting Yes 3.6 Part-Time 35.0 1 4 200 Laptop 70 0.75 34 Male 22 Senior Retailing/Marketing Yes 2.6 Full-Time 40.0 1 4 1400 Laptop 800 0.85 35 Female 19 Junior Retailing/Marketing Undecided 3.4 Part-Time 40.0 1 5 500 Laptop 300 0.13 36 Female 26 Junior Accounting Yes 3.3 Part-Time 60.0 1 4 450 Desktop 300 0.75 37 Male 21 Senior Management Yes 3.1 Part-Time 40.0 1 4 500 Laptop 100 5.00 38 Female 21 Sophomore Accounting Yes 2.5 Part-Time 60.0 2 3 500 Laptop 600 0.90 39 Male 24 Junior Economics/Finance Yes 2.8 Part-Time 50.0 1 6 600 Laptop 50 1.00 40 Male 19 Sophomore Retailing/Marketing Yes 2.5 Unemployed 50.0 2 5 300 Laptop 100 1.00 41 Male 22 Junior Accounting Yes 3.2 Full-Time 60.0 1 4 680 Desktop 200 0.50 42 Female 20 Junior Retailing/Marketing No 3.3 Part-Time 30.0 1 4 600 Laptop 350 1.00 43 Female 22 Senior Retailing/Marketing Undecided 3.5 Unemployed 40.0 2 5 300 Laptop 50 6.00 44 Female 21 Senior Retailing/Marketing No 3.9 Part-Time 30.0 1 5 100 Laptop 900 1.00 45 Female 21 Senior International Business No 3.0 Part-Time 30.0 2 5 650 Desktop 500 0.50 46 Female 21 Senior Management Undecided 3.8 Part-Time 60.0 1 4 650 Laptop 150 1.00 47 Female 20 Junior Retailing/Marketing Yes 3.5 Unemployed 60.0 1 3 350 Laptop 200 0.25 48 Male 19 Sophomore Undecided Undecided 2.5 Part-Time 80.0 2 4 500 Laptop 150 3.00 49 Female 21 Senior Economics/Finance Yes 3.2 Part-Time 47.5 2 4 220 Laptop 105 1.00 50 Female 21 Senior Economics/Finance Undecided 3.0 Part-Time 45.0 1 3 520 Laptop 105 0.70 51 Female 21 Junior Management No 3.5 Unemployed 35.0 2 4 600 Tablet 100 1.00 52 Male 21 Senior Management No 3.0 Part-Time 50.0 1 4 500 Laptop 200 5.00 53 Female 21 Senior Retailing/Marketing Undecided 3.7 Part-Time 40.0 3 4 300 Laptop 700 10.00 54 Male 21 Junior Retailing/Marketing No 3.4 Part-Time 40.0 1 5 500 Laptop 300 0.50 55 Male 21 Senior Other Yes 3.4 Part-Time 50.0 1 4 250 Desktop 700 2.50 56 Female 21 Senior Retailing/Marketing No 3.1 Part-Time 50.0 1 1 300 Laptop 300 3.50 57 Female 21 Senior International Business Yes 3.4 Part-Time 42.0 1 1 200 Laptop 100 3.80 58 Female 21 Senior International Business No 2.4 Part-Time 40.0 1 3 1000 Laptop 10 1.00 59 Female 20 Junior CIS No 2.9 Part-Time 40.0 2 4 350 Laptop 250 0.10 60 Female 20 Sophomore CIS No 2.5 Part-Time 55.0 1 4 500 Laptop 500 1.00 61 Female 23 Senior Accounting Yes 3.5 Part-Time 30.0 2 3 490 Laptop 50 1.00 62 Female 23 Senior Economics/Finance No 3.2 Part-Time 70.0 2 3 250 Laptop 0 2.00

Explanation / Answer

Ans:

the numeric value of the slope coefficient (b1) of this model is -0.131.

2) Using the regression model from the previous problem, what is the interpretation of the slope coefficient b1? Assume Salary is measured in $1,000's.

Ans: For increasing an unit on age decrease the mean salaty by 0.131. In $1000, it will reduce $131 at the mean salary.

3) Using the previous regression model, what salary would you expect a student who is 24 years old to expect? Round your answer to 1 decimal and express in $1,000's (same format the Salary variable has in the Excel dataset).

Regression line

Salary= 51.319-0.131*Age

When age=24, the salary

Salary=51.319-0.131*24=$48.175

Threfore actual salary= 48.175*1000= $ 48175.

4)Using the previous regression model, what is the p-value associated with the hypothesis test of significance between Age and Salary Expectation? Round your answer to 3 decimal places.

Answer: p-value= 0.905.

Ans: The estimated p-value of Age is 0.905. Hence, we can not reject the null hypothesis and conclude that there is not a statistically significant relationship between Age and Salary Expectation at the 5% significance level.

Coefficients Standard Error t Stat P-value Intercept 51.319 23.073 2.224 0.030 Age -0.131 1.090 -0.120 0.905

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1) Using Excel, create a simple linear regression to predict the Salary Expectat

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