# University of Maryland University College Statistics Discussion

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Question 1

Suppose you went to the Bureau of Labor Statistics web site and found several predictors of your salary. State three possible predictors and a simulated multilinear regression equation along with their p-values. What do your p-values tell you about your predictors?

How does multilinear regression analysis apply to your profession? Specify the dependent value and the set of predictors. Where would you get your data?

1. Question 2

In your environment (business or personal), please give an application of exponential smoothing and WHY you would use only this technique.

In your environment (business or personal), please give an application of trend projection and WHY you would use only this technique.

In your environment (business or personal), please give an application of moving average and WHY you would use only this technique.

Student 1

1. a) The three possible predictors would be 1) market salary 2) Experience level 3) Level of Education. We can use logistic regression analysis for the data set. the p values can be calculated for the given multi regression equation

y=B+ax1+bx2+cx3

y=independent variable

b=y intercept

a, b, c are constant values

b) I am a loan officer so the market level right now depending on the company and its policies would be helpful to predict the present salary. My past education and me currently pursuing an MBA a in finance can be an important variable paired with my current financial background would be an variable to predict my possible salary. Most of the data is already available. The market value of the salary can be found on job portals.

Student 2

A possible multilinear regression equation for a prediction of salary could look like:

Annual Salary = \$12,000 + \$3,000(Years of Education) + \$4,000(Years of Experience) + \$1,000(Avg. Market Salary)

This demonstrates that the annual salary will be equal to \$12,000, plus an additional \$3,000 for each year of education, plus an additional \$4,000 for each year of experience, and lastly, plus an additional \$1,000 based on the average salary in the market – if the average salary is up, an individual will receive a higher salary amount.

Through analyzing the p-values of these predictors, one can determine the probability of the data occurring if the null hypothesis is true. This, in turn, allows you to determine whether or not the results are statistically significant.

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