rema994
Please read below for direction on completing your final project. …
Please read below for direction on completing your final project.
Part 1 –
Read REPORT 4.2 on page 174 of the text.
The report involves two new executives recently hired at Skyhigh Construction, Inc. and the decision they need to make when selecting their salary plan. After reading the report and evaluating the data provided, answer this question:
Which salary plan, option 1 or 2, is best for each new hire and explain why.
Your analysis should be between 1 and 2 pages,
Part 2 –
Access the data set House Price. Select two comparable college towns (DO NOT USE AMES IOWA) and:
Develop a model that predicts the sale price of a house in college city 1.
Develop a model that predicts the sale price of a house in college city 2.
Develop a model that predicts the sale price of a house using both college city 1 and 2 data.
Compare and contrast your models and their results.
Which model is the best predictor and why? Be as specific as possible.
Compute expected values to evaluate payment plans for Grossman and Arroyo.
(Hint) See case study completed on page 254-255 of the text for assistance
the analysis should be between 3 and 4 pages-2000-3000–words, not including the title page or reference page or appendices.
Report 4.2. Senior executives at Skyhigh Construction, Inc., participate in a pick-your-salary plan. They choose salaries in a range between $125,000 and $150,000. By choosing a lower salary, an executive has an opportunity to make a larger bonus. If Skyhigh does not generate an operating profit during the year, then no bonuses are paid. Skyhigh has just hired two new senior executives, Allen Grossman and Felicia Arroyo. Each must decide whether to choose Option 1: a base pay of $125,000 with a possibility of a large bonus or Option 2: a base pay of $150,000 with a possibility of a bonus, but the bonus would be one-half of the bonus under Option 1. Grossman, 44 years old, is married with two young children. He bought his home at the height of the market and has a rather large monthly mortgage payment. Arroyo, 32 years old, just completed her MBA at a prestigious Ivy League university. She is single and has no student loans due to a timely inheritance upon entering graduate school. Arroyo just moved to the area, so she has decided to rent an apartment for at least one year. Given their personal profiles, inherent perceptions of risk, and subjective views of the economy, Grossman and Arroyo construct their individual probability distributions with respect to bonus outcomes shown in Table 4.10.
TABLE 4.10 Grossman’s and Arroyo’s Probability Distributions
Probability
Bonus (in $) Grossman Arroyo
0 0.35 0.20
50,000 0.45 0.25
100,000 0.10 0.35
150,000 0.10 0.20
In a report, use the sample information
– Compute expected values to evaluate payment plans for Grossman and Arroyo.
– Help Grossman and Arroyo decide whether to choose Option 1 or Option 2 for his/her compensation package.
Part 2:
10652 160000 3/16/2016 3 1 1002 8712 Single Family 2011 Waterloo-Cedar Falls, IA University of Northern Iowa
10653 218500 5/12/2016 2 1 1163 13503.6 Single Family 2011 Waterloo-Cedar Falls, IA University of Northern Iowa
10654 355000 2/5/2016 4 3 3074 11761.2 Single Family 2012 Waterloo-Cedar Falls, IA University of Northern Iowa
10655 320000 6/7/2016 3 2 1870 13068 Single Family 2012 Waterloo-Cedar Falls, IA University of Northern Iowa
10656 359100 4/8/2016 5 4.5 2119 11325.6 Single Family 2013 Waterloo-Cedar Falls, IA University of Northern Iowa
10657 349646 6/13/2016 3 1.75 1949 14374.8 Single Family 2015 Waterloo-Cedar Falls, IA University of Northern Iowa
10658 288000 4/16/2016 4 4 2710 10890 Single Family 2012 Waterloo-Cedar Falls, IA University of Northern Iowa
10659 330000 7/1/2016 4 3 2702 13068 Single Family 2012 Waterloo-Cedar Falls, IA University of Northern Iowa
Also, should not missing the linear regression output / screen prints.
Note
Tip: Pay particular attention on understanding and practicing regression analysis, as this will be on the final project.
More info on Final Project Part 2:
Final Part 2: Regression
Develop a model that predicts the sale price of a house in college city 1.
Develop a model that predicts the sale price of a house in college city 2.
Here you are doing 2 regression analysis
For example
Split the data in 2, for two specific college cities Only ; college city 1, college city 2
Regression analysis, predict; home prices (y) ~ square feet (x) for college city 1
Regression analysis, predict; home prices (y) ~ square feet (x) for college city 2
Develop a model that predicts the sale price of a house using both college city 1 and 2 data.
Here you are doing 1 more regression analysis
Using both college city 1 and 2 data combined.
Compare and contrast your regression models and their results.
