WILLING TO NEGOTIATE !! HELLO, THESE ASSIGNMENTS ARE FOR A QUANTITATE METHODS CLASS!! (QMB3600) THERE IS AN EXCEL WORKSHEET WHERE YOU PLUG IN THE NUMERS FROM THE WORD HOMEWORK ASSIGNMENTS AND THEN A

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WILLING TO NEGOTIATE !!

HELLO, THESE ASSIGNMENTS ARE FOR A

QUANTITATE METHODS CLASS!! (QMB3600)

THERE IS AN EXCEL WORKSHEET WHERE YOU PLUG IN THE NUMERS FROM THE WORD HOMEWORK ASSIGNMENTS AND THEN ANSWER THE QUESTIONS. THE ANSWERS NEEDS TO BE IN COMPLETE SENTENCES!!

EACH QUESTION HAS AN EXCEL SHEET ASSIGNED TO IT!!

—-> ASSIGNMENTS ARE ATTACHED

WILLING TO NEGOTIATE !! HELLO, THESE ASSIGNMENTS ARE FOR A QUANTITATE METHODS CLASS!! (QMB3600) THERE IS AN EXCEL WORKSHEET WHERE YOU PLUG IN THE NUMERS FROM THE WORD HOMEWORK ASSIGNMENTS AND THEN A
Homework 5 1) Thirteen student were admitted to the undergraduate business program at Whatsamatta University 2 years ago. The following table indicates what their grade-point average was after 2 years at the University. Also listed is their SAT score that they scored when they were in high school (maximum score is 2400). Student SAT Score GPA Student SAT Score GPA 1263 2.90 1443 2.53 1131 2.93 2187 3.22 1755 3.00 1503 1.99 2070 3.45 1839 2.75 1824 3.66 2127 3.90 1170 2.88 1098 1.60 1245 2.15 a) Using the SAT score to predict their GPA is there a meaningful relationship between the SAT score and their GPA? How do you know? b) If a student has a SAT score of 1200, what do you think their GPA will be in two years? c) If a student has a SAT score of 2400, what do you think their GPA will be in two years? 2) The following data gives the selling price, square footage, number of bedrooms, and the age of a house in years. These houses have been sold in a specific neighborhood over the last six months. Selling Price ($) Square Footage Bedrooms Age (years) 84,000 1,670 30 79,000 1,339 25 91,500 1,712 30 120,000 1,840 40 127,500 2,300 18 132,500 2,234 30 145,000 2,311 19 164,000 2,377 155,000 2,736 10 168,000 2,500 172,500 2,500 174,500 2,479 175,000 2,400 177,500 3,124 184,000 2,500 195,500 4,062 10 195,000 2,854 a) Using square footage develop a model to predict the selling price of the house. How well does the model fit the data? What percentage of the selling price is explained by the model? b) Using the number of bedrooms develop a model to predict the selling price of the house. How well does the model fit the data? What percentage of the selling price is explained by the model? c) Using the age of the house develop a model to predict the selling price of the house. How well does the model fit the data? What percentage of the selling price is explained by the model? d) Which of the models estimated in parts a – d best fits the data? Why did you select that model? 3) The total expenses of a hospital is determined by many different factors but two of these factors are the number of beds and the number of patients that are admitted to the hospital. Data was collected on 14 hospitals and is listed in the table. Hospital Number of beds Admissions (1,000s) Total expenses ($1,000,000 215 77 57 336 160 127 520 230 157 135 43 24 35 14 210 155 93 140 53 45 90 410 159 99 10 50 18 12 11 65 16 11 12 42 29 15 13 110 28 21 14 305 98 63 a) Develop a model using the number of beds to predict the total expenses of a hospital. How well does the model fit the data? b) Develop a model using the number of admissions to predict the total expenses of a hospital. How well does the model fit the data? c) Develop a model using the number of beds and admissions to predict the total expenses of a hospital. How well does the model fit the data? d) Do you think that both variables (number of beds and admissions) should be included in our prediction model? Why or Why not? 4) A sample of 20 automobiles was taken and the miles per gallon (MPG), horsepower, and total weight were recorded. Develop a regression model to predict MPG based on weight and horsepower. MPG Horsepower Weight (lbs) 44 67 1,844 44 50 1,998 40 62 1,752 37 96 1,980 37 66 1,797 34 63 2,199 35 90 2,404 32 99 2,611 30 63 3,236 28 91 2,606 26 94 2,580 26 88 2,507 25 124 2,922 22 97 2,434 20 114 3,248 21 102 2,812 18 114 3,382 18 142 3,197 16 153 4,380 16 139 4,036 a) Develop a regression model using weight and horsepower to predict the MPG. How well does the data fit the model? b) Let us suppose that your automobile has 83 horsepower and a weight of 2,381 pounds. What is your expected MPG?
WILLING TO NEGOTIATE !! HELLO, THESE ASSIGNMENTS ARE FOR A QUANTITATE METHODS CLASS!! (QMB3600) THERE IS AN EXCEL WORKSHEET WHERE YOU PLUG IN THE NUMERS FROM THE WORD HOMEWORK ASSIGNMENTS AND THEN A
Homework 6 1) The following are corporate AAA bond interest rates for the last 12 months: 9.5, 9.3, 9.4, 9.6, 9.8, 9.7, 9.8, 10.5, 9.9, 9.7, 9.6, and 9.6. a) Develop a three- and four-month moving average for this time series. Which moving average provides the better forecast? Why? b) Based on your decision in part a, what is the forecast for bond interest rates for next month? 2) The sales of vacuum cleaners at Lowenthal’s Industrial Supply over the last thirteen months are given below. Month Sales Month Sales 12 14 14 17 16 10 12 10 11 14 15 12 16 17 13 11 11 a) Use a weighted moving average for three periods to forecast sales for next month. Use a weight of 3 for the most recent period, 2 for the middle period, and 1 for the third previous period. What is the forecasted number of vacuum sales for next month? b) Use a weighted moving average for three periods to forecast sales for next month. Use a weight of 4 for the most recent period, 3 for the middle period and 2 for the third previous period. What is the forecasted number of vacuum sales for next month? c) Which forecast of sales should Lowenthal use and why? 3) The following data is the unemployment rate in a metropolitan community over the last 10 years. Use exponential smoothing with a smoothing constant of a = 0.2, 0.4, 0.6, and 0.8 to find the best forecast for unemployment for next year. Using mean absolute deviation which forecast constant provides the best prediction of unemployment? Why? Year Rate Year Rate 7.2 5.5 7.0 6.7 6.2 7.4 5.0 6.8 5.3 10 6.1 4) Use the following data from the DJIA to answer the questions below. Year DJIA Year DJIA 2013 13,104 2003 8,342 2012 12,392 2002 10,022 2011 11,577 2001 10,791 2010 10,431 2000 11,502 2009 8,772 1999 9,213 2008 13,262 1998 7,908 2007 12,460 1997 6,448 2006 10,718 1996 5,117 2005 10,784 1995 3,834 2004 10,453 1994 3,754 a) Use trend line analysis to develop a forecast for 2014, 2015 and 2016. b) Use exponential smoothing with trend to develop a forecast for 2014. Use a = 0.8 and b = 0.2. c) Compare the linear trend and exponential smoothing using MSE. Which forecasting technique should you use and why? 5) Mayfair Department Store was shut down during the months of July and August due to severe flooding in the downtown area. The store’s insurance policy provides revenues to the department store in cases of natural disaster. Sales data for the preceding 6 months are listed below. Month Sales (1,000s) Month Sales (1,000s) January 184.72 April 210.36 February 167.84 May 255.57 March 205.11 June 261.19 a) Develop a forecast for July and August using exponential smoothing with a = 0.4. (Hint: use the July forecast as actual sales for July in developing the August forecast.) Comment on the use of exponential smoothing for forecasts more than one period in the future. b) Use linear trend to predict sales for July and August. What are the forecasted amounts? c) Mayfair’s insurance company has offered $240,000 per month for the lost sales. Should Mayfair accept the settlement offer? Why? If Mayfair should reject the offer, what is the amount that Mayfair should make as a counter offer?

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