1.Students in a management science class have just received their grades on their first test. The instructor has provided information about the first test grades in some previous classes as well as the final average for the same students. Some of the grades have been sampled and are as follows: STUDENT 1 2 3 4 5 6 7 8 9 1st test grade 98 77 88 80 96 61 66 95 69 Final Average 93 78 84 73 84 64 64 95 76 A) Develop a regression model that could be used to predict the final average in the course based on the first test grade B) Predict the final average of a student who made an 83 on the first test. C) Give the values of r and r^2 for this model. Interpret the value of r^2 in the context of this problem. 2. Steve Caples, a real estate appraiser in Lake Charles, Louisiana, has developed a regression model to help appraise residential housing in Lake Charles area. The model was developed using recently sold homes in a particular neighborhood. The price (Y) of the house is based on the square footage (X) of the house. The model is: Y=13,473+37.65x The coefficient of correlation for the model is 0.63. A) Use the model to predict the selling price of a house that is 1,860 square feet. B) A house with 1,860 square feet recently sold for $95,000. Explain why this is not what the model predicted. C) If you were going to use multiple regression to develop an appraisal model, what other quantitative variables might be included in the model? D) What is the coefficient of determination for this model?
Paper#13640 | Written in 18-Jul-2015Price : $25