Ch16

4. For the questions 1-3 we found out a couple different cost functions that could be useful for Delta when predicting salaries for 2003 and 2004. The multiple regressions may be most reliable considering it uses multiple variables, but the single R square is better than multiple regressions.

6. Although you are basically satisfied with the analysis thus far, you are concerned about the

4. Based on your analysis in (1) – (3) above, what is your overall conclusion regarding the

b. Then, we also determine the regression equation and correlation between North's engine cost and the average age of fleet. Please refer to Annex 3 for the QM results.

22) Which statement is true of a regression line that is superimposed on the scatter plot?

The case involves the decision to locate a new store at one of two candidate sites. The decision will be based on estimates of sales potential, and for this purpose, you will need to develop a multiple regression model to predict sales. Specific case questions are given in the textbook, and the necessary data is in the file named pamsue.xls.

Pam and Susan’s department stores are in the process of opening a new business unit. There are two locations that are being considered for the new store and decision is based upon estimates of sales for both of them. My job is to use data gathered from each store as well census data in store’s trading zones to predict sales at both of the sites that are being consider for their newest store.

4) Use exponential regression (or find a constant ratio) to determine an equation for the data.

29. A distribution center for a chain of electronics supply stores fills and ships orders to retail outlets. A random sample of orders is selected as they are received and the dollar amount of the order (in thousands of dollars) is recorded, and then the time (in hours) required to fill the order and have it ready for shipping is determined. A scatterplot showing the times as the response variable and the dollar amounts (in thousands of dollars) as the predictor shows a linear trend. The least squares regression line is determined to be: yˆ= 0.76 +1.8x. A plot of the residuals versus the dollar amounts showed no pattern, and the following values were reported: Correlation r +0.90; R 2 = 0.81; standard deviation of the residuals is 0.48. What percentage of the variation in the times required to prepare an order for shipping is accounted for by the fitted line?

10. Refer to the model and estimates in the previous question. You want to test that

Meanwhile, yearly house price inflation rates in the top 20 cities are running in line with the national trend. The cities with the highest rates of increase are Seattle (+12%), Portland (+10%) and Dallas (+9%). Lower tier property prices appear to be more volatile than their high end counterparts in both Seattle and Portland. Meanwhile, the three cities with the lowest rates of house price inflation are New York (+3%), Washington (+4%) and Cleveland (+5%). Furthermore, rising house prices appear to be having an adverse impact on affordability. According to the National Association of Realtors, rising prices are offsetting higher disposable incomes and stable mortgage rates, and affordability has consequently been declining since January 2015. Partly driving the increase in prices is a lack of available supply of existing single family homes for sale. The number of months’ of unsold inventory was just below 4 in March and availability has been gradually falling since 2014. Additionally, there is a relatively tight supply situation for new single family homes for sale, which is also helping to support prices.

As you can see, housing in Miami has been less affordable relative to rest of the country for the entirety of the last decade. Based on this history the expectation would be that this trend will continue, and looking at the end of 2013 on the graph, the lines appear to diverge. The U.S. housing affordability rises back above 160 while the Miami-Dade line drops below 120, meaning Miami housing became less affordable while housing a whole in the rest of the U.S. became more affordable.

b) The same article reported the mean size was more than 2100 square feet? Can we conclude that the mean size of homes sold in the Gulshan area is more than 2100 square feet? Use the 0.05 significance level. What is the

In this report, the question “How much of the changes in the median selling price of homes in a city can be explained by the changes in median income of that city?” is answered. Home ownership is an important aspect of one’s life stages, and home prices are determined by demand and supply. The demand curve is affected by the one’s income, such that as one’s income increases, one is more willing to pay a higher price for the same quantity of goods (Baye & Prince, 2014). However, there are many other factors that might affect the demand curve, e.g. no. of children, in the household, the perceived quality of education in the school district, or the number of job positions (filled or open) around the city. According to Burda

The procedure for this model is to collect several periods of history relating to the independent and dependent variables themselves, establish the relationship that minimizes mean squared error of forecast vs actual using linear or non-linear and singular or multiple regression analysis.