Forecasting – Simple Linear Regression Applications Essay

May June July August September October November December Average Total Rooms Occupancy Occupied Percent 126 150 154 162 163 159 156 162 154 186 149 118 153 51.6% 61.4% 63.1% 66.2% 66.7% 65.3% 64.0% 66.4% 63.2% 76.4% 61.0% 48.3% 62.8% Total ADR $140.27 $139.29 $141.80 $140.20 $143.72 $141.90 $139.11 $141.54 $145.08 $157.36 $148.66 $137.38 $143.03

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.

We have data out of 250 stores. The data include demographics, economics, sales of the stores, compositions of those sales as well as sales behavior per households. There are 31 variables being consider for each store and those variables range from sales,

AJ DAVIS is a department store chain, which has many credit customers and wants to find out more information about these customers. A sample of 50 credit customers is selected with data collected on the following five variables:

The business literature involving human capital shows that education influences an individual’s annual income. Combined, these may influence family size. With this in mind, what should the real estate builder be particularly concerned with when analyzing the multiple regression model?

When we use multiple regressions to estimate the salary cost which one of the variable is the one that has the best R square in the single regression part, we definitely have a bigger R square. In this case also the R square for multiple regressions is bigger than the R square for single regression so it’s an improvement compare to the model estimated in question 2.

Using MINITAB run the multiple regression analysis using the variables CALLS, TIME, and YEARS to predict SALES. State the equation for this multiple regression model.

This case involves an investigation of the factors that affect the sale price of Oceanside condominium units. It represents an extension of an analysis of the same data by Herman Kelting (1979). Although condo sale prices have increased dramatically over the past 20 years, the relationship between these factors and sale price remain about the same. Consequently, the data provide valuable insight into today’s condominium sales market.

The mission of the hotel is to cater to a professional clientele who is likely to increase the revenues of Amber Inn. These kinds of patrons do not worry about price since most of the times they are covered by the Company they work for and they tend to have a prolonged stay. The necessities of those customers are more regular than the individuals that are on a vacation trip; thus, there is no difficulty to find out what do to. For instance, internet services would suffice those on a business trip whereas those on a leisure trip would worry about entertainment such as live music. Some researchers suggested that, the publicity is unnecessary in determining which hotel to choose. Internet can be a primary factor, as a sales element such as hotels.com. I advise excluding or decreasing ad for almost 4.4 million, lowering the

1. Assess the decisions made by the hotel’s management in relation to the various offers received

4. Analysis: Open the Forte Hotel Data (Conjoint, 3 Analysis) data set in My Marketing Engineering, which has competed Steps 5 and 6 in the Tutorial for you. Follow Step 7 in the Tutorial, ((ME>XL==>Conjoint==>Run Analysis) and assess the viability of the four specific hotel concepts that Forte is exploring for the State College area. Base this evaluation on the preferences of a sample of 40 business travelers on that sheet (Exhibit 2) and the cost estimates summarized in Exhibit 3. The base cost to build each hotel room (without the attributes and options listed in Exhibit 3) is expected to be about $40,000 for a 150- to 200-room hotel, regardless of the mix of room

What factors lead to variations in demand for rooms at a hotel such as the Accra Beach?

Run the regression Report your answer in the format of equation 5.8 (Chapter 5, p. 152) in the textbook including and the standard error of the regression (SER). Interpret the estimated slope parameter for LOT. In the interpretation, please note that PRICE is measured in thousands of dollars and LOT is measured in acres.

The manager of Snowpea Restaurant collected data on the new four delivery people to find the possible cause of the complaints. Figure two shows the descriptive statistics for the preparation time, in minutes, from when the orders are placed until they are ready to be delivered to the customers. Based on the descriptive statistics of the sample data for the preparation time, the deliverers two and four have a mean preparation time slightly higher than the total mean of the four people, which could contribute to a longer delivery time of the orders. However, the mean of those two delivery people are not significantly higher, so the preparation time could not be the cause of the delays in delivery.

Chan, ESW & Wong, SCK 2006, ‘Hotel selection: when price is not the issue’, Journal of Vacation Marketing, vol. 12, no. 2, pp. 142-159, (Online Sage).