Business statistics lab assignment 4

Project-4

QBA337- Applied Business Statistics

Due-07-30-2021 at 11:59 PM

• You can discuss this lab-work with any other student in the class.

• You can get help or clarification from the instructor.

• You are permitted to use the course notes, books, the internet, Rscript posted on blackboard, and any other materials you like.

• For each question, you need to fully interpret your results. Your report should be typed. You should include your code as an appendix and cite your output properly.

• You can upload your lab-work file under lab assignment folder in Blackboard.

• Late submission is not accepted.

• Project-3’s data are available under Data folder.

• Use of RStudio is required for all project in this class.

1. Imagine that you have been asked to join the team supporting a young New York City chef

who plans to create a new Italian restaurant in Manhattan. The stated aims of the restaurant

are to provide the highest quality Italian food utilizing state-of-the art decor while setting a

new standard for high-quality service in Manhattan. The creation and the initial operation

of the restaurant will be the basis of a reality TV show for the US and international markets

(Including Australia). You have been told that the restaurant is going to be located no fur-

ther south than the Flatiron District and it will be either east or west of Fifth Avenue.

You have been asked to determine the pricing of the restaurants dinner menu such that

it is competitively positioned with other high-end Italian restaurants in the target area. In

particular, your role in the team is to analyze the pricing data that have been collected in

order to produce a regression model to predict the price of dinner. Actual data from surveys

of customers of 168 Italian restaurants in the target area are available. The data are in the

form of the average of customer views on. Use Nyc.xls data

Y = Price = The price (in $US) of dinner (including one drink and a tip) x1 = Food = customer rating of food (out of 30)

x2 = Decor = Customer rating of the decor (out of 30)

x3 = Service = customer rating of the service (out of 30)

x4 = East = dummy variable = 1(0) if the restaurant is east(west) of Fifth Avenue.

In particular you have been asked to

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(a) [10 pts] Develop a regression model that directly predicts the price of dinner ( in

dollars) using a subset or all of four potential predictor variables listed above.

(b) [10 pts] Determine which of the predictor variables Food, Decor and Service has the

largest estimated effect on price? Is this effect also the most statistically significant.

(c) [10 pts] If the aim is to choose the location of the restaurant so that the price achieved for

dinner is maximized, should the new restaurant be on the east or west of Fifth Avenue?

(d) [10 pts] Does it seems possible to achieve a price premium for ” setting a new standard

for high-quality service in Manhattan” for Italian restaurants?

2. The file lab-3.xlsx contain data on 150 homes that were sold recently in a particular commu-

nity.

(a) [10 pts] Find a table of correlation between all of the variables. Do the correlation

between Price and each of the other variables have the sign(positive or negative) you

would expect? Explain briefly.

(b) [10 pts] Run a regression of Price versus rooms. What does the 95% confidence level

for the coefficient of Rooms tell you about the effect of Rooms on Price for the entire

population of such homes?

(c) [10 pts] Run a multiple regression of price versus Home Size, Lot Size, Rooms, and

Bathrooms. What is the 95% confidence interval for the coefficient of Rooms now? Why

you think it can be so different from the one in part (b)? Based on this regression, can

you reject the null hypothesis that the population regression coefficient of room is zero

versus a two-tailed alternative? What does this mean?

3. Consumer Research, Inc., is an independent agency that conducts research on consumer at-

titudes and behaviors for a variety of firms. In one study, a client asked for an investigation

of consumer characteristics that can be used to predict the amount charged by credit card

users. Data(consumer.xls) were collected on annual income, household size, and annual

credit card charges for a sample of 50 consumers. The following data are contained in the file

Consumer.

Managerial Report

(a) Use methods of descriptive statistics to summarize the data. Comment on the findings.

(b) [10 pts] Develop estimated regression equations, first using annual income as the in-

dependent variable and then using household size as the independent variable. Which

variable is the better predictor of annual credit card charges? Discuss your findings.

(c) [10 pts] Develop an estimated regression equation with annual income and household

size as the independent variables. Discuss your findings.

(d) [10 pts] What is the predicted annual credit card charge for a three-person household

with an annual income of $40,000?

(e) [10 pts] Discuss the need for other independent variables that could be added to the

model. What additional variables might be helpful?

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4. Forecasting Food and Beverage Sales The Vintage Restaurant, on Captiva Island near

Fort Myers, Florida, is owned and operated by Karen Payne. The restaurant just completed

its third year of operation. During those three years, Karen sought to establish a reputation

for the restaurant as a high-quality dining establishment that specializes in fresh seafood.

Through the efforts of Karen and her staff, her restaurant has become one of the best and

fastest-growing restaurants on the Island.

To better plan for future growth of the restaurant, Karen needs to develop a system that

will enable her to forecast food and beverage sales by month for up to one year in advance.

The data set (vintage.xls) shows the value of food and beverage sales ($1,000s) for the first three years of operation:

Managerial Report

Perform an analysis of the sales data for the Vintage Restaurant. Prepare a report for Karen

that summarizes your findings, forecasts, and recommendations. Include the following:

(a) [10 pts] A time series plot. Comment on the underlying pattern in the time series.

(b) [30 pts] Using the dummy variable approach, forecast sales for January through Decem-

ber of the fourth year. How would you explain this model to Karen?

Assume that January sales for the fourth year turn out to be $295,000. What was your forecast error? If this error is large, Karen may be puzzled about the difference between your

forecast and the actual sales value. What can you do to resolve her uncertainty about the

forecasting procedure?

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