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 Only one word document (.doc or .docx) by one student from each group,

 Pay attention to the due date and try to make timely submissions (penalty for late submissions)

 Put a table in the first page and include names, student IDs, and a group photo for verification.

 The solution will be briefly discussed in class (in the first session after the due date).

Chapter 7: Linear Regression

You have been invited to help Alexa, a former student in College of Business Administration at CSUSM,

who is studying the waiting time of clients at San Marcos DMV office. Total Waiting Time of a client starts

from the moment they check in with the receptionists until they fully receive the service and leave the

office. Before proposing any recommendations to the DMV officials, she needs to understand why some

clients need to wait longer. As part of her analysis, she would like to run multiple regression models to

predict Total Waiting Time based on some potential predictors. She has managed to find the waiting time

and some other information like appointment type, time of the day, number of workers, number of

customers waiting in the system, etc., for a sample of 150 customers (Refer to CH7 for the sample data).

1. Run a regression model to predict waiting time based on all the predictors provided in the file.

For categorical variables, please define appropriate dummy variables.

 Write down the resulting equation.

 What is the estimated impact of having appointment on the waiting time?

 How much of the variation in waiting time does your model explain?

 Check the multicollinearity and the necessary conditions for the residuals, and

comment on the significance of the predictors.

2. Now add the squared number of working staff (quadratic factor) as a predictor and run another

model with all predictors including the squared staff number.

 Write down the resulting equation.

 What is the estimated impact of appointment type on the waiting time?

 How much of the variation in waiting time does the new model explain?

 Check the multicollinearity and the necessary conditions for the residuals, and

comment on the significance of the predictors.

3. Now, remove all the non-significant variables, and run another model with only significant

variables.

4. Which model would you prefer and why? What is your prediction for the waiting time of a 60-

year old customer with special needs who also has a scheduled morning appointment for vehicle

registration, arriving at a time when 10 staff are working and 70 clients are waiting in the office.

5. Can you help her come up with more independent variables that can potentially explain clients’

waiting time?

Chapter 8: Time Series and Forecasting

The CSUSM Restaurant just finished its fourth year of operation. Through the great efforts of its manager

and staff, this restaurant has become one of the most popular and fastest-growing restaurants in the SD

County.

The manager has recently decided to improve the capacity planning process of the restaurant. To do so,

they need to come up with an effective forecasting procedure to predict the monthly sales of foods for up

to a year in advance (12 months). Data file CH8 shows the value of food sales (\$100s) for the first four

years of operation.

You, as the new forecasting analyst, have been asked to propose a new forecasting procedure with a

summary of your method, forecasts, and recommendations. Include the followings:

1. A time series plot. Comment on the underlying pattern in the time series. What forecasting

technique(s) do you recommend based on your visualization?

2. Do you consider moving average as an effective method for forecasting the food sales of this

restaurant? Why?

3. Using dummy variables for seasonality effects, forecast sales for January through December of

the fifth year.

4. Add a trend effect to your forecasting model in (3) and forecast sales for January through

December of the fifth year again with the new model.

5. Which model is expected to give more accurate forecasts, (3) or (4)? Calculate MSE for both

techniques.

6. Assume that restaurant’s sales in January of the fifth year turn out to be \$175,000. What was your

forecast (and what is the forecasting error)? How do you explain this error to the manager?

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