A restaurant manger, Coleman, at the Four Seasons Hotel wants to predict/forecast a number of meals...

A restaurant manger, Coleman, at the Four Seasons Hotel wants to predict/forecast a number of meals to be prepared for the breakfast since the labor costs and cost of good sold are vey high and does not want to create high volume of waste and manage the inventory in a proper way.

He looks through the previous data (2016) to determine the relationship between the number of guest stayed at the hotel and number of meals served from the following data:

Number of guest stayed at the hotel (Guest) Number of meals (breakfast) served (Meals)

Guest Meals

23 69

29 95

29 102

35 118

42 126

46 125

50 138

54 178

64 156

66 184

76 176

78 225

Run the regression analysis using MegaStat (Excel)
Run the Scatter Plot
Determine the relationship: Y = A + BX (whether this data is good to RUN/USE for constructing the relationship between

Y = (describe which one is used for Y):

X = (describe/identify which one is used for X):

A = (A refers to ?) and provide a number

B = (B refers to ?) and provide a number

Sara wants to prepare the breakfast for tomorrow based on the guests number of 70. Determine how many meals to be prepared for tomorrow?

Solutions

Expert Solution

orchestra answered 1 year ago

Next > < Previous

Related Solutions

Data on advertising expenditures and revenue (in thousands of dollars) for the Four Seasons Restaurant follow....

Data on advertising expenditures and revenue (in thousands of dollars) for the Four Seasons Restaurant follow. Advertising Expenditures Revenue 1 20 2 33 4 44 6 40 10 52 14 53 20 54 a. Let X equal advertising expenditures and Y equal revenue. Complete the estimated regression equation below (to 2 decimals). y =( )+( )x b. Compute the following (to 1 decimal). SSE ( ) SST ( ) SSR ( )MSR ( ) MSE ( ) c. Test whether...

1. The managers of the Ada Hotel forecast the breakfast covers based on number of guest...

1. The managers of the Ada Hotel forecast the breakfast covers based on number of guest rooms. Following table presents the number of rooms sold and breakfast covers for the past year, 2017. Room Guests Breakfast Covers January 1,200 1,400 February 1,020 1,220 March 1,250 1,420 April 1,100 1,320 May 1,380 1,520 June 1,400 1,580 July 1,520 1,650 August 1,550 1,690 September 1,450 1,400 October 1,210 1,380 November 1,080 1,240 December 1,050 1,220 Required: 1. Determine the coefficient of correlation...

1. A restaurant wants to forecast its weekly sales. Historical data (in dollars) for 15 weeks...

1. A restaurant wants to forecast its weekly sales. Historical data (in dollars) for 15 weeks are shown below. Time Period Observation 1 1,586 2 1,622 3 1,417 4 1,310 5 1,228 6 1,342 7 1,580 8 1,303 9 1,283 10 1,559 11 1,386 12 1,586 13 1,665 14 1,638 15 1,361 MSE values based on the two- and three-period moving average are 33,931.58 and 31,048.14, respectively. Find the best single exponential smoothing model by evaluating the MSE from 0.1...

11.29) A builder wants to predict the relationship between house size (as measured by number of...

11.29) A builder wants to predict the relationship between house size (as measured by number of rooms). and selling price. Two difference neighbourhoods are compare. A random sample of 215 houses is selected, with the results as follows. House Number of rooms Selling Price ($000) Neighbourhood 1 8 345 0 2 8 360 1 3 6 325 0 4 8 400 1 5 7 350 1 6 9 360 0 7 13 405 0 8 6 299 0 9 8...

A hotel chain wants to estimate the average number of rooms rented daily in each month....

A hotel chain wants to estimate the average number of rooms rented daily in each month. The population of rooms rented daily is assumed to be normally distributed for each month with a standard deviation of 24 rooms. During January, a sample of 16 days has a sample mean of 48 rooms. This information is used to calculate an interval estimate for the population mean to be from 40 to 56 rooms. What is the level of confidence of this...

A local tire dealer wants to predict the number of tires sold each month. He believes...

A local tire dealer wants to predict the number of tires sold each month. He believes that the number of tires sold is a linear function of the amount of money invested in advertising. He randomly selects 6 months of data consisting of tire sales (in thousands of tires) and advertising expenditures (in thousands of dollars). Based on the data set with 6 observations, the simple linear regression model yielded the following results. ?X =24 ?X2=124 ?Y = 42 ?Y2 =338...

Subjects