Problem set 8

profileglangsing
problem_set_8.xlsx
Home>Business & Finance homework help>Accounting homework help>Problem set 8

10.55

Source Degrees of Freedom Sum of Squares Mean Square (Variance) F 10.55 Consider an experiment with four groups, with eight
Among Groups c - 1 = ? SSA = ? MSA = 80 FSTAT = ? values in each. For the ANOVA summary table below, fill in
Within Groups n - c = ? SSW = 560 MSW = ? all the missing results:
Total n - 1 = ? SST = ?

10.57

Source Degree of Freedom Sum of Squares Mean Squares F 10.57 The Computer Anxiety Rating Scale (CARS) measures
Among Majors 5 3172 an individual’s level of computer anxiety, on a scale
Within Majors 166 21246 from 20 (no anxiety) to 100 (highest level of anxiety).
Total 171 24418 Researchers at Miami University administered CARS to
172 business students. One of the objectives of the study was
Major n Mean to determine whether there are differences in the amount of
Marketing 19 44.37 computer anxiety experienced by students with different majors.
Management 11 43.18 They found the following:
Other 14 42.21
Finance 45 41.8 a. Complete the ANOVA summary table.
Accountancy 36 37.56 b. At the 0.05 level of significance, is there evidence of a
MIS 47 32.21 difference in the mean computer anxiety experienced by
different majors?

10.59ERWaiting

Main Satellite 1 Satellite 2 Satellite 3 10.59 A hospital conducted a study of the waiting time in  require the use of the "One-Way ANOVA"
120.08 30.75 75.86 54.05 its emergency room. The hospital has a main campus and
81.90 61.83 37.88 38.82 three satellite locations. Management had a business objective
78.79 26.40 68.73 36.85 of reducing waiting time for emergency room cases that
63.83 53.84 51.08 32.83 did not require immediate attention. To study this, a random
79.77 72.30 50.21 52.94 sample of 15 emergency room cases that did not require immediate
47.94 53.09 58.47 34.13 attention at each location were selected on a particular
79.88 27.67 86.29 69.37 day, and the waiting time (measured from check-in to
48.63 52.46 62.90 78.52 when the patient was called into the clinic area) was measured.
55.43 10.64 44.84 55.95 The results are stored in
64.06 53.50 64.17 49.61
64.99 37.28 50.68 66.40 a. At the 0.05 level of significance, is there evidence of a
53.82 34.31 47.97 76.06 difference in the mean waiting times in the four locations?
62.43 66.00 60.57 11.37
65.07 8.99 58.37 83.51
81.02 29.75 30.40 39.17

10.61 Coffe Sales

59 cents 69 cents 79 cents 89 cents 10.61 The per-store daily customer count (i.e., the mean number  require the use of the "One-Way ANOVA"
964 953 942 920 of customers in a store in one day) for a nationwide convenience
972 950 937 918 store chain that operates nearly 10,000 stores has been
962 959 945 925 steady, at 900, for some time. To increase the customer count,
968 955 948 919 the chain is considering cutting prices for coffee beverages. The
975 960 945 915 question to be determined is how much to cut prices to increase
960 954 941 906 the daily customer count without reducing the gross margin on
coffee sales too much. You decide to carry out an experiment in
a sample of 24 stores where customer counts have been running
almost exactly at the national average of 900. In 6 of the
stores, the price of a small coffee will now be $0.59, in 6 stores
the price of a small coffee will now be $0.69, in 6 stores, the
price of a small coffee will now be $0.79, and in 6 stores, the
price of a small coffee will now be $0.89. After four weeks of
selling the coffee at the new price, the daily customer count in
the stores was recorded and stored in
a. At the 0.05 level of significance, is there evidence of a
difference in the daily customer count based on the price
of a small coffee?

11.25

Media Under 36 36-50 50+ 11.25 Where people turn for news is different for various
Local TV 107 119 133 age groups. A study indicated where different age groups
National TV 73 102 127 primarily get their news:
Radio 75 97 109 At the 0.05 level of significance, is there evidence of a
Local Newspaper 52 79 107 significant relationship between the age group and where
Internet 95 83 76 people primarily get their news? If so, explain the
relationship.

12.1

12.1 Fitting a straight line to a set of data yields the following
prediction line:
Yi = 2 + 5Xi

12.4Pet Food

Shelf Space Sales Aisle Location 12.4 The marketing manager of a large supermarket (assume "Calories" is the "x" variable and "Fat" is the "y" variable)
5 160 0 chain has the business objective of using
5 220 1 shelf space most efficiently. Toward that goal, she would
5 140 0 like to use shelf space to predict the sales of pet food. Data
10 190 0 is collected from a random sample of 12 equal-sized stores,
10 240 0 with the following results
10 260 1
15 230 0 a. Construct a scatter plot.
15 270 0 For these data, and
15 280 1 b. Interpret the meaning of the slope, in this problem.
20 260 0 c. Predict the weekly sales of pet food for stores with 8 feet
20 290 0 of shelf space for pet food.
20 310 1

12.9 Rent

Rent Size 12.9 An agent for a residential real estate company has the (assume "Calories" is the "x" variable and "Fat" is the "y" variable)
950 850 business objective of developing more accurate estimates of
1600 1450 the monthly rental cost for apartments. Toward that goal, the
1200 1085 agent would like to use the size of an apartment, as defined
1500 1232 by square footage to predict the monthly rental cost. The
950 718 agent selects a sample of 25 apartments in a particular residential
1700 1485 neighborhood and collects the following data (stored
1650 1136 in
935 726 a. Construct a scatter plot.
875 700 b. Use the least-squares method to determine the regression
1150 956 coefficients b0 and b1.
1400 1100 c. Interpret the meaning of b0 and b1 in this problem.
1650 1285 d. Predict the monthly rent for an apartment that has 1,000
2300 1985 square feet.
1800 1369
1400 1175
1450 1225
1100 1245
1700 1259
1200 1150
1150 896
1600 1361
1650 1040
1200 755
800 1000
1750 1200

12.17 Restaurants

Location Food Décor Service Summated Rating Coded Location Cost 12.17 In Problem 12.5 on page 441, you used the summated  require the use of the "Regression" function within the Data Analysis menu in Excel
City 21 19 20 60 0 62 rating to predict the cost of a restaurant meal. For those data, (assume "Calories" is the "x" variable and "Fat" is the "y" variable)
City 24 24 20 68 0 67 SSR = 6,951.3963 and SST = 15,890.11.
City 22 14 14 50 0 23
City 27 23 24 74 0 79 a. Determine the coefficient of determination, r2 and interpret
City 20 13 19 52 0 32 its meaning.
City 19 11 18 48 0 38 b. Determine the standard error of the estimate.
City 21 23 20 64 0 46 c. How useful do you think this regression model is for predicting
City 19 17 19 55 0 43 audited sales?
City 21 16 19 56 0 39
City 16 15 17 48 0 43
City 20 26 19 65 0 44
City 23 15 17 55 0 29
City 22 23 21 66 0 59
City 21 16 20 57 0 56
City 19 16 18 53 0 32
City 25 22 22 69 0 56
City 22 12 17 51 0 23
City 21 12 16 49 0 40
City 22 19 20 61 0 45
City 17 15 19 51 0 44
City 23 18 21 62 0 40
City 21 17 20 58 0 33
City 23 23 21 67 0 57
City 19 17 17 53 0 43
City 22 16 19 57 0 49
City 21 20 20 61 0 28
City 19 16 16 51 0 35
City 24 20 24 68 0 79
City 19 18 17 54 0 42
City 19 11 12 42 0 21
City 23 16 18 57 0 40
City 19 20 23 62 0 49
City 19 18 18 55 0 45
City 23 20 21 64 0 54
City 25 21 22 68 0 64
City 20 20 17 57 0 48
City 18 14 17 49 0 41
City 24 19 20 63 0 34
City 22 24 21 67 0 53
City 18 15 17 50 0 27
City 22 17 21 60 0 44
City 23 20 22 65 0 58
City 21 19 21 61 0 68
City 22 26 20 68 0 59
City 18 18 18 54 0 61
City 23 17 20 60 0 59
City 22 14 18 54 0 48
City 24 24 25 73 0 78
City 19 21 18 58 0 65
City 20 15 19 54 0 42
Suburban 22 17 21 60 1 53
Suburban 22 18 21 61 1 45
Suburban 20 13 17 50 1 39
Suburban 21 16 20 57 1 43
Suburban 24 19 20 63 1 44
Suburban 19 16 16 51 1 29
Suburban 22 22 21 65 1 37
Suburban 23 16 20 59 1 34
Suburban 18 19 19 56 1 33
Suburban 18 17 17 52 1 37
Suburban 22 17 22 61 1 54
Suburban 22 17 20 59 1 30
Suburban 19 22 17 58 1 49
Suburban 21 12 19 52 1 44
Suburban 15 20 16 51 1 34
Suburban 22 20 22 64 1 55
Suburban 20 18 18 56 1 48
Suburban 18 16 18 52 1 36
Suburban 22 16 21 59 1 29
Suburban 22 21 23 66 1 40
Suburban 19 19 19 57 1 38
Suburban 24 18 20 62 1 38
Suburban 25 21 24 70 1 55
Suburban 24 21 20 65 1 43
Suburban 20 13 17 50 1 33
Suburban 18 19 18 55 1 44
Suburban 22 15 19 56 1 41
Suburban 18 15 20 53 1 45
Suburban 23 25 21 69 1 41
Suburban 20 22 22 64 1 42
Suburban 20 19 17 56 1 37
Suburban 24 19 22 65 1 56
Suburban 24 27 24 75 1 60
Suburban 21 18 21 60 1 46
Suburban 17 14 18 49 1 31
Suburban 23 15 22 60 1 35
Suburban 24 21 21 66 1 68
Suburban 25 17 22 64 1 40
Suburban 21 19 20 60 1 51
Suburban 23 12 24 59 1 32
Suburban 21 15 19 55 1 28
Suburban 19 19 18 56 1 44
Suburban 26 13 18 57 1 26
Suburban 19 18 20 57 1 42
Suburban 21 11 16 48 1 37
Suburban 27 20 23 70 1 63
Suburban 24 20 20 64 1 37
Suburban 19 11 16 46 1 22
Suburban 23 21 20 64 1 53
Suburban 24 18 22 64 1 62

12.21 Rent

Rent Size 12.21 In Problem 12.9 on page 442, an agent for a real  require the use of the "Regression" function within the Data Analysis menu in Excel
950 850 estate company wanted to predict the monthly rent for apartments, (assume "Calories" is the "x" variable and "Fat" is the "y" variable)
1600 1450 based on the size of the apartment Using the results of that problem,
1200 1085
1500 1232 a. determine the coefficient of determination, and interpret
950 718 its meaning.
1700 1485 b. determine the standard error of the estimate.
1650 1136 c. How useful do you think this regression model is for predicting
935 726 the monthly rent?
875 700 d. Can you think of other variables that might explain the
1150 956 variation in monthly rent?
1400 1100
1650 1285
2300 1985
1800 1369
1400 1175
1450 1225
1100 1245
1700 1259
1200 1150
1150 896
1600 1361
1650 1040
1200 755
800 1000
1750 1200

12.43 Restaurants

Location Food Décor Service Summated Rating Coded Location Cost 12.43 In Problem 12.5 on page 441, you used the summated  require the use of the "Regression" function within the Data Analysis menu in Excel
City 21 19 20 60 0 62 rating of a restaurant to predict the cost of a meal. (assume "Calories" is the "x" variable and "Fat" is the "y" variable)
City 24 24 20 68 0 67 Using the results of that problem, b1 = 1.2409 and Sb1 b1 = 1.2409 = 0.1421.
City 22 14 14 50 0 23
City 27 23 24 74 0 79 a. At the 0.05 level of significance, is there evidence of a
City 20 13 19 52 0 32 linear relationship between the summated rating of a
City 19 11 18 48 0 38 restaurant and the cost of a meal?
City 21 23 20 64 0 46 b. Construct a 95% confidence interval estimate of the
City 19 17 19 55 0 43 population slope, b1.
City 21 16 19 56 0 39
City 16 15 17 48 0 43
City 20 26 19 65 0 44
City 23 15 17 55 0 29
City 22 23 21 66 0 59
City 21 16 20 57 0 56
City 19 16 18 53 0 32
City 25 22 22 69 0 56
City 22 12 17 51 0 23
City 21 12 16 49 0 40
City 22 19 20 61 0 45
City 17 15 19 51 0 44
City 23 18 21 62 0 40
City 21 17 20 58 0 33
City 23 23 21 67 0 57
City 19 17 17 53 0 43
City 22 16 19 57 0 49
City 21 20 20 61 0 28
City 19 16 16 51 0 35
City 24 20 24 68 0 79
City 19 18 17 54 0 42
City 19 11 12 42 0 21
City 23 16 18 57 0 40
City 19 20 23 62 0 49
City 19 18 18 55 0 45
City 23 20 21 64 0 54
City 25 21 22 68 0 64
City 20 20 17 57 0 48
City 18 14 17 49 0 41
City 24 19 20 63 0 34
City 22 24 21 67 0 53
City 18 15 17 50 0 27
City 22 17 21 60 0 44
City 23 20 22 65 0 58
City 21 19 21 61 0 68
City 22 26 20 68 0 59
City 18 18 18 54 0 61
City 23 17 20 60 0 59
City 22 14 18 54 0 48
City 24 24 25 73 0 78
City 19 21 18 58 0 65
City 20 15 19 54 0 42
Suburban 22 17 21 60 1 53
Suburban 22 18 21 61 1 45
Suburban 20 13 17 50 1 39
Suburban 21 16 20 57 1 43
Suburban 24 19 20 63 1 44
Suburban 19 16 16 51 1 29
Suburban 22 22 21 65 1 37
Suburban 23 16 20 59 1 34
Suburban 18 19 19 56 1 33
Suburban 18 17 17 52 1 37
Suburban 22 17 22 61 1 54
Suburban 22 17 20 59 1 30
Suburban 19 22 17 58 1 49
Suburban 21 12 19 52 1 44
Suburban 15 20 16 51 1 34
Suburban 22 20 22 64 1 55
Suburban 20 18 18 56 1 48
Suburban 18 16 18 52 1 36
Suburban 22 16 21 59 1 29
Suburban 22 21 23 66 1 40
Suburban 19 19 19 57 1 38
Suburban 24 18 20 62 1 38
Suburban 25 21 24 70 1 55
Suburban 24 21 20 65 1 43
Suburban 20 13 17 50 1 33
Suburban 18 19 18 55 1 44
Suburban 22 15 19 56 1 41
Suburban 18 15 20 53 1 45
Suburban 23 25 21 69 1 41
Suburban 20 22 22 64 1 42
Suburban 20 19 17 56 1 37
Suburban 24 19 22 65 1 56
Suburban 24 27 24 75 1 60
Suburban 21 18 21 60 1 46
Suburban 17 14 18 49 1 31
Suburban 23 15 22 60 1 35
Suburban 24 21 21 66 1 68
Suburban 25 17 22 64 1 40
Suburban 21 19 20 60 1 51
Suburban 23 12 24 59 1 32
Suburban 21 15 19 55 1 28
Suburban 19 19 18 56 1 44
Suburban 26 13 18 57 1 26
Suburban 19 18 20 57 1 42
Suburban 21 11 16 48 1 37
Suburban 27 20 23 70 1 63
Suburban 24 20 20 64 1 37
Suburban 19 11 16 46 1 22
Suburban 23 21 20 64 1 53
Suburban 24 18 22 64 1 62