The following data are for 30 observations involving twocategorical variables, x and y. The categories for x are A, B, andC; the categories for y are 1 and 2.
Observation x y Observation x y 1 A 2 16 B 1 2 B 2 17 C 2 3 B 2 18 B 2 4 C 1 19 C 2 5 B 2 20 B 2 6 C 1 21 C 1 7 B 2 22 B 2 8 C 1 23 C 1 9 A 1 24 A 1 10 B 2 25 B 2 11 A 1 26 C 1 12 B 2 27 C 1 13 C 1 28 A 1 14 C 1 29 B 2 15 C 1 30 B 1
Develop a crosstabulation for the data, with x as the rowvariable and y as the column variable.
y 1 2 Total A x B C Total
Compute the row percentages (to 1 decimal).
y 1 2 Total A % % % x B % % % C % % %
Compute the column percentages (to 1 decimal).
y 1 2 A % % x B % % C % % Total % %
The following data are for 30 observations involving twocategorical variables, x and y. The categories for x are A, B, andC; the categories for y are 1 and 2.
| Observation | x | y | Observation | x | y | |
| 1 | A | 2 | 16 | B | 1 | |
| 2 | B | 2 | 17 | C | 2 | |
| 3 | B | 2 | 18 | B | 2 | |
| 4 | C | 1 | 19 | C | 2 | |
| 5 | B | 2 | 20 | B | 2 | |
| 6 | C | 1 | 21 | C | 1 | |
| 7 | B | 2 | 22 | B | 2 | |
| 8 | C | 1 | 23 | C | 1 | |
| 9 | A | 1 | 24 | A | 1 | |
| 10 | B | 2 | 25 | B | 2 | |
| 11 | A | 1 | 26 | C | 1 | |
| 12 | B | 2 | 27 | C | 1 | |
| 13 | C | 1 | 28 | A | 1 | |
| 14 | C | 1 | 29 | B | 2 | |
| 15 | C | 1 | 30 | B | 1 |
Develop a crosstabulation for the data, with x as the rowvariable and y as the column variable.
| y | ||||
| 1 | 2 | Total | ||
| A | ||||
| x | B | |||
| C | ||||
| Total | ||||
Compute the row percentages (to 1 decimal).
| y | ||||
| 1 | 2 | Total | ||
| A | % | % | % | |
| x | B | % | % | % |
| C | % | % | % | |
Compute the column percentages (to 1 decimal).
| y | ||||
| 1 | 2 | |||
| A | % | % | ||
| x | B | % | % | |
| C | % | % | ||
| Total | % | % | ||
Related questions
In each of the cases below, assume that Division X has a productthat can be sold either to outside customers or to Division Y ofthe same company for use in its production process. The managers ofthe divisions are evaluated based on their divisional profits.
| Case | |||||||
| A | B | ||||||
| Division X: | |||||||
| Capacity in units | 106,000 | 108,000 | |||||
| Number of units being sold tooutside customers | 106,000 | 87,000 | |||||
| Selling price per unit tooutside customers | $ | 59 | $ | 28 | |||
| Variable costs per unit | $ | 27 | $ | 13 | |||
| Fixed costs per unit (based oncapacity) | $ | 10 | $ | 5 | |||
| Division Y: | |||||||
| Number of units needed forproduction | 21,000 | 21,000 | |||||
| Purchase price per unit nowbeing paid to an outside supplier | $ | 53 | $ | 26 | |||
6.
value:
5.00 points
Required information
Required:
1-a. Refer to the data in case A above. Assume in this case that$2 per unit in variable selling costs can be avoided onintracompany sales.
1-b. If the managers are free to negotiate and make decisions ontheir own, will a transfer take place?
| Yes | |
| No |
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7.
value:
5.00 points
Required information
2-a. Refer to the data in case B above. In this case, there willbe no savings in variable selling costs on intracompany sales.Determine the transfer price of the selling division
2-b. If the managers are free to negotiate and make decisions ontheir own, will a transfer take place?
| No | |
| Yes |
2-c. What is the range of transfer price the managers of bothdivisions should agree?
PROBLEM 1: Analyze the following data using LINEAR TRENDANALYSIS. How many cars will be sold in time period 13?
TIME PERIOD | NUMBER OF CARS SOLD |
1 | 70 |
2 | 80 |
3 | 66 |
4 | 74 |
5 | 64 |
6 | 76 |
7 | 72 |
8 | 83 |
9 | 82 |
10 | 76 |
11 | 84 |
12 | 80 |
PROBLEM 2: A sample of 20 cars was taken, and the miles pergallon (MPG), horsepower, and total weight were recorded.
Develop a simple regression model to predict the dependentvariable, MPG, using horsepoweras the independent variable.
Develop a simple regression model to predict the dependentvariable, MPG, using weight asthe independent variable.
Using R², which of the 2 causal forecasting models, A or B, isbetter?
Develop a multiple regression model to predict the dependentvariable, MPG, using horsepowerand weight as the 2 independent variables.
Using R², how does the multiple regression model compare witheach of the 2 simple regression models?
MPG (Y) | HORSEPOWER (X1) | WEIGHT (X2) |
44 | 67 | 1844 |
44 | 50 | 1998 |
40 | 62 | 1752 |
37 | 69 | 1980 |
37 | 66 | 1797 |
34 | 63 | 2199 |
35 | 90 | 2404 |
32 | 99 | 2611 |
30 | 63 | 3236 |
28 | 91 | 2606 |
26 | 94 | 2580 |
26 | 88 | 2507 |
25 | 124 | 2922 |
22 | 97 | 2434 |
20 | 114 | 3248 |
21 | 102 | 2812 |
18 | 114 | 3382 |
18 | 142 | 3197 |
16 | 153 | 4380 |
16 | 139 | 4036 |
give answer to the problem 1 and 2 in excel.
