2. Following data is amount of Assessed Value for an office building for a given floor area, number of office space, number of entrances, and the age of the offic building. Use excel to …. Obtain a Scatter plot of Floor Area as the independent and Assessed value as the dependent variable. Note the linear regression Model Predict the assessed value for 6000 sq ft. FloorArea (Sq.Ft.) Offices Entrances Age AssessedValue ($'000) 4790 4 2 8 1796 4720 3 2 12 1544 5940 4 2 2 2094 5720 4 2 34 1968 3660 3 2 38 1567 5000 4 2 31 1878 2990 2 1 19 949 2610 2 1 48 910 5650 4 2 42 1774 3570 2 1 4 1187 2930 3 2 15 1113 1280 2 1 31 671 4880 3 2 42 1678 1620 1 2 35 710 1820 2 1 17 678 4530 2 2 5 1585 2570 2 1 13 842 4690 2 2 45 1539 1280 1 1 45 433 4100 3 1 27 1268 3530 2 2 41 1251 3660 2 2 33 1094 1110 1 2 50 638 2670 2 2 39 999 1100 1 1 20 653 5810 4 3 17 1914 2560 2 2 24 772 2340 3 1 5 890 3690 2 2 15 1282 3580 3 2 27 1264 3610 2 1 8 1162 3960 3 2 17 1447 3960 3 2 17 1447 1280 1 1 45 433 5000 4 2 31 1878 1280 1 1 45 433 3660 3 2 38 1567 3960 3 2 17 1447 4880 3 2 42 1678
2. Following data is amount of Assessed Value for an office building for a given floor area, number of office space, number of entrances, and the age of the offic building. Use excel to ….
- Obtain a
Scatter plot of Floor Area as the independent and Assessed value as the dependent variable. - Note the linear regression Model
- Predict the assessed value for 6000 sq ft.
|
FloorArea (Sq.Ft.) |
Offices |
Entrances |
Age |
AssessedValue ($'000) |
|
4790 |
4 |
2 |
8 |
1796 |
|
4720 |
3 |
2 |
12 |
1544 |
|
5940 |
4 |
2 |
2 |
2094 |
|
5720 |
4 |
2 |
34 |
1968 |
|
3660 |
3 |
2 |
38 |
1567 |
|
5000 |
4 |
2 |
31 |
1878 |
|
2990 |
2 |
1 |
19 |
949 |
|
2610 |
2 |
1 |
48 |
910 |
|
5650 |
4 |
2 |
42 |
1774 |
|
3570 |
2 |
1 |
4 |
1187 |
|
2930 |
3 |
2 |
15 |
1113 |
|
1280 |
2 |
1 |
31 |
671 |
|
4880 |
3 |
2 |
42 |
1678 |
|
1620 |
1 |
2 |
35 |
710 |
|
1820 |
2 |
1 |
17 |
678 |
|
4530 |
2 |
2 |
5 |
1585 |
|
2570 |
2 |
1 |
13 |
842 |
|
4690 |
2 |
2 |
45 |
1539 |
|
1280 |
1 |
1 |
45 |
433 |
|
4100 |
3 |
1 |
27 |
1268 |
|
3530 |
2 |
2 |
41 |
1251 |
|
3660 |
2 |
2 |
33 |
1094 |
|
1110 |
1 |
2 |
50 |
638 |
|
2670 |
2 |
2 |
39 |
999 |
|
1100 |
1 |
1 |
20 |
653 |
|
5810 |
4 |
3 |
17 |
1914 |
|
2560 |
2 |
2 |
24 |
772 |
|
2340 |
3 |
1 |
5 |
890 |
|
3690 |
2 |
2 |
15 |
1282 |
|
3580 |
3 |
2 |
27 |
1264 |
|
3610 |
2 |
1 |
8 |
1162 |
|
3960 |
3 |
2 |
17 |
1447 |
|
3960 |
3 |
2 |
17 |
1447 |
|
1280 |
1 |
1 |
45 |
433 |
|
5000 |
4 |
2 |
31 |
1878 |
|
1280 |
1 |
1 |
45 |
433 |
|
3660 |
3 |
2 |
38 |
1567 |
|
3960 |
3 |
2 |
17 |
1447 |
|
4880 |
3 |
2 |
42 |
1678 |
3. Is there a statistical significance difference between the five cities in their ability to sell the three different type of autos? How about the different in car models? (conduct an analysis of variance using excel to determine this problem. Make sure you conuct a full fledge eight step hypotheis test for both supervisors and shifts.)
|
|
Toyota |
Honda |
Ford |
|
San Diego |
30 |
31 |
33 |
|
Escondido |
23 |
26 |
33 |
|
El Cajon |
34 |
35 |
20 |
|
LA |
30 |
24 |
40 |
|
SF |
25 |
39 |
49 |
For the different car models.
3a What will be your Ho?
3b what is your Ha?
3c What is your level of
3d What is your test statistics?
3e Why did you use the specific test statistics?
3f What is your decision rule?
3g Do your excel statistical print out
3h What is your decision?
3i What is your conclusion
3K What is your recommendation?
For the different cities:
3a What will be your Ho?
3b what is your Ha?
3c What is your level of significance?
3d What is your test statistics?
3e Why did you use the specific test statistics?
3f What is your decision rule?
3g Do your excel statistical print out
3h What is your decision?
3i What is your conclusion?
3K What is your recommendation?
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