Create and interpret scatterplots of Price vs SQFeet and Price vs Age 2. Run the best subsets procedure. Identify the best model to use and why. 3. Run the regression procedure using the model you chose in #2 a. Identify the regression equation. b. Interpret all c

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Problem 1 (You are trying to predict Price.)
Note: Mtn View=1 if there is a mountain view, 0 otherwise. The rest of the variables
should be self explanatory.
1. Create and interpret scatterplots of Price vs SQFeet and Price vs Age
2. Run the best subsets procedure. Identify the best model to use and why.
3. Run the regression procedure using the model you chose in #2
a. Identify the regression equation.
b. Interpret all coefficients
c. Identify and interpret the R-Square
For parts d-e, use the p-value value approach for each test (use .05 for
significance level)
d. Perform an F Test for overall significance of the model
e. Perform a T test for slope for the age variable
f. Find a 95% confidence interval for the slope of the SqFeet variable
g. Create a prediction, CI, and PI for 1 new set of x values (any valid
numbers you want), and interpret each.
h. Run the residual plots and indicate if they show any problems with
the model.
4. Discuss how good the model is for predicting home prices.
 
Problem 2 (This was given to me by the assessment coordinator and needs to be
collected for AACSB accrediting).
Q1: Pat Jones is the general manager at Denny’s Muffler, which has 3 locations and
27 employees. Currently, the office manager at 3 stores put input their weekly sales
results into a spreadsheet and e-mail that spreadsheet to Pat on Friday by noon.
Each week, Pat handles between 120 to 350 records of sales data, and another 100
to 300 records of customer data. Pat would be like to keep track of weekly sales,
which customers are associated with each sale, the parts used for each sale, what
work was done for each customer at each sale, and which parts were used for each
sale. If Pat wants to improve his efficiency and effectiveness, which of the following
applications would be the best for collecting and storing Pat’s data?
1) Excel
2) SAP
3) Access
4) Minitab
5) Teams
6) MySQL

Q2: Raj Miller runs an 8-person sales team at a small paper company that has 22
employees and $6,500,000 of revenue per year. Raj needs to give a presentation of
second quarter sales results to company management. As part of the presentation,
Raj would like to create some charts and pivot tables to summarize the quarterly
data.
To be successful at this presentation, which of the following technologies would be
the best choice (or choices) for creating charts and making his in-person
presentation to company management? (Indicate all correct answers)
1) SAP
2) Excel
3) Access
4) Powerpoint
5) Oracle

Q3: Jami is an intern on a sales team at a large insurance company. On the first day
of the internship, Jami is asked to download a list of all of her friends and contacts
from Facebook and upload them to the company database. The team lead explains
"This database keeps track of all of our employees, and well as their customers and
prospects. This makes it really easy to avoid redundant customer interactions, and
also keeps us from cannibalizing customers and business from one another."
Jami is wondering if there are ethical issues she should be concerned about. Do you
think there are ethical issues involved with this situation? If yes- what are those
issues, specifically, and how would you advise Jami?
A
B
E
F
G
1
Price
Bathrooms Garage spaceMtn View
SqFeet
1000
Age
Bedrooms
110000
28
3
1
1
3
133500
1400
23
3
1
1
1
4
112500
1248
58
3
4
1
1
5
141750
1106
12
1
1
1
6
195250
2112
78
2
6.
2
1
7
132250
1078
33
2
1
1
1
8
136000
952
13
2
2
9
162750
1100
1
1
1
10
148500
1040
17
3
1
2
1
11
123500
1416
27
4
2
1
1
12
142250
1150
25
3
2
13
145500
1220
17
3
2
1
14
155250
1464
28
3
2
2.
1
15
150750
1228
15
2
1
16
150900
1132
1
3
4
2
17
144000
1132
3
4
2
1
18
151900
1132
1
3
4
2
1
19
161500
2100
28
2
2
1
20
155750
1270
3
2
1
21
157250
1362
23
3
4
2
1
22
152900
1120
1
3
3
2
1
23
145250
1025
1
3
5
2
1
24
164750
1290
19
3
3
2
1
25
152500
1260
22
3
4
2
1
26
150750
1085
1
3
4
2
1
27
144750
1312
41
3
4
2
1
28
148250
1489
34
3
4
1
1
29
174500
1540
24
3
4
2
1
30
215500
2112
12
4
4
2
1
31
155750
1351
1
4
5
2
1
32
167900
1351
1.
4
4
2
1
33
148500
1193
5
3
4
2
1
34
152500
1200
1
4
4
2
1
35
167200
1256
1
3
4
2
1
36
159500
1058
1
3
4
2
1
37
153600
1210
13
3
4
2
1
38
168000
1380
1
4
2
1
39
166100
1301
7
4
2
1
40
165200
1375
8.
3
4
2
1
41
174900
1437
8.
3
4
2
1
42
160250
1330
1
3
4
2
1
43
165100
1361
1
3
4
1
44
170500
1594
16
3
3
2
1
45
155900
1186
2
3
4
2
1
46
167700
1336
1
3
4
2
47
165400
1325
5
3
4
1
48
163500
1352
3
4
2
1
49
178500
1354
7
3
2
1
50
164000
1318
1
3
4
2
1
Transcribed Image Text:A B E F G 1 Price Bathrooms Garage spaceMtn View SqFeet 1000 Age Bedrooms 110000 28 3 1 1 3 133500 1400 23 3 1 1 1 4 112500 1248 58 3 4 1 1 5 141750 1106 12 1 1 1 6 195250 2112 78 2 6. 2 1 7 132250 1078 33 2 1 1 1 8 136000 952 13 2 2 9 162750 1100 1 1 1 10 148500 1040 17 3 1 2 1 11 123500 1416 27 4 2 1 1 12 142250 1150 25 3 2 13 145500 1220 17 3 2 1 14 155250 1464 28 3 2 2. 1 15 150750 1228 15 2 1 16 150900 1132 1 3 4 2 17 144000 1132 3 4 2 1 18 151900 1132 1 3 4 2 1 19 161500 2100 28 2 2 1 20 155750 1270 3 2 1 21 157250 1362 23 3 4 2 1 22 152900 1120 1 3 3 2 1 23 145250 1025 1 3 5 2 1 24 164750 1290 19 3 3 2 1 25 152500 1260 22 3 4 2 1 26 150750 1085 1 3 4 2 1 27 144750 1312 41 3 4 2 1 28 148250 1489 34 3 4 1 1 29 174500 1540 24 3 4 2 1 30 215500 2112 12 4 4 2 1 31 155750 1351 1 4 5 2 1 32 167900 1351 1. 4 4 2 1 33 148500 1193 5 3 4 2 1 34 152500 1200 1 4 4 2 1 35 167200 1256 1 3 4 2 1 36 159500 1058 1 3 4 2 1 37 153600 1210 13 3 4 2 1 38 168000 1380 1 4 2 1 39 166100 1301 7 4 2 1 40 165200 1375 8. 3 4 2 1 41 174900 1437 8. 3 4 2 1 42 160250 1330 1 3 4 2 1 43 165100 1361 1 3 4 1 44 170500 1594 16 3 3 2 1 45 155900 1186 2 3 4 2 1 46 167700 1336 1 3 4 2 47 165400 1325 5 3 4 1 48 163500 1352 3 4 2 1 49 178500 1354 7 3 2 1 50 164000 1318 1 3 4 2 1
51
172750
1415
3
1
52
149000
1337
2
3
4
2
53
176000
2028
25
4
4
2
54
205500
1820
21
3
4
2
55
216750
2182
1
4
4
3
1
56
181250
2600
37
5
3
1
57
229500
1930
1
3
4
3
58
207500
1834
2
3
4
3
59
206250
1933
3
4.
3
60
265400
2172
1
3
4
3
61
219250
2193
4
4
2
1
62
272100
2018
1
4
5
4
63
229600
2310
1
4
5
3
1
64
261000
2256
1
4
4
3
65
274600
2710
20
4
4
1
66
82200
1450
40
3
3
2
67
93300
1728
61
4
1
2.
68
111400
1976
37
4
4
1
69
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1608
7
3
1
1
70
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1532
41
3
2
2
71
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1288
5
3
4.
72
65950
1320
9
3
4
2
73
90100
1416
41
2
4
2
74
110200
2022
36
4
3
75
122500
1550
42
3
2
2
76
93900
1568
40
3
3
2
77
84800
1968
40
3
3
78
103600
1536
22
3
4
2
79
121000
1872
39
4
4
2.
80
102150
2200
31
4
1
81
129600
1763
29
3
4
2
82
82950
2073
71
3
2
83
111700
1563
52
3
1
3
84
127150
2080
28
5
2
85
139300
1968
47
3
3
2
86
111900
1340
9
3
4
3
87
96400
1730
12
3
4
2
88
106250
2168
2
4
1
89
126100
2320
22
4
4
1
90
121950
1750
8
3
4
91
150950
2000
39
3
4
2
92
142100
2073
44
3
2.
2
93
173200
2000
20
3
3
2
94
157850
1900
45
6.
3
95
164700
2400
38
4
4
2
96
130100
3000
43
4
5
97
123900
2172
1
5
6
3
98
180500
2515
51
2
4
2
99
169900
2500
49
4
6
3
100
213000
2345
44
5
4
4
Transcribed Image Text:51 172750 1415 3 1 52 149000 1337 2 3 4 2 53 176000 2028 25 4 4 2 54 205500 1820 21 3 4 2 55 216750 2182 1 4 4 3 1 56 181250 2600 37 5 3 1 57 229500 1930 1 3 4 3 58 207500 1834 2 3 4 3 59 206250 1933 3 4. 3 60 265400 2172 1 3 4 3 61 219250 2193 4 4 2 1 62 272100 2018 1 4 5 4 63 229600 2310 1 4 5 3 1 64 261000 2256 1 4 4 3 65 274600 2710 20 4 4 1 66 82200 1450 40 3 3 2 67 93300 1728 61 4 1 2. 68 111400 1976 37 4 4 1 69 90250 1608 7 3 1 1 70 72500 1532 41 3 2 2 71 70600 1288 5 3 4. 72 65950 1320 9 3 4 2 73 90100 1416 41 2 4 2 74 110200 2022 36 4 3 75 122500 1550 42 3 2 2 76 93900 1568 40 3 3 2 77 84800 1968 40 3 3 78 103600 1536 22 3 4 2 79 121000 1872 39 4 4 2. 80 102150 2200 31 4 1 81 129600 1763 29 3 4 2 82 82950 2073 71 3 2 83 111700 1563 52 3 1 3 84 127150 2080 28 5 2 85 139300 1968 47 3 3 2 86 111900 1340 9 3 4 3 87 96400 1730 12 3 4 2 88 106250 2168 2 4 1 89 126100 2320 22 4 4 1 90 121950 1750 8 3 4 91 150950 2000 39 3 4 2 92 142100 2073 44 3 2. 2 93 173200 2000 20 3 3 2 94 157850 1900 45 6. 3 95 164700 2400 38 4 4 2 96 130100 3000 43 4 5 97 123900 2172 1 5 6 3 98 180500 2515 51 2 4 2 99 169900 2500 49 4 6 3 100 213000 2345 44 5 4 4
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