Walden University Unit 7 Correlation Coefficient & Regression Model Questions Does undergraduate success predict graduate success? While most people comple

Walden University Unit 7 Correlation Coefficient & Regression Model Questions Does undergraduate success predict graduate success? While most people complete their bachelor’s degree during the daytime while taking multiple classes and not working full-time, those getting an MBA are typically taking one or two courses at a time, in the evening or on weekends, and while working and even supporting a family. Yet one would expect those who perform better in their bachelor’s degree will perform better in their master’s. Using a significance level of .05, test whether there is a correlation between the BS GPA and the MBA GPA. Also, answer the following:

a) What is the correlation coefficient & how strong is it?

b) What is the best fit regression equation that can predict the MBA GPA from the BS GPA?

c) What percent of the variability in the MBA GPA can be explained by the regression model?

d) What would you expect a student’s MBA GPA to be if he/she had a 3.50 BS GPA?

To get an even better model for predicting MBA performance, let’s look at many variables. Create a multiple regression model predicting the MBA GPA using the BS GPA, the Hours studied per week, the Gender of the student, whether the student works full-time, and the student’s age. Use a .05 significance level. After you create your model, predict the MBA GPA 40-year old student that studies 6 hours per week, works full-time, and had a 3.00 BS GPA.

Note! MULTIPLE REGRESSION Exel File – I have already set up this file…..you need to enter the desired predictions….do not use whether the student works full-time or not.

Correlation & Regression Reading Assignment Correlation
Correlation & Regression
Data
Observation
Obs 1
Obs 2
Obs 3
Obs 4
Obs 5
Obs 6
Obs 7
Obs 8
Obs 9
Obs 10
Obs 11
Obs 12
Obs 13
Obs 14
Obs 15
Obs 16
Obs 17
Obs 18
Obs 19
Obs 20
Obs 21
Obs 22
Obs 23
Obs 24
Obs 25
Obs 26
Obs 27
Obs 28
Obs 29
Obs 30
Obs 31
Obs 32
Obs 33
X-Data
APPR
237000
164000
219000
194000
127000
223000
298000
249000
202000
245000
269000
193000
250000
228000
180000
261000
161000
193000
186000
196000
237000
182000
197000
309000
164000
178000
245000
223000
172000
225000
199000
239000
160000
Y-Data
PRICE
263000
182000
242000
214000
140000
245000
300000
272000
221000
267000
292000
209000
271000
246000
194000
281000
173000
207000
199000
209000
252000
193000
209000
320000
173000
187000
257000
233000
180000
234000
207000
248000
166000
REGRESSION STATISTICS
Observations
Correlation coefficient (r)
Coefficient of determination (r-squared)
Standard error of the estimate
REGRESSION EQUATION
Slope
Intercept
PRICE = 8335.829 + 0.961 (APPR)
a) The correlation coefficient = .954, wh
b) PRICE = 8335.829 + .961 (APPRAISA
c) Coefficient of determination = 91%,
d) For an appraised value of $250,000,
100
0.954
91%
13074.015
0.961
8335.829
PREDICTING WITH THE REGRESSION EQUATION
X value
250000
Confidence Level
95%
Predicted Y value
248464.633
Confidence Interval
248464.633
Prediction Interval
248464.633
+
+
3280.542
26151.535
HYPOTHESIS TEST FOR CORRELATION
Null hypothesis: Slope = 0 (no correlation)
Level of Significance
0.05
t-Statistic (computed)
31.3884
p-value
0.0000
Decision
Reject the null hypothesis
Conclusion
Conclude that correlation exists.
ANOVA
Regression
Error
Total
SS
168405461829.9620
16751128170.0385
185156590000.0000
CORRELATION GUIDELINES
Step 1: input the data (note which is the dependent variable)
df
1
98
99
MS
168405461829.9620
170929879.2861
Correlation
Obs 34
Obs 35
Obs 36
Obs 37
Obs 38
Obs 39
Obs 40
Obs 41
Obs 42
Obs 43
Obs 44
Obs 45
Obs 46
Obs 47
Obs 48
Obs 49
Obs 50
Obs 51
Obs 52
Obs 53
Obs 54
Obs 55
Obs 56
Obs 57
Obs 58
Obs 59
Obs 60
Obs 61
Obs 62
Obs 63
Obs 64
Obs 65
Obs 66
Obs 67
Obs 68
Obs 69
Obs 70
171000
178000
210000
304000
195000
267000
202000
228000
195000
202000
174000
305000
267000
224000
171000
217000
193000
236000
173000
253000
248000
148000
178000
231000
169000
192000
296000
276000
158000
228000
216000
197000
263000
214000
218000
306000
183000
177000
183000
216000
312000
200000
273000
206000
232000
198000
205000
176000
308000
269000
225000
172000
217000
193000
236000
172000
251000
246000
147000
176000
228000
166000
189000
290000
270000
154000
222000
210000
191000
254000
207000
210000
294000
176000
Step 2: assess the scatter diagram for linearity (if not linear, STOP)
Step 3: hypothesis test for correlation (if correlation does not exist, STOP)
Step 4: evaluate regression statistics (if correlation is weak, reconsider its value)
©2007 DrJimMirabella.com
Correlation
Obs 71
Obs 72
Obs 73
Obs 74
Obs 75
Obs 76
Obs 77
Obs 78
Obs 79
Obs 80
Obs 81
Obs 82
Obs 83
Obs 84
Obs 85
Obs 86
Obs 87
Obs 88
Obs 89
Obs 90
Obs 91
Obs 92
Obs 93
Obs 94
Obs 95
Obs 96
Obs 97
Obs 98
Obs 99
Obs 100
Obs 101
Obs 102
Obs 103
Obs 104
Obs 105
Obs 106
Obs 107
234000
131000
249000
173000
230000
203000
133000
234000
313000
261000
212000
256000
281000
202000
238000
189000
273000
168000
203000
194000
205000
247000
190000
206000
323000
197000
207000
251000
192000
208000
224000
125000
237000
164000
218000
192000
126000
221000
295000
245000
199000
240000
263000
188000
221000
175000
253000
155000
187000
179000
188000
227000
174000
188000
294000
179000
188000
227000
174000
188000
Correlation
Obs 108
Obs 109
Obs 110
Obs 111
Obs 112
Obs 113
Obs 114
Obs 115
Obs 116
Obs 117
Obs 118
Obs 119
Obs 120
Obs 121
Obs 122
Obs 123
Obs 124
Obs 125
Obs 126
Obs 127
Obs 128
Obs 129
Obs 130
Obs 131
Obs 132
Obs 133
Obs 134
Obs 135
Obs 136
Obs 137
Obs 138
Obs 139
Obs 140
Obs 141
Obs 142
Obs 143
Obs 144
Correlation
Obs 145
Obs 146
Obs 147
Obs 148
Obs 149
Obs 150
Obs 151
Obs 152
Obs 153
Obs 154
Obs 155
Obs 156
Obs 157
Obs 158
Obs 159
Obs 160
Obs 161
Obs 162
Obs 163
Obs 164
Obs 165
Obs 166
Obs 167
Obs 168
Obs 169
Obs 170
Obs 171
Obs 172
Obs 173
Obs 174
Obs 175
Obs 176
Obs 177
Obs 178
Obs 179
Obs 180
Obs 181
Correlation
Obs 182
Obs 183
Obs 184
Obs 185
Obs 186
Obs 187
Obs 188
Obs 189
Obs 190
Obs 191
Obs 192
Obs 193
Obs 194
Obs 195
Obs 196
Obs 197
Obs 198
Obs 199
Obs 200
Correlation
rrelation coefficient = .954, which indicates a very strong positive correlation.
= 8335.829 + .961 (APPRAISAL)
cient of determination = 91%, meaning that 91% of variability in the Price can be explained by the regression model.
appraised value of $250,000, we would predict a sales price of $248,464.63.
F
985.2313
F crit
3.9381
Correlation
PRICE = 8335.829 + 0.961 (APPR)
350000
300000
250000
PRICE
200000
150000
100000
50000
0
0
50000
100000
150000
200000
250000
300000
APPR
r = 0.954
r-squared = 0.91
350000
ID
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
Gender
0
1
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
1
1
1
1
1
1
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
Major
No Major
No Major
No Major
No Major
No Major
No Major
No Major
No Major
No Major
No Major
No Major
No Major
No Major
No Major
No Major
No Major
Finance
No Major
No Major
Finance
No Major
No Major
No Major
No Major
No Major
No Major
No Major
No Major
No Major
Finance
No Major
No Major
No Major
Finance
No Major
Finance
Finance
No Major
Finance
No Major
Finance
Finance
No Major
Finance
Finance
Employ
Unemployed
Full Time
Part Time
Full Time
Full Time
Unemployed
Full Time
Full Time
Part Time
Full Time
Part Time
Full Time
Full Time
Full Time
Part Time
Full Time
Full Time
Part Time
Full Time
Unemployed
Full Time
Part Time
Full Time
Full Time
Part Time
Full Time
Part Time
Unemployed
Part Time
Full Time
Full Time
Unemployed
Full Time
Full Time
Part Time
Part Time
Full Time
Unemployed
Full Time
Part Time
Full Time
Full Time
Full Time
Unemployed
Full Time
Age
39
55
43
56
38
54
30
37
38
42
52
35
37
53
51
40
33
53
43
35
57
32
59
48
34
53
35
38
37
46
44
31
51
47
56
42
44
54
51
42
45
55
47
43
57
MBA_GPA
2.82
4
3.45
2.61
3.5
4
3
2.5
2.84
3.72
3.21
3.44
3.65
3.02
3.03
3.8
4
3.26
3.53
3.75
3.15
3.66
3.36
3.79
2.85
3.74
3.23
3.52
3.32
2.89
2.83
2.93
3.71
3.47
3.52
2.83
3.64
2.96
3.59
3.33
3.38
3.44
3.31
3.03
3.26
BS GPA
3
4
3.5
4
3.3
3.05
4
3.6
3.05
3.7
3.5
3.55
2.78
3.3
3.25
4
3.5
3.5
3.75
3.9
3.2
3.75
3.45
2.55
3.05
3.9
4
3.7
3.45
3.1
3.05
3.1
3.8
2.6
3.8
4
3.55
3.1
3.9
3.9
3.6
3.35
3.9
3.25
3.4
Hrs_Studying
10
15
3
4
5
5
6
6
6
6
6
6
6
6
6
6
6
7
6
7
6
8
8
8
8
8
2
2
2
2
1
1
1
4
4
4
6
6
6
6
6
6
7
7
7
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
1
1
1
1
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Finance
No Major
Finance
Finance
Finance
No Major
Finance
No Major
Finance
Finance
Marketing
Marketing
Marketing
Leadership
Leadership
Marketing
Marketing
Marketing
Marketing
Marketing
No Major
No Major
No Major
No Major
Marketing
Leadership
Leadership
Leadership
Leadership
Leadership
No Major
Leadership
No Major
Leadership
Leadership
Leadership
Leadership
Leadership
Leadership
No Major
Marketing
Marketing
No Major
Finance
Finance
Finance
Finance
Full Time
Part Time
Full Time
Full Time
Full Time
Full Time
Full Time
Full Time
Unemployed
Full Time
Part Time
Full Time
Full Time
Part Time
Full Time
Full Time
Full Time
Full Time
Full Time
Full Time
Unemployed
Full Time
Part Time
Full Time
Part Time
Full Time
Part Time
Full Time
Full Time
Full Time
Full Time
Full Time
Full Time
Full Time
Unemployed
Full Time
Full Time
Full Time
Full Time
Full Time
Full Time
Full Time
Part Time
Full Time
Full Time
Full Time
Full Time
36
58
46
53
59
49
34
46
46
33
56
39
51
55
38
33
34
31
37
46
31
47
54
52
43
44
34
59
45
30
32
32
40
48
51
30
31
35
33
35
31
38
46
45
59
58
46
3.04
2.98
2.8
3.75
3.64
3.65
3.18
3.44
3.06
3.51
3.33
2.81
3.64
3.05
2.85
3.56
2.92
3.35
3.46
3.59
3.11
3.65
3.17
2.97
3.77
3.21
3.17
3.65
2.94
3.53
3.65
3.61
3.7
2.91
3.09
3.77
3.79
3.59
3.38
4
2.97
3.44
3.64
3.48
2.76
3.73
2.91
4
3.1
3.05
3.75
3.65
3.8
3.3
4
3.15
3.75
3.4
3.05
3.8
3.4
3.25
3.6
3.1
3.5
3.35
3.75
3.2
3.7
3.5
3.1
3.9
3.2
3.15
3.65
3.1
3.7
3.6
3.7
3.9
3.1
3.25
3.95
3.8
3.6
3.5
3.5
3.1
3.65
3.55
3.4
3.1
3.8
3.05
7
7
7
3
3
3
3
3
3
10
2
2
8
7
3
7
5
7
10
8
6
8
7
5
8
6
6
10
5
8
7
8
8
5
6
9
8
7
8
8
8
8
8
8
8
8
8
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Finance
Finance
Finance
Finance
Finance
Finance
Finance
Finance
No Major
Marketing
Marketing
Leadership
Leadership
No Major
Leadership
No Major
No Major
No Major
No Major
No Major
No Major
No Major
No Major
No Major
Leadership
Leadership
Leadership
Leadership
Finance
No Major
No Major
Finance
Finance
Finance
Finance
Finance
Finance
Finance
Finance
Finance
Leadership
Leadership
Leadership
Finance
Finance
Finance
Finance
Full Time
Part Time
Full Time
Full Time
Full Time
Full Time
Full Time
Full Time
Unemployed
Full Time
Part Time
Full Time
Full Time
Full Time
Full Time
Part Time
Full Time
Full Time
Part Time
Full Time
Full Time
Unemployed
Full Time
Full Time
Part Time
Unemployed
Full Time
Part Time
Full Time
Full Time
Part Time
Full Time
Unemployed
Part Time
Full Time
Part Time
Full Time
Unemployed
Part Time
Full Time
Full Time
Full Time
Full Time
Unemployed
Full Time
Part Time
Full Time
35
53
31
50
38
50
48
53
53
30
32
42
56
46
49
32
36
42
37
31
31
42
39
47
28
28
52
35
38
44
38
52
53
53
31
47
51
37
46
48
54
48
36
39
28
45
31
3.78
3.5
3.13
3.14
3.24
3.56
3.16
3.53
3.7
3.3
4
3.5
3.39
3.65
2.78
3.44
3.88
2.84
3.53
3.22
3.56
3.2
3.56
3.41
3.56
3.34
2.56
3.76
3.55
3.88
3.31
3.09
3.82
3.01
3.66
3.64
3.59
3.49
3.13
3.83
3.04
3.91
3.56
3.96
3.46
3.22
3.27
3.95
3.4
3.15
3.25
3.3
3.5
3.25
3.55
3.15
3.35
3.6
3.4
3.4
3.8
3.7
3.6
3.95
3.95
3.6
3.3
3.8
3.25
3.3
3.6
3.7
3.6
3.6
3.8
3.45
3.9
3.45
3.15
4
3.2
3.85
3.7
3.65
3.55
3.2
3.9
3.15
4
3.7
4
3.4
3.15
3.2
9
7
6
6
6
7
6
7
6
6
7
7
7
8
8
7
9
9
7
6
8
6
6
7
8
7
7
8
7
8
7
6
9
6
8
8
7
7
6
8
6
10
8
9
7
6
6
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
1
1
1
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
1
1
1
0
1
1
1
1
1
1
1
0
1
1
1
1
0
1
1
1
0
1
1
1
0
1
Finance
Finance
Finance
Finance
Finance
Finance
Finance
Leadership
Leadership
Leadership
Leadership
No Major
No Major
No Major
Marketing
Marketing
Marketing
Marketing
Marketing
Marketing
Marketing
Marketing
Marketing
Marketing
Marketing
Marketing
Leadership
Leadership
Leadership
Leadership
Leadership
Leadership
Leadership
Leadership
Leadership
Leadership
Leadership
Leadership
Leadership
Leadership
Leadership
Leadership
Leadership
Leadership
Leadership
Leadership
Leadership
Full Time
Part Time
Full Time
Part Time
Unemployed
Full Time
Part Time
Full Time
Unemployed
Part Time
Unemployed
Part Time
Full Time
Unemployed
Full Time
Unemployed
Full Time
Full Time
Unemployed
Unemployed
Part Time
Full Time
Unemployed
Full Time
Part Time
Unemployed
Full Time
Part Time
Full Time
Part Time
Unemployed
Full Time
Part Time
Unemployed
Part Time
Full Time
Full Time
Part Time
Full Time
Unemployed
Full Time
Full Time
Full Time
Full Time
Full Time
Unemployed
Part Time
47
35
52
52
55
52
46
31
33
45
50
33
37
33
46
55
30
51
35
40
29
52
27
51
56
35
46
39
31
52
35
32
44
43
38
54
30
38
45
48
43
34
54
36
45
55
45
3.43
3.85
3.89
3.37
3.32
3.54
3.8
3.74
3.6
2.6
3.8
2.67
3.95
3.56
3.79
3.93
3.79
3.71
3.05
3.22
3.85
3.82
3.23
3.56
3.53
3.62
3.8
3.47
3.64
3.03
3.17
3.22
3.92
3.82
3.26
3.8
3.2
3.46
3.67
4
3.66
3.96
3.75
3.83
3.55
3.36
3.21
3.45
3.95
3.9
3.45
3.3
3.55
3.9
3.85
3.45
3.55
3.3
3.45
4
3.75
3.75
4
3.85
3.85
3.35
3.2
3.95
3.95
3.95
3.65
3.65
4
3.95
3.35
3.65
3.15
3.25
3.2
4
3.95
3.55
3.85
3.2
3.35
3.75
3.4
3.85
4
3.85
3.85
3.2
3.35
3.25
7
9
8
7
6
7
8
8
7
7
6
7
9
8
8
9
8
8
6
6
9
9
9
7
7
9
9
6
7
5
6
6
10
9
7
8
6
6
8
7
8
10
8
8
6
6
6
187
188
189
190
191
192
193
194
195
196
197
198
199
200
1
0
1
1
1
1
1
1
1
1
1
1
1
1
Leadership
Leadership
Leadership
Leadership
Leadership
Leadership
Leadership
Leadership
Leadership
Leadership
Leadership
Leadership
Leadership
Leadership
Part Time
Part Time
Full Time
Full Time
Full Time
Full Time
Unemployed
Full Time
Unemployed
Unemployed
Unemployed
Unemployed
Unemployed
Full Time
34
54
36
24
34
45
33
22
27
33
36
34
55
33
2.97
3.99
3.07
3.65
3.67
3.06
3.98
3.93
3.41
3.43
3.7
3.76
3.9
3.23
3.15
4
3.15
3.65
3.85
3.35
3.7
4
3.3
3.5
3.65
3.75
3.9
3.3
5
10
6
7
8
6
8
10
6
7
7
8
8
6
Works FT
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
1
0
1
0
0
1
Variable descriptions
Gender = 0 (female), 1 (male)
Major = student’s major
Age = age of student in years
MBA_GPA = overall GPA in the MBA program
BS_GPA = overall GPA in the BS program
Hrs_Studying = average hours studied per week
Works FT = 0 (No), 1 (Yes)
0
0
0
1
1
1
0
1
1
0
1
0
1
0
1
1
0
1
1
1
0
1
0
1
1
1
0
0
0
1
1
1
1
1
0
1
1
)
1
1
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
1
0
0
1
1
1
0
1
1
1
0
1
1
1
1
1
0
1
1
1
1
1
1
0
1
1
1
1
1
1
1
1
1
1
1
1
0
0
1
1
1
1
0
1
1
1
1
1
1
1
1
0
1
1
1
1
1
1
1
1
1
1
1
1
1
0
1
1
1
1
1
1
1
1
0
1
1
0
0
1
1
1
1
1
1
1
1
1
1
1
0
1
1
0
1
0
1
0
1
Data
Multiple Regression
Sl.No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
Y
1
X1
MBA Ones BS
2.82
1
3
4
1
4
3.45
1
3.5
2.61
1
4
3.5
1
3.3
4
1
3.05
3
1
4
2.5
1
3.6
2.84
1
3.05
3.72
1
3.7
3.21
1
3.5
3.44
1
3.55
3.65
1
2.78
3.02
1
3.3
3.03
1
3.25
3.8
1
4
4
1
3.5
3.26
1
3.5
3.53
1
3.75
3.75
1
3.9
3.15
1
3.2
3.66
1
3.75
3.36
1
3.45
3.79
1
2.55
2.85
1
3.05
3.74
1
3.9
3.23
1
4
3.52
1
3.7
3.32
1
3.45
2.89
1
3.1
2.83
1
3.05
2.93
1
3.1
3.71
1
3.8
X2
X3
STUDY GENDER
10
0
15
1
3
0
4
0
5
1
5
0
6
0
6
0
6
0
6
0
6
0
6
0
6
0
6
0
6
0
6
1
6
0
7
0
6
0
7
0
6
0
8
1
8
1
8
1
8
1
8
1
2
1
2
1
2
1
2
0
1
0
1
0
1
0
X4
AGE
39
55
43
56
38
54
30
37
38
42
52
35
37
53
51
40
33
53
43
35
57
32
59
48
34
53
35
38
37
46
44
31
51
X5
X6
Page 1
X7
X8
X9
X10
Data
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
3.47
3.52
2.83
3.64
2.96
3.59
3.33
3.38
3.44
3.31
3.03
3.26
3.04
2.98
2.8
3.75
3.64
3.65
3.18
3.44
3.06
3.51
3.33
2.81
3.64
3.05
2.85
3.56
2.92
3.35
3.46
3.59
3.11
3.65
3.17
2.97
3.77
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2.6
3.8
4
3.55
3.1
3.9
3.9
3.6
3.35
3.9
3.25
3.4
4
3.1
3.05
3.75
3.65
3.8
3.3
4
3.15
3.75
3.4
3.05
3.8
3.4
3.25
3.6
3.1
3.5
3.35
3.75
3.2
3.7
3.5
3.1
3.9
4
4
4
6
6
6
6
6
6
7
7
7
7
7
7
3
3
3
3
3
3
10
2
2
8
7
3
7
5
7
10
8
6
8
7
5
8
0
0
1
0
0
0
0
0
0
0
1
0
1
1
1
1
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
47
56
42
44
54
51
42
45
55
47
43
57
36
58
46
53
59
49
34
46
46
33
56
39
51
55
38
33
34
31
37
46
31
47
54
52
43
Page 2
Data
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
3.21
3.17
3.65
2.94
3.53
3.65
3.61
3.7
2.91
3.09
3.77
3.79
3.59
3.38
4
2.97
3.44
3.64
3.48
2.76
3.73
2.91
3.78
3.5
3.13
3.14
3.24
3.56
3.16
3.53
3.7
3.3
4
3.5
3.39
3.65
2.78
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3.2
3.15
3.65
3.1
3.7
3.6
3.7
3.9
3.1
3.25
3.95
3.8
3.6
3.5
3.5
3.1
3.65
3.55
3.4
3.1
3.8
3.05
3.95
3.4
3.15
3.25
3.3
3.5
3.25
3.55
3.15
3.35
3.6
3.4
3.4
3.8
3.7
6
6
10
5
8
7
8
8
5
6
9
8
7
8
8
8
8
8
8
8
8
8
9
7
6
6
6
7
6
7
6
6
7
7
7
8
8
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
44
34
59
45
30
32
32
40
48
51
30
31
35
33
35
31
38
46
45
59
58
46
35
53
31
50
38
50
48
53
53
30
32
42
56
46
49
Page 3
Data
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
3.44
3.88
2.84
3.53
3.22
3.56
3.2
3.56
3.41
3.56
3.34
2.56
3.76
3.55
3.88
3.31
3.09
3.82
3.01
3.66
3.64
3.59
3.49
3.13
3.83
3.04
3.91
3.56
3.96
3.46
3.22
3.27
3.43
3.85
3.89
3.37
3.32
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3.6
3.95
3.95
3.6
3.3
3.8
3.25
3.3
3.6
3.7
3.6
3.6
3.8
3.45
3.9
3.45
3.15
4
3.2
3.85
3.7
3.65
3.55
3.2
3.9
3.15
4
3.7
4
3.4
3.15
3.2
3.45
3.95
3.9
3.45
3.3
7
9
9
7
6
8
6
6
7
8
7
7
8
7
8
7
6
9
6
8
8
7
7
6
8
6
10
8
9
7
6
6
7
9
8
7
6
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
1
32
36
42
37
31
31
42
39
47
28
28
52
35
38
44
38
52
53
53
31
47
51
37
46
48
54
48
36
39
28
45
31
47
35
52
52
55
Page 4
Data
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
3.54
3.8
3.74
3.6
2.6
3.8
2.67
3.95
3.56
3.79
3.93
3.79
3.71
3.05
3.22
3.85
3.82
3.23
3.56
3.53
3.62
3.8
3.47
3.64
3.03
3.17
3.22
3.92
3.82
3.26
3.8
3.2
3.46
3.67
4
3.66
3.96
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3.55
3.9
3.85
3.45
3.55
3.3
3.45
4
3.75
3.75
4
3.85
3.85
3.35
3.2
3.95
3.95
3.95
3.65
3.65
4
3.95
3.35
3.65
3.15
3.25
3.2
4
3.95
3.55
3.85
3.2
3.35
3.75
3.4
3.85
4
7
8
8
7
7
6
7
9
8
8
9
8
8
6
6
9
9
9
7
7
9
9
6
7
5
6
6
10
9
7
8
6
6
8
7
8
10
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
1
1
1
0
1
1
1
1
1
1
1
0
1
1
1
1
0
1
1
1
0
52
46
31
33
45
50
33
37
33
46
55
30
51
35
40
29
52
27
51
56
35
46
39
31
52
35
32
44
43
38
54
30
38
45
48
43
34
Page 5
Data
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
3.75
3.83
3.55
3.36
3.21
2.97
3.99
3.07
3.65
3.67
3.06
3.98
3.93
3.41
3.43
3.7
3.76
3.9
3.23
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3.85
3.85
3.2
3.35
3.25
3.15
4
3.15
3.65
3.85
3.35
3.7
4
3.3
3.5
3.65
3.75
3.9
3.3
8
8
6
6
6
5
10
6
7
8
6
8
10
6
7
7
8
8
6
1
1
1
0
1
1
0
1
1
1
1
1
1
1
1
1
1
1
1
54
36
45
55
45
34
54
36
24
34
45
33
22
27
33
36
34
55
33
©2007 DrJimMirabella.com
Page 6
A
B
C
D
E
1 Multiple Regression Results
2
3
0
1
2
3
4
Intercept
BS
STUDY GENDER
5
b 1.35688 0.4978 0.0436
0.016
6
s(b)
0.2567 0.07052 0.0119 0.0448
7
t 5.28577 7.05944 3.6721 0.3571
8 p-value
0.0000 0.0000 0.0003 0.7214
9
10
VIF 1.2332 1.2949 1.0709
11
F
4
AGE
8E-05
0.0023
0.0346
0.9725
G
H
I
J
K
L
5
6
7
8
9
10
1.0155
12 ANOVA Table
13
Source
SS
df
MS
F
F Critical p -value
14
Regn. 8.76338
4
2.1908 26.864
2.418 0.0000
s
15
Error 15.9032 195
0.0816
R2 0.3553
Adjusted R2
16
Total 24.6665 199
0.124
17
18
19 Prediction Interval
20
21 Given X
BS
STUDY GENDER AGE
22
1
2000
1
1
3
4
6
23
24
25
26
27
28
29
30
1-a
95%
(1-a) P.I. for Y for given X
997.024 + or – 277.7
©2007 DrJimMirabella.com
1-a
95%
0.2856
0.342
(1-a) P.I. for E[Y | X]
997.02 + or – 277.7
M
A
B
C
D
E
F
1 Correlation matrix
2
3 Correlation
1
2
3
4
4 Coefficients
BS
STUDY GENDER AGE
5
1
BS 1.0000
6
2
STUDY 0.4231 1.0000
7
3 GENDER 0.0087 0.2233 1.0000
8
4
AGE -0.0721 -0.0739 -0.0979 1.0000
9
5
10
6
11
7
12
8
13
9
14 10
15
16
Y
MBA 0.5525 0.4356 0.0787 -0.0503
17
18 P-values
1
2
3
4
19
BS
STUDY GENDER AGE
20
1
BS
21
2
STUDY 0.0000*
22
3 GENDER 0.9021 0.0015*
23
4
AGE 0.3106 0.2983
0.168
24
5
25
6
26
7
27
8
28
9
29 10
30
31
Y
MBA 0.0000* 0.0000* 0.2682 0.4792
32
77
78
79
©2007 DrJimMirabella.com
G
H
I
J
K
L
5
6
7
8
9
10
5
6
7
8
9
10
* = significant at .05 level

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