# ISSCM 491 Survey Data Using PhStat My Occupational group is Group # 3 Admin Support Please Use The Program PhStat!!! there will be a file you can downloa

ISSCM 491 Survey Data Using PhStat My Occupational group is Group # 3 Admin Support

there will be a file you can download ( PHStat.xlam PHStat.xlam )if you do not have PhStat, and also another file to show you how to use PhStat to get the answers. ( Ph Stat Handout 2016.pdf )

– everything else should be pretty straight forward in the excel spreadsheet it shows and tells you exactly what to do. – the file for this assignment is hw8.xlsx. Do not do more than is asked please.

Also the Survey Data ISSCM491.xlsx file is attached as (if needed) to look at this for this homework assignment (if it is) mentioned inside hw8.xlsx

Townes.HW1.xlsx will show you the The Correct way on how to answer and submit this assignment, so you could take a look at that to help how to format to answer the questions. (Note that this homework has 2 questions)
Homework 8
Question 1.
The Child Health and Development collected data to study the relationship between the infant weights
and several related variables.
A sample of 40 observations are given in the next worksheet
Model Building – Stepwise Regression (Backward Elimination Method):
(a) Follows the process steps described below to obtain a significant regression model.
Step 1. Determine the estimated regression equation containing ALL six independent variables
(gestation, height, smoke, age, weight, and parity).
(a) What is the estimated regression equation with these six independent variables?
(b) What percent of the total variation in baby weights is explained by this regression?
(c) Which independent variables are NOT significant in this model at a = 0.05?
Step 2. Eliminate non-significant variables, one at a time, until you obtain a regression model
with all independent variables significant.
Process steps:
Step 2a. Remove the non-significant independent variable(s), one at a time, starting
with the variable that has the highest p-value first. Obtain the new
regression model.
Step 2b. Check the independent variables for significance at a=0.05..
(i) If all independent variables are significant, stop. This is the significant model.
(ii) If some independent variables in the model are not significant, repeat Step 2.
(b) These process steps described above will result in a regression model that has significant
independent variables only. What is the significant model found after completing the process
steps above?
(c) Now use the stepwise regression available in PHStat. Run stepwise regression (using
backward elimination approach) to obtain a significant regression model.
What is the model found by the PHStat stepwise regression?
(d) Compare the significant regression models obtained in (b) and (c). Are they different? If
they are different, which model is performing better?
(e) Using the model you found to be performing better in (d), construct 95 percent confidence
interval estimate for the mean baby weights for the newborns with the following values:
(i)
(i)
Baby 1
Baby 2
gestation
height
smoke
age
weight
parity
283
64
1
22
140
0
gestation
height
smoke
age
weight
parity
283
64
1
25
160
1
(f) Which confidence interval estimate has a larger interval? Why?
When you run stepwise regression in question (c),
if you get an error message like the following, that
means the PHStat failed. Please copy the data into a
NEW FILe and run the stepwise regression from this new
file.
bwt
gestation
height
smoke
age
weight
parity
Variables
128
125
114
130
116
81
124
125
110
125
138
142
115
102
140
133
127
104
119
152
123
143
131
141
129
113
119
109
104
131
110
148
137
117
115
98
136
121
132
93
290
286
290
285
248
256
287
292
262
279
294
284
278
280
294
276
290
274
275
301
284
273
308
319
277
282
292
295
280
282
293
279
283
283
302
250
303
276
285
264
64
64
66
63
66
60
62
65
66
63
64
66
60
55
61
63
66
62
67
65
65
66
65
67
66
59
62
63
68
66
64
71
65
63
67
56
68
71
63
60
0
0
0
1
0
1
1
0
0
1
0
0
1
0
0
0
0
1
0
0
0
0
0
0
1
0
1
1
1
0
1
0
0
0
0
1
0
1
0
1
22
21
30
23
28
30
27
22
25
23
40
39
23
38
25
22
35
20
42
29
20
19
40
20
30
36
33
23
27
21
28
27
20
27
22
35
20
23
25
36
118
139
160
128
135
148
105
122
140
104
125
132
102
140
103
119
165
115
156
150
120
135
160
140
142
140
118
103
146
126
135
189
157
108
135
122
148
152
140
100
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
1
0
0
1
0
1
0
1
1
1
1
0
1
0
1
0
1
1
1
0
Bwt
Gestation
Height
Smoke
Age
Weight
122.50
15.428911
283.3
64.1
0.4
27.4
0.4
Parity
When you run stepwise reg
if you get an error message
means the PHStat failed. Pl
NEW FILe and run the stepwis
file.
Bwt
180
160
140
120
100
4900.00000
4900.0000
Baby weights in ounces
Length of pregnancy in days
Mother’s height in inches
=1 if mother is smoker. = 0 Nonsmoker
Mother’s age in years
Mother’s pregnancy weight
= 0 if the baby is first born
=1 otherwise
Bins
90
100
110
120
130
140
150
160
170
180
en you run stepwise regression in question (c),
ou get an error message like the following, that
ans the PHStat failed. Please copy the data into a
W FILe and run the stepwise regression from this new
Bwt vs Gestation
Bwt
180
160
140
120
100
80
60
40
240
260
280
Gestation
300
320
340
Mid
95
105
115
125
135
145
155
165
175
180
4900.0
4900.
bwt
gestation
height
Weight
smoke
age
128
125
114
130
116
81
124
125
110
125
138
142
115
102
140
133
127
104
119
152
123
143
131
141
129
113
119
109
104
131
110
148
137
117
115
98
136
121
132
93
290
286
290
285
248
256
287
292
262
279
294
284
278
280
294
276
290
274
275
301
284
273
308
319
277
282
292
295
280
282
293
279
283
283
302
250
303
276
285
264
64
64
66
63
66
60
62
65
66
63
64
66
60
55
61
63
66
62
67
65
65
66
65
67
66
59
62
63
68
66
64
71
65
63
67
56
68
71
63
60
118
139
160
128
135
148
105
122
140
104
125
132
102
140
103
119
165
115
156
150
120
135
160
140
142
140
118
103
146
126
135
189
157
108
135
122
148
152
140
100
0
0
0
1
0
1
1
0
0
1
0
0
1
0
0
0
0
1
0
0
0
0
0
0
1
0
1
1
1
0
1
0
0
0
0
1
0
1
0
1
22
21
30
23
28
30
27
22
25
23
40
39
23
38
25
22
35
20
42
29
20
19
40
20
30
36
33
23
27
21
28
27
20
27
22
35
20
23
25
36
122.50
15.428911
283.3
64.1
0.4
27.4
4900.00000
4900.0000
Question 2
(Y)
(\$1,000)
MARKET
REAL ESTATE DATA
(X1)
(X2)
(X3)
(Sq Feet)
AREA
(\$1,000)
(X4)
(Yes/No) (Number)
ASSESS BASEM BEDRM
128
No
1
101
No
1
(Area)
LOCAT
Loc 2
Loc 2
1
2
139
172
952
1255
3
4
5
178
180
183
1663
1575
1209
88
110
125
No
No
No
1
1
1
Loc 1
Loc 3
Loc 3
6
7
8
9
10
183
198
205
205
216
1689
1330
1437
1598
1377
81
147
143
103
123
No
Yes
Yes
Yes
Yes
1
1
1
1
1
Loc 1
Loc 1
Loc 2
Loc 1
Loc 1
1607
1225
1610
1157
1262
1426
1185
1580
1215
1444
1808
1925
1577
1010
1365
1342
1460
1573
1425
1629
1589
1855
1769
1768
1505
1824
1888
1607
1848
1792
1881
1936
1832
1873
1769
1691
1855
1824
1957
963
1178
1992
1545
1846
1593
2030
1920
1904
2170
2049
1159
1558
1467
2034
2004
2270
2275
2186
2319
2272
115,707
1,652.96
332.024
123
99
114
132
119
79
125
103
125
156
139
165
139
123
125
123
99
128
121
136
128
132
143
156
103
145
134
178
143
150
167
174
165
125
154
167
183
185
174
125
130
141
143
152
136
176
178
167
165
178
108
112
141
163
169
125
128
165
176
130
9,636
137.66
25.997
Yes
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
1
2
2
2
2
2
2
2
2
2
2
2
2
3
3
2
3
3
2
3
3
2
3
3
2
3
3
2
3
3
2
3
3
2
3
3
2
3
3
3
4
4
3
4
4
3
4
4
3
4
4
4
4
4
4
4
4
4
4
4
185
2.64
Loc 1
Loc 3
Loc 1
Loc 1
Loc 1
Loc 1
Loc 1
Loc 3
Loc 2
Loc 3
Loc 1
Loc 1
Loc 1
Loc 2
Loc 2
Loc 1
Loc 2
Loc 3
Loc 2
Loc 3
Loc 3
Loc 3
Loc 3
Loc 1
Loc 2
Loc 3
Loc 3
Loc 3
Loc 3
Loc 3
Loc 1
Loc 1
Loc 3
Loc 2
Loc 2
Loc 2
Loc 3
Loc 2
Loc 3
Loc 2
Loc 2
Loc 3
Loc 1
Loc 2
Loc 2
Loc 1
Loc 2
Loc 3
Loc 1
Loc 2
Loc 1
Loc 3
Loc 1
Loc 1
Loc 3
Loc 3
Loc 2
Loc 1
Loc 3
Loc 3
11
224
12
161
13
172
14
172
15
180
16
189
17
200
18
200
19
200
20
209
21
222
22
229
23
229
24
145
25
172
26
185
27
191
28
207
29
207
30
209
31
209
32
211
33
211
34
213
35
213
36
222
37
224
38
227
39
231
40
231
41
235
42
235
43
240
44
240
45
240
46
242
47
246
48
268
49
268
50
167
51
189
52
216
53
218
54
222
55
224
56
238
57
246
58
249
59
257
60
266
61
191
62
194
63
196
64
231
65
255
66
255
67
260
68
262
69
266
70
275
SUM 15045
AVG 214.929
St Dev 31.193
Dummy Variables:
X3 = Basement
X3 = 1 Finished Basement (Yes)
X3 = 0 No Finished Basement (No)
X5 = Location (Location 2 or not)
X5 = 1 if in location 2
X5 = 0 if not in location 2
X6 = Location (Location 3 or not)
X6 = 1 if in location 3
X6 = 0 if not in location 3
CORRELATION COEFFICIENTS
MARKET
AREA
ASSESS
BASEM
BEDRM
MARKET AREA ASSESS BASEM BEDRM LOCAT
1
0.8617
1
0.6924 0.5266
1
0.7016 0.5180 0.4405
1
0.4897 0.4787 0.4197 0.2761 1.0000
Question:
To answer the regression questions below, you first need to enter numeric
values (0 or 1) for BASEMENT column (X3), and 0 or 1 for LOCATION
variables (X5 and X6) as defined above.
(a) Use stepwise regression to determine the best Regression Model to
predict the market value of the houses. Which variables are included
in the model determined by stepwise regression?
(b) What is the estimated regression equation with these variables?
(c) What percentage of the total variations in market VALUEs
is explained by this regression model?
(d1) Using this regression model, calculate the predicted market
VALUE for a house in location 1 with a BASEMENT, AREA=1652
and ASSESS=137.
(d2) Using this regression model, calculate the predicted market
VALUE for a house in location 2 with a BASEMENT, AREA=1652
and ASSESS=137.
(d3) Using this regression model, calculate the predicted market
VALUE for a house in location 3 with a BASEMENT, AREA=1652
and ASSESS=137.
Page 4 of 4 (Homework 8)
Max
Min
275
139
Dr. Guder
USING EXCEL and PHStat
Page 0
Lecture
to Use
How
Class 1
Data Analysis
Data –> Data Analysis –> Histogram…..
2
Class 1
PHStat
PHStat –> Descriptive Statistics–> Histogram and Polygons
2a
Descriptive Statistics (Excel)
Descriptive Statistics (PHStat)
Class 1
Data Analysis
Data –> Data Analysis –> Descriptive Statistics…..
3
150
Class 2
Data Analysis
PHStat –> Descriptive Statistics–> Descriptive Summary…
4
150
Normal Distribution (probability Calculation)
Class 2
PHStat
PHStat –> Probability and Prob Distributions –> Normal …
5, 6
246
Mean (sigma known)
Class 2
PHStat
PHStat –> Confidence Interval –> Estimate for the Mean, sigma known …
7
302
Mean (sigma unknown)
Class 2
PHStat
PHStat –> Confidence Interval –> Estimate for the Mean, sigma unknown …
7
302
Proportion
Class 2
PHStat
PHStat –> Confidence Interval –> Estimate for the Proportion …
8
303
Mean
Class 2
PHStat

PHStat –> Sample Size –> Determination for the Mean
8
303
Proportion
Class 2
PHStat
PHStat –> Sample Size –> Determination for the Proportion
8
303
Mean (sigma known)
Class 3
PHStat
PHStat –> One-Sample Tests –> Z Test for the Mean, sigma known …
9
339
Mean (sigma unknown)
Class 3
PHStat
PHStat –> One-Sample Tests –> t Test for the Mean, sigma unknown …
10
339
Proportion
Class 3
PHStat
PHStat –> One-Sample Tests –> Z Test for the Proportion …
10
341
Data – Service Employees
Histogram, Frequency Distribution
Handout Textbook
Page(s)
Page(s)
1
95
Interval Estimation for
Sample Size for Estimating
Hypothesis Testing for
One-Sample Tests
Two-Sample Tests
Differences in Two Means (independent populat
Class 3
PHStat
PHStat —> Two-Sample Tests (Summarized Data) –> Pooled Variance t Test for…
11
382
Differences in Two Means (related populations)
Class 3
PHStat
PHStat –> Two-Sample Tests (Unsummarized) –> Paired t Test …
12
384
Class 4
Data Analysis
Analysis of Variance (ANOVA)
One-Way ANOVA
Tukey-Kramer Procedure Using PHStat
Two-Way ANOVA (Excel)
PHStat
Class 4
Two-Way ANOVA (PHStat)
Correlation
Data Analysis
PHStat
Class 6
Data Analysis
Data –> Data Analysis –> Anova: Single Factor
13
PHStat –> Multiple-Sample Tests –> One-Way ANOVA (Check Tukey-Kramer Proc)
15
424
Data –> Data Analysis –> Anova: Two Factor
14
426
PHStat –> Multiple-Sample Tests –> Two-Way ANOVA
16
Data –> Data Analysis –> Correlation …
17
Regression Analysis
Simple Linear Regression
Multiple Regression
Class 6
PHStat
PHStat –> Regression –> Simple Linear Regression …
18-19
520-523
Classes 7, 8
PHStat
PHStat –> Regression –> Multiple Regression …
20-21
564-569
Class 9
Excel
Drawing Line Chart (Graph) in Excel 2013
Line Chart
Starting PHStat
(MS Excel 2003, 2010, and 2013)
Step 0. Save the PHStat file (PHStat.xlam) to your desktop. When saved, it will be a shortcut looking an Excel file with the title PHStat2 or PHStat
Step 1. Double click on this shortcut. Choose Enable Macros option. This will add another pane called Add-in
Step 2. Select Add-in to get to PHStat
22
SERVICE EMPLOYEES (Occup Group = 10) DATA
(Explanations on pages 2-3 are based on the data shown below)
Page 1
Page 2
USING EXCEL TO OBTAIN HISTOGRAM and FREQUENCY DISTRIBUTION
Choose the following:
Data —> Data Analysis —> Histogram —> (Fill out the window as shown below)
Output/Result:
Frequency
0
1
9
12
4
2
1
Histogram
Frequency
Bins
15.0
25.0
35.0
45.0
55.0
65.0
75.0
15
10
5
0
15.0 25.0 35.0 45.0 55.0 65.0 75.0
Bins
USING PHStat TO OBTAIN HISTOGRAM
Page 2a
Choose the following:
PHStat  Descriptive Statistics  Histogram and Polygons
You need to provide Cell ranges for three different data:
 Variable Cell Range (The area/column that has the INCOME data – D5.D59 in my case.
Note D5 has the title/Label)
 Bins Cell Range (The area where you entered the interval points as 20, 30, 40, 50, 60, 70,
80, 90)
 Midpoints Cell Range (The area where you entered midpoints of the bin values as 25,
35, 45, 55, 65, 75, 85)
Suppose the data values are given in column D (D5.D59), where D5
has the column title/label. Bins (interval points) are entered in the
range S2.S10 and Midpoints are entered in the range T2.T9 as
shown below.
You will then enter the ranges in the
Histogram & Polygons windows as shown
below:
The result will be similar to the following:
USING EXCEL TO OBTAIN DESCRIPTIVE STATISTICS
Page 3
Choose the following:
Data —> Data Analysis —> Descriptive Statistics… —> (Fill out the window as shown below)
Output/Result:
INCOME (000)
Mean
31.483
Standard Error
2.300
Median
29.4
Mode
#N/A
Standard Deviation 12.388
Sample Variance 153.451
Kurtosis
-0.112
Skewness
0.855
Range
44
Minimum
15.3
Maximum
59.3
Sum
913
Count
29
Confidence Level (95%)
4.712
WORKed
Mean
Standard Error
Median
Mode
Standard Deviation
Sample Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence Level(95%)
EDUC
20.690
2.090
21
24
11.254
126.650
0.945
0.816
46
3
49
600
29
4.281
Mean
Standard Error
Median
Mode
Standard Deviation
Sample Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence Level (95%
12.862
0.343
13
14
1.846
3.409
-0.129
-0.478
7
9
16
373
29
0.702
USING PHSTAT TO OBTAIN DESCRIPTIVE STATISTICS (Single Group)
PHStat —> Descriptive Statistics –> Descriptive Summary –> (Select) Single Group Variable
–> (Fill out the window as shown below)
Descriptive Summary
INCOME
Mean
31.483
Median
29.4
Mode
#N/A
Minimum
15.3
Maximum
59.3
Range
44
Variance
153.451
Standard Deviation
12.388
Coeff. of Variation
39.35%
Skewness
0.855
Kurtosis
-0.112
Count
29
Standard Error
2.300
USING PHSTAT TO OBTAIN DESCRIPTIVE STATISTICS (Multiple Groups)
PHStat —> Descriptive Statistics –> Descriptive Summary –> (Select) Single Group Variable
–> (Fill out the window as shown below)
Descriptive Summary
0 (Female)
Mean
Median
Mode
Minimum
Maximum
Range
Variance
Standard Deviation
Coeff. of Variation
Skewness
Kurtosis
Count
Standard Error
22.991
21.6
#N/A
15.3
33.7
18.4
38.809
6.230
27.10%
0.477
-1.096
11
1.878
1 (Male)
36.672
33.9
#N/A
21.1
59.3
38.2
154.739
12.439
33.92%
0.501
-0.938
18
2.932
Page 4
Page 5
USING PHStat TO OBTAIN PROBABILITY FOR NORMAL DISTRIBUTION
Choose the following:
PHStat —> Probability & Prob Distributions —> Normal… —> (Fill out the window as shown below)
Example 1(a) – Class 2
Example 1(b) – Class 2
Output/Result:
Example 1(a)
Example 1(b)
Common Data
Mean
Standard Deviation
Common Data
68
2
Probability for a Range
From X Value
68
To X Value
70
Z Value for 68
0
Z Value for 70
1
P(X Estimate for Proportion… —> (Fill out the window as shown below)
Data
Sample Size
Number of Successes
Confidence Level
600
300
0.99
Intermediate Calculations
Sample Proportion
Z Value
Standard Error of the Proportion
Interval Half Width
0.5
-2.576
0.020
0.0526
Confidence Interval
Interval Lower Limit
Interval Upper Limit
0.447
0.553
USING PHStat TO DETERMINE the SAMPLE SIZE (n) for Mean
PHStat —> Sample Size —> Determination for the Mean…—> (Fill out the window as shown below)
Example 5
Data
Population Standard Deviation
Sampling Error
Confidence Level
4
1
95%
Intemediate Calculations
Z Value
Calculated Sample Size
-1.960
61.463
Result
Sample Size Needed
62
USING PHStat TO DETERMINE the SAMPLE SIZE (n) for Proportion
PHStat —> Sample Size —> Determination for the Proportion…—> (Fill out the window as shown below)
Example 8 – Class 2
Data
Estimate of True Proportion
Sampling Error
Confidence Level
0.50
0.03
0.99
Intermediate Calculations
Z Value
Calculated Sample Size
-2.58
1843.03
Result
Sample Size Needed
1844
USING PHStat FOR HYPOTHESIS TESTING for MEAN
Page 9
Choose the following:
PHStat —> One-Sample Tests —> Z Test for the Mean, sigma known… —>
(Fill out the window as shown below)
Example 1 – Class 3
Data
=
Null Hypothesis
Level of Significance
Population Standard Deviation
Sample Size
Sample Mean
64
0.05
0.5
25
63.85
Intermediate Calculations
Standard Error of the Mean
Z Test Statistic
0.1
-1.50
Two-Tail Test
Lower Critical Value
Upper Critical Value
p-Value
-1.960
1.960
0.1336
Do not reject the null hypothesis
PHStat —> One-Sample Tests —> Z Test for the Mean, sigma known… —>
Example 2 – Class 3
Data
Null Hypothesis
=
Level of Significance
Population Standard Deviation
Sample Size
Sample Mean
700
0.05
75
225
704
Intermediate Calculations
Standard Error of the Mean
Z Test Statistic
5
0.80
Upper-Tail Test
Upper Critical Value
p-Value
Do not reject the null hypothesis
1.645
0.212
PHStat —> One-Sample Tests —> t Test for the Mean, sigma unknown… —>
Page 10
(Fill out the window as shown below)
Example 4 – Class 3
Data
=
Null Hypothesis
Level of Significance
Sample Size
Sample Mean
Sample Standard Deviation
700
0.10
64
710
48
Intermediate Calculations
Standard Error of the Mean
Degrees of Freedom
t Test Statistic
6
63.00
1.6667
Upper-Tail Test
Ca
Lower Critical Value
Upper Critical Value
p-Value
-1.6694
1.2951
0.0503
Reject …