Course Hero has millions of student submitted documents similar to the one

below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.

Course Hero has millions of student submitted documents similar to the one below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.

6
Spreadsheet AGE
Module Assignment
First, save this spreadsheet to your computer files, adding your name to the filename (e.g. SmithM6Assign.xls).
Save in .xls format.
Open the file on your computer, work on the problems, re-saving your file in .xls format always
You may copy the data and/or problems to additional sheets in this workbook or work on this page.
To add a sheet, click Shift+F11. Or right-click on the sheet tab, which also allows you to rename the tab.
Attach the file in the assignment drop box for Module 6.
For this Problem, we will use the Bears data set provided in the Appendix B data on the CD or online -- click Tools for Success.
Copy the data set into this spreadsheet.
Please organize your work so that it is easily understood. Some people put each problem on a separate sheet with labelled tabs.
Be sure to show your calculations and/or paste in results from StatCrunch, DDXL, etc.
Remember to use 4-Step problem solving process: Declare, Strategize, Execute, Deduce. Always characterize the distributions.
Problem 1 (25 points)
Find the correlations of various pairs of data column: HeadLength, HeadWidth, Neck, Length, Chest, Weight (there will be 15 pairs)
Show and discuss a scatterplot with regression for the best-correlated pair, and one for the least-correlated pair.
HdLngth
HdLngth
HdWidth
Neck
Length
Chest
Weight
HdWidth
Neck
Length
Chest
Weight
1
1
<< fill in yellow cells
1
1
1
1
Problem 2 (25 points)
It is easier to measure Head Length and Head Width than anything else. Using Head Length as the Explanatory variable,
give regression equations for Neck, Length, Chest, Weight (as response variables), and provide each correlation value R (there will be 4)
Using the value of 12.95 for Head Length, predict each response variable. Which are you most / least confident about?
Note: the regression equation is a linear equation of the form: y = m*x + b, where m is the slope and b is the y-intercept
m and b are coefficients, and x and y are variables. Linear regression fits a line to the points, and determines the coefficients.
Using the Excel regression tool in the Analysis Toolpak, the coefficients are given in the bottom table.
StatCrunch regression provides the coefficients as intercept and slope
Statdisk correlation and regression calls the coefficients y-intercept and slope
DDXL simple regression refers to the y-intercept as the Constant coefficient
y
x value is 12.95 and you must calculate the predicted response variable y, for each of the 4 cases
x
0
m
Problem 3 (25 points)
Similar to problem 2, use Head Width as the Explanatory variable, and
give regression equations for Neck, Length, Chest, Weight (as response variables), and provide each correlation value R (there will be 4)
Using the value of 6.19 for Head Width, predict each response variable. Which are you most / least confident about?
y
This is similar to problem 2, except x value is 6.19
enter coeff's m and b
x
0
result
Problem 4 (25 points)
Since 12.95 is average Head Length, and 6.19 is average Head Width, comment on the calculated responses in problems 2 and 3,
and compare these responses with the average values of the data columns Neck, Length, Chest, Weight. (perhaps a table?)
Could scientists use the Head Length and Head Width and a database of previous data to make reasonable predictions about
other data for the bear population? Explain your point of view.
Neck
average
calc-P2
calc-P3
Length
Chest
b
12.95
result
Weight
<< enter averages from the respective data columns in Bears Data
<< enter calculated values from problem 2
<< enter calculated values from problem 3
m
b
6.19
enter coeff's m and b
MONTH
19
55
81
115
104
100
56
51
57
53
68
8
44
32
20
32
45
9
21
177
57
81
21
9
45
9
33
57
45
21
10
82
70
10
10
34
34
34
58
58
11
23
70
11
83
35
16
16
17
17
17
8
83
18
SEX
7
7
9
7
8
4
7
4
9
5
8
8
8
8
8
8
9
9
9
9
9
9
9
9
9
9
9
9
9
9
10
10
10
10
10
10
10
10
10
10
11
11
10
11
11
11
4
4
5
5
5
8
11
6
HEADLEN HEADWTH
1
1
1
1
2
2
1
1
2
2
1
1
2
1
2
1
1
2
1
1
2
2
1
1
1
1
1
2
2
1
1
2
2
1
1
1
1
1
2
1
1
1
1
2
2
1
1
1
1
2
2
2
1
1
11
16.5
15.5
17
15.5
13
15
13.5
13.5
12.5
16
9
12.5
14
11.5
13
13.5
9
13
16
12.5
13
13
10
16
10
13.5
13
13
14.5
9.5
13.5
14.5
11
11.5
13
16.5
14
13.5
15.5
11.5
12
15.5
9
14.5
13.5
10
10
11.5
11.5
11
10
15.5
12.5
5.5
9
8
10
6.5
7
7.5
8
7
6
9
4.5
4.5
5
5
8
7
4.5
6
9.5
5
5
5
4
6
4
6
5.5
6.5
5.5
4.5
6.5
6.5
5
5
7
6.5
5.5
6.5
7
6
6.5
7
5
7
8.5
4
5
5
5
4.5
4.5
8
8.5
NECK
LENGTH
16
28
31
31.5
22
21
26.5
27
20
18
29
13
10.5
21.5
17.5
21.5
24
12
19
30
19
20
17
13
24
13.5
22
17.5
21
20
16
28
26
17
17
21
27
24
21.5
28
16.5
19
28
15
23
23
15.5
15
17
15
13
10
30.5
18
53
67.5
72
72
62
70
73.5
68.5
64
58
73
37
63
67
52
59
64
36
59
72
57.5
61
54
40
63
43
66.5
60.5
60
61
40
64
65
49
47
59
72
65
63
70.5
48
50
76.5
46
61.5
63.5
48
41
53
52.5
46
43.5
75
57.3
CHEST
26
45
54
49
35
41
41
49
38
31
44
19
32
37
29
33
39
19
30
48
32
33
28
23
42
23
34
31
34.5
34
26
48
48
29
29.5
35
44.5
39
40
50
31
38
55
27
44
44
26
26
30.5
28
23
24
54
32.8
WEIGHT
80
344
416
348
166
220
262
360
204
144
332
34
140
180
105
166
204
26
120
436
125
132
90
40
220
46
154
116
182
150
65
356
316
94
86
150
270
202
202
365
79
148
446
62
236
212
60
64
114
76
48
29
514
140
HEADLEN
Problem 1 (25 points)
Find the correlations of various pairs of data column: HeadLength, HeadWidth, Neck, Length, Chest, Weight (there will be 15 pairs)
Show and discuss a scatterplot with regression for the best-correlated pair, and one for the least-correlated pair.
HdLngth
HdLngth
HdWidth
Neck
Length
Chest
Weight
HdWidth
1
0.75
0.88
0.92
0.86
0.83
Neck
1
0.82
0.74
0.78
0.78
Length
Chest
Weight
<< fill in yellow cells
1
1
0.87
0.93
0.93
0.89
0.86
1
0.96
1
Least Correlated
HeadWidth Vs Length
90
80
70
60
50
LENGTH
Linear Regression for LENGTH
40
30
20
10
0
3
4
5
6
7
8
9
10
11
We can see that the points are not so close to the line. Therefore this correlation is not strong
HeadWidth Vs Length
600
500
400
WEIGHT
300
Linear Regression for WEIGHT
200
100
0
15
20
25
30
35
40
45
50
55
60
We can see that the points are very close to the line. Therefore this correlation is very strong strong
HEADWTH
NECK
LENGTH
CHEST
WEIGHT
11
16.5
15.5
17
15.5
13
15
13.5
13.5
12.5
16
9
12.5
14
11.5
13
13.5
9
13
16
12.5
13
13
10
16
10
13.5
13
13
14.5
9.5
13.5
14.5
11
11.5
13
16.5
14
13.5
15.5
11.5
12
15.5
9
14.5
13.5
10
10
11.5
11.5
11
10
15.5
12.5
5.5
9
8
10
6.5
7
7.5
8
7
6
9
4.5
4.5
5
5
8
7
4.5
6
9.5
5
5
5
4
6
4
6
5.5
6.5
5.5
4.5
6.5
6.5
5
5
7
6.5
5.5
6.5
7
6
6.5
7
5
7
8.5
4
5
5
5
4.5
4.5
8
8.5
16
28
31
31.5
22
21
26.5
27
20
18
29
13
10.5
21.5
17.5
21.5
24
12
19
30
19
20
17
13
24
13.5
22
17.5
21
20
16
28
26
17
17
21
27
24
21.5
28
16.5
19
28
15
23
23
15.5
15
17
15
13
10
30.5
18
53
67.5
72
72
62
70
73.5
68.5
64
58
73
37
63
67
52
59
64
36
59
72
57.5
61
54
40
63
43
66.5
60.5
60
61
40
64
65
49
47
59
72
65
63
70.5
48
50
76.5
46
61.5
63.5
48
41
53
52.5
46
43.5
75
57.3
26
45
54
49
35
41
41
49
38
31
44
19
32
37
29
33
39
19
30
48
32
33
28
23
42
23
34
31
34.5
34
26
48
48
29
29.5
35
44.5
39
40
50
31
38
55
27
44
44
26
26
30.5
28
23
24
54
32.8
80
344
416
348
166
220
262
360
204
144
332
34
140
180
105
166
204
26
120
436
125
132
90
40
220
46
154
116
182
150
65
356
316
94
86
150
270
202
202
365
79
148
446
62
236
212
60
64
114
76
48
29
514
140
Correlation Matrix
HEADLEN
HEADLEN
1
HEADWTH
0.75
NECK
0.88
LENGTH
0.92
CHEST
0.86
WEIGHT
0.83
HEADWTH
1
0.82
0.74
0.78
0.78
NECK
1
0.87
0.93
0.93
LENGTH
1
0.89
0.86
CHEST
1
0.96
WEIGHT
1
Stategize: Make an orderly list. Use function CORREL. Rank the correlations.
Execute: HeadLength
HeadLength
HeadLength
HeadLength
HeadLength
HeadWidth
Least Correlated
HeadWidth
HeadWidth
HeadWidth
Neck
Neck
Neck
Length
Length
Best Correlated
Chest
HeadWidth
Neck
Length
Chest
Weight
Neck
Length
Chest
Weight
Length
Chest
Weight
Chest
Weight
Weight
0.75
0.88
0.92
0.86
0.83
0.82
0.74
0.78
0.78
0.87
0.93
0.93
0.89
0.86
0.96
Problem 2 (25 points)
It is easier to measure Head Length and Head Width than anything else. Using Head Length as the Explanatory variable,
give regression equations for Neck, Length, Chest, Weight (as response variables), and provide each correlation value R (there will be 4)
Using the value of 12.95 for Head Length, predict each response variable. Which are you most / least confident about?
Model C
Most confident
The R square and R in the length are the highest I confident that
Least confident
Model A
The R square and R in the Weight are the smallest I confident that
HEADLEN WEIGHT
11
16.5
15.5
17
15.5
13
15
13.5
13.5
12.5
16
9
12.5
14
11.5
13
13.5
9
13
16
12.5
13
13
10
16
10
13.5
13
13
14.5
9.5
13.5
14.5
11
11.5
13
16.5
14
13.5
15.5
11.5
12
15.5
9
14.5
13.5
10
10
11.5
11.5
11
10
15.5
12.5
80
344
416
348
166
220
262
360
204
144
332
34
140
180
105
166
204
26
120
436
125
132
90
40
220
46
154
116
182
150
65
356
316
94
86
150
270
202
202
365
79
148
446
62
236
212
60
64
114
76
48
29
514
140
NECK
LENGTH
CHEST
16
28
31
31.5
22
21
26.5
27
20
18
29
13
10.5
21.5
17.5
21.5
24
12
19
30
19
20
17
13
24
13.5
22
17.5
21
20
16
28
26
17
17
21
27
24
21.5
28
16.5
19
28
15
23
23
15.5
15
17
15
13
10
30.5
18
53
67.5
72
72
62
70
73.5
68.5
64
58
73
37
63
67
52
59
64
36
59
72
57.5
61
54
40
63
43
66.5
60.5
60
61
40
64
65
49
47
59
72
65
63
70.5
48
50
76.5
46
61.5
63.5
48
41
53
52.5
46
43.5
75
57.3
26
45
54
49
35
41
41
49
38
31
44
19
32
37
29
33
39
19
30
48
32
33
28
23
42
23
34
31
34.5
34
26
48
48
29
29.5
35
44.5
39
40
50
31
38
55
27
44
44
26
26
30.5
28
23
24
54
32.8
Problem 2 (25 points)
HEADLEN WEIGHT
It is easier to measure Head Length and Head Width than anything else. Using Head Length as the Explanatory variable ,
give regression equations for Neck, Length, Chest, Weight (as response variables), and provide each correlation value R (there will be 4)
Using the value of 12.95 for Head Length, predict each response variable. Which are you most / least confident about?
Note: the regression equation is a linear equation of the form: y = m*x + b, where m is the slope and b is the y-intercept
m and b are coefficients, and x and y are variables. Linear regression fits a line to the points, and determines the coefficients.
Using the Excel regression tool in the Analysis Toolpak, the coefficients are given in the bottom table.
StatCrunch regression provides the coefficients as intercept and slope
Statdisk correlation and regression calls the coefficients y-intercept and slope
DDXL simple regression refers to the y-intercept as the Constant coefficient
y
x value is 12.95 and you must calculate the predicted response variable y, for each of the 4 cases
Explanatory variable
Response variable
HEADLEN
WEIGHT
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.834185028
R Square
0.695864661
Adjusted R Square
0.690015904
Standard Error
67.81430364
Observations
54
ANOVA
df
Regression
Residual
Total
Intercept
HEADLEN
SS
MS
F
Significance F
1 547146.7848488 547146.8 118.9765 4.752016E-015
52 239136.5484846 4598.78
53 786283.3333333
Coefficients Standard Error
t Stat
P-value
Lower 95% Upper 95%
Lower 95.0%pper 95.0%
U
-430.98153 57.0305555603 -7.557028 6.41E-010 -545.4217118 -316.5413 -545.4217 -316.5413
47.3895677
4.344623307 10.90764 4.75E-015 38.6714432177 56.10769 38.67144 56.10769
182.7134
result
x
m
b
12.95 47.38957 -430.9815
enter coeff's m and b
11
16.5
15.5
17
15.5
13
15
13.5
13.5
12.5
16
9
12.5
14
11.5
13
13.5
9
13
16
12.5
13
13
10
16
10
13.5
13
13
14.5
9.5
13.5
14.5
11
11.5
13
16.5
14
13.5
15.5
11.5
12
15.5
9
14.5
13.5
10
10
11.5
11.5
11
10
15.5
12.5
80
344
416
348
166
220
262
360
204
144
332
34
140
180
105
166
204
26
120
436
125
132
90
40
220
46
154
116
182
150
65
356
316
94
86
150
270
202
202
365
79
148
446
62
236
212
60
64
114
76
48
29
514
140
Problem 2 (25 points)
HEADLEN NECK
It is easier to measure Head Length and Head Width than anything else. Using Head Length as the Explanatory variable,
give regression equations for Neck, Length, Chest, Weight (as response variables), and provide each correlation value R (there will be 4)
Using the value of 12.95 for Head Length, predict each response variable. Which are you most / least confident about?
Note: the regression equation is a linear equation of the form: y = m*x + b, where m is the slope and b is the y-intercept
m and b are coefficients, and x and y are variables. Linear regression fits a line to the points, and determines the coefficients.
Using the Excel regression tool in the Analysis Toolpak, the coefficients are given in the bottom table.
StatCrunch regression provides the coefficients as intercept and slope
Statdisk correlation and regression calls the coefficients y-intercept and slope
DDXL simple regression refers to the y-intercept as the Constant coefficient
y
x value is 12.95 and you must calculate the predicted response variable y, for each of the 4 cases
Explanatory variable
Response variable
HEADLEN
NECK
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.884806974
R Square
0.78288338
Adjusted R Square .778708061
0
Standard Error 2.653484621
Observations
54
ANOVA
df
Regression
Residual
Total
Intercept
HEADLEN
SS
MS
F Significance F
1 1320.202340459 1320.202 187.5026 7.03E-019
52 366.1309928741 7.040981
53 1686.333333333
Coefficients Standard Error
t Stat
P-value Lower 95%Upper 95%
Lower 95.0%pper 95.0%
U
-9.59845131
2.2315307237 -4.301286 7.50E-005 -14.07635 -5.120557 -14.07635 -5.120557
2.327828979
0.1699994029 13.69316 7.03E-019
1.9867 2.668958
1.9867 2.668958
20.54693
result
x
m
b
12.95 2.327829 -9.598451
enter coeff's m and b
11
16.5
15.5
17
15.5
13
15
13.5
13.5
12.5
16
9
12.5
14
11.5
13
13.5
9
13
16
12.5
13
13
10
16
10
13.5
13
13
14.5
9.5
13.5
14.5
11
11.5
13
16.5
14
13.5
15.5
11.5
12
15.5
9
14.5
13.5
10
10
11.5
11.5
11
10
15.5
12.5
16
28
31
31.5
22
21
26.5
27
20
18
29
13
10.5
21.5
17.5
21.5
24
12
19
30
19
20
17
13
24
13.5
22
17.5
21
20
16
28
26
17
17
21
27
24
21.5
28
16.5
19
28
15
23
23
15.5
15
17
15
13
10
30.5
18
Problem 2 (25 points)
HEADLEN LENGTH
It is easier to measure Head Length and Head Width than anything else. Using Head Length as the Explanatory variable,
give regression equations for Neck, Length, Chest, Weight (as response variables), and provide each correlation value R (there will be 4)
Using the value of 12.95 for Head Length, predict each response variable. Which are you most / least confident about?
Note: the regression equation is a linear equation of the form: y = m*x + b, where m is the slope and b is the y-intercept
m and b are coefficients, and x and y are variables. Linear regression fits a line to the points, and determines the coefficients.
Using the Excel regression tool in the Analysis Toolpak, the coefficients are given in the bottom table.
StatCrunch regression provides the coefficients as intercept and slope
Statdisk correlation and regression calls the coefficients y-intercept and slope
DDXL simple regression refers to the y-intercept as the Constant coefficient
y
x value is 12.95 and you must calculate the predicted response variable y, for each of the 4 cases
Explanatory variable
Response variable
HEADLEN
LENGTH
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.919951335
R Square
0.84631046
Adjusted R Square .843354892
0
Standard Error 4.235234314
Observations
54
ANOVA
df
Regression
Residual
Total
Intercept
HEADLEN
SS
MS
F Significance F
1 5136.220095677 5136.22 286.3444 8.50E-023
52 932.734904323 17.93721
53
6068.955
Coefficients Standard Error
t Stat
P-value Lower 95%Upper 95%
Lower 95.0%pper 95.0%
U
-0.8599962
3.5617525051 -0.241453 0.810154 -8.007175 6.287183 -8.007175 6.287183
4.591479335
0.2713365282 16.92171 8.50E-023 4.047003 5.135956 4.047003 5.135956
58.59966
result
x
m
b
12.95 4.591479 -0.859996
enter coeff's m and b
11
16.5
15.5
17
15.5
13
15
13.5
13.5
12.5
16
9
12.5
14
11.5
13
13.5
9
13
16
12.5
13
13
10
16
10
13.5
13
13
14.5
9.5
13.5
14.5
11
11.5
13
16.5
14
13.5
15.5
11.5
12
15.5
9
14.5
13.5
10
10
11.5
11.5
11
10
15.5
12.5
53
67.5
72
72
62
70
73.5
68.5
64
58
73
37
63
67
52
59
64
36
59
72
57.5
61
54
40
63
43
66.5
60.5
60
61
40
64
65
49
47
59
72
65
63
70.5
48
50
76.5
46
61.5
63.5
48
41
53
52.5
46
43.5
75
57.3
Problem 2 (25 points)
HEADLEN
It is easier to measure Head Length and Head Width than anything else. Using Head Length as the Explanatory variable,
give regression equations for Neck, Length, Chest, Weight (as response variables), and provide each correlation value R (there will be 4)
Using the value of 12.95 for Head Length, predict each response variable. Which are you most / least confident about?
Note: the regression equation is a linear equation of the form: y = m*x + b, where m is the slope and b is the y-intercept
m and b are coefficients, and x and y are variables. Linear regression fits a line to the points, and determines the coefficients.
Using the Excel regression tool in the Analysis Toolpak, the coefficients are given in the bottom table.
StatCrunch regression provides the coefficients as intercept and slope
Statdisk correlation and regression calls the coefficients y-intercept and slope
DDXL simple regression refers to the y-intercept as the Constant coefficient
y
x value is 12.95 and you must calculate the predicted response variable y, for each of the 4 cases
Explanatory variable
Response variable
HEADLEN
CHEST
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.862456042
R Square
0.743830424
Adjusted R Square .738904086
0
Standard Error 4.778503793
Observations
54
ANOVA
df
Regression
Residual
Total
Intercept
HEADLEN
SS
MS
F Significance F
1 3447.732804026 3447.733 150.9905 5.31E-017
52
1187.3731219 22.8341
53 4635.105925926
Coefficients Standard Error
t Stat
P-value Lower 95%Upper 95%
Lower 95.0%pper 95.0%
U
-13.066495
4.0186319318 -3.251478 0.002018 -21.13047 -5.00252 -21.13047 -5.00252
3.761816627
0.306141888 12.28782 5.31E-017 3.147498 4.376135 3.147498 4.376135
35.64903
result
x
m
b
12.95 3.761817 -13.0665
enter coeff's m and b
CHEST
11
16.5
15.5
17
15.5
13
15
13.5
13.5
12.5
16
9
12.5
14
11.5
13
13.5
9
13
16
12.5
13
13
10
16
10
13.5
13
13
14.5
9.5
13.5
14.5
11
11.5
13
16.5
14
13.5
15.5
11.5
12
15.5
9
14.5
13.5
10
10
11.5
11.5
11
10
15.5
12.5
26
45
54
49
35
41
41
49
38
31
44
19
32
37
29
33
39
19
30
48
32
33
28
23
42
23
34
31
34.5
34
26
48
48
29
29.5
35
44.5
39
40
50
31
38
55
27
44
44
26
26
30.5
28
23
24
54
32.8
Problem 3 (25 points)
Similar to problem 2, use Head Width as the Explanatory variable, and
give regression equations for Neck, Length, Chest, Weight (as response variables), and provide each correlation value R (there will be 4)
Using the value of 6.19 for Head Width, predict each response variable. Which are you most / least confident about?
Model B
Most confident
The R square and R in the neck are the highest I confident that
Least confident
Model C
The R square and R in the length are the smallest I confident that
HEADWTH
WEIGHT
NECK
LENGTH
CHEST
5.5
9
8
10
6.5
7
7.5
8
7
6
9
4.5
4.5
5
5
8
7
4.5
6
9.5
5
5
5
4
6
4
6
5.5
6.5
5.5
4.5
6.5
6.5
5
5
7
6.5
5.5
6.5
7
6
6.5
7
5
7
8.5
4
5
5
5
4.5
4.5
8
8.5
80
344
416
348
166
220
262
360
204
144
332
34
140
180
105
166
204
26
120
436
125
132
90
40
220
46
154
116
182
150
65
356
316
94
86
150
270
202
202
365
79
148
446
62
236
212
60
64
114
76
48
29
514
140
16
28
31
31.5
22
21
26.5
27
20
18
29
13
10.5
21.5
17.5
21.5
24
12
19
30
19
20
17
13
24
13.5
22
17.5
21
20
16
28
26
17
17
21
27
24
21.5
28
16.5
19
28
15
23
23
15.5
15
17
15
13
10
30.5
18
53
67.5
72
72
62
70
73.5
68.5
64
58
73
37
63
67
52
59
64
36
59
72
57.5
61
54
40
63
43
66.5
60.5
60
61
40
64
65
49
47
59
72
65
63
70.5
48
50
76.5
46
61.5
63.5
48
41
53
52.5
46
43.5
75
57.3
26
45
54
49
35
41
41
49
38
31
44
19
32
37
29
33
39
19
30
48
32
33
28
23
42
23
34
31
34.5
34
26
48
48
29
29.5
35
44.5
39
40
50
31
38
55
27
44
44
26
26
30.5
28
23
24
54
32.8
Problem 3 (25 points)
Similar to problem 2, use Head Width as the Explanatory variable, and
give regression equations for Neck, Length, Chest, Weight (as response variables), and provide each correlation value R (there will be 4)
Using the value of 6.19 for Head Width, predict each response variable. Which are you most / least confident about?
HEADWTH
This is similar to problem 2, except x value is 6.19
y
182.6084
result
Explanatory variable
Response variable
HEADWTH
WEIGHT
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.783483689
R Square
0.613846691
Adjusted R Square
0.606420666
Standard Error
76.4130867
Observations
54
ANOVA
df
Regression
Residual
Total
Intercept
HEADWTH
SS
MS
F Significance F
1 482657.4227111 482657.4 82.66154 2.50E-012
52 303625.9106222 5838.96
53 786283.3333333
Coefficients Standard Error
t Stat
P-value Lower 95%Upper 95%
Lower 95.0%pper 95.0%
U
-208.002063 44.2332339819 -4.702393 1.93E-005 -296.7625 -119.2416 -296.7625 -119.2416
63.10347198
6.9406717194 9.091839 2.50E-012
49.176 77.03095
49.176 77.03095
x
m
b
6.19 63.10347 -208.0021
enter coeff's m and b
WEIGHT
5.5
9
8
10
6.5
7
7.5
8
7
6
9
4.5
4.5
5
5
8
7
4.5
6
9.5
5
5
5
4
6
4
6
5.5
6.5
5.5
4.5
6.5
6.5
5
5
7
6.5
5.5
6.5
7
6
6.5
7
5
7
8.5
4
5
5
5
4.5
4.5
8
8.5
80
344
416
348
166
220
262
360
204
144
332
34
140
180
105
166
204
26
120
436
125
132
90
40
220
46
154
116
182
150
65
356
316
94
86
150
270
202
202
365
79
148
446
62
236
212
60
64
114
76
48
29
514
140
Problem 3 (25 points)
Similar to problem 2, use Head Width as the Explanatory variable, and
give regression equations for Neck, Length, Chest, Weight (as response variables), and provide each correlation value R (there will be 4)
Using the value of 6.19 for Head Width, predict each response variable. Which are you most / least confident about?
HEADWTH
This is similar to problem 2, except x value is 6.19
y
20.54198
result
Explanatory variable
Response variable
HEADWTH
NECK
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.818765163
R Square
0.670376393
Adjusted R Square
0.664037477
Standard Error 3.269483836
Observations
54
ANOVA
df
Regression
Residual
Total
Intercept
HEADWTH
SS
MS
F Significance F
1 1130.478056606 1130.478 105.7557 3.92E-014
52 555.8552767274 10.68952
53 1686.333333333
Coefficients Standard Error
t Stat
P-value Lower 95%Upper 95%
Lower 95.0%pper 95.0%
U
1.637905351
1.8926057008 0.865423 0.390782 -2.159886 5.435697 -2.159886 5.435697
3.053970437
0.296970257 10.28376 3.92E-014 2.458056 3.649885 2.458056 3.649885
x
m
6.19
b
3.05397 1.637905
enter coeff's m and b
NECK
5.5
9
8
10
6.5
7
7.5
8
7
6
9
4.5
4.5
5
5
8
7
4.5
6
9.5
5
5
5
4
6
4
6
5.5
6.5
5.5
4.5
6.5
6.5
5
5
7
6.5
5.5
6.5
7
6
6.5
7
5
7
8.5
4
5
5
5
4.5
4.5
8
8.5
16
28
31
31.5
22
21
26.5
27
20
18
29
13
10.5
21.5
17.5
21.5
24
12
19
30
19
20
17
13
24
13.5
22
17.5
21
20
16
28
26
17
17
21
27
24
21.5
28
16.5
19
28
15
23
23
15.5
15
17
15
13
10
30.5
18
Problem 3 (25 points)
Similar to problem 2, use Head Width as the Explanatory variable, and
give regression equations for Neck, Length, Chest, Weight (as response variables), and provide each correlation value R (there will be 4)
Using the value of 6.19 for Head Width, predict each response variable. Which are you most / least confident about?
HEADWTH
This is similar to problem 2, except x value is 6.19
y
58.59354
result
Explanatory variable
Response variable
HEADWTH
LENGTH
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.735446819
R Square
0.540882024
Adjusted R Square
0.532052832
Standard Error 7.320107107
Observations
54
ANOVA
df
Regression
Residual
Total
Intercept
HEADWTH
SS
MS
F Significance F
1 3282.588661052 3282.589 61.26065 2.39E-010
52 2786.366338948 53.58397
53
6068.955
Coefficients Standard Error
t Stat
P-value Lower 95%Upper 95%
Lower 95.0%pper 95.0%
U
26.38042856
4.2373894891 6.225632 8.46E-008 17.87748 34.88337 17.87748 34.88337
5.204056377
0.6648921352 7.826918 2.39E-010 3.869853 6.53826 3.869853 6.53826
x
m
b
6.19 5.204056 26.38043
enter coeff's m and b
LENGTH
5.5
9
8
10
6.5
7
7.5
8
7
6
9
4.5
4.5
5
5
8
7
4.5
6
9.5
5
5
5
4
6
4
6
5.5
6.5
5.5
4.5
6.5
6.5
5
5
7
6.5
5.5
6.5
7
6
6.5
7
5
7
8.5
4
5
5
5
4.5
4.5
8
8.5
53
67.5
72
72
62
70
73.5
68.5
64
58
73
37
63
67
52
59
64
36
59
72
57.5
61
54
40
63
43
66.5
60.5
60
61
40
64
65
49
47
59
72
65
63
70.5
48
50
76.5
46
61.5
63.5
48
41
53
52.5
46
43.5
75
57.3
Problem 3 (25 points)
Similar to problem 2, use Head Width as the Explanatory variable, and
give regression equations for Neck, Length, Chest, Weight (as response variables), and provide each correlation value R (there will be 4)
Using the value of 6.19 for Head Width, predict each response variable. Which are you most / least confident about?
HEADWTH
This is similar to problem 2, except x value is 6.19
y
35.64157
result
Explanatory variable
Response variable
HEADWTH
CHEST
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.778527096
R Square
0.60610444
Adjusted R Square .598529525
0
Standard Error 5.925414049
Observations
54
ANOVA
df
Regression
Residual
Total
Intercept
HEADWTH
SS
MS
F Significance F
1 2809.358279923 2809.358 80.01469 4.22E-012
52 1825.747646003 35.11053
53 4635.105925926
Coefficients Standard Error
t Stat
P-value Lower 95%Upper 95%
Lower 95.0%pper 95.0%
U
5.840762385
3.4300436925 1.702824 0.094572 -1.042124 12.72365 -1.042124 12.72365
4.814346282
0.5382108679 8.945093 4.22E-012 3.734347 5.894345 3.734347 5.894345
x
m
b
6.19 4.814346 5.840762
enter coeff's m and b
CHEST
5.5
9
8
10
6.5
7
7.5
8
7
6
9
4.5
4.5
5
5
8
7
4.5
6
9.5
5
5
5
4
6
4
6
5.5
6.5
5.5
4.5
6.5
6.5
5
5
7
6.5
5.5
6.5
7
6
6.5
7
5
7
8.5
4
5
5
5
4.5
4.5
8
8.5
26
45
54
49
35
41
41
49
38
31
44
19
32
37
29
33
39
19
30
48
32
33
28
23
42
23
34
31
34.5
34
26
48
48
29
29.5
35
44.5
39
40
50
31
38
55
27
44
44
26
26
30.5
28
23
24
54
32.8
Problem 4 (25 points)
Since 12.95 is average Head Length, and 6.19 is average Head Width, comment on the calculated responses in problems 2 and 3,
and compare these responses with the average values of the data columns Neck, Length, Chest, Weight. (perhaps a table?)
Could scientists use the Head Length and Head Width and a database of previous data to make reasonable predictions about
other data for the bear population? Explain your point of view.
average
calc-P2
calc-P3
Neck
Length
Chest
Weight
20.56
58.62
35.66
182.89 << enter averages from the respective data columns in Bears Data
20.55
58.60
35.65
182.71 << enter calculated values from problem 2
20.54
58.59
35.64
182.61 << enter calculated values from problem 3
As we can see the results are very similar to the real averages of the samples.
This results happened because we use the averages of our explanatory variables
Yes they can since we use a large sample and our models are pretty well predictors!
We also need to worry about variation in actual bears vs. predicting the average
WEIGHT
NECK
LENGTH
CHEST
80
344
416
348
166
220
262
360
204
144
332
34
140
180
105
166
204
26
120
436
125
132
90
40
220
46
154
116
182
150
65
356
316
94
86
150
270
202
202
365
79
148
446
62
236
212
60
64
114
76
48
29
514
140
16
28
31
31.5
22
21
26.5
27
20
18
29
13
10.5
21.5
17.5
21.5
24
12
19
30
19
20
17
13
24
13.5
22
17.5
21
20
16
28
26
17
17
21
27
24
21.5
28
16.5
19
28
15
23
23
15.5
15
17
15
13
10
30.5
18
53
67.5
72
72
62
70
73.5
68.5
64
58
73
37
63
67
52
59
64
36
59
72
57.5
61
54
40
63
43
66.5
60.5
60
61
40
64
65
49
47
59
72
65
63
70.5
48
50
76.5
46
61.5
63.5
48
41
53
52.5
46
43.5
75
57.3
26
45
54
49
35
41
41
49
38
31
44
19
32
37
29
33
39
19
30
48
32
33
28
23
42
23
34
31
34.5
34
26
48
48
29
29.5
35
44.5
39
40
50
31
38
55
27
44
44
26
26
30.5
28
23
24
54
32.8

**Find millions of documents on Course Hero - Study Guides, Lecture Notes, Reference Materials, Practice Exams and more. Course Hero has millions of course specific materials providing students with the best way to expand their education.**

Below is a small sample set of documents:

U. Memphis - ENGL - 1020

Bryant HulbertMarch 17, 2008English 1020Tonya P. ParhamMalcolm XMalcolm X was actually a pretty descent movie.Afterreading the book and then watching the movie, I can say thatSpike Lee did a pretty good job at putting together the mostimportant a

CUNY Baruch - ECON - 1001

MA 611 ProbabilityFinal ExaminationDecember 10, 2012to be emailed or handed in on December 19, 2012 by 6:00pm(I) Show your work. I may not give credit for a correct answer if you donot show how you got it.(II) Where possible, give answers as fractio

CUNY Baruch - ECON - 1001

The average returns, standard deviations and betas for three funds are given below along withdata for the S&P 500 index. The risk free return during the sample period is 6%.FundABCS& P 500A v g . R e tu rn1 3 .6 %1 3 .1 %1 2 .4 %1 2 .0 %S t. D

CUNY Baruch - ECON - 1001

Statistics for Behavioral Sciences Final ExamFor the following variables, please indicate if the variable is categorical or continuous:1. Gender: categorical2. Age: continuous3. Income (in amount of dollars earned per month): continuous4. Income (cla

CUNY Baruch - ECON - 1001

Instructions for Statistics Final Project (Report)Keep in MindThe purpose of this project is to demonstrate your ability to use the statistical techniques welearned in class to analyze a topic of your own and to communicate your conclusions. To thisen

CUNY Baruch - ECON - 1001

527 Trigonometric IntegralsTrigonometric Integrals Kwai Bon ChiuP. 1Today we will look into integrals of the form sinmx cos n x dx and secmx tan n x dxwhere either m or n is a postive integer.e.g. sin x cos x dx3 cos x sin x dx4cos 3 x si

CUNY Baruch - ECON - 1001

528 Trigonometric SubstitutionTrigonometric Substitution Kwai Bon ChiuP. 1Today we will look into another integration techniquewhich is Trigonometric Substitution. You shouldrealize that, in comparison, integration techniques arefar more interestin

CUNY Baruch - ECON - 1001

530 Partial FractionsPartial Fractions Kwai Bon ChiuP. 123andare added togetherx+32x 1the result can be expressed as a single compound fraction.If the two fractions232 ( 2 x 1) + 3( x + 3)7x + 7+==( x + 3)( 2 x 1)( x + 3)( 2 x 1)x + 3

CUNY Baruch - ECON - 1001

540 Numerical IntegrationNumerical Integration Kwai Bon ChiuP. 1Some elementary functions simply donot have integration. For examplesin x 2 ,1 x3 ,cos x,xx cos xIf we need to evaluate a definite integralinvolving a function f(x) whose antider

CUNY Baruch - ECON - 1001

550 L'Hopital's Rule< LHopitals Rule > Kwai Bon ChiuRecall:P. 11. limx 22. limx2x2 4x2x 2x23x 2 1x 4 x 23. lime x cos x4. limx 02xTranscendental functionAlgebraic function Kwai Bon ChiuP. 21550 L'Hopital's RuleLHopitals RuleLet

CUNY Baruch - ECON - 1001

555 SequenceSequence Kwai Bon ChiuP. 1Definition of SequenceA sequence cfw_an is a special kind of function (mapping) whosedomain is a set of positive integers.The Sequence cfw_an = cfw_ a1, a2, a3, a4, , an, 1st term of the sequencenth term of t

CUNY Baruch - ECON - 1001

580 Taylor PolynomialsTaylor Polynomials&Approximation Kwai Bon ChiuP. 11st degree polynomialfunctionP ( x) = a0 + a1 x12nd degree polynomialfunctionP2 ( x) = a0 + a1 x + a2 x 23rd degree polynomialfunctionP3 ( x) = a0 + a1 x + a2 x 2 + a3

CUNY Baruch - ECON - 1001

585 Power Series IPower Series I Kwai Bon ChiuP. 1Power SeriesPower Series is an infinite series in the formMore generally, series of the formseries centered at c.Note : a (x c )n =0nn a (x c )n =0nna xn =0nnis a power= a0 + a1 ( x c

CUNY Baruch - ECON - 1001

590 Power Series IIPower Series II(Taylor and Maclaurin Series)If f is represented by a power series f ( x) = an ( x c ) fornall x in an open interval I containing c, then an =f ( n ) (c )andn!f ' ' (c )f ( n ) (c )2f ( x) = f (c) + f ' (c)( x

Berkeley - PROBABILIT - 200

CUNY City - MGMT - 3100

Assignment #4 InventoryQuestion 1 (10 points)Review Question #1. In what ways are service sector inventory problems different from typicalmanufacturing inventory problems? (You will need to consult the text for more detailed answer).Question 2 (10 poi

CUNY City - MGMT - 3100

Assignment #5 Waiting line management (Queue)Task 1 (Excel)a)Submit the entire assignment in Excel (2 points)b)Submit a portion of the assignment in Excel (1 points)c)Use formula in Excel to solve the problem (leave the formula in the cell) (3 poin

CUNY City - MGMT - 3100

Assignment #6 Project Management (PM)Question 1 (25 points)You are given the task of producing a new CD for an aspiring artist. In the past, you know that thistype of assignment can be broken into the following activitiesImmediate TimePredecessor Nee

CUNY City - MGMT - 3100

Assignment #7 ForecastingTask 1 (Excel)a)Submit the entire assignment in Excel (2 points)b)Submit a portion of the assignment in Excel (1 points)c)Use formula in Excel to solve the problem (leave the formula in the cell) (3 points)d)Submit only M

CUNY City - MGMT - 3100

Informatica 35 (2011) 289321289Regression Test Selection Techniques: A SurveySwarnendu Biswas and Rajib MallDept. of Computer Science and EngineeringIIT Kharagpur, India - 721302E-mail: cfw_swarnendu, rajib@cse.iitkgp.ernet.inManoranjan Satpathy an

CUNY City - MGMT - 3100

Production & Operation Management courseInventory ManagementIntroductionAn inventory is a stock or store of goods. A typical firm has about 30 percent of itscurrent assets and as much as 90 percent of its working capital invested in inventory.Because

CUNY City - MGMT - 3100

Management JeopardyHistoricalViewsEthics &SocialResponsibilityGlobalizationPlanning andControllingStrategicManagement100100100100100200200200200200300300300300300400400400400400500500500500500All questions and answers w

CUNY City - MGMT - 3100

Equilibrium uniqueness with perfect complementsEilon Solan and Nicolas VieilleMay 15, 2003JEL classication: D50Keywords: Equilibrium uniqueness, perfect complements, networks.IntroductionIt is sometimes the case that some economic goods are perfect

CUNY City - MGMT - 3100

MGT 3121Chapters M13Topics Service and inventory? Managing cost of inventoryInventory?! Types of inventory systemsService and inventory?Service co. need inventory? Serice co. needs inventory? Serice is intangible. Special issues for serice co.

CUNY City - MGMT - 3100

MGT 3121Chapter M14Waiting LineManagementProblems of queuing (Q) orwaiting line management The need and implication of having a Q Goal of waiting-line management Characteristics of waiting-linemanagement Estimating the performance ofdifference

CUNY City - MGMT - 3100

MGT 3121Chapter M15ProjectManagementCharacteristics of a project Involves many sub-tasks Sub-tasks has "dependencies" Many sub-tasks may be active at thesame time (run in parallel) Often it is a one-time eventExamples of projectTerminologies C

CUNY City - MGMT - 3100

The Porsche ShopThe Porsche Shop specializes in the restoration of vintage Porsche automobiles.One of Jensen's regular customer asked him to prepare an estimate for therestoration of a 1964 model 357SC Porsche. Jensen broken this job into thefour acti

CUNY City - MGMT - 3100

STAT503Lecture Notes: Chapter 51Chapter 5:Sampling DistributionsSeptember 18, 20095.1 IntroductionThe variability among random samples from the same population is called sampling variability.A probability distribution that characterizes an aspect

CUNY City - MGMT - 3100

File: mod07, Module 07: Strategy and Strategic ManagementMultiple Choice1. A comprehensive action plan that identifies long term direction for an organization iscalled a(n) _.a) competitive advantageb) strategyc) objectived) ideaAns: bResponse: S

CUNY City - MGMT - 3100

7. Group & TeamUnderstanding Key Conceptl Groups involve two or more people working together regularly to achieve common goals.l Synergy is the creation of a whole greater than the sum of its parts.l Social loafing occurs when people work less hard in

CUNY City - MGMT - 3100

18The Application ofQueueing TheoryQueueing theory has enjoyed a prominent place among the modern analytical techniquesof OR. However, the emphasis thus far has been on developing a descriptive mathematical theory. Thus, queueing theory is not directl

CUNY City - MGMT - 3100

5 Interpersonal Conflict Management Styles of Jordanian ManagersKamil KozanWriters on organizations have long stressed the need for an organizational science applicable tonon-Western as well as Western cultures. In a review of the literature M. N. Kigg

Ashford University - BUS - 311

Date Taken:Time Spent:Points Received:Question Type:True/FalseMultiple ChoiceGrade Details - All Questions12/17/201240 min , 35 secs13 / 20 (65%)# Of Questions:416# Correct:491. Question :A person who holds a freehold estate only for his

Ashford University - BUS - 311

Date Taken:Time Spent:Points Received:Question Type:True/FalseMultiple ChoiceGrade Details - All Questions12/10/201212 min , 34 secs15 / 20 (75%)# Of Questions:317# Correct:2131. Question :The party to whom a promise is made in the making

Ashford University - BUS - 311

These are the automatically computed resultsof your exam. Grades for essay questions, andcomments from your instructor, are in the"Details" section below.Question Type:True/FalseMultiple ChoiceGrade Details - All QuestionsDate Taken:Time Spent:P

Ashford University - BUS - 311

Contracts Within Business1Running head: CONTRACTSContracts, What Are They? How Do They Work?Naomi L JohnsonBUS311 Business Law IInstructor Marla Muse12/17/2012AbstractIn this paper, I will reflect on the operation of contracts. Business law shows

Ashford University - BUS - 311

Discuss Question #25 in Chapter 31: Should the law allow an employer to fire an employeewithout having a good reason? Conduct research to provide examples to support your position oneach question, using your own personal employment experiences when poss

Ashford University - BUS - 311

Discuss Questions #27 and # 28 in Chapter 28: Why is it important to society that the law protectintellectual property? Should an individual who is offered an employment position sign anemployment contract that contains a restrictive covenant? Conduct r

Allen University - ECON - 123

UNIVERSITY OF MAURITIUSFACULTY OF ENGINEERINGFIRST SEMESTER EXAMINATIONSDECEMBER 2007PROGRAMMEBEng (Hons) Mechatronics - Level 3MODULE NAMEFactory AutomationDATEMonday10December 2007MODULE CODEMECH 3060(5)TIME9:30 11:30 HoursDURATION2 Hou

Allen University - ECON - 123

UNIVERSITYUNIVERSITY OF MAURITIUSFACULTYFACULTY OF ENGINEERINGFIRST SEMESTER EXAMINATIONSDECEMBER 2008PROGRAMMEBEng (Hons) Mechatronics Level 3MODULE NAME Factory AutomationDATEWednesdayMODULE CODEMECH 3060(5)3 December 2008TIME13:30 15:30

Syracuse - EAR - 105

EarthSciencetest#310/15/2012Pyroclasticflowsextremelyfast,unabletooutrunitVolcanoesandMagnetismCanstandrelativelyclosesometimesinordertovieweruption,shouldbewearing properequipment3maintectonicsitedonearthproducinglava:OceanridgessubductionzonesH

Syracuse - EAR - 105

EAR 105 EXAM 1 Review Sheet. The questions below are take home messages of what I was trying to achieve during lecture. DISCLAIMER: This is not necessarily a comprehensive list of things that may be on the exam,

Syracuse - EAR - 105

Geology 305Fall 2011, TTh 9:00 10:20 amQuiz 2, Chs 1, 24, 2, & 3Version #1KEY(NOTE: question numbers and order may vary from your test)MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.1. Mechanis

Syracuse - EAR - 105

Exam 4 Review Sheet Geologic Time (Lecture was called Catastrophes in Earths History ) When did life appear on Earth? What is the Goldilocks Scenario and what is the Habitable Zone? Why is Earths molten oute

Syracuse - EAR - 105

EAR 105 Lecture outline: Introduction: Fall 2012Topic 1. The Essence of ScienceWhat is Earth Science?Scientific methodHypothesis, theory, lawStructure of the Earth (classical divisions - chemical)Know about the four spheresHydrosphere, biosphere, g

Syracuse - EAR - 105

EAR105 Topic 2: Origin of the Universe and Earth, Partial lecture summaryCHAPTERS 2, 3 and 4There is a lot of information presented in Chapters 2, 3 and 4 and we cannot hope to cover it all.Use these notes as a guide to what you should be studyingThe

Syracuse - EAR - 105

Topic 3: MineralsChapter 5 in textbookWhat are clouds of dust and gas (Nebula) made of?Element - fundamental component of matter that cannot be broken down into simplerparticles by ordinary chemical meansAtom - the smallest unit of an element that ha

Syracuse - EAR - 105

Topic 4: RocksChapter 6Know that rocks are classified according to mineral content, how they formed and textureKnow the three major rock types and how they formedUnderstand the rock cycle (link this later to plate tectonics)Major Rock TypesIGNEOUSF

Syracuse - EAR - 105

Topic 5: Plate Tectonics and structure of the EarthChapter 5WHAT YOU NEED TO KNOW:The different layers of the Earth (crust, mantle, core, lithosphere, asthenosphere etc)Continental drift (Wegner and his idea, evidence for Pangea)How the theory of pla

Syracuse - EAR - 105

What is the overall goal of science?(a) Discover and develop concepts that support political doctrine(b) Discover and develop concepts that support religious doctrine(c) Discover underlying patterns in the natural world and to use this knowledge to pre

Texas A&M - ENTO - 208

208 INSECTSMallophaga (Biting Lice)Anoplura (Sucking Lice)Siphonaptera (Fleas)1. Cat Flea2. Dog Flea3. Human Flea4. Oriental Rat Flea5. Sticktight Flea6 Chigoe Flea7. Wild Rodent FleasDiptera (Flies)Family Culicidae (Mosquitoes)A. Subfamily T

Texas A&M - FIVS - 205

Chapter 10: Crime Scene InvestigationThe Scene of the crime must always be properly managed and investigated in the bestmanner possible.High quality crime scene investigation is a simple methodical process. It is not rigid, butfollows a set of princip

Texas A&M - FIVS - 205

Forensics Test #4Crime Scene Investigation: Analysis of suspected site of crime. ( blood insects material,evidence associated with someone who was assaulted, evidence that has to be stored inmetal paint cans, compounds such as explosives or accelerants

Texas A&M - FIVS - 205

Crime scene investigation- The analysis of suspected cite of a crimeCollecting evidence that has to be stored in cans, can be related to explosives.1. Defining what your crime scene is.a. Size scope of the scene can varya.i. Like 9/11b. Primary scene

Texas A&M - FIVS - 205

Forensic NursingMost of Exam on Forensic nursingit is the application of forensic science combined with the biological and psychological educationof a registered nurse and the scientific investigation, evidence collection and preservation,analysis, pr

Texas A&M - FIVS - 205

Glass, Hair, FiberDemonstration on Friday guest speaker on Monday Exam next FridayDifferent types of glasses- domestic windows, cars, headlamp covers, light bulbs, glasses areproduced differently from different chemistries.Busted head lamp in a parkin

Texas A&M - FIVS - 205

BIOLOGY 111MCKNIGHTFALL 2011EXAM 2NAME_MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.1) During glycolysis, when each molecule of glucose is catabolized to two molecules of pyruvate,most of the

Texas A&M - FIVS - 205

Serial Offenders: Linking Cases by Modus Operandi and SignatureChapter Author: Robert D. KeppelCan tell the strength the physical nature of the person and behavior about the killerPost of offense behaviorRitualistic after the crime has occurredCan be

Texas A&M - FIVS - 205

1 forensic evidence is the same as other evidence in the manners of: 1 court approvedinfo that the trier of fact2 it is distinguished from nonscientifically generated info: witness statements,circumstances data.3it allows the trier of fact to accept

Texas A&M - FIVS - 205

Questions asked of a QDEIs the document forged?Is the signature genuine?Wills contracts checksDid the nurse/doctor/caregiver alter medical records?Did the same person sign two documents?Did the suspected person write the anonymous letter?Was the do

Texas A&M - FIVS - 205

Forensic Science Benchmark 1 Study Guidehttp:/podcasts.shelbyed.k12.al.us/adishman/files/2011/10/forensics-bm-1-studyguide-2011.docCrime Scene Investigation1.Describe the first priority and second priority of securing the crime scene.2.Distinguish b