Academics higher education (B.Tech)

1. Six Sigma’s Statistical Approach to Draw Inferences for Academic
Excellence : A Case of an Engineering Branch
By
Dr. Bikram Jit Singh
Professor
MMDU, Mullana

2. DEFINE PHASE

3. S. NO Name Roll no Applied Maths Applied Physics Electrical Engineering Communication Skill Applied Chemistry Manufacturing Process
1 Amit 75111003 6 12 5 37 2 29
2 Lokesh Pratap Singh 75111013 2 10 1 30 6 13
3 W Vishno Mohan 75111031 28 43 30 43 50 51
4 Divyanshu Badyal 75111042 0 7 0 27 1 12
5 Mayank Tandon 75112028 5 5 0 42 0 6
6 Abhinav Das 75114001 0 1 0 25 6 7
7 Achhru Kant 75114002 18 37 10 45 48 47
8 Alok Kumar 75114003 6 14 9 36 10 24
9 Amit Kanga 75114004 29 45 25 38 43 45
10 Amit Kumar Singh 75114005 17 36 9 42 30 38
11 Anish Kumar 75114006 2 11 3 15 16 37
12 Anmol Ranjan 75114007 24 41 12 39 33 52
13 Dhol Puriya Jayraj Mohan Kumar 75114013 11 37 15 27 24 45
14 Durgesh Kumar Singh 75114014 0 7 1 30 2 25
15 Gopal Kumar 75114016 1 7 2 24 0 0
16 Hashim Parwez 75114019 16 29 9 34 35 48
17 Immam Hassan 75114020 0 8 2 24 15 35
18 Jay Prakash 75114022 18 31 11 28 2 40
19 Jayasurya Vind 75114024 19 28 8 24 24 32
20 Kapil 75114025 0 5 1 38 9 33
21 Karan Sharma 75114026 40 53 24 43 50 38
22 Manas Kumar Singh 75114028 4 28 8 33 24 39
23 Mandeep Sood 75114029 24 44 18 31 35 42
24 Mayank Kumar 75114030 2 30 12 34 43 47
25 MD Shahanwaz Khan 75114031 0 12 3 36 6 31
26 MD Washim Akram 75114032 24 17 11 33 14 34
27 Mukesh Kumar 75114033 0 2 0 15 0 10
28 Nitin Kumar 75114035 3 24 3 24 16 24
29 Parag Priyank 75114037 2 9 6 43 24 36
30 Patvinder Singh 75114038 0
31 Raghav Mehnidiratta 75114039 14 24 13 43 32 38
32 Rahul Pandey 75114040 1 14 9 36 26 44
33 Raj Kumar Munda 75114041 5 18 3 33 24 38
34 Rajesh Kumar Singh 75114042 0 1 2 24 4 32
35 RajRanjan Srivastva 75114043 44 38 20 34 32 47
36 Ramjee Bind 75114044 42 25 24 28 35 41
37 Ranjan Kumar Kushwaha 75114045 5 15 2 33 19 48
38 Ravi Kumar 75114046 0 2 6 34 4 10
39 Rohit Kumar 75114047 0 38 13 45 43 0
40 Sanjay Kumar 75114048 26 35 16 34 46 40
41 Sanjeet 75114050 0 8 0 15 2 24
42 Shahid Ansari 75114053 27 45 12 39 32 43
43 Sheshnath Singh 75114055 19 29 14 33 24 44
44 Shiv Kumar 75114056 8 34 9 43 42 45
45 Shivam Sharma 75114057 26 28 15 38 32 37
46 Shravan Kumar 75114058 24 24 17 48 26 50
47 Shubham Verma 75114060 5 25 5 33 17 30
48 Sourav Gupta 75114061 13 14 11 32 20 41
49 Sunil Kumar 75114063 46 37 14 34 41 45
50 Vikash Kumar gupta 75114066 15 30 4 29 29 42
51 Vishal 75114068 36 38 16 43 46 50
52 Vishal Kumar Tiwari 75114069 47 42 24 35 35 51
53 Vivek Dhiman 75114070 16 31 12 43 25 36
Result of Mechanical 1st semester

4. 60483624120-12
60483624120-12
18
16
14
12
10
8
6
4
2
0
18
16
14
12
10
8
6
4
2
0
Data
Frequency
13.85 14.09 52
23.62 14.20 52
9.596 7.585 52
33.63 7.867 52
23.15 15.49 52
34.54 13.88 52
Mean StDev N
Applied Maths
Applied Physics
Electrical Engineering
Communication Skill
Applied Chemistry
Manufacturing Process
Variable
Histogram of Applied Math, Applied Phys, Electrical E, ...
Normal
Performance of Students in each Subjest

5. No of students having supplies in 1 subject, 2 sub 3

6. Number of Supplies 47 37 22 22 8 3
Percent 33.8 26.6 15.8 15.8 5.8 2.2
Cum % 33.8 60.4 76.3 92.1 97.8 100.0
Subject
O
ther
M
anufacturing
Process
Applied
Physics
Applied
Chem
istry
Applied
M
aths
Electrical Engineering
140
120
100
80
60
40
20
0
100
80
60
40
20
0
NumberofSupplies
Percent
Other8
2222
37
47
Pareto Chart of Subject
EE Subject has 33.8% Supplies (Maximum)

8. MEASURE PHASE

9. 544536271890-9
LSL (24 Marks) USL (60 Marks)
Total N 53
Subgroup size 1
Mean 9.4151
StDev (overall) 7.6270
StDev (within) 7.7571
Process Characterization
Cp 0.77
Cpk -0.63
Z.Bench -1.88
% Out of spec (expected) 97.00
PPM (DPMO) (expected) 969960
Actual (overall)
Pp 0.79
Ppk -0.64
Z.Bench -1.91
% Out of spec (observed) 90.57
% Out of spec (expected) 97.21
PPM (DPMO) (observed) 905660
PPM (DPMO) (expected) 972080
Potential (within)
Capability Statistics
3
9
25
9
10
0
0
0
30
15
Capability Histogram
Are the data inside the limits?
Actual (overall) capability is what the customer experiences.
shifts and drifts were eliminated.
Potential (within) capability is what could be achieved if process
Capability Analysis for Electrical E
Process Performance Report
Distribution of Marks in External of EE Subject

10. 60
HighLow
Z.Bench = -1.91
544536271890-9
LSL USL
Actual (overall) capability is what the customer experiences.
spec limits.
percentage of parts from the process that are outside the
-- The defect rate is 97.21%, which estimates the
Conclusions
Upper Spec 60
Target *
Lower Spec 24
Customer Requirements
Mean 9.4151
Standard deviation 7.6270
Actual (overall) capability
Pp 0.79
Ppk -0.64
Z.Bench -1.91
% Out of spec 97.21
PPM (DPMO) 972080
Process Characterization
Actual (overall) Capability
Are the data inside the limits?
Comments
Capability Analysis for Electrical E
Summary Report
How capable is the process?
Conclusion from EE Result

11. Roll Number External marks Internal marks
1 75111003 5 27
2 75111013 1 11
3 75111031 30 38
4 75111042 0 0
5 75112028 0 0
6 75114001 0 14
7 75114002 10 28
8 75114003 9 23
9 75114004 25 30
10 75114005 9 24
11 75114006 3 22
12 75114007 12 29
13 75114013 15 29
14 75114014 1 16
15 75114016 2 16
16 75114019 9 35
17 75114020 2 25
18 75114022 11 26
19 75114024 8 34
20 75114025 1 24
21 75114026 24 34
22 75114028 8 28
23 75114029 18 29
24 75114030 12 26
25 75114031 3 24
26 75114032 11 30
27 75114033 0 15
28 75114035 3 20
29 75114037 6 18
30 75114038 18
31 75114039 13 27
32 75114040 9 26
33 75114041 3 25
34 75114042 2 24
35 75114043 20 32
36 75114044 24 34
37 75114045 2 22
38 75114046 6 18
39 75114047 13 34
40 75114048 16 34
41 75114050 0 16
42 75114053 12 30
43 75114055 14 29
44 75114056 9 25
45 75114057 15 31
46 75114058 17 27
47 75114060 5 21
48 75114061 11 26
49 75114063 14 34
50 75114066 4 23
51 75114068 16 34
52 75114069 24 38
53 75114070 12 23

12. Comparison of Internal and External Marks in EE Subject
4035302520151050-5
4035302520151050-5
35
30
25
20
15
10
5
0
-5
-10
35
30
25
20
15
10
5
0
-5
-10
Internal marks
Externalmarks Scatterplot of External marks vs Internal marks
Pearson Correlation in between External marks and Internal marks is 0.761
It implies Internal marks secured by students are strongly influencing the
performance in end semester exams. Hence “Internal Criteria of giving Marks” is
highly related with External result. It must be optimized (factors affecting
external result more, should have high weightages in terms of marks) to further
lure and motivate the students for good result in External exams…..

13. 1st
(12 marks)
2nd
(12 marks)
1 75114029 Mandeep Sood 37 12 8 6 7 18 37
2 75114041 Raj Kumar Munda 68 19 5 7 6 18 43
3 75114050 Sanjeet 69 19 10 6 8 20 47
4 75114052 Shabbar Hussain Khan 0 0 0 0 0 0 0
5 75114053 Shahid Ansari 82 24 11 9 10 22 56
TOTAL
(60 marks)
MAHARISHI MARKANDESHWAR UNIVERSITY,SADOPUR(AMBALA)
Subject Name: Sem & Branch: Session: Subject Code:
S.No. ROLL NO. NAME % Lab Attd
Attd. Marks
(40%)
(24 marks)
Viva Voice
Avg. Viva
Marks
(20%)
(12 marks)
Practical File
(40%)
(24 marks)
INTERNAL CRITERIA FOR PRACTICALS
Subject Name: Electrical Engg. Sem & Branch: 1st A group Subject Code: EE-101
1st 2nd 3rd
1 75114001 Abhinav Das 50 10 2 a 0 1 3 14
2 75114002 Achhru Kant 81 16 14 15 14 6 6 28
3 75114003 Alok Kumar 77 14 13 10 0 3 6 23
4 75114004 Amit Kanga 77 14 27 25 25 10 6 30
5 75114005 Amit Kumar Singh 88 16 13 21 10 6 2 24
MAHARISHI MARKANDESHWAR UNIVERSITY,SADOPUR(AMBALA)
Mid Term Tests
TOTAL (40 marks)
CLASS WORK
(Assignments/
Tutorials)
(20%)
(8 marks)
Best of two
AVG MARKS
(40%)
(16 marks)
Attd.
Marks
(40%)
(16 marks)
% Lecture
Attendance
NAMEROLL NO.S.No.
INTERNAL MARKS CRITERIA FOR THEORY

14. ANALYSE PHASE

15. Root Cause Analysis of Poor Result in EE
Result
Poor
Motivation
Material
Attendance
Evaluation
Method
Infra structure
Personnel
Instructors
Quality of Teachers
IQ Level of Students
Library with study Room
Sports Facilities
Extra Curricular Activities
Labs with Equipments
Tutorial Rooms
Class Rooms
College Campus
Lectures
Guest or Expert
Audio-Visual Aids
Studies with
Experimental
Communication
Effective
Interaction
Student-Teacher
Students
Listening Skills of
Teaching Style
Practicals or Lab work
Class Room Behaviour
Assignments / Tutorials
Quiz and surprise Tests
Mid Semester Tests
Sports Activities
Attendance in
Extra Curricular
Attendance in
Lab work
Attendance in
MST Attendance
Attendance
Lecture / Tutorial
Self Study Hours
Material
Out of Syllabus Teaching
Lecture Notes
Old Edition Books /
Free Lectures
Policies
Management
Workmanship
Lab Instructors's
Students Interest
Willingness
Teacher's
Cause-and-Effect Diagram

16. Controlled Factors Noise Factors Critical Factors
1. IQ Level of Students
2. Quality of Teachers
3. Instructors
4. College Campus
5. Class Rooms
6. Tutorial Rooms
7. Labs with Equipments
8. Library with study Room
9. Student-Teacher Interaction
10. Effective Communication
11. Experimental Studies with theory
12. Audio-Visual Aids
13. Guest or Expert Lectures
14. Old Edition Books / Lecture Notes
15. Out of Syllabus Teaching Material
16. Self Study Hours
17. Free Lectures
18. Extra Curriculum Activities
1. Teaching Style
2. Listening Skills of Students
3. Student-Teacher Interaction
4. Effective Communication
5. Quiz and surprise Tests
6. Attendance in Extra
7. Curricular Activities
8. Attendance in Sports Activities
9. Teacher's Willingness
10. Students Interest
11. Lab Instructors's Workmanship
12. Management Policies
- MST Attendance
- Lecture Attendance
- Lab Attendance
- MST-1
- MST-2
- MST-3
- Assignments /
Tutorials
- Practical Files
- Viva-Voice during
Practicals
Brainstorming Session (without ignoring the real constraints)

17. Roll Nos Lecture Attd. MST-1 MST-2 MST-3 MST Attd. Assignments Lab Attd. Viva-Voice Practical
File75114001 10 2 0 0 2.0 3 6 1 8
75114002 16 14 15 14 3.0 6 24 8 19
75114003 14 13 10 0 3.0 6 19 9 20
75114004 14 27 25 25 3.0 6 22 10 22
75114005 16 13 21 10 3.0 2 19 7 16
75114007 14 22 0 16 2.0 7 14 7 16
75114012 0 0 0 0 0.0 0 0 0 0
75114013 14 9 21 22 3.0 7 14 7 18
75114014 8 0 0 4 1.0 7 17 8 18
75114016 12 0 0 0 0.0 4 17 8 16
75114019 16 23 18 36 3.0 8 22 8 22
75114020 12 3 6 10 3.0 6 19 7 20
75114022 16 22 17 11 3.0 2 22 10 22
75114024 16 19 26 20 3.0 8 24 9 19
75114025 14 14 0 0 1.0 6 19 10 20
75114026 14 29 30 38 3.0 7 22 11 22
75114032 16 8 22 8 3.0 7 22 8 20
75114066 16 13 17 3 3.0 2 17 6 16
75111013 6 0 6 2 2.0 2 0 0 0
75111031 16 19 31 48 3.0 8 24 9 20
75114006 12 9 1 14 3.0 6 22 7 19
75114028 14 15 16 14 3.0 8 24 9 18
75114030 14 3 14 10 3.0 7 22 8 18
75114031 14 10 8 6 3.0 7 22 10 20
75114033 10 0 3 0 3.0 4 6 7 16
75114035 10 12 0 19 2.0 4 6 5 12
75114037 8 15 1 16 3.0 4 12 4 12
75114038 10 2 20 8 3.0 2 19 8 19
75114039 14 0 20 30 2.0 4 22 8 19
75114040 14 7 17 20 3.0 5 19 6 18
75114042 16 3 7 6 3.0 5 24 7 20
75114043 16 22 28 24 3.0 6 22 6 18
75114044 16 26 23 24 3.0 8 22 7 18
75114045 10 15 0 8 1.0 7 22 7 19
75114046 8 0 13 0 1.0 7 12 5 16
75114047 16 28 26 41 3.0 6 14 6 16
75114048 16 26 28 44 3.0 5 22 8 18
75114069 16 30 31 48 3.0 8 22 7 18
75114029 14 13 8 42 3.0 6 12 7 18
75114041 12 18 21 30 3.0 4 19 6 18
75114050 10 0 2 6 2.0 5 19 8 20
75114052 0 0 0 0 0.0 0 0 0 0
75114053 16 30 23 36 3.0 2 24 10 22
75114055 16 16 15 24 3.0 6 24 6 20
75114056 14 24 19 24 3.0 2 22 10 22
75114057 14 23 21 36 3.0 6 24 10 22
75114058 10 24 16 42 3.0 5 12 5 12
75114060 12 8 4 10 2.0 6 17 8 20
75114061 14 0 12 17 2.0 7 12 6 18
75114063 16 27 25 32 3.0 7 24 9 20
75114068 16 16 23 37 3.0 7 24 9 20
75114070 10 19 12 14 3.0 6 12 6 16
75111003 16 0 14 10 2 6 24 7 18

19. 987654321
6
5
4
3
2
1
0
Component Number
Eigenvalue
0.055810.141940.196350.24118
0.36931
0.51960
0.69062
1.41289
5.37230
Scree Plot of Lecture Attd., ..., Practical File

20. 0.40.30.20.10.0
0.4
0.3
0.2
0.1
0.0
-0.1
-0.2
-0.3
-0.4
-0.5
First Component
SecondComponent
Practical FileViva-Voice
Lab Attd.
Assignments
MST Attd.
MST-3
MST-2
MST-1
Lecture Attd.
Practical FileViva-Voice
Lab Attd.
Assignments
MST Attd.
MST-3
MST-2
MST-1
Lecture Attd.
Loading Plot of Lecture Attd., ..., Practical File

21. 30
15
0
30150
40
20
0
30150
MST-2
MST-1
MST-3
MST-2
Matrix Plot of MST-1, MST-2, MST-3

22. 20
10
0
1680
3.0
1.5
0.0
20100
LabAttd.
Lecture Attd.
MSTAttd.
Lab Attd.
Matrix Plot of Lecture Attd., Lab Attd., MST Attd.

23. 20
10
0
1050
8
4
0
20100
PracticalFile
Viva-Voice
Assignments
Practical File
Matrix Plot of Viva-Voice, Practical File, Assignments

24. 3952272664729194273549402036383716514350461145143233430813441718515283453233121481210419243222251
78.73
85.82
92.91
100.00
Observations
Similarity
Dendrogram
Single Linkage, Euclidean Distance

25. Group Name Critical Factors Tool / Technique
Used
Attendance MST Attendance Multi- Regression
AnalysisLecture Attendance
Lab Attendance
Mid Semester Tests MST-1 One Way ANOVA for
inter MST analysis
and then
Orthogonal
Regression for
average MST marks
MST-2
MST-3
Written & Oral
Submissions
Assignments/
Tutorials
Stepwise Regression
(Backward Step
Method)
Practical Files
Viva-Voice
Analytical Plan

26. Roll no MST Attendance Lecture Attendance Lab Attendance External Result
75111003 66.7 82.0 86.0 5
75111013 66.7 10.0 0.0 1
75111031 100.0 91.0 84.0 30
75114001 66.7 50.0 10.0 0
75114002 100.0 81.0 85.0 10
75114003 100.0 77.0 68.0 9
75114004 100.0 77.0 75.0 25
75114005 100.0 88.0 66.0 9
75114006 66.7 63.0 81.0 3
75114007 66.7 72.0 48.0 12
75114013 100.0 79.0 57.0 15
75114014 33.3 42.0 58.0 1
75114016 0.0 67.0 56.0 2
75114019 100.0 87.0 79.0 9
75114020 100.0 62.0 68.0 2
75114022 100.0 84.0 78.0 11
75114024 100.0 100.0 85.0 8
75114025 66.7 71.0 67.0 1
75114026 100.0 75.0 78.0 24
75114028 100.0 78.0 85.0 8
75114029 100.0 78.0 37.0 18
75114030 100.0 72.0 83.0 12
75114031 100.0 76.0 82.0 3
75114032 100.0 87.0 78.0 11
75114033 100.0 54.0 17.0 0
75114035 66.7 57.0 18.0 3
75114037 66.7 43.0 35.0 6
75114038 100.0 57.0 68.0 0
75114039 66.7 71.0 76.0 13
75114040 100.0 73.0 69.0 9
75114041 100.0 66.0 68.0 3
75114042 100.0 90.0 78.0 2
75114043 100.0 83.0 76.0 20
75114044 100.0 90.0 75.0 24
75114045 66.7 59.0 76.0 2
75114046 33.3 47.0 37.0 6
75114047 100.0 86.0 48.0 13
75114048 100.0 94.0 76.0 16
75114050 66.7 53.0 69.0 0
75114053 100.0 81.0 82.0 12
75114055 100.0 89.0 81.0 14
75114056 100.0 72.0 82.0 9
75114057 100.0 76.0 85.0 15
75114058 100.0 58.0 36.0 17
75114060 100.0 68.0 59.0 5
75114061 66.7 72.0 35.0 11
75114063 100.0 94.0 85.0 14
75114066 100.0 83.0 56.0 4
75114068 100.0 81.0 84.0 16
75114069 100.0 94.0 75.0 24
75114070 100.0 55.0 37.0 12

27. 20100-10
99
90
50
10
1
Residual
Percent
151050-5
20
10
0
-10
Fitted Value
Residual
1612840-4-8-12
12
9
6
3
0
Residual
Frequency
50454035302520151051
20
10
0
-10
Observation Order
Residual
Normal Probability Plot Versus Fits
Histogram Versus Order
Residual Plots for External Result

28. Regression Analysis: External Reslt Vs MST Attd., Lecture & Lab Attd.
Predictor Coef SE Coef T P
Constant -10.482 4.365 -2.40 0.020
MST Attendance 0.07080 0.04808 1.47 0.048
Lecture Attendance 0.22756 0.08185 2.78 0.008
Lab Attendance -0.03769 0.05680 -0.66 0.510
S = 6.36405 R-Sq = 33.0% R-Sq(adj) = 28.7%
Analysis of Variance
Source DF SS MS F P
Regression 3 937.07 312.36 7.71 0.000
Residual Error 47 1903.55 40.50
Total 50 2840.63

29. Attendance is statistically significant (p < 0.05).
The relationship between External Result and MST
> 0.50.10.050
NoYes
P = 0.001
accounted for by the regression model.
17.39% of the variation in External Result can be
100%0%
R-sq (adj) = 17.39%
increase.
MST Attendance increases, External Result also tends to
The positive correlation (r = 0.44) indicates that when
10-1
0.44
1007550250
30
20
10
0
MST Attendance
ExternalResult
causes Y.
A statistically significant relationship does not imply that X
a desired value or range of values for External Result.
or find the settings for MST Attendance that correspond to
to predict External Result for a value of MST Attendance,
If the model fits the data well, this equation can be used
Y = - 3.268 + 0.1490 X
relationship between Y and X is:
The fitted equation for the linear model that describes the
Y: External Result
X: MST Attendance
Is there a relationship between Y and X?
Fitted Line Plot for Linear Model
Y = - 3.268 + 0.1490 X
Comments
Regression for External Result vs MST Attendance
Summary Report
% of variation accounted for by model
Correlation between Y and X
Negative No correlation Positive

30. Attendance is statistically significant (p < 0.05).
The relationship between External Result and Lecture
> 0.50.10.050
NoYes
P = 0.000
accounted for by the regression model.
27.79% of the variation in External Result can be
100%0%
R-sq (adj) = 27.79%
to increase.
Lecture Attendance increases, External Result also tends
The positive correlation (r = 0.54) indicates that when
10-1
0.54
1007550250
30
20
10
0
Lecture Attendance
ExternalResult
causes Y.
A statistically significant relationship does not imply that X
External Result.
that correspond to a desired value or range of values for
Attendance, or find the settings for Lecture Attendance
to predict External Result for a value of Lecture
If the model fits the data well, this equation can be used
Y = - 7.791 + 0.2426 X
relationship between Y and X is:
The fitted equation for the linear model that describes the
Y: External Result
X: Lecture Attendance
Is there a relationship between Y and X?
Fitted Line Plot for Linear Model
Y = - 7.791 + 0.2426 X
Comments
Regression for External Result vs Lecture Attendance
Summary Report
% of variation accounted for by model
Correlation between Y and X
Negative No correlation Positive

31. Attendance is statistically significant (p < 0.05).
The relationship between External Result and Lab
> 0.50.10.050
NoYes
P = 0.024
accounted for by the regression model.
8.17% of the variation in External Result can be
100%0%
R-sq (adj) = 8.17%
increase.
Lab Attendance increases, External Result also tends to
The positive correlation (r = 0.32) indicates that when
10-1
0.32
806040200
30
20
10
0
Lab Attendance
ExternalResult
causes Y.
A statistically significant relationship does not imply that X
desired value or range of values for External Result.
find the settings for Lab Attendance that correspond to a
to predict External Result for a value of Lab Attendance, or
If the model fits the data well, this equation can be used
Y = 2.821 + 0.1084 X
relationship between Y and X is:
The fitted equation for the linear model that describes the
Y: External Result
X: Lab Attendance
Is there a relationship between Y and X?
Fitted Line Plot for Linear Model
Y = 2.821 + 0.1084 X
Comments
Regression for External Result vs Lab Attendance
Summary Report
% of variation accounted for by model
Correlation between Y and X
Negative No correlation Positive

32. Overall Regression Equation
External Result = - 10.5 + 0.0708 MST Attendance + 0.228 Lecture Attendance
- 0.0377 Lab Attendance

33. Internal Analysis
MST-1 MST-2 MST-3
14 15 14
13 10 0
27 25 25
13 21 10
9 21 22
23 18 36
3 6 10
22 17 11
19 26 20
29 30 38
8 22 8
13 17 3
19 31 48
9 1 14
15 16 14
3 14 10
10 8 6
0 3 0
15 1 16
2 20 8
7 17 20
3 7 6
22 28 24
26 23 24
28 26 41
26 28 44
30 31 48
13 8 42
18 21 30
30 23 36
16 15 24
24 19 24
23 21 36
24 16 42
8 4 10
27 25 32
16 23 37
19 12 14

34. One-way ANOVA: MST-1, MST-2, MST-3
Source DF SS MS F P
Factor 2 555 278 2.27 0.109
Error 87 10635 122
Total 89 11190
S = 11.06 R-Sq = 4.96% R-Sq(adj) = 2.78%
Individual 95% CIs For Mean Based on Pooled StDev
Level N Mean StDev ----+---------+---------+---------+---
MST-1 30 16.03 8.82 (-----------*----------)
MST-2 30 17.70 8.49 (-----------*----------)
MST-3 30 21.93 14.72 (-----------*----------)
------+---------+---------+---------+---
14.0 17.5 21.0 24.5
Pooled StDev = 11.06

35. MST-3MST-2MST-1
50
40
30
20
10
0
Data
Individual Value Plot of MST-1, MST-2, MST-3
MST-3MST-2MST-1
50
40
30
20
10
0
Data
Boxplot of MST-1, MST-2, MST-3

36. Roll Number External marks MST Marks
75111003 5 5
75111013 1 3
75111031 30 14
75114001 0 1
75114002 10 6
75114003 9 3
75114004 25 10
75114005 9 6
75114006 3 8
75114007 12 0
75114013 15 8
75114014 1 1
75114016 2 0
75114019 9 11
75114020 2 7
75114022 11 8
75114024 8 10
75114025 1 4
75114026 24 13
75114028 8 6
75114029 18 9
75114030 12 5
75114031 3 3
75114032 11 7
75114033 0 1
75114035 3 6
75114037 6 6
75114038 6
75114039 13 9
75114040 9 7
75114041 3 9
75114042 2 3
75114043 20 10
75114044 24 10
75114045 2 5
75114046 6 3
75114047 13 12
75114048 16 13
75114050 0 1
75114053 12 12
75114055 14 7
75114056 9 9
75114057 15 11
75114058 17 12
75114060 5 3
75114061 11 5
75114063 14 11
75114066 4
75114068 16 11
75114069 24 14
75114070 12 7

37. Orthogonal Regression Analysis: External marks versus MST Marks
Error Variance Ratio (External marks/MST Marks): 1.42
Coefficients
Predictor Coef SE Coef Z P Approx 95% CI
Constant -5.46468 2.14562 -2.5469 0.011 (-9.67001, -1.25935)
MST Marks 2.21093 0.28104 7.8668 0.000 ( 1.66009, 2.76176)
Error Variances
Variable Variance
External marks 7.43182
MST Marks 5.23368

38. 20100-10
99
90
50
10
1
Residual
Percent
15105
20
10
0
-10
Fitted Value
Residual
151050-5-10
8
6
4
2
0
Residual
Frequency
50454035302520151051
20
10
0
-10
Observation Order
Residual
Normal Probability Plot Versus Fits
Histogram Versus Order
Residual Plots for External marks

39. 14121086420
30
20
10
0
-10
MST Marks
Externalmarks
12
24
16
14
11
5
17
15
9
14
12
0
16
13
6
2
24
20
2
3
9
13
6
3
0
11
3
12
18
8
24
1
8
11
2
9
2
1
15
12
3
9
25
9
10
0
30
1
5
Plot of External marks vs MST Marks with Fitted Line
Regression Equation: External marks = - 5.465 + 2.211 MST Marks

40. Roll Number File Viva-Voice Assignment External marks
75111003 18 7 6 5
75111013 0 0 2 1
75111031 20 9 8 30
75114001 8 1 3 0
75114002 19 8 6 10
75114003 20 9 6 9
75114004 22 10 6 25
75114005 16 7 2 9
75114006 19 7 6 3
75114007 16 7 7 12
75114013 18 7 7 15
75114014 18 8 7 1
75114016 16 8 4 2
75114019 22 8 8 9
75114020 20 7 6 2
75114022 22 10 2 11
75114024 19 9 8 8
75114025 20 10 6 1
75114026 22 11 7 24
75114028 18 9 8 8
75114029 18 7 6 18
75114030 18 8 7 12
75114031 20 10 8 3
75114032 20 8 6 11
75114033 16 7 4 0
75114035 12 5 4 3
75114037 12 4 4 6
75114038 19 8 2
75114039 19 8 4 13
75114040 18 6 5 9
75114041 18 6 4 3
75114042 20 7 5 2
75114043 18 6 6 20
75114044 18 7 8 24
75114045 19 7 7 2
75114046 16 5 7 6
75114047 16 6 6 13
75114048 18 8 5 16
75114050 20 8 5 0
75114053 22 10 2 12
75114055 20 6 6 14
75114056 22 10 2 9
75114057 22 10 6 15
75114058 12 5 5 17
75114060 20 8 6 5
75114061 18 6 7 11
75114063 20 9 7 14
75114066 16 6 2 4
75114068 20 9 7 16
75114069 18 7 8 24
75114070 16 6 6 12

41. Stepwise Regression: External marks versus File, Viva-Voice, Assignment
Backward elimination. Alpha-to-Remove: 0.1
Response is External marks on 3 predictors, with N = 50
N(cases with missing observations) = 1 N(all cases) = 51
Step 1 2 3
Constant -2.722 -2.492 1.696
File 0.04
T-Value 0.08
P-Value 0.940
Viva-Voice 0.71 0.78
T-Value 0.71 1.59
P-Value 0.480 0.119
Assignment 1.20 1.21 1.48
T-Value 2.05 2.13 2.69
P-Value 0.046 0.038 0.010
S 7.01 6.94 7.05
R-Sq 17.51 17.50 13.08
R-Sq(adj) 12.13 13.99 11.27
Mallows Cp 4.0 2.0 2.5

42. 8765432
8765432
30
25
20
15
10
5
0
30
25
20
15
10
5
0
Assignment
Externalmarks
Scatterplot of External marks vs Assignment
Regression Equation: External Result = 1.696 + 1.479 Assignment Marks

43. Category Critical Factors Existing
Weightage
(Marks)
Impact Coefficient Trend Proposed
Internal
Theory
(40 Marks)
MST Attendance 0% (0) + 0.0708 Add by 5%
Lecture Attendance 40% (16) + 0.228 Increase by 10%
MST Marks 40% (16) + 2.211 Increase by 15%
Assignments /
Tutorials
20% (8) +1.479 Increase by 12%
Internal
Practicals
(60 Marks)
Lab Attendance 40% (24) -0.0377 Decrease by 17%
Practical Files 40% (24) p = 0.940,
(non-significant)
Decrease by 20%
Viva-Voice 20% (12) P = 0.119,
(non-significant)
Decrease by 5%
Inferences from Statistical Analysis of Internal Factors

44. Suggestions to Optimize Internal Criteria for Good ‘External Theory Result’
Category Critical Factors Existing
Weightage
(Marks)
Internal
Theory
(80 Marks)
MST Attendance 0% (05)
Lecture Attendance 40% (20)
MST Marks 40% (30)
Assignments /
Tutorials
20% (25)
Internal
Practicals
(45 Marks)
Lab Attendance 40% (24)
Practical Files 40% (24)
Viva-Voice 20% (12)