UNIVERSITI TUN HUSSEIN ONN MALAYSIA
Group Assignment Engineering Statistics
BEE 35202 SEMESTER 1 SESSION 2024/2025
Due: 2 Jan 2025
Assessment Plan
Tool CLO Weightage Due Date
Proposal guidelines briefing 13-17 Oct 2024
Week 2
Proposal submission 1 3 % 14 Nov 2024
Week 6 Assignment Report (Manual
Solution) 1 7% 3 Jan 2024
Week 13
Assignment-software demonstration 2 5 %
Assignment-Q&A/presentation 3 5 %
TOTAL 20 %
1. CLOs:
i.
Apply the concepts of statistics involving probability distribution, sampling distribution and hypothesis testing in solving mathematical problems (LOD10-PLO1-C4)
(knowledge)
ii.
Perform engineering statistics calculations using suitable mathematical tools (LOD5- PLO5-P2) (practical skills)
iii.
Discuss all the types of distribution that will be used in sampling technique for solving
mathematical problems (LOD13-PLO12-A2) (lifelong learning)
2. Knowledge and skills to acquire:
i.
Cognitive skills to perform and analyze Simple Linear Regression (SLR) from a data set.
ii.
Practical skills in using appropriate software to calculate SLR.
iii.
Life-long learning skills to find relevant information to understand and perform linear regression.
3. Guidelines:
i.
5 members maximum per group. Choose your group members.
ii.
At any time during the assignment work, discuss with your lecturer if any members are unwilling to cooperate or create issues for the group.
iii.
Students caught for plagiarism (with the other groups) will be penalized with ZERO mark (0) for the assignment.
4. Tasks:
4.1 Find a set of engineering data with the following requirements:
a)
30 data, 1 independent and 1 dependent variable.
b)
From any engineering field: electrical, electronic, mechanical, civil, chemical, etc.
c)
From FYP thesis in 2022, journal and conference papers or websites.
d)
Publicly accessible.
e)
Cite the source of your dataset.
4.2 Complete the following tasks using suitable software of your choice:
a) Find the minimum, maximum, mean and standard deviation for each variable.
b) Plot the scatter diagram and determine the linear regression line.
c) Assuming a linear relationship, use the least squares method to find the simple linear regression model.
d) Check the significance (α=0.05) of the simple linear regression model in (c).
e) Interpret the meaning of the intercept and slope in this problem. Please comment any impossible event.
f) Test the hypothesis H1: β
0≠ 0 and H1: β
1≠ 0 by taking 5% level of significance.
g) Evaluate the regression model (good or not) by assessing the regression model assumptions as follows:
i. residuals variances are similar ii. residuals are normally distributed iii. residuals are independent of each other iv. existence of significant outliers
Those assumptions can be assessed by plotting residuals vs predicted dependent variable (from the model), residual vs independent variable and/or residuals vs frequency for each data point.
h) Compute the Pearson correlation coefficient, r, and interpret its meaning.
i) Compute the coefficient of determination, R
2,and interpret its meaning.
j) Discuss the effect of outliers’ data points on the coefficient of determination.
k) Discuss the similarities and differences between r and .
l) Performed hypothesis testing to conclude whether linear correlation for the
population, at = 0.05 is significant or not .
(Hint:
0 12
: 0, : 0, 2
1
test
H H T r n
r
= = −
− ) State the conclusion of the testing.
m) Conclude whether the regression model can accurately predict the dependent variable value.
CLO Task Expectation from Students
Apply the concepts of statistics involving probability distribution, sampling distribution and hypothesis testing in solving mathematical problems (LOD10-PLO1- C3) (knowledge)
Tasks 4.1 and 4.2. Able to:
•
find the min, max, mean and standard deviation values from the data
•
create scatter plot and linear regression line using the selected software
•
produce the regression model
•
assess whether the regression model is good or not for prediction
•
Check the significance of the regression model and
correlation coefficient
•
understand whether the coefficient of determination and Pearson correlation values truly reflect the relationship between variables
Perform engineering statistics calculations using suitable mathematical tools (LOD5-PLO5-P2) (practical skills)
Use suitable software to complete tasks in 4.2.
Able to:
•
Use suitable
modules/functions in the software to complete given tasks
•
Produce correct answers Discuss all the types of
distribution that will be used in sampling technique for solving mathematical problems (LOD13-PLO12- A2) (lifelong learning)
•
Discuss the answers from the tasks in 4.2 based on the knowledge and information acquired from various sources.
•
Demonstrate strong interest to further
explore/analyze the tasks in 4.2 independently
Able to:
•
Discuss the answers and justify that the answers are correct
•
Demonstrate strong interest in
further explore/analyze the
given tasks independently
5. Deliverables:
i.
A 5-page (maximum) proposal must be submitted with the following elements:
a. 30 data from any engineering fields, with a unit of measurement b. source of data
c. explanation about the data – brief information, engineering field, used in what application/system, whether sample of data or all data from the source d. independent and dependent variables
e. software to be used to complete the tasks and relevant functions – with justification
ii.
A 10-page (maximum) final report must be submitted at the end of the project. The report should include the following information:
a. Introduction
− brief information about the linear regression
− report organization
b. Tasks in 4.1 (data requirements)
− brief information about the data, with an appropriate diagram if necessary
− source of data
− independent and dependent variables
c. Software
− brief information about the software and relevant functions
− reasons to choose the software
d. Tasks in 4.2 (results, discussion, conclusion)
− from (a) to (m), in separate subsections
− describe the modules/functions used in the software
e. References
− Follow IEEE reference styles
iii.
At the end of the project, software demonstration, either online or through video presentation (upload to YouTube, maximum duration of 5 minutes) will be conducted with the following focus:
a. Steps to complete tasks in 4.2 using the selected software b. Explanation of the results in 4.2
A Q&A session will discuss the software demonstration and the answers to task 4.2.
Questions will be directed to all group members.
Rubric evaluation forms for proposals, reports, software demonstrations, and Q&A are
APPENDIX I
GROUP ASSIGNMENT EVALUATION RUBRIC –PROPOSAL (COGNITIVE)
No. Item (CLO,
Level) Excellent [4 marks] Good [3 marks] Average [2 marks] Fair [1 mark] Poor [0 marks] Weigh-
tage Total 1
Meeting data requirements (CLO1)
30 data from any engineering field, data is suitable for
regression analysis
30 data from any engineering field, data is somewhat suitable for regression analysis
Half of the 30 data, data is somewhat suitable for regression analysis
Data is not suitable for regression analysis
Very little or no data
5
2
Source of data, information about the data (CLO1)
The source of data is available, accessible, clear and complete information about the data
Source of data is available and accessible, but incomplete
information about the data
The source of data is available but inaccessible data, incomplete
information about the data
The source of data is unavailable,
inaccessible, and unclear information about the data
No information about the source
and data 2
3 Software selection (CLO1)
Clear information about the software and functions, strong reasoning
Clear information about the software and functions,
reasoning is somewhat strong
Unclear about the software and functions, weak reasoning
Unclear about the software and functions, very weak reasoning
No reasons at all.
3
4
Independent and dependent variables (CLO1)
Identify clearly the independent and dependent variables, clear justification
Part of the variables are identified clearly, and the justification is partly clear
Variables and justification are somewhat clear
Not clearly identified, weak, no justification
Not identified at
all. 5
/ 60
APPENDIX II
GROUP ASSIGNMENT EVALUATION RUBRIC –FINAL REPORT (COGNITIVE)
No. Item (CLO,
Level) Excellent [4 marks] Good [3 marks] Average [2 marks] Fair [1 mark] Poor [0 marks] Weigh-
tage Total 1
Introduction (CLO1)
Clear and complete introduction about linear regression, clear report organization
The introduction about the linear regression and report organization are clear but incomplete
The introduction about the linear regression and report organization is somewhat clear
The introduction about the linear regression and report are unclear
No introduction
2
2
Task 4.1: data requirements (CLO1)
Clear and complete information about the data, meet
requirements, suitable and accessible data, clear explanation about independent and dependent variables
Clear information about the data but incomplete, meets requirements, clear explanation about the variables
Information about the data is somewhat clear and meets some of the requirements, and independent and dependent variables are somewhat clear
Incomplete
information about the data does not meet requirements, not suitable and
inaccessible data, and independent and dependent variables are unclear
No information about the data
5
3 Software selection (CLO1)
Clear and complete information about the software and functions, strong reasoning for the software selection
Information about the software and
functions is clear but weak reasoning
Information about the software and
functions are somewhat clear, but weak reasoning
Information about the software and
functions is unclear, and there are no reasons
No information about the
software 3
4
Task 4.2(a, b):
min, max, mean, std.
deviation, scatter plot, regression model (CLO1)
Correct and well- organized results and discussion, clear information about the modules/functions used in the software
Correct results but not well organized, clear information about the
modules/functions used in the software
Results are somewhat clear, some results are correct, but
incomplete information about functions in the software
Results are mostly incorrect,
unorganized, and unclear about functions in the software
No results and discussion
5
No. Item (CLO,
Level) Excellent [4 marks] Good [3 marks] Average [2 marks] Fair [1 mark] Poor [0 marks] Weigh-
tage Total 5
Task 4.2(c-g):
Regression model, regression coefficient testing, evaluation of regression model (CLO1)
Correct and well- organized results and discussion, clear information about the modules/functions used in the software
Correct results but not well organized, clear information about the
modules/functions used in the software
Results are somewhat clear, some results are correct, but
incomplete information about functions in the software
Results are mostly incorrect,
unorganized, unclear about functions in the software
No results and discussion
5
6
Task 4.2(h- l):
Pearson correlation coefficient, coefficient of determination and
correlation hypothesis testing, (CLO1)
Correct and well- organized results and discussion, clear information about the modules/functions used in the software
Correct results but not well organized, clear information about the
modules/functions used in the software
Results are somewhat clear, some results are correct, but
incomplete information about functions in the software
Results are mostly incorrect,
unorganized, and unclear about functions in the software
No results and discussion
5
7
Task 4.2(m):
conclusion (CLO1)
Clearly conclude the results of regression analysis
The conclusion is clear but incomplete
The conclusion is somewhat clear and incomplete
Conclusion is unclear No conclusion
3 8
References (CLO1)
Reliable and sufficient references follow IEEE styles format
completely
References are reliable but insufficient
References are somewhat reliable and sufficient
References are unreliable and insufficient
No references
2
/ 120
APPENDIX III
GROUP ASSIGNMENT EVALUATION RUBRIC –SOFTWARE DEMONSTRATION (PRACTICAL SKILLS)
No. Item (CLO, Level) Very Good [4 marks] Good [3 marks] Average [2 marks] Fair [1 mark] Poor [0 marks] Weigh-
tage Total 1
Software proficiency (CLO2)
Use correct/appropriate functions/methods in the software to produce results for all tasks
Use
correct/appropriate functions/methods in the software to produce results for most tasks
Use
correct/appropriate functions/methods in the software to produce results for some of the tasks
Use
correct/appropriate functions/methods in the software to produce results for a few of the tasks
Not use the
correct/appropriate functions/methods to produce results for all tasks
5
2
Correctness of results (CLO2)
Produce correct answers for all tasks using the software
Produce more than half correct answers
Produce less than half correct answers
Produce a few correct answers
All answers are
wrong 5
/ 40
APPENDIX IV
GROUP ASSIGNMENT EVALUATION RUBRIC –Q&A(LIFE LONG LEARNING)
No. Item (CLO, Level) Very Good [4 marks] Good [3 marks] Average [2 marks] Fair [1 mark] Poor [0 marks] Weigh-
tage Total 1
Discussion of answers for all tasks (CLO3)
Discuss clearly answers for all tasks
Discuss clearly answers for some tasks
Discuss clearly answers for a few tasks
The discussion is somewhat clear overall
Not clearly discuss answers for all tasks
5 2
Demonstrate interest in further exploring/analyzin g the given tasks (CLO3)
Demonstrate a strong interest in further
exploring/analyzing the given task
Demonstrate
acceptable interest to further
explore/analyze the given tasks
Demonstrate moderate interest in further
exploring/analyzing the given tasks
Demonstrate very little interest in further exploring/analyzing the given tasks
Not demonstrate any interest
5
/ 40
