代写DTS206TC、c/c++，Java程序代做
XJTLU Entrepreneur College (Taicang) Cover Sheet
Module code and Title DTS206TC Applied Linear Statistical Models
School Title School of AI and Advanced Computing
Assignment Title Coursework (Individual Report)
Submission Deadline 23:59 16th March (Sunday)
Final Word Count N/A
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School of Artificial Intelligence and Advanced Computing
Xi’an Jiaotong-Liverpool University
DTS206TC Applied Linear Statistical Models
Coursework
Due: Sunday March. 16th, 2024 @ 11:59pm
Weight: 40%
Maximum score: 100 points
Learning Outcomes Assessed
• A. Demonstrate knowledge and understanding of basic principles of R programming language.
• B. Demonstrate understanding of the significance of linear regression models and ANOVA
tables.
• C. Show understanding of the rationale and assumptions of linear regression models.
• E. Carry out and interpret linear regressions and analyses of variance, and derive basic theoretical
results.
Submission Policy
1. Submission Format
• Each student must submit both report and codes:
(a) The final report in PDF format.
(b) The code in .R format. If multiple code files are to be submitted, please create a code
folder.
2. File Naming
• The files and folders should be named as follows: StudentID_report.pdf, StudentID_code.R,
or StudentID_codes.zip if you are submitting a folder with code.
3. All submissions must be written in English.
4. Please do NOT include the data in the folder if the data is more than 80M. If you would like
to share the data, please upload it to any e-Drive and paste the share link in the report (as
reference or footnote).
5. Coverpage should be inserted in the report.
6. Page limit: No more than 16 pages.
2
Late Policy
5% of the total marks available for the assessment shall be deducted from the assessment mark for
each working day after the submission date, up to a maximum of five working days.
Avoid Plagiarism
• Do not submit work from other students.
• Do not share code/work to other students.
• Do not copy code/work from other students.
• Do not use content generated by AI tools.
1 Coursework Overview
This coursework aims to provide students with practical experience in data analysis, linear regression,
and ANOVA analysis using the R programming language. The task will involve exploring a dataset
of your choice, performing various statistical analyses, and interpreting the results with a focus on
understanding and applying the key principles of linear regression models, ANOVA, and diagnostics.
The overall goal is to demonstrate your ability to use R to perform a thorough analysis, assess the
fit of the model, and address any issues or violations of regression assumptions through appropriate
diagnostic and remedial measures.
The coursework is divided into the following key sections:
2 Data Analysis & Visualization (15 marks)
1. Describe the dataset and the variables of interest (5 Marks)
• Provide a clear description of the dataset you have chosen for your analysis. Include
relevant details such as the source of the data, the variables it contains, and the key
characteristics of the data. Highlight which variables are of particular interest in your
analysis.
• Include the dataset name and source, and a summary of the variables (both dependent
and independent variables), and a brief discussion of why you have chosen these variables
for analysis.
• For example, you can use datasets from sources like the UCI Machine Learning Repository
or Kaggle competitions, such as the Boston Housing Dataset or the Student Performance
Dataset. These are just a few examples; feel free to choose a dataset that aligns with your
interests.
2. Perform Exploratory Data Analysis (EDA) using R functions/packages (5 Marks)
• Perform EDA to understand the structure of your data, identify any patterns, and detect
potential issues (such as missing values or outliers).
• Summary statistics (mean, median, standard deviation, etc.).
• Identify any missing values or outliers.
3
• Use R functions (e.g., summary(), str(), head(), summary(), etc.) to gain insights into the
dataset.
3. Visualize the relationships between variables using scatter plots, histograms, etc. (5
Marks)
• Use appropriate graphical techniques (e.g., scatter plots for continuous variables, histograms
for distribution of individual variables).
• Plot relationships between independent and dependent variables.
• Discuss the insights gained from the visualizations.
3 Linear Regression (20 Marks)
1. Perform Simple Linear Regression Analysis (5 Marks)
• Use R to fit a linear regression model (e.g., lm() function).
• Ensure the choice of dependent and independent variables is well-justified.
2. Specify the Regression Model, Explaining the Choice of Independent and Dependent
Variables (5 Marks)
• Write the equation of the regression model.
• Explain the rationale behind selecting each variable for the model (e.g., why certain variables
are considered independent and others dependent).
3. Interpret the Regression Coefficients (5 Marks)
• Provide an interpretation of the regression coefficients, including their magnitude, direction,
and significance.
• Explain the meaning of the slope and intercept in the context of the problem.
• Provide interpretations of each coefficient in relation to the dependent variable.
4. Assess the Goodness-of-Fit of the Model (R2, Adjusted R2) (5 Marks)
• Calculate and interpret R2 and adjusted R2
.
• Assess how well the model fits the data and whether any improvements are necessary.
4 ANOVA Analysis (15 Marks)
1. Construct the ANOVA Table (5 Marks)
• Construct the ANOVA table using R, ensuring it accurately displays all key metrics (SSR,
SSE, SSTO, df, F-value, etc.).
• Ensure the format is correct and all calculations are accurate, consistent with the regression
model results.
2. Interpret the ANOVA table (5 Marks)
• Explain the meaning of each metric in ANOVA Table.
4
• Briefly explain how to compute SSR, SSE, and SSTO, and describe their significance in
ANOVA.
• Discuss the significance of factors on the dependent variable, and determine whether the
independent variables significantly impact the dependent variable.
3. Applying the F-Test (5 Marks)
• Explain the basic principle of the F-test, including how F-values are calculated and their
application in ANOVA.
• Based on the F-test results, assess the overall significance of the independent variables in
the regression model, and explain how this affects the conclusions of the study.
5 Diagnostics & Remedial Measures (15 Marks)
1. Perform Diagnostic Checks for Linear Regression Models (8 Marks)
• Residuals vs Fitted: Check for linearity (patterns indicate non-linearity).
• Residuals vs Leverage: Check for homoscedasticity (fluctuations indicate heteroscedasticity).
• Residuals vs Time: Check for independence (trends suggest violation).
• Q-Q Plot: Assess normality (deviations indicate non-normality).
• Histogram: Verify if distribution is bell-shaped.
2. Identify and Address Violations of Assumptions (7 Marks)
• Discuss Violations. Describe observed issues (e.g., non-linearity, heteroscedasticity) and
their impact.
• Implement appropriate remedial measures to address any issues identified.
6 Conclusion (5 Marks)
• Provide a clear summary of the linear regression results, including model performance and key
coefficients.
• Discuss the implications of the results and any insights gained from the analysis.
7 Report Writing (30%)
1. Structure and Organization (15 Marks)
• Clear and Concise Manner, with Appropriate Headings and Subheadings.
• Clarity and Organization of the Report. The report should be cohesive, with ideas flowing
logically. Transitions between sections should be smooth.
• The report should maintain a high standard of academic professionalism, with formal
language, correct grammar, and proper formatting.
2. Analytical Depth and Accuracy (10 Marks)
5
• Provide a thorough, well-explained regression analysis. This includes data analysis, model
specification, assumption checks, and interpretation of results.
• All R code should run correctly, producing accurate outputs.
3. Technical Demonstration and Originality (5 Marks)
• Include relevant R code snippets demonstrating the analysis and visualization steps.
• The code should be well-commented to explain the methodology and logic behind it.
• The report should demonstrate independent thought and creativity. Any external resources
should be properly cited.
END
6
Marking Criteria
Excellent Good Satisfactory Poor
1. Data Analysis & Visualization (15 marks)
1.1 Describe the dataset and
the variables (5 marks)
Clear and detailed description,
include rationale for variable
choice.
(4-5 marks)
Clear description including
dataset source, variables, and
key characteristics.
(2-3 marks)
Brief description with minimal
details about the dataset.
(1 mark)
Not relevant, missing
(0 mark)
1.2 Exploratory Data Analysis
(EDA) (5 marks)
Comprehensive summary,
including missing values,
outliers, and visualizations.
(4-5 marks)
Summary statistics and
identification of missing
values or outliers.
(2-3 marks)
Basic summary statistics
provided without
visualization.
(1 mark)
Not relevant, missing
(0 mark)
1.3 Visualize the relationships
(5 marks)
Effective use of various plots
with clear insights.
(4-5 marks)
Basic visuals with some
insights but lacking detail.
(2-3 marks)
Poor or missing visuals.
(1 mark)
Not relevant, missing
(0 mark)
2. Linear Regression (20 marks)
2.1 Simple Linear Regression
Analysis (5 marks)
Fits the regression model in R,
correctly specifies variables
with justification.
(4-5 marks)
Fits the model but lacks clarity
in specifying dependent or
independent variables.
(2-3 marks)
Attempts to fit the model but
fails to specify variables
correctly.
(1 mark)
No attempt to fit a model or
entirely irrelevant response.
(0 mark)
2.2 Specify the Regression
Model and Explain Variable
Choice (5 marks)
Clear equation and variable
choice, linking them to
theoretical or practical
considerations.
(4-5 marks)
Writes the regression equation
correctly and provides a
general explanation of
variables.
(2-3 marks)
Specifies the regression
equation with errors and offers
a vague or incorrect
explanation of variable choice.
(1 mark)
Fails to provide a regression
equation or explanation.
(0 mark)
2.3 Interpret the regression
coefficients (5 marks)
Accurately interprets the
intercept and slope in context,
Partial interpretation, misses
context or detail.
Provides a superficial
interpretation of coefficients
Fails to interpret the
coefficients or gives incorrect
highlighting direction,
magnitude, and significance.
(4-5 marks)
(2-3 marks) without context or meaning.
(1 mark)
interpretations.
(0 mark)
2.4 Assess the Goodness-of Fit of the Model (5 marks)
Correctly calculates and
interprets R
2 and adjusted R
2
,
with clear implications and
critique.
(4-5 marks)
Calculates R
2 and adjusted R
2
but provides limited or unclear
interpretation.
(2-3 marks)
Attempts to calculate R
2 or
adjusted R
2 but provides an
incorrect or irrelevant
interpretation.
(1 mark)
Fails to calculate R
2 or
adjusted R
2
.
(0 mark)
3. ANOVA Analysis (15 marks)
3.1 Construct the ANOVA
Table (5 marks)
Accurately constructs the
ANOVA table in R with
correct metrics, formatting,
and error-free calculations.
(4-5 marks)
Provides a general
interpretation with missing
details, errors in calculations.
(2-3 marks)
Incomplete or incorrect
ANOVA table.
(1 mark)
No attempt to construct the
ANOVA table or entirely
irrelevant submission.
(0 mark)
3.2 Interpret the ANOVA Table
(5 marks)
Accurately explains each
ANOVA metric, their
computation, and significance.
Clearly discusses the impact of
independent variables on the
dependent variable.
(4-5 marks)
Gives a basic interpretation of
the metrics with some errors in
explanation and computation.
Discusses the significance of
factors but lacks detail or
accuracy.
(2-3 marks)
Minimal or incorrect
interpretation of the ANOVA
table.
Little to no attempt to explain
metric computations or their
significance.
(1 mark)
No interpretation of the
ANOVA table or entirely
irrelevant explanation.
(0 mark)
3.3 Applying the F-Test (5
marks)
Correctly explains the F-test
principle, formula, and its role
in ANOVA. Uses F-test results
to assess the significance of
independent variables and
Provides a basic explanation of
the F-test with minor errors or
omissions. Mentions the F-test
significance but lacks clarity
or depth in interpreting the
Incorrect or minimal
explanation of the F-test.
Fails to assess the significance
of F-test results or link them to
study conclusions.
No attempt to explain or apply
the F-test.
(0 mark)
clearly links them to the
study's conclusions.
(4-5 marks)
results.
(2-3 marks)
(1 mark)
4. Diagnostics & Remedial Measures (15 Marks)
4.1 Perform Diagnostic
Checks (8 marks)
Accurately analyzes all
diagnostic plots, and identifies
key issues (e.g., non-linearity,
heteroscedasticity,
independence, non-normality).
(7-8 marks)
Analyzes most of the
diagnostic plots but may miss
or misinterpret some key
aspects and identifies at least
some major violations.
(4-6 marks)
Analyzes only a few
diagnostic plots, missing key
checks or misinterpreting
some plots. Identifies only a
few violations or issues with
minimal justification.
(2-3 marks)
Fails to analyze the diagnostic
plots or provides incorrect
analyses.
Does not identify key issues or
misinterprets them.
(0-1 marks)
4.2 Identify and Address
Violations (7 marks)
Clearly identifies all violations
and their impact, applying
appropriate remedies with
strong justification.
(6-7 marks)
Identifies most violations,
applies suitable remedies, but
with less detailed justification.
(4-5 marks)
Mentions some violations with
limited explanation and
applies basic remedies.
(2-3 marks)
Fails to identify or address
violations effectively.
(0-1 marks)
5. Conclusion (5 marks)
5 Conclusion (5 marks) Clear summary of results with
key coefficients and model
performance. Insightful
discussion on implications and
conclusions.
(4-5 marks)
Basic summary with some
discussion on key results and
implications, but lacks depth.
(2-3 marks)
Minimal summary with
limited interpretation of
results.
(1 mark)
No summary or incorrect
interpretations.
(0 mark)
6. Report Writing （30 marks）
6.1 Structure and Organization
(15 marks)
Well-structured, clear
headings, smooth flow,
Clear structure, minor flow
issues, few grammatical
Unclear structure, inconsistent
headings, multiple
Disorganized, missing
headings, frequent errors, poor
minimal errors, professional
language and formatting.
(12-15 marks)
errors, consistent formatting.
(9-11 marks)
grammatical errors, formatting
issues.
(5-8 marks)
formatting.
(0-4 marks)
6.2 Analytical Depth and
accuracy (10 marks)
Clear, thorough analysis with
accurate R code and correct
outputs.
(8-10 marks)
Solid analysis with minor
missing details or code errors.
(5-7 marks)
Incomplete analysis with
multiple errors in code or
explanations.
(3-4 marks)
Lacks analysis, significant
code errors or incorrect
outputs.
(0-2 marks)
6.3 Technical Demonstration
(5 marks)
Relevant, well-commented R
code with clear methodology
and independent thought.
Proper citations.
(4-5 marks)
Basic R code with minimal
comments. Some originality
and external resources cited.
(2-3 marks)
Limited R code with unclear
comments. Minimal
originality, vague resource
use.
(1 mark)
No R code or explanation. No
originality or citations.
(0 mark)