代写DTS206TC、c/c++，Java程序代做

日期：2025-03-13 08:21

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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and learning purposes, please type “yes” here.

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Collusion and the Fabrication of Data (available on Learning Mall Online). With reference to this

policy I certify that:

 My work does not contain any instances of plagiarism and/or collusion.

My work does not contain any fabricated data.

By uploading my assignment onto Learning Mall Online, I formally declare

that all of the above information is true to the best of my knowledge and

belief.

Scoring – For Tutor Use

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Stage of

Marking

Marker

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Learning Outcomes Achieved （F/P/M/D）

(please modify as appropriate)

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Score

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1

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Moderation

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The original mark has been accepted by the moderator

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another tutor (please circle):

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2

nd Marker if

needed – green

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For Academic Office Use Possible Academic Infringement (please tick as appropriate)

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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)