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Overview and Rationale
It is important for you to be able to describe data numerically and graphically and using
multiple regression to predict influential variables. In this assignment you will use R in a
hands-on experience on data analytics as a review.
Course Outcomes
This assignment is directly linked to the following key learning outcomes from the course
syllabus:
• Describe data numerically and graphically and predict influential variables for real
world business problems
Assignment Summary
There are two parts of this assignment:
Part A: Use R functions to describe data numerically and graphically.
Part B: Use R functions to build a multiple regression model for real world data.
You will then report your work and findings in a 1000 word paper.
Use the following supporting materials for R syntax, data sets and tools:
• Using R for Data Analysis and Graphics by J H Maindonald.
• Quick R
Follow the instructions below for each part of the assignment:
Part A
Use the “Trees” data or another data set that is part of R. Then, use the functions in sections
2.5, 3.5 and 3.6 of “Using R for Data Analysis and Graphics” to describe data numerically
and construct the graphs to describe data graphically. Follow the steps below.
1. Invoke R and use the “Tree” dataset
2. Find the 5 summary numbers in the data
3. Graph a straight line regression
4. Create Histograms and density plots
5. Create Boxplots
6. Normal probability plots
Include your code and results in your report.
Part B
Use the “Rubber” and “oddbooks” data sets, or choose two use other appropriate data sets,
in R. Then use the functions in section 5.4 of “Using R for Data Analysis and Graphics” to
build multiple regression models.
In addition, you need to install the DAAG package before you can complete this part of the
assignment. Follow the steps below:
1. Load the MASS and ggplot2 libraries and use the “Rubber” data set
2. Load the DAAG library and use the “oddblocks” data set
3. Build multiple regression models using summary(), log(), lm() and
ggcorrplot()
Include your code and results in your report. Be sure to show the model with insights,
correlation matrix and explanations.
Report
Your assignment/project should have a good cover/title page, introduction of what the
goals of the project and the methods you use. It also should follow APA format with at least
1000 words (excluding title page and references page) and references page. In the body of
your project you should incorporate the R codes and R outputs with interpretation of your
results. You need to make sense of your results to make good points to show your
understanding of the course material and its application to the dataset.
Graphs, figures, charts, tables are very useful to increase visual effects to impress your
readers. You also should do your best to give insight and understanding to the project with
a good conclusion. Please use subtitles to make your assignment more reader friendly as
well.
Format & Guidelines
The report should follow the following format:
(i) Title page
(ii) Introduction
(iii) Analysis
(iv) Conclusion/Interpretations
(v) References
And be 1000 words in length and presented in the APA format
Assignment Rubric
Category Meets Standards Approaching Standards Below Standards
Introduction
Introduction provides a
brief and intelligible
overview of the goals and
methods of the
assignment
Introduction provides an
overview of the goals and
methods of the
assignment, but is
ambiguous or not concise
Does not introduce
project goals, project
questions or methods.
Analysis
Provides all R code and
the outputs. Includes
interpretation of the
output, graphs, figures,
charts, and tables and the
significance of the results
in the analysis.
Provides R codes and
outputs, but the R code
does not match the
outputs or is missing
some code or outputs.
Includes limited
interpretations, charts,
and tables and the
significance of the results
in the analysis.
Does not provide R code
or its outputs or minimal
R code is provided.
Includes few
interpretations, charts, or
tables. Does not identify
the significance of the
results in the analysis
Data
Visualizations
Data visualizations are
appropriate for the level
and type of analysis.
Graphs, figures and tables
communicate insights and
significance to the reader.
Data visualization are
useful for the level and
type of analysis, but
graphs, figures and tables
do not clearly
communicate significance
of the results to the
reader.
Data visualization are
used minimally or not at
all. If graphs, figures and
tables are used, it is
unclear what they are
intended to communicate
or why.
Interpretation &
Conclusions
The conclusion
summarizes and makes
sense of the results,
making good points that
reflect clear
understanding of the
assignment material.
The conclusion
summarizes and makes
sense of the results,
making good points that
reflect a basic
understanding of the
assignment material.
The conclusion does not
summarize or attempt to
make sense of the results.
Conclusions do not reflect
an understanding or
reflect a
misunderstanding of the
material
Report: Writing
Mechanics, Title
Page, & References
There are no noticeable
errors in grammar,
spelling, and punctuation;
and completely correct
usage of title page,
citations, and references.
The report contains
approximately of 1000
words
There are very few errors
in grammar, spelling, and
punctuation; and
completely correct usage
of title page, citations, and
references. The report
contains approximately
1000 words
There are more than five
errors in grammar,
spelling, and punctuation;
or the usage of title page,
citations, and references
are incomplete; or the
report contains far less
than 1000 words
Overview and Rationale
It is important for you to be able to describe data numerically and graphically and using
multiple regression to predict influential variables. In this assignment you will use R in a
hands-on experience on data analytics as a review.
Course Outcomes
This assignment is directly linked to the following key learning outcomes from the course
syllabus:
• Describe data numerically and graphically and predict influential variables for real
world business problems
Assignment Summary
There are two parts of this assignment:
Part A: Use R functions to describe data numerically and graphically.
Part B: Use R functions to build a multiple regression model for real world data.
You will then report your work and findings in a 1000 word paper.
Use the following supporting materials for R syntax, data sets and tools:
• Using R for Data Analysis and Graphics by J H Maindonald.
• Quick R
Follow the instructions below for each part of the assignment:
Part A
Use the “Trees” data or another data set that is part of R. Then, use the functions in sections
2.5, 3.5 and 3.6 of “Using R for Data Analysis and Graphics” to describe data numerically
and construct the graphs to describe data graphically. Follow the steps below.
1. Invoke R and use the “Tree” dataset
2. Find the 5 summary numbers in the data
3. Graph a straight line regression
4. Create Histograms and density plots
5. Create Boxplots
6. Normal probability plots
Include your code and results in your report.
Part B
Use the “Rubber” and “oddbooks” data sets, or choose two use other appropriate data sets,
in R. Then use the functions in section 5.4 of “Using R for Data Analysis and Graphics” to
build multiple regression models.
In addition, you need to install the DAAG package before you can complete this part of the
assignment. Follow the steps below:
1. Load the MASS and ggplot2 libraries and use the “Rubber” data set
2. Load the DAAG library and use the “oddblocks” data set
3. Build multiple regression models using summary(), log(), lm() and
ggcorrplot()
Include your code and results in your report. Be sure to show the model with insights,
correlation matrix and explanations.
Report
Your assignment/project should have a good cover/title page, introduction of what the
goals of the project and the methods you use. It also should follow APA format with at least
1000 words (excluding title page and references page) and references page. In the body of
your project you should incorporate the R codes and R outputs with interpretation of your
results. You need to make sense of your results to make good points to show your
understanding of the course material and its application to the dataset.
Graphs, figures, charts, tables are very useful to increase visual effects to impress your
readers. You also should do your best to give insight and understanding to the project with
a good conclusion. Please use subtitles to make your assignment more reader friendly as
well.
Format & Guidelines
The report should follow the following format:
(i) Title page
(ii) Introduction
(iii) Analysis
(iv) Conclusion/Interpretations
(v) References
And be 1000 words in length and presented in the APA format
Assignment Rubric
Category Meets Standards Approaching Standards Below Standards
Introduction
Introduction provides a
brief and intelligible
overview of the goals and
methods of the
assignment
Introduction provides an
overview of the goals and
methods of the
assignment, but is
ambiguous or not concise
Does not introduce
project goals, project
questions or methods.
Analysis
Provides all R code and
the outputs. Includes
interpretation of the
output, graphs, figures,
charts, and tables and the
significance of the results
in the analysis.
Provides R codes and
outputs, but the R code
does not match the
outputs or is missing
some code or outputs.
Includes limited
interpretations, charts,
and tables and the
significance of the results
in the analysis.
Does not provide R code
or its outputs or minimal
R code is provided.
Includes few
interpretations, charts, or
tables. Does not identify
the significance of the
results in the analysis
Data
Visualizations
Data visualizations are
appropriate for the level
and type of analysis.
Graphs, figures and tables
communicate insights and
significance to the reader.
Data visualization are
useful for the level and
type of analysis, but
graphs, figures and tables
do not clearly
communicate significance
of the results to the
reader.
Data visualization are
used minimally or not at
all. If graphs, figures and
tables are used, it is
unclear what they are
intended to communicate
or why.
Interpretation &
Conclusions
The conclusion
summarizes and makes
sense of the results,
making good points that
reflect clear
understanding of the
assignment material.
The conclusion
summarizes and makes
sense of the results,
making good points that
reflect a basic
understanding of the
assignment material.
The conclusion does not
summarize or attempt to
make sense of the results.
Conclusions do not reflect
an understanding or
reflect a
misunderstanding of the
material
Report: Writing
Mechanics, Title
Page, & References
There are no noticeable
errors in grammar,
spelling, and punctuation;
and completely correct
usage of title page,
citations, and references.
The report contains
approximately of 1000
words
There are very few errors
in grammar, spelling, and
punctuation; and
completely correct usage
of title page, citations, and
references. The report
contains approximately
1000 words
There are more than five
errors in grammar,
spelling, and punctuation;
or the usage of title page,
citations, and references
are incomplete; or the
report contains far less
than 1000 words