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Descriptive Statistics and Regression Analysis with R
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.

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