data留学生代写、代做R设计、analytics代做、R编程语言调试

日期：2020-05-26 10:16

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.

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