MATH1041代写、代做R编程、代写RStudio数据、R代写代做

日期：2018-10-11 09:50

MATH1041 Statistics for Life and Social Science

Semester 2, 2018

MATH1041 Computing Assignment

Assignment release date: The assignment will be released to all students on Wednesday

the 26th of September on Moodle (see “Assessments Information” section).

Submission due date: Thursday 11th October (Week 11) before 6pm (Sydney

time).

Please submit your assignment through Moodle, see the “Assessments Information” section

on Moodle for further information regarding online submission. You must submit

a neatly typed assignment converted to pdf format.

Data: A data set (in the text file format) will be sent to you via email at your official

university email address (see page 2 of this document for further details).

Assignment length: No more than SEVEN single-sided A4 pages including this

cover sheet as the first page. Also, please make sure that you include your name and

zID somewhere in the assignment.

Q1 /6

Q2 /19

Q3 /17

Q4 /18

Total /60

1

Obtaining the data via email and reading it into RStudio

The data (that is, your data set) are available in a text file with a name similar to:

“z3141593.txt”, (where z3141593 in the text file name is replaced by your unique student

zID number). This text file has been sent to you via email at your official

university email address. PLEASE CHECK YOUR UNIVERSITY EMAILS

REGULARLY TO MAKE SURE THAT YOU HAVE OBTAINED YOUR

DATA SET. Please email Dr Jakub Stoklosa (j.stoklosa@unsw.edu.au) if haven’t

received your data set yet.

The first step is to read the data into RStudio. The data format is simple and similar

to what you have already done in the Introduction labs. Follow the instructions given in

section R1.4 “How to import a text file into RStudio”of the RStudio “How-To-Manual”

available on Moodle. Once you’ve uploaded the data then you are ready to start your

analysis!

Computing assignment format

Here are some more details that may assist you:

Regarding the overall assignment structure, this is up to you, just remember to keep

it clear and concise. If you are answering questions in the given order (that is, 1a),

b), etc.), then this is fine. You don’t need to re-write the assignment question again.

You are required to type up your entire assignment (rather than scanning and taking

screenshots). If you are using Word you should use the equation editor for any

maths notation. If you don’t have Word then please use the School computers.

Please convert and submit your assignment in pdf.

You are asked to produce SIX graphs/plots for this assignment. You are required to

produce these in RStudio. You may want to use the par(mfrow=c(2,3)) function

to construct all six graphs per plot (this is optional), see Section R1.4 “Transforming

data using RStudio”of the RStudio “How-To-Manual” available on Moodle.

We recommend adding some working out for some of the questions involving calculations.

But try to keep your solutions brief and concise (since there is a page limit).

It’s good practice for the exam and in case you get the wrong answer you have some

workings to gain marks from. Your working could consist of RStudio commands or

perhaps the main steps on how you arrived at your answer. You don’t need to add

all of your R-code!

Keeping your results to 2 or 3 decimal places should be fine.

There is no requirement for font size and line spacing but obviously don’t make

things too small.

2

Scenario

A team of researchers were interested in studying the impacts of drought on sheep livestock

in farms around New South Wales and Queensland, Australia. In particular, the

researchers wanted to compare the average body weight of sheep from five years ago (when

there was little drought) to now (Spring, 2018) where drought is of serious concern.

To obtain their data, the research team decided to collect a random sample of sheep from

a very large sheep population on a farm affected by the drought. This random sample of

data consists of sheep body weight measurements (measured in kilograms), head-to-tail

length measurements (measured in metres) and their gender (male/female).

The text file contains your unique data of length n in separate rows consisting of 3

variables: BW which corresponds to sheep body weights, HTL which corresponds to sheep

head-to-tail lengths, and SEX which corresponds to gender (0 = Female and 1 = Male).

Your job is to assist the research team by analysing the data set provided to you.

The Analysis Tasks

The questions you need to answer in your assignment submission are given below. Please

make sure your assignment is converted to pdf format.

1. (a) Calculate the sample mean and sample standard deviation of your sample of

sheep body weight (BW) measurements.

(b) Produce a normal quantile plot of your sample of sheep body weight measurements

(see Section R2.6 “How to produce a normal quantile plot using

RStudio”). Include this plot in your submitted assignment, properly labelled.

(c) By referring to the normal quantile plot obtained in Part 1b briefly discuss if

the sheep body weights are approximately normally distribution.

2. Let μ be the population mean body weight (in kg) of sheep (of any gender) on the

farm now (Spring, 2018). The research team decided to compare the current sheep

mean body weight with the mean from five years ago. The known mean body weight

for sheep from five years ago was 60kg.

(a) Test the hypothesis that μ is equal to 60. You must summarize all steps:

state the null (H0) and alternative hypotheses (Ha) relevant to the research

objectives stated in this scenario, the value of a suitable test statistic, the

sampling distribution for this statistic, a P-value, your summary of significance

and conclusion in plain language.

(b) Some assumptions need to be made for the sampling distribution of the test

statistic (as given in Part 2a) to be valid. State these assumptions.

3

(c) Discuss whether the assumptions from Part 2b are satisfied?

(d) Produce a 95% confidence interval for μ, the mean body weight of sheep. For

this question you may assume that it is appropriate to use a t-distribution.

Make sure you write down all the required steps to calculate this interval.

Does this confidence interval include the value 60?

Explain whether your confidence interval is consistent with your conclusions

from the hypothesis test in Part 2a.

3. The research team were also interested in studying:

the relationship between body weight and gender; and

the relationship between body weight and head-to-tail length.

(a) Produce a comparative boxplot for sheep body weight against gender. Include

this plot in your submitted assignment, properly labelled.

(b) Describe any differences or similarities in the distribution of body weight of

sheep for the different genders using your comparative boxplot from Parts 3a.

(c) Construct an appropriate graphical summary to visualize the relationship between

body weight and head-to-tail length. Include this plot in your assignment,

properly labelled.

(d) Summarize the key features of your plot from Part 3c.

(e) Suggest an appropriate numerical summary to quantify the linear relationship

between body weight and head-to-tail length. Report and comment on this

value.

(f) The research team wanted to predict sheep body weight from head-to-tail

length measurement by fitting a linear regression model. Would you recommend

the research team do this? Explain briefly.

4. The research team decided to investigate the head-to-tail length (HTL) measurement

in more detail.

(a) Produce a five number summary for the HTL measurements.

(b) Produce a histogram for the HTL measurements. Include this histogram in your

submitted assignment properly labelled.

(c) In MATH1041, we looked at the effect of transforming data. Using the HTL

measurements, perform: (1) a log transformation; and (2) a square-root transformation,

and produce a histogram for each of these. Include these histograms

in your submitted assignment properly labelled.

(d) Summarize the key features of each histogram from Parts 4b and 4c (that is,

the raw data, and each of the transformations). Please comment on central

location, spread, and (any) skewness/symmetry.

(e) Do you think these transformations reduced any skewness? Explain briefly.