代写ETF2121编程、代写Java，Python程序

日期：2021-10-21 10:56

ETF2121 - ETF5912 Data Analysis in Business

Semester 2, 2021

Assignment 3

Due Date: Friday 22 October 2021 (Week 12)

Due by 11.55pm, Melbourne time

This is an individual assignment worth 15%. You can submit your assignment early if

you wish. Unless otherwise specied, feel free to report any decimal places you like when

presenting your answers. Please include EViews outputs in your answers.

You are required to complete your answers in a Word document and then upload your

completed le in PDF through Moodle submission. Your assignment can be either (i)

entirely handwritten or (ii) entirely typed or (iii) a mixture of handwritten and typed answers.

You may take photos of handwritten answers and paste them on a Word document.

If you prefer not to use Microsoft Word, feel free to try using apps such as Camscanner

(available on iPhones and Android phones) to convert photos of your handwritten answers

directly into PDF. This app can combine multiple pages into one PDF document.

You can access Microsoft Word via the MoVE website https://move.monash.edu/

Please save your Word document as PDF with its ?le name as your name and student ID.

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Special Consideration

If you are experiencing interferences with your studies that are outside of your control (e.g.

illness, carer responsibilities), you may be eligible for special consideration. You can request

a short extension of up to ?ve calendar days by contacting Kew (Chief Examiner) via the

following email: hsein.kew@monash.edu

You can request a longer extension of more than ?ve calendar days by contacting the uni-

versity via the following website:

https://www.monash.edu/students/admin/exams/changes/special-consideration

Question 1 [2 marks]

An insurance company is thinking about o¤ering discounts on its life insurance policies to

nonsmokers. As part of its analysis, it randomly selects 1600 men who are 60 years old

and asks them if they smoke at least one packet of cigarettes per day and if they have ever

su¤ered from heart disease.

The table below shows that 500 of those in the smokers group su¤ered from heart disease,

while 282 of those in the non-smokers group su¤ered from heart disease.

Su¤er from heart disease

Group Yes No Total

Smokers 500 500 1000

Non-smokers 282 318 600

Construct a 95% con?dence interval for the di¤erence of population proportions of men

su¤ering from heart disease for smokers versus non-smokers, and interpret.

2

Question 2

Please include EViews outputs in your answers.

For ETF2121 students, this question will be marked out of 15 and this mark will be converted

to a mark out of 7 marks.

For ETF5912 students, this question will be marked out of 15 and this mark will be converted

to a mark out of 5 marks.

It is widely believed that workers with more education have, on average, higher wages than

workers with less education. The data set comprises a random sample of 500 full-time workers

from age 25 to 60 and is stored in the EViews work?le wages.wf1. It includes the following

variables for the workers.

wage = hourly wages in dollars

educ = years of education

exper = years of job experience

Consider the following model

wagei = 0 + 1 educi + 2 experi + "i:

(a) [1 mark] Use EViews to run a regression of wage on educ and exper: Write down the

sample regression line. Report the results to 4 decimal places.

(b) [0.5 marks] Jenny has 13 years of education and 13 years of job experience. What is

Jenny?s predicted hourly wages?

(c) [0.5 marks] John has 14 years of education and 13 years of job experience. What is

John?s predicted hourly wages?

(d) [0.5 marks] Calculate the di¤erence in predicted hourly wage between John and Jenny;

ie. John?s predicted hourly wages minus Jenny?s predicted hourly wages.

(e) [0.5 marks] In part (a), what is the value of ^1?

(f) [2 marks] Compare your answers in part (d) and (e). Are they the same? Explain why

or why not?

Consider the following non-linear regression model:

wagei = 0 + 1 educi + 2 experi + 3 exper

2

i + "i:

(g) [3 marks] Use EViews to run a regression of wage on educ; exper and exper2: Is there

evidence that exper has a nonlinear e¤ect on wage? Do the six steps of the test. Use

= 0:05:

3

Consider the following regression model:

ln (wagei) = 0 + 1 educi + 2 experi + "i:

(h) [2 marks] Use EViews to run a regression of ln (wage) on educ and exper: Interpret the

coe￠ cient 2:

(i) [2 marks] Using = 0:05; test to determine whether each of the independent variables

is linearly related to ln (wage) by using the p-value approach; ie. test the following

hypotheses, H0 : j = 0 vs HA : j 6= 0 for j = 1; 2:

(j) [2 marks] Test the overall utility of the model by using the p-value approach. Use

= 0:05:

(k) [1 mark] When testing the overall utility of the model, why do we prefer to use only one

test for testing jointly all the slope parameters in part (j) rather than multiple tests

for testing each independent variable as conducted in part (i)?

Question 3 [6 marks]

You are employed as an analyst in a consulting ?rm in Melbourne. Your consulting ?rm has

a consulting contract with a major housing construction company. You have been asked by

your manager to write a brief report that uses statistical techniques that you have learnt in

ETF2121-ETF5912 lecture material from Week 1 to Week 9 (inclusive) to characterise the

housing market in Melbourne.

The housing construction company wants to target its building plans. The company is

interested in knowing how housing prices (dependent variable) are a¤ected by the size of

the house and the number of bedrooms. The company is interested to know whether four-

bedroom houses sell for more than three-bedroom houses. Also the di¤erence in the price of

a house that has a nice view compared to a house that does not have a nice view.

The construction company has given you a data set in an Excel ?le (housing.xls) that contains

information about 88 randomly selected houses in Melbourne to undertake this assignment.

The Excel ?le contains the following variables:

1. hprice is the selling housing prices in dollars

2. hsize is the size of house in square-meters

3. bdr is the number of bedrooms

4. view = 1 if house has a nice view

= 0 if house does not have a nice view

Please refer to Tutorial 1 (Week 2) Question B1 part (a) if you would like to revise on how

to read the data in the Excel ?le into EViews. Remember that every tutorial is recorded.

4

Your brief report should contain all of the empirical results using the data provided. For

example, your brief report could include simple/multiple regression models, hypothesis

testing and interpretation of the empirical results.

The aim of this brief report is to allow students to undertake statistical analysis by using

the techniques taught in lectures to investigate a real-world problem. This question is inten-

tionally open ended and so there are not necessarily "right or wrong answers". The

quality of your brief report counts. For example, if you wrote "So many people wear heavy

coats during winter because they want to stay warm" would receive more marks than if you

wrote "So many people wear heavy coats during winter because they are fashion-conscious".

Remember you are an analyst writing a brief report for your boss :) Of most importance

is a correct justi?cation of your empirical results. Feel free to use Excel functions to report

some of the empirical results if you wish. You can type your brief report, but feel free to

handwrite some parts or handwrite all of your brief report if you like.

Your brief report, ideally, does not exceed 600 words, excluding tables and graphs.

Question 4. ETF5912 students ONLY

This question will be marked out of 10 and this mark will be converted to a mark out of 2

marks.

Absenteeism is a serious employment problem in most countries. It is estimated that ab-

senteeism reduces potential output by more than 10%. A management consulting ?rm

launched a project to learn more about the problem. They randomly selected 100 companies

to participate in a one-year study. For each company, they recorded the average number

of days employee absent and several independent variables thought to a¤ect absenteeism.

The dataset is stored in the EViews work?le ?absent.wf1?. The dataset has the following

variables.

ABSENT - average number of days employee absent

WAGE - average employee wage (in dollar)

PT - percentage of part-time employees

SHIFT_no - availability of shiftwork (1 = no; 0 = yes)

The variable SHIFT_no is a dummy (indicator) variable. Shiftwork is an employment prac-

tice designed to divide the day into day-shift and night-shift. Some companies only have

day-shift and hence no availability of shiftwork (ie. SHIFT_no = 1). Some companies have

both day-shift and night-shift and hence availability of shiftwork (ie. SHIFT_no = 0).

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Consider the multiple regression model

ABSENTi = 0 + 1 WAGEi + 2 PTi + 3 SHIFT_noi + "i:

(a) [2 marks] Do you think 1 and 3 will have obvious anticipated signs? Justify your

answer.

(b) [1 mark] Use EViews to estimate the multiple regression model. Interpret the coe￠ cient

3.

(c) [2 marks] Explain whether the following statement is true or false: holding ?xed all

other independent variables, increasing the WAGE by $1000 is associated, on average,

with an increase in the ABSENT by 2 days.

(d) [2 marks] Can we infer, at the 5% signi?cance level, that SHIFT_no is related to

absenteeism? Do the six steps of the test.

(e) [3 marks] Test the hypothesis that neither PT nor SHIFT_no a¤ects absenteeism. Use

= 0:05: