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Forecasting Total Employment in Australia using Regression Analysis

Data Sets and Instructions

Questions:

You are repeating the overall case study from Report 1 but using a regression forecasting instead.
You have been employed as a consultant for a joint project by the Labour force Association of Australia and the Australian Government Treasury.

As part of your role in the Business Analytics and Data Analytics team, you have been asked to forecast total employment (i.e. total employed), as part of a wider report being commissioned by the above collaboration – on Australia’s Labour Force Status.

Questions 
You are to use the exact same data sets that you used in Report

1. You do not need to re-download the data. If your data is lost, the instructions for obtaining it are repeated
Obtain the ABS statistics for Labour Force
For the purposes of this report you are to consider the Total Employed Labour Force data. There are three series in Table 1: Original, Seasonally-adjusted, and Trend (please choose carefully throughout this report!)
For the purposes of this report, only consider the data from August 2011 to July 2020 as the sample of data that is available to you – that is, ignore any recent observations. 
This means that the first actual observation in your Excel file is from August 2011 and your last actual observation in your Excel file is from July 2020.

Use Excel and no other statistical software for the purposes of this report. 
 You may use Minitab for constructing correlograms.

This report will require two separate submissions.
The numerical responses need to be submitted via a quiz tool in iLearn. 
The written responses need to be submitted via a PDF uploaded via Turn-It-In in iLearn.
Instances of plagiarism will be dealt with according to the relevant policies and procedures.

Numerical responses to be submitted via a quiz tool on iLearn:

Exercise 1 – Application (10 marks)
As for Report 1:
For the purposes of this report, only consider the data from August 2011 to July 2020 as the sample of data that is available to you – that is, ignore any recent observations. 
This means that the first actual observation in your Excel file is from August 2011 and your last actual observation in your Excel file is from July 2020.
For the Seasonally-adjusted data for the Employed total (Series ID: A84423043C) available in Table 1: 
Forecast the out-of-sample values for every month in the period August 2020 – July 2021 (both months inclusive) using a simple linear regression with an intercept, and time (t = 1, 2, 3,… ) as the explanatory variable.

Exercise 2 – Application (10 marks)
As for Report 1:
For the purposes of this report, only consider the data from August 2011 to July 2020 as the sample of data that is available to you – that is, ignore any recent observations. 
This means that the first actual observation in your Excel file is from August 2011 and your last actual observation in your Excel file is from July 2020.
For the Original data for the Employed total (Series ID: A84423085A) available in Table 1: Forecast the out-of-sample values for every month in the period August 2020 – July 2021 (both months inclusive) using a multiple linear regression with an intercept, time as an explanatory variable, and 11 dummy variables for all months except January – hence use January as the base.

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