Question

Requirements

The task is basically to establish a relation between the two variables by applying statistical measures. The relation should be clear after that and enough to form an interpretation of about 1000 words where the enitre method is explained and the results are analyzed.

Below is a little background of the topic and the research question to help you get an idea. I have attached the file as well that contains that data on which relation needs to be built.

Introduction

One of the most important dimensions to look at when assessing the financial development of any country is to first look at its availability of credit as highlighted by Levine and Warusawitharana (2014). Finance for any company in other terms is the credit that is made available in order to proceed with normal operations. Productivity on the other hand simply reflects the efficiency of a company and how well it is able to make use of the available resources. Productivity has a positive relation with profitability (Borowiecki.K, 2013), which is why the latter can be used as a measure of productivity.

The aim of this research paper is to find whether there is a link between the level of debt financing and the resultant productivity of that business. Normally, it is believed that more money leads to more productivity, irrespective of what the source of that money is. The research done by Levine and Warusawitharana (2014) concludes that there is a positive relation between debt and the expected productivity and that finance overall is good for any firm’s growth. This research aims to find out how the debt financing affects productivity of businesses in a particular industry by assessing in terms of profitability. It will establish the relation by looking at the profitability of that industry and comparing it to the increase and decrease in debt financing in those respective years. Contrary to majority researches, there have been some researches explaining about the threshold of finance, after which the productivity starts to decrease and is therefore negatively impacted (Cecchetti and Kharroubi, 2012). This paper will try to look into this phenomenon by taking the construction industry as the main subject of assessment for sake of data that is collected and then later analyzed. Based on industry level data, we will be able to tell whether the two are positively linked or whether the threshold theory in recent researches proves its point.

Focused research question

Is there a relationship between business productivity and debt financing in the construction industry?

The secondary data that will be studied to establish a relation has been taken from the Australian Bureau of Statistics. From there, in the section of general business characteristics, two excel files were downloaded. One of the files had details about the debt financing that was done in different industries and the other one had details about the profitability of those industries comparing it with that of previous years. The two variables in this would be debt financing as independent variable and profitability increase/decrease being the dependent variable. Since all the data is in numerical form and explains different statistics, this is clearly a quantitative research. Qualitative research will also be done to gather support for the topic as present in past available literature. As for the industry, the main focus will only be the construction industry and using all statistics relevant to it. To be more specific, the construction industry in Australia will be the focus of the entire report.

		%	%
no	Year	Debt financing obtained	Increase in productivity
1	2005-2006	85.6	39.1
2	2006-2007	84.5	45.7
3	2007-2008	96	39.3
4	2008-2009	88.3	43.8
5	2009-2010	87.8	38.9
6	2010-2011	83.7	41.8
7	2011-2012	89.4	29.8
8	2012-2013	86.6	34
9	2013-2014	80.8	37.3
10	2014-2015	79	34.1
11	2015-2016	84.7	23.3
12	2016-2017	91.6	23.8

Answer 1

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.108886896
R Square	0.011856356
Adjusted R Square	-0.086958008
Standard Error	7.539421779
Observations	12
ANOVA
	df	SS	MS	F	Significance F
Regression	1	6.820359033	6.820359033	0.119986161	0.736227305
Residual	10	568.4288076	56.84288076
Total	11	575.2491667
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	50.66842982	42.66673225	1.187539498	0.262456566	-44.398974	145.7358336
Debt financing obtained	-0.170636954	0.492614862	-0.346390186	0.736227305	-1.268251266	0.926977359

p-value =0.736227305 >0.05

there is not significant relationship