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p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 28.0px ‘Helvetica Neue’; -webkit-text-stroke: #000000} p.p2 {margin: 0.0px 0.0px 0.0px 0.0px; font: 16.0px ‘Helvetica Neue’; -webkit-text-stroke: #000000} li.li3 {margin: 0.0px 0.0px 0.0px 0.0px; font: 19.0px ‘Helvetica Neue’; -webkit-text-stroke: #000000} span.s1 {font-kerning: none} ul.ul1 {list-style-type: disc} Review of quantitative research design Inferential statistical methods are widely used in research in order to conduct tests of differences for dependent or independent samples, analyse associations between different types of variables and establish relationships using models between dependent and independent variable(s). For this week’s Key Concept Exercise you will evaluate examples of inferential statistical analysis by discussing empirical results and the strengths, weakness and suitability of different statistical estimation and hypothesis testing procedures. In approximately 500 words, answer TWO of the FOUR questions in the file provided here on Inferential Statistical Analysis:
Document Preview:
Key Concept Exercise
Research Methods
Week 5: Inferential Statistical Analysis
Question 1
The below table shows the results of an OLS regression of US real GDP growth rates (REALGDP)
on changes of oil prices (OIL), interest rate (INTERESTRATE) and inflation rates (INFLATION)
(monthly data from 1990 to 2013):
REALGDP=CONSTANT+a*OIL+b*INTERESTRATE+c*INFLATION
Coefficient T-stat p-value
CONSTANT 0.015 12.454 0.000
OIL -0.037 -4.565 0.003
INTERSTRATE -0.012 -5.564 0.032
INFLATION -0.004 -1.56 0.145
2
(a) Discuss the statistical significance of the parameters, interpret the sign and magnitude of the
estimates, and overall fit of the model.
(b) Are the results in line with the predictions of the theory and why?
Question 2
The below table shows the results of Mann-Whitney tests of comparing the distribution of
productivity between male (1) and female (0), postgraduate (1) and undergraduate (0), and
trained (1) and non-trained (0) employees, using independent samples from a company.
Ranks
SEX N Mean Rank Sum of Ranks
Productivity .00 28 25.48 713.50
1.00 25 28.70 717.50
Total 53
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