POLS6024: Introduction to Quantitative Research Methods- Statistical Computing & Graphics- R Studio- Essay Writing Assignment

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MAS7804
Essay Writing Assignment:

Task:

PART A: Descriptive statistics

This question uses the Living Costs and Food Survey (LCF2013) dataset. Recode as appropriate. Interpret all results statistically and substantively.
1) Describe what is being measured and the level of measurement for the following variables: P344pr, A093r, SexHRP, A094r, G018r.

2) Using the appropriate measures, report and interpret the central tendency and dispersion for P550tpr, P425r, A121r, and G019r. You should report
your output in a table or plots.

3) Graphically display the distribution of P344pr by Gorx. How does P344pr vary between and within Gorx? Interpret your results.

PART B: Inferential Statistics: Confidence intervals, chi square and t-tests

This questions use the LCF2013 dataset. You want to gain some additional insight into the relationships/differences in the Living Cost and Food Survey. Recode as appropriate and explain your recoding decisions. State a null hypothesis and alternative hypothesis. Be sure to interpret all of the results statistically and substantively.

1) Calculate and interpret a 95% confidence interval for the sample mean of P550tpr. Explain your working.

2) Calculate and interpret a 99% confidence interval for the sample proportion working full time (A093r) of those in employment or looking for work (i.e. A093r!= “Economically inactive”). Explain your working.

3) Create and report a cross tabulation between G018r and A121r for those living in the North West and Merseyside region. Describe any patterns
observed in the table and determine if there is a statistically significant association.

4) Report the strength of the association using the appropriate test. Interpret what this can tell you about the relationship between G018r and A121r.

5) Report and interpret the mean gross normal weekly household income (P344pr) for those who work (full-time or part-time) in higher managerial,
administrative and professional occupations versus those who work (fulltime or part-time) in lower social class occupations (A094r) in the full
sample (i.e. living in all regions in the UK).

6) Is there a statistically significant difference in mean gross normal weekly household income (P344pr) between those who work in higher managerial, administrative and professional occupations versus those who work in lower social class occupations (A094r)? State a null hypothesis
and alternative hypothesis. Explain why you chose this test and whether the data meet the assumptions to conduct the test.

PART C: Correlation and linear regression

This question uses the LCF2013 dataset. You are a researcher interested in explaining differences in household expenditure (P550tpr). You will need to perform a series of steps to answer this question. When interpreting the model, you should report the sample size, interpret the p-value for the F-test and interpret the adjusted R-squared.

For each variable, interpret the coefficient estimate and whether that coefficient is statistically significant.

1) State a research hypothesis on the relationship between P550tpr and P344pr. Give a brief explanation as to why you would expect this
hypothesis.

2) Report the correlation between P550tpr and P344pr. Graphically display and statistically test the relationship between these variables. Interpret
your results both statistically and substantively.

3) Estimate and present output from a simple regression model using P550tpr as the dependent variable and P344pr as the explanatory variable.

4) Check for heteroskedasticity in your model using a plot and an appropriate post-estimation function. Interpret the results and correct you model, if
necessary.

5) Interpret your model statistically and substantively drawing on your hypothesis above.

6) Estimate expenditure when the value of your explanatory variable is £1,200. Indicate why it may not be appropriate to use your model to make
this prediction.

7) Comment on the limitations of your model and whether you can infer causality.

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Posted on : April 18th, 2018
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