Applied Quantitative Methods Below are the questions composing the final. Please, have your solutions
Applied Quantitative Methods
Below are the questions composing the final. Please, have your solutions reflect your energies by showing clear and thorough work for each problem; your efforts are valuable and should be treated so. Good luck and enjoy the challenge!
Assume that we were provided the data sets mar1500 and country.sav and commissioned to evaluate, summarize, and formulate conclusions from the samples. In particular, the client is interested in knowing if a certain hypothesis is correct. For each question we will seek the following:
- Test the data for normality and outliers then plot to check for linearity.
II If necessary, remove outliers (which ones did you remove, why, and was it significant?)
III. Where required, perform the appropriate hypothesis test (assume a = 0.01) and post hoc tests and explain your conclusions. Also, calculate the R2 of any accompanying hypothesis tests (what are these, what are they for, and what do they conclude?).
IV. If needed, transform nonlinear data so that linearity is preserved.
V. Create a regression model for the given variables. Is it a reliable model? Why or why not?
VI. Test the residual's normality and constant variance of the regression model. How do they compare with the data from part I?
With each question, include the necessary graphs and charts along with explication of these results and their meaning.
- Using the mar1500.sav data, determine if the age of the runner has significant bearing on their performance. Use the hours and agecat8 variables for you analysis.
- Again using the mar1500 data, analyze the data to determine if both agecat8 and sex affect the runners finishing time.
3) What, if any, is the relationship between birth rates and the number of doctor? Does a linear model well describe their association? Use the country.sav data with variables birthrat and dots.
4) Again, using the country.sav data, use multiple linear regression techniques to find a model that `best' describes death rates (deathrat variable). Verify that this model and the factors you selected are the best.
Deliverable: Word Document
