Part 1 (30 marks):

Hypothesis testing received several criticisms, based on the suggested literature (please see literature in Moodle page) discuss the argument presented against hypothesis testing and the argument presented to address those criticism.

Part 2 (40 marks):

1. Using suitable sample data of your choice, you are required to perform and report on multiple regression model.

In your report you should:

1. State clearly the context, data description, and objectives of the analysis undertaken. ( 5 marks)

1. Describe carefully all independent/dependent variables in your analysis. ( 5 marks)

1. Perform and report the results of tests to check that the Gauss Markov and other relevant assumptions are not violated. ( 10 marks)

1. Run the analysis in SPSS, R or other software of your choice. Describe the model building stages and present and discuss the output from your final model. (20 marks)

Part 3 (30 marks)

In this part you will reduce the data set using PCA and apply regression using the reduced dataset.

a) Using your own words, describe and explain the use of Principal Components Analysis (PCA) as a dimension reduction technique. (maximum 500 words)

(5 marks)

b) Apply PCA on your dataset to reduce the dataset, when conducting PCA, ensure that you justify the number of components and all extraction methods used. Component matrices, Scree plots, Total Variance Explained, Communalities, and component plots should all be presented and summarised. Make sure you save the Factor Component scores.

(10 marks).

c) Use the output of your Principal Component Analysis to build a Multiple Linear Regression model. Select principal components based on your recommendation in part 1 that will be your new independent variables.

(10 marks)

d) Compare the performance of models in part 2 and 3? What can you conclude?

(5 marks)

pur-new-sol

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