MS4034 - Multiple Linear Regression Model Assignment

Assignment Task

Instructions

1. The project should be carried out in R.

2. Your project report must be submitted in a pdf format (you can easily do it by writing your answers in a Microsoft Word document and then printing to pdf). You should also submit the R file you used for analysis, as well as the dataset used.

3. The project report should have as its first page your name and student number.

4. Your R file should be called studentname.R, i.e. ”JamesSweeney.r”

5. The project report and R file should be submitted online at the assignment page on Sulis. If there are issues uploading to Sulis, email your project files to james.

Write an R file which does the following, summarising your findings in a research report:

1. Go to kaggle.com or similar and find a dataset of interest to you.

(a) Some of the examples we have seen already in the labs such as the housing dataset were obtained from kaggle.

(b) Here’s a clickable link: kaggle datasets

(c) Note the dataset you choose is one that should have the possibility of being modelled using multiple linear regression, with data manipulations via transformations etc as required.

(d) Within the submitted report you should provide a short introduction to the dataset and outline the motivation for your interest in this project.

2. For your chosen dataset first carry out descriptive analytics - summarise and explore each variable in the dataset, providing the appropriate summary statistics, visualisations and confidence intervals.

3. Now engage in diagnostic analytics - explore for statistically significant relationships between variables of interest using hypothesis tests as appropriate

4. Explore the data from a model fitting perspective using scatter plots and correlation. Note some of the predictor variables may have a non-linear relationship with the variable you wish to predict so evaluation of model residuals is key. Explore model fit and summarise the interpretation of each variable in the fitted model. Interpret the parameters of the fitted model for your best fitting model and present conclusions from it.

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