The project will involve the analysis of real world data (Boston dataset with house pricing) in which linear or non-linear regression analysis is appropriate and will give the details of the analysis used.
The dataset must contain at least eight predictors and one response.
The project report will be single spaced typed with 1 inch margins with appropriate tables and figures will be included in the technical report and correctly cross-referenced.
The goal is for the student to learn not only how to complete a specific analysis but also how to correctly and clearly communicate the results and their implications.
All equations should be created using publication quality software such as Mathtype or LaTeX, etc.
The R code used to complete your analysis will be attached as an appendix to the report.
The code should be well commented and in runnable form.

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These solutions may offer step-by-step problem-solving explanations or good writing examples that include modern styles of formatting and construction of bibliographies out of text citations and references. Students may use these solutions for personal skill-building and practice. Unethical use is strictly forbidden.

Multiple Linear regression

The data describes the Housing Values in Suburbs of Boston. The dataset has 506 rows and 14 columns. The Boston Housing data contains information on neighborhoods in Boston for which several measurements are taken into account. The variables are ‘mdv, median value of owner-occupied homes \$1000s which is a response variable, crm= per capita crime rate by town, zn=proportion of residential land zoned for lots over 25,000 sq.ft., indus= proportion of non-retail business acres per town, chas=Charles River dummy variable, nox=nitrogen oxides concentration (parts per 10 million), rm=average number of rooms per dwelling, age=proportion of owner-occupied units built prior to 1940, dis=weighted mean of distances to five Boston employment centres, rad=index of accessibility to radial highways, tax=full-value property-tax rate per \$10,000, ptratio =pupil-teacher ratio by town, black=proportion of blacks by town, lstat=lower status of the population (percent).

This is the dataset available in R. So we can load MASS package to view this dataset
#To see the number of rows and columns...

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Solution.R and Solution.docx.

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