Suppose you are working as an analyst in a company that sells a wide range of similar products on its platform. Your team believes that there should be some relationship between the ratings of a product and the number of purchases, as a popular product that is bought frequently must have a good rating. The outcome of the model is that you should be able to estimate the product rating based on the number of purchases of that item.

Please refer to the following data to perform your calculations.

Dataset - Project Part 1 (Excel)

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Question 1 - Linear Regression Equation (2 marks) What will be the general equation of the model built among the two variables, the ratings of a product and the number of purchases?

Question 2 - Regression on MS Excel (4 marks) Now that you have identified the regression equation, perform the function of regression analysis on the data set in MS Excel. The analysis report tab should be named Regression and should also have a line fit plot and a residual plot.

Question 3 - Linear Regression Model (3 marks) Based on the scatterplots available, analyse if the linear regression model can be built between the product rating and the number of purchases of the product? Explain your answer.

Question 4 - Correlation Coefficient (3 marks) What is the value of the correlation coefficient between the two variables and what does it signify?

Part 2

The data provided below is the company data, wherein 139 different companies submitted an application to get an investment from one of the leading investment firms.

The operations team of the investment firm have made a decision (labelled as 'Target' variable) by analysing all the companys information and coded 1 for those companies that seem riskier to invest in, i.e., companies that are likely to get bankrupted.

With the help of the sample data provided, help the investment firm to decide on a few other metrics based on the questions asked below.

Dataset - Project Part 2 (R