Introduction to Business Analytics — DU Bachelor of Management Studies Previous Year Question Papers

(iii) Accuracy (8) Write short notes on any three of the following: (a) K-means Clustering (b) Decision Tree approach for Classification and (c) Testing of Assumptions in Linear Regression (d) web aDd Social Media Analytics (S*:=tS) 5. Show your working clearly in your answer sheet. What are the differences between descriptive, predictive, and prescriptive analytics? Explain how these types of analytics can be combined to provide comprehensive business insights, using an example from a business context. (15) (a) The following is a dataset template of students who wish to take admission to a prestigious university: GMAT Score CGPA Work (a) What is Big Data Analytics and why is it important? What are the main challenges and benefits of working with Big Data in business? (7) (b) Consider a dataset named inventory-data which contains information on stock levels, sales and lead times for products in a retail store. One of the variables is days-out-of-stock, which tracks the number of days a product was unavailable for sale. Write a suitable Python or R code to calculate the following for the days-out-of-stock variable: In this dataset, the outcome 'Admitted' is coded as I for admission and 0 for reiection. Write down the steps and provide Python or R code for building a logistic regression model using this dataset. (7) (b) Consider the following confusion matrix obtained from a classification model: (7) Pre Positive l0 4 Positive Negative ( iii) Standard Deviation (ri) Regarding the above table, calculate the following: (b) The box office success of Bollywood movies was analyzed using a logistic regression model. In this analysis, the target variable'Box Office Success'(Y) is coded as 1 for success and 0 for failure. The independent variable (X) is the budget, measured in crores of rupees. The relevant coefficients and intercept values obtained from the model are presented in the following table. (i) A production house is making a movie with a 100-crore budget. What is the probability of success for this movie?Reference values for e*: 3 (a) What are the main differences between simple regression models and rnultiple regression models? How do you interpret the coefficients ( B ) in Simple Linear Regression compared to those in Multiple Linear Regression? (7) (b) The following is the dataset template of companies producing automobiles. Marketing Expense (in lacs) Sales (in Crores) Suppose the narne of the datasel is 'auto'. Build a suitable regression model to predict the sales of a company. Write down the steps with python/R codesfor building the model using the above data template.(8) We aim to build a model to estimate sales based on the advertising budget allocated to YouTube, Facebook, and Newspapers. After running the regression analysis, we obtained the following output: (ii) Calculate the budget for which box office success and failure are equally likely. (8) lm(formula Sales Newspaper, data : marketing) Min lQ Median 3Q Max