Description

Business Analytics

Sep 2026 Examination

 

Q1 A regional logistics company is preparing a performance dashboard for its monthly delivery review. The operations analyst has compiled shipment data in Excel, including order number, client ID, product code, date of delivery, and delivery amount. During validation, the analyst notices several blank cells in the delivery amount column and a few repeated transaction rows that may have been entered twice during manual uploads. The COO wants the dashboard released quickly because it will guide pricing revisions and branch-level incentives. However, the finance team is concerned that incomplete and duplicated records may distort averages, misstate branch performance, and lead to poor strategic decisions. Using the concepts of data cleansing, recommend how the analyst should identify and treat the missing delivery amounts and duplicate records in Excel before management uses the dashboard. How should deletion and imputation be applied in this case to protect analytical reliability and business decision-making? (10 Marks)

Ans 1.

Introduction

Data cleaning is the primary option to protect against an executive manager sees a dashboard due to the fact that dirty data distorts averages and rankings without anyone noting the problem until bad decisions are taken. For example, the logistics organization has blank cells in the delivery amount column and the duplicate rows of transaction created through manually uploaded files. Pushing this straight into an online dashboard to guide prices revisions and branch rewards can be extremely risky. Finance wants speed. The COO desires speed. finance needs accuracy and both of them are important. The analyst has to resolve any missing values and duplicates through

Its Half solved only

Buy Complete assignment from us

Price – 190/  assignment

NMIMS Online University Complete SolvedAssignments  session SEPT  2026

Last date AUG 2026

buy cheap assignment help online from us easily

we are here to help you with the best and cheap help

Contact No – 8791514139 (WhatsApp)

OR

Mail us-  [email protected]

Our website – www.assignmentsupport.in

 

Q2 A company’s help desk function has experienced customer complaints about slow response times and inconsistent service quality. Management introduced process changes, additional agent training, and closer KPI monitoring, then collected data on response time, ticket closure rates, and customer satisfaction before and after the intervention. The analytics team formulated a null hypothesis stating there was no significant difference in performance after the changes and used hypothesis testing to assess improvement. Results indicated statistically significant gains in response time, while business analytics suggested a positive correlation between training and satisfaction. Executives now want to determine whether these findings justify broader investment and permanent operational redesign across the support organization. Critically evaluate the help desk team’s use of hypothesis testing and business analytics to justify operational changes. To what extent does the evidence support sustained investment in training, process redesign, or technology, and what additional evaluation would strengthen the managerial recommendation? (10 Marks)

Ans 2.

Introduction

Hypothesis testing gives management a disciplined way to separate true improvement from random fluctuations But statistical relevance alone is not enough to establish that a business decision is correct. In this case the help desk team carried out the study prior to and following of response times, ticket closure and satisfaction levels after making changes to the process in training and KPI monitoring. Time to response improved with statistics and the training program showed positive correlations with satisfaction. The executives are now trying to determine what the implications of these findings are for permanent improvement and greater investments. A

 

 

Q3 (A) A logistics company has built a simple linear regression model to predict delivery costs using shipment volume. Initial results appear promising, but the operations director notices that prediction errors seem larger for high-volume shipments and that weekly observations may be influencing one another. The analyst also suspects that the relationship may not remain perfectly straight across all shipment levels. Because the model will be used to negotiate vendor contracts and set future pricing, the company cannot rely on superficial outputs alone. Senior management therefore asks for a rigorous review of the model assumptions so that any hidden weaknesses can be identified before the forecasts are embedded into strategic decisions. Develop a diagnostic and corrective action plan for this regression project that ensures valid interpretation and forecasting. Your plan should specify how the company should test linearity, independence, homoscedasticity, normality of residuals, and autocorrelation, and how managers should redesign the analysis if any assumption is violated. (5 Marks)

Ans 3 (A).

Introduction

Regression models used in discussions with vendors is not based on an attractive R squared. Director’s observations of that there were more mistakes at higher volumes and possible correlation between daily observations point to common assumptions and errors that should be

 

Q3 (B) A healthcare network has developed a multiple regression model to predict post-surgery recovery duration using patient age, medical history, treatment type, and hospital stay characteristics. While the medical director is optimistic about using the model for staffing and bed planning, the operations team worries that good-looking predictions may hide specification problems. They need a disciplined approach to judge whether the model truly fits the data and whether any assumptions appear violated. Because the hospital relies on spreadsheet-based analysis, the review process must be understandable to both clinicians and administrators. You have been asked to design a fit-assessment framework before the model is adopted systemwide. Construct a comprehensive model-fit evaluation plan for the hospital’s regression model using r-squared, adjusted r-squared, residual analysis, F-statistic, and SSE. How would you integrate these measures into a managerial review system that helps leaders decide whether the model is reliable enough for operational and patient-care planning? (5 Marks)

Ans 3 (B).

Introduction

Hospitals planning staffing and bed allocation around models that use regression require more than optimistic predictions. An organized fit assessment framework including r squared, adjusted r squared, residual analysis, F-statistic and SSE gives clinicians and administrators a common, logical basis for judging whether the model is truly reliable.

Concept and Application

Using R Squared and Adjusted R Squared

R-squared displays the share of variance in the duration of recovery that the model can explain.