# MMPC-005 Quantitative Analysis for Managerial Applications Solved Assignment 2022-2023

Contents

## MMPC-005 Quantitative Analysis for Managerial Applications Solved Assignment 2022-2023

Course Code : MMPC-005
Course Title : Quantitative Analysis for Managerial Applications
Assignment Code : MMPC-005/TMA/JULY/2022
Coverage : All Blocks

Title Name

#### MMPC-005 Solved Assignment 2022-2023

University IGNOU
Service Type Solved Assignment (Soft copy/PDF)
Course MBA and MBA (Banking & Finance)
Language ENGLISH
Semester 2022-2023 Course: MBA
Session July 2022 and January 2023 sessions
Short Name MMPC-005
Assignment Code MMPC-005/TMA/JULY/2022
Product Assignment of MBA and MBA (Banking & Finance) 2021-2022 (IGNOU)
Submission Date (Last date of submission for July 2022 session is 31st October, 2022 and for January 2023
session is 30th April, 2023

Note: Attempt all the questions and submit this assignment to the coordinator of your study
centre. (Last date of submission for July 2022 session is 31st October, 2022 and for
January 2023 session is 30th April, 2023).
1. The income of a group of 10,000 persons was found to be normally distributed with mean
Rs.750 per month and a standard deviation of Rs. 50, show that of this group about 95%
has income exceeding Rs. 668 and only 5% had income exceeding Rs. 832. (area between
750 and 668 = 0.4495, area between 750 and 832 = 0.4495).
2. Why is forecasting so important in business? Explain the application of forecasting for
long term decisions.
3. What do you understand by Primary Data? What are the various methods of collecting
primary data? Also, mention what points to be kept in mind while designing the
questionnaire?
4. The means of two large samples of sizes 1000 and 2000 are 67.5 and 68.0 respectively.
Test the quality of the means of the two populations each with standard deviation of 2.5.
(z table value at α0.05= -1.96).
5. Write short notes on any two of the following:-
(a) Mathematical Properties of Arithmetic Mean
(b) Stratified Sampling
(c) Exponential Distribution
(d) Time Series Analysis

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