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MS-95 Solved Assignment

MS-95 Solved Assignment

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  • Version: 2021 Jan - June



Soft Copy: Yes
Downloadable File: Yes
University: IGNOU
Course: Master of Business Administration
File: PDF/ZIP

Q&A of MS-95 Solved Assignment 2021 - Research Methodology for Management Decisions

Q. "Analysis of covariance is a combination of the two techniques - analysis of variance and regression". Explain the statement along with the uses of analysis of covariance.

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Q. Compare and contrast the various attitude measurement techniques. When will you use each of them? Discuss briefly.

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Q. What type of information is there in the "cover and the title page and introductory page of a report"?

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Q. "Regression analysis is probably the most widely applied technique amongst the analytical models of association used in business research". Do you agree? Give reasons to support your answer.

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Q. In an agricultural field experiment, three different fertilizers were used on sample plots and yields were recorded. On the basis of these data, test the hypothesis that there is no significant difference in yields from three different fertilizers (using The Kruskal-Wallis Test).
Yield (in MT)
Fertilizer A 2.48 3.25 3.94 3.45 3.0 4.0 3.6 3.87
Fertilizer B 2.84 3.1 3.5 2.27 3.88 2.87 3.27 2.8
Fertilizer C 3.4 3.17 2.85 2.46 3.15 2.69 2.88 3.44

Answer......

Product Details: Mba MS-95 Solved Assignment 2021

Course: IGNOU MBA (Master of Business Administration)
Session: Jan - June 2021
Subject: Research Methodology for Management Decisions

Ignou Mba MS-95 Assignments - Old Sample Answers

Q. What is a Regression Analysis?
Answer. Time series analysis is the term used to describe a set of statistical tools that are useful for identifying patterns of demand that repeat periodically - in other words, patterns that are driven by time. The other most widely used tool for demand forecasting is regression analysis. This statistical tool is useful when the analyst has reason to believe that some measurable factor other than time is affecting demand. Regression analysis begins with the identification of two categories of variables: dependent variables and independent variables....... Regression models are built using a data set of historical values. They are used to evaluate the relationship between independent and dependent variables in an existing data set and produce a mathematical framework that can be extrapolated to values of the independent variables not present in the data set......... A diverse range of regression models exists, and the appropriate model to employ for a given task depends on the nature of the dependent variable being predicted. In some cases, an explicit value must be predicted-say, the total amount of revenue a new user will spend over the user's lifetime.............. In other cases, the value predicted by the regression model is not numeric but categorical; following from the example above, if, instead of the total revenue a new user will spend over the users lifetime, a model was constructed to predict whether or not the user would ever contribute revenue, the model would be predicting for a categorical (in this case, binary) variable: revenue or no revenue............. Imagine you are a consultant working in a purchasing department whose input into business decision making process is welcomed within the firm. The Purchasing Manager believes that by working more closely with suppliers, subsequent delivery performance will improve. His idea of working more closely means visiting suppliers on a regular basis to discuss business issues.........


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