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MS-08 SOLVED ASSIGNMENT 2015 PROVIDED BY WWW.MYIGNOU.IN

Course Code: MS-08

Course Title: Quantitative Analysis for Managerial Applications

Assignment Code: MS-08/TMA/SEM-I/2015

Coverage: All Blocks

Q1.

Ans:

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Q2.

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Ans:

Q3. The marks obtained in Statistical Methods paper in MBA First Semester

examination of a Management Institute, followed normal distribution with

mean 75 and standard deviation 10. If 250 students appeared at the

examination, estimate the number of students, scoring:

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(i)Less than 70 marks,

(ii)More than 90 marks.

Ans:

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Q4) A manufacturer of LCD TV claims that it is becoming quite popular, and that

about 5% homes are having LCD TV. However, a dealer of conventional TVs

claims that the percentage of homes with LCD TV is less than 5%. A sample of

400 household is surveyed, and it is found that only 18 household have LCD TV.

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est at 1% level of significance whether the claim of the company is tenable.

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Q5. Define correlation & Regression ? What is the difference between the two?

Give examples of a situation, where you will use them.

Ans:

Correlation and regression analysis are related in the sense that both deal with relationships

among variables. The correlation coefficient is a measure of linear association between two

variables. Values of the correlation coefficient are always between -1 and +1. A correlation

coefficient of +1 indicates that two variables are perfectly related in a positive linear sense, a

correlation coefficient of -1 indicates that two variables are perfectly related in a negative

linear sense, and a correlation coefficient of 0 indicates that there is no linear relationship

between the two variables. For simple linear regression, the sample correlation coefficient is

the square root of the coefficient of determination, with the sign of the correlation

coefficient being the same as the sign of b1, the coefficient of x1 in the estimated regression

equation.

Correlation makes no a priori assumption as to whether one variable is dependent on the

other(s) and is not concerned with the relationship between variables; instead it gives an

estimate as to the degree of association between the variables. In fact, correlation analysis

tests for interdependence of the variables.

As regression attempts to describe the dependence of a variable on one (or more)

explanatory variables; it implicitly assumes that there is a one-way causal effect from the

explanatory variable(s) to the response variable, regardless of whether the path of effect is

direct or indirect. There are advanced regression methods that allow a non-dependence

based relationship to be described (eg. Principal Components Analysis or PCA) and these

will be touched on later.

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The best way to appreciate this difference is by example.

Take for instance samples of the leg length and skull size from a population of elephants. It

would be reasonable to suggest that these two variables are associated in some way, as

elephants with short legs tend to have small heads and elephants with long legs tend to have

big heads. We may, therefore, formally demonstrate an association exists by performing a

correlation analysis. However, would regression be an appropriate tool to describe a

relationship between head size and leg length? Does an increase in skull size cause an

increase in leg length? Does a decrease in leg length cause the skull to shrink? As you can

see, it is meaningless to apply a causal regression analysis to these variables as they are

interdependent and one is not wholly dependent on the other, but more likely some other

factor that affects them both (eg. food supply, genetic makeup).

Consider two variables: crop yield and temperature. These are measured independently, one

by the weather station thermometer and the other by Farmer Giles' scales. While correlation

anaylsis would show a high degree of association between these two variables, regression

anaylsis would be able to demonstrate the dependence of crop yield on temperature.

However, careless use of regression analysis could also demonstrate that temperature is

dependent on crop yield: this would suggest that if you grow really big crops you'll be

guaranteed a hot summer!

Neither regression nor correlation analyses can be interpreted as establishing cause-and-

effect relationships. They can indicate only how or to what extent variables are associated

with each other. The correlation coefficient measures only the degree of linear association

between two variables. Any conclusions about a cause-and-effect relationship must be based

on the judgment of the analyst.

Difference between the two

Correlation Regression

Correlation examines the relationship between two

variables using a standardized unit. However, most

applications use raw units as an input.

Regression examines the relationship between one

dependent variables and one or more independent

variables. Calculations may us either raw unit values, or

standardized units as input.

The calculation is symmetrical, meaning that the order of

comparison does NOT change the result.

The calculation is NOT symmetrical. So one variable is

assigned the dependent role (the values being predicted)

and one or more the independent role (the values

hypothesize to impact the dependent variable).

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Correlation coefficients indicate the strength of a

relationship. Regression shows the effect of one unit change in an

independent variable on the dependent variable.

Correlation removes the effect of different measurement

scales. Therefore, comparison between different models is

possible since the rho coefficient is in standardized units.

Linear regression using raw unit measurement scales can

be used to predict outcomes. For example, if a model

shows that spending more money on advertising will

increases sales, then one can say that for every added $ in

advertising our sales will increase by β.

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