rm mkt final

8/8/2019 RM MKT Final

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CORRELATION

Correlation is the statistical tool with the help

of which these relationships between two or

more than two variables is studied.

Correlation analysis refers to the techniques

used in measuring the closeness of the

relationship between the variables.


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Pos t v and N at vcorrelat on

S ple,Part al and Mult plecorrelat on

near and Non-linearcorrelation


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Specific Example

For sevenrandom summer

days, a personrecorded thetemperature andtheir water

consumption, duringa three-hour periodspent outside.

Temperature (F)

WaterConsumption

(ounces)

75 16

83 20

85 25

85 27

92 32

97 48

99 48


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How would you describe the graph?


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Methods of studying correlation

Commonly used methods for studying the correlation

between two variables are

Scatter diagram method.

Karl Pearson's coefficient of correlation (covariance

method).

Two-way frequency table.

Rank method.

Concurrent deviations method.


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REGRESSION

The meaning of the term Regression is the

act of returning or going back.

Regression is the statistical tool with the help

of which we are in a position to estimate or to

predict the unknown values of one variable

from known values of another variable.


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Correlation Vs. Regression Analysis

Correlation literally means the relationship between

two or more variables which vary. Regression means

stepping back or returning to the average value.

Correlation need not imply cause and effect

relationship between the variable under study.

Regression clearly indicates the cause and effectrelationship.


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Correlation coefficient is a linear relationship

between x and y and is independent of the units of

measurement. It is pure number lying between +

or 1. Regression is absolute measure and the

variable x and y are dependent on each other.

Correlation analysis is confined to only to study of

linear relationship between the variables and

therefore, has limited applications. Regression

analysis has wider application as it studies linearas well as non-linear relationship between the

variables.


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Regression: 3 ain Purposes

To describe (or model)

To predict (or estimate)

To control (or administer)


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Relationship Analysis

The examination of the association between

two or more variables. In marketing, some of

the more apparent relationships include

associations between advertising and sales,

company size and advertising budget, supply

and demand for products, and customersatisfaction and customer loyalty.


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DIFFERENCE BETWEENCORRELATIONANDDIFFERENCE BETWEENCORRELATIONAND

REGRESSIONREGRESSION

The objective of

regression analysis is to

study the nature of

relationship between thevariables

It is a measure of degree ofrelationship between the

variables

CORRELATION

REGREESSION


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INTERPRETING THECOEFFICIENTINTERPRETING THECOEFFICIENT

OFCORRELA

TIONOF

CORRELA

TION

When r= +1, there is a perfectpositive correlation

When r= -1, there is a perfectnegative correlation

When r= 0, there is no correlationbetween the variables

The closer r is to +1 or -1, the closerthe relationship between thevariables


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CONVERSION OF CORRELATION

INTO REGRESSION Regression= Correlation + Prediction

predicting y based on x

e.g., predicting.

throwing points (y)

based on distance from target (x)

Regression equation

formula that specifies a line

y = bx + a

plug in a x value (distance from target) and predict y (points) note

y= actual value of a score

y= predict value


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Steps to Reaching a Solution

Draw a scatter plot of the data.

Visually, consider the strength of the linearrelationship.

If the relationship appears relatively strong,find the correlation coefficient as a numericalverification.

If the correlation is still relatively strong, thenfind the simple linear regression line.


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DECISION TI E

If correlation analysis is a popular andinformative statistical method, why shouldresearchers bother using the somewhatintimidating multivariate statisticaltechniques? Do you feel there is reallymuch to gain from these methods?


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Vote of thanks

We are heartily thankful to r.

R K Singh sir who gave us thisproject which in turn became a

great learning experience of

management as well as its

practical application.

We would also like to thank

BI S faculty and staff

members for their support andfinally we thank our family,

friends and colleagues for their

helping nature.

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