RESEARCH METHODOLOGY
A Study on Dependent and Independent variables
Under the Guidance of
Dr. MADHUMITA GUHA MAJUMDAR
Prepared By:
Finance-D
Group-8
ATISH JAIN (10SBCM0135)
GUNJAN AGGARWAL (10SBCM0267)
JUTHIKA BORA (10SBCM0391)
KAUSTUBH UDGAONKAR (10SBCM0271)
P.V.SARAN KUMAR (10SBCM0526)
REGRESSION ANALYSIS
INTRODUCTION
Regression analysis is a statistical tool for the investigation of relationships between variables.
Usually, the investigator seeks to ascertain the causal effect of one variable upon another—the
effect of a price increase upon demand, for example, or the effect of changes in the money
supply upon the inflation rate. To explore such issues, the investigator assembles data on the
underlying variables of interest and employs regression to estimate the quantitative effect of the
causal variables upon the variable that they influence. The investigator also typically assesses the
“statistical significance” of the estimated relationships, that is, the degree of confidence that the
true relationship is close to the estimated relationship.
CASE BACKGROUND
The case is about the Preference of customers satisfaction towards the hotels based on
various factors and this preference is measured on a five point scale (‘1’ strongly agree ‘2’ agree
‘3’ no opinion ‘4’ disagree ‘5’ strongly disagree). The various factors considered in this case are
1. service
2. quantity
3. quality
4. neatness
5. time of served
6. customer satisfaction
In this case ‘customer satisfaction’ is taken as dependent variable and the remaining
factors are taken as independent variable. The analysis shows how these independent factors
influence the dependent variable(i.e. customer satisfaction). The analysis is mainly to know the
significance and does the customers should prefer the hotels. The sample size is 25.
METHODOLOGY
Here we have taken five independent variables which are ‘metric variable’. These factors are of
numeric type because the values fed are the numbers and thus they are measured on interval
scale or ratio scale.
The following is the input table that shows the sample size of 25 customer
4 1 4 5 3 4
3 5 4 4 5 4
4 5 5 2 2 3
5 3 5 5 3 5
2 3 4 5 4 5
1 3 3 4 5 3
2 5 1 1 5 3
4 5 1 3 5 2
4 4 2 2 5 4
4 3 4 4 2 1
1 4 5 3 5 4
4 3 3 2 3 4
3 3 5 5 5 5
5 3 4 2 5 5
5 3 5 4 2 3
1 5 1 1 1 1
2 3 2 4 1 2
3 3 5 3 3 4
4 4 5 2 2 4
5 1 4 1 1 5
3 3 1 2 1 2
5 4 2 2 2 3
1 3 2 4 5 4
1 2 3 4 5 3
1 1 2 4 2 3
ANALYSIS
Variables Entered/Removed(b)
Variables Variables
Model Entered Removed Method
1 timeofserve
d, quality,
quantity, . Enter
service,
neatness(a)
a All requested variables entered.
b Dependent Variable: customer satisfaction
Model Summary
Adjusted R Std. Error of
Model R R Square Square the Estimate
1 .790(a) .954 .525 .823
a Predictors: (Constant), time of served, quality, quantity, service, neatness
Using this model 95.4% variation can be explained.
ANOVA(b)
Sum of
Model Squares Df Mean Square F Sig.
1 Regression 21.305 5 4.261 6.298 .001(a)
Residual 12.855 19 .677
Total 34.160 24
a Predictors: (Constant), time of served, quality, quantity, service, neatness
b Dependent Variable: customer satisfaction
Since the ANOVA is less than 0.05 thus we can infer that the model is statistically significant .
Coefficients(a)
Unstandardized Standardized
Coefficients Coefficients
Model B Std. Error Beta t Sig.
1 (Constant) 2.344 .928 2.526 .021
Service .150 .130 .188 1.153 .263
Quantity -.417 .161 -.422 -2.589 .018
quality .393 .135 .490 2.909 .009
neatness -.228 .161 -.255 -1.421 .171
timeofserved .434 .119 .580 3.642 .002
a Dependent Variable: customersatisfaction
The equation which can be derived is:
customer satisfaction=2.344+0.150(service)-.417(quantity)+.393(quality)-.228(neatness)
+.434(time of served).Except service and neatness remaining are significant.
FACTOR ANALYSIS
INTRODUCTION
Factor analysis is a method for investigating whether a number of variables of interest Y1, Y2, : :
:, Yl, are linearly related to a smaller number of unobservable factors F1, F2, : : :, Fk .
The fact that the factors are not observable disquali¯es regression and other methods previously
examined. We shall see, however, that under certain conditions the hypothesized factor model
has certain implications, and these implications in turn can be tested against the observations.
Exactly what these conditions and implications are, and how the model can be tested, must be
explained with some care.
CASE BACKGROUND
The case is about the Preference of the refrigerator based on various factors The various
factors considered in this case are:
a) size
b) volume
c) utility
d) colour
e) price
f) brand
g) service
h) dooring system
ANALYSIS
From the first test i.e KMO test we could determine whether the data that we have taken is
enough to conduct a research and whether the sample is good.
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling
Adequacy. .664
Bartlett's Test of Approx. Chi-Square 49.306
Sphericity Df 28
Sig. .008
From the output generated we could determine that with a KMO factor of 0.664 the
Sample is good to conduct a research and the research could also be carried on.
The significance level is .008 which shows that the samples are significant to determine a
solution.
Total Variance Explained
Component Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings
% of
Total % of Variance Cumulative % Total % of Variance Cumulative % Total Variance Cumulative %
1 2.979 37.241 37.241 2.979 37.241 37.241 2.117 26.467 26.467
2 1.357 16.964 54.205 1.357 16.964 54.205 1.865 23.317 49.784
3 1.221 15.260 69.465 1.221 15.260 69.465 1.574 19.681 69.465
4 .840 10.497 79.962
5 .566 7.078 87.040
6 .456 5.700 92.740
7 .293 3.657 96.397
8 .288 3.603 100.000
Extraction Method: Principal Component Analysis.
Here we also determined that by converting them into three factors we have found 70% of the
relevant data while the remaining 30% data are irrelevant could be left alone.
Then the most important topic of the factor analysis is Rotated Component Matrix
Using Varimax and Kaiser Normalisation.
Here in this analysis we would be able to determine as to which all factors we would combine to
form the 3 Major Factors.
Rotated Component Matrix(a)
Component
1 2 3
size .430 .404 .647
volume .760 .080 -.049
utility .863 -.048 .088
colour .716 .279 .329
Price .039 .078 .895
Brand .016 .591 .230
Service .310 .762 -.355
Dooringsystem .010 .825 .239
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 5 iterations.
By using the Varimax with Kaiser Normalisation we could find out that
1. Volume, utility and color could be taken together as 1st Factor. The name I
would suggest is customer satisfaction.
2. Service, brand and doo ring system could be combined together as 2nd
factor as all these three factors form a very good positive correlation in the
2nd factor and the name I would suggest is customer satisfaction.
3. And the final factor would consist of price and size which all fall in a very
smooth category in the 3rd Factor. The name for the 3rd factor would be
value for money.
These are the Various Analysis that we could do based on the Factor Analysis that
we did on our Research.
RECOMMENDATIONS
Factor Analysis differs from researcher to researcher, what we have taken here is
by considering the most likely situation. If a Company wants to give its customer,
Satisfaction then he will consider the factors of service brand and doo ring system.
Hence by our research we got the above details in the refrigerator business. And by
collecting data from 25 persons and by conducting Factor Analysis we could
determine the above details.
