sectionb group2 apparel analysis final

7/27/2019 SectionB Group2 Apparel Analysis Final

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Apparel Industry

by

Aman Anshu(13PGP061)

Minu Pandey(13PGP091)

Puneet Manot(13PGP102)

Bhavana

Ziradkar(13PGP118)

Indian Institute of Management Raipur Page 1


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Introduction

The apparel and textile industry occupies a unique and important

place in India. One of the earliest industries to come into existence in the

country, the sector accounts for 14% of the total Industrial production,

conduces to about 30% of the total exports and is the second largestemployment creator after agriculture.

The Indian textiles industry that already has an overwhelming

presence in the economic life of the country has been given a further

boost with the scrapping of quotas in global trade of textiles and clothing.

In the post quota period, the size of industry has expanded from US$ 37

billion in 2004-05 to US$ 49 billion in 2006-07. During this period, while

the domestic market has grown from US$ 23 billion to US$ 30 billion,

exports have increased from around US$ 14 billion to US$ 19 billion.As a matter of fact, the apparel and textile is the largest foreign

exchange earning sector in the country. Being the 2nd highest employer of

raw labour it gives a direct employment provider to over 35 million people

and with continuing growth momentum, the role of this sector in Indian

economy is bound to increase.

Objective

1. Collection of sample data of readymade garments producing

companies.

2. Descriptive analysis of the data

3. Preparation of contingency tables for producers as per their Net

Profit Margin and consumers according to segment classification.

4. Using stratified random sampling technique to obtain a sample from

which inferences can be made.

Brief Summary of Data used

The sources of our data are:

www.Crisilresearch.com

www.wazir.in (Annexure)

We obtained the Net profit margin of 11 companies (for 5 years each),

dealing with the manufacture of readymade garments from CrisilResearch. The companies are:

http://www.crisilresearch.com/http://www.wazir.in/http://www.crisilresearch.com/http://www.wazir.in/


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S.N. Name of Company

1 Arvind Ltd.

2 Bhandari Hosiery Exports Ltd.

3 Celebrity Fashions Ltd.

4 Gokaldas Exports Ltd.

5 Kewal Kiran Clothing Ltd.

6 Page Industries Ltd.

7 Provogue(India) Ltd.

8 Raymond Ltd.

9 Samtex Fashions Ltd.

10 Virat Industries Ltd.

11 Zodiac Clothing Ltd.

We obtained the contingency table for the consumption of garments

according to the following three categories Mens Wear, Womens Wear

and Kids Wear - based on two types of products Branded and Un-branded

from a report published by Wazir Advisors a statistics for the year 2011.

Descriptive Summary Population

The population size N = 5*11=55

Population data.xlsx

Attachment 2: Population data

Descriptives

Statistic Std. ErrorMean 3.9271 1.04266

95% Confidence Interval forMean

LowerBound

1.8367



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UpperBound

6.0175

Std. Deviation 7.73256

Skewness -.467 .322

(Skewness)/(Std. Error of Skewness) = -1.45109 (which is greater than -2 and

less than 2).

Hence we can apply Empirical Rule to obtain ranges of data and density of

data.

Range of Data Empirical Rule

Density

Density (calculated from

Population)Min Maximum

-3.81 11.66 90% 67%

-11.54 19.40 95% 95%-19.27 27.12 99% 98%

Inference: 95% chances that a selected period has a net profit margin in

between -11.84 and 19.40

The histogram plot for the population data:

Using Tests of Normality Kolmogorov-Smirnov Test

Net Profit Margin

Kolmogorov-Smirnova Shapiro-Wilk

Statistic df Sig. Statistic df Sig..101 55 .200* .980 55 .472Ho: The Population is Normal (p> )

H1: The Population is not normal (p< )

Here, significant value p= 0.2 Level of significance = 0.05 (95%

confidence level) As p > : Null Hypothesis (Ho) that population

is normally distributed is accepted.

Probability Contingency Table for Net Profit

MarginThe mean of the population of yearly Net Profit Margins is 3.9271%

Using this we create a contingency table for mutually exclusive and collectively

exhaustive criteria

a) NPM>5 b) NPM


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Count Contingency Table

NPM>Mean

NPMMean

NPMMean

MarginalProbabilit JointProbabil Conditional RevisedProbability



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y ityProbability

Arvind Ltd. 0.09091 0.05455 0.00496 0.10714Bhandari HosieryExports Ltd. 0.09091 0.00000 0.00000 0.00000

Celebrity Fashions Ltd. 0.09091 0.00000 0.00000 0.00000Gokaldas Exports Ltd. 0.09091 0.01818 0.00165 0.03571Kewal Kiran ClothingLtd. 0.09091 0.09091 0.00826 0.17857Page Industries Ltd. 0.09091 0.09091 0.00826 0.17857Provogue(India) Ltd. 0.09091 0.09091 0.00826 0.17857Raymond Ltd. 0.09091 0.00000 0.00000 0.00000Samtex Fashions Ltd. 0.09091 0.00000 0.00000 0.00000Virat Industries Ltd. 0.09091 0.09091 0.00826 0.17857Zodiac Clothing Ltd. 0.09091 0.07273 0.00661 0.14286

Total 0.04628 1.00000

NPM


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Virat Industries Ltd. 0.09091 0.00000 0.00000 0.00000Zodiac Clothing Ltd. 0.09091 0.01818 0.00165 0.03704

Total 0.04463 1.00000

The Contingency calculation can be found in the attached excel sheet:

contingency tablewith pie.xlsx

Sampling Technique

Population consisted of all the companies of the textile and apparel

industry.

The parameter taken into consideration was the net profit of all these

companies.

We could identify data of each company as strata. Random samples

were taken from each stratum.

Stratified Random Sampling has been used for normalization of the

data with net profit of the samples as statistics.

Stratified randomsample.xlsx

Attachment 1: Stratified random sample

Descriptive Summary Sample

The sample size selected is n =3*11=33



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DescriptiveSample NPM

Statistic Std. Error

Mean 4.3021 1.39947

95% Confidence Interval for

Mean

Lower Bound 1.4515

Upper Bound 7.1527

Std. Deviation 8.03932

Skewness -.648 .409

Tests of Normality

Kolmogorov-Smirnova Shapiro-Wilk

Statistic df Sig. Statistic df Sig.

SampleNPM .106 33 .200* .970 33 .489

Here, significant value p= 0.2

Level of significance Alpha (a) = 0.05 (95% confidence level)As p > Alpha (a) - Null Hypothesis (Ho) that population is normally

distributed is accepted.

Hence t-sample test and Z tests can be carried out for the sample. From the data it can be assumed that:

o Case 1 H0: the net profit margin (population mean

()) 5



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H1: the net profit margin (population mean

()) < 5

o Case 2 H0: the net profit margin (population mean ())

3.92

H1: the net profit margin (population mean ()) ,

Hence we may accept Ho

Asthe probability does not lie within the rejection ratio, Ho is accepted.

Hence, with 95% confidence, we can say the net profit margin will be greater

than 5%

Case 2:

Tabulated value of z is 1.6449

Zstat= (x - )/ (^/ n)x=4.3021= 3.9271

^ =s= 8.03932

n= 33

Zstat= (4.3021 3.9271)/(8.03932/ 33)= (0.375)/ (8.03932/5.744)

= (0.375)/ (1.399)=.26804Also from the one sample t-test

One-Sample Test

Test Value = 3.9271

t df Sig. (2-tailed) Mean Difference 95% Confidence Interval of the

Difference

Lower Upper

SampleNPM .268 32 .790 .37502 -2.4756 3.2256



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We observe that p value is greater than the value of,

Therefore, |cal z| ,

Hence we may accept Ho

Asthe probability does not lie within the rejection ratio, Ho is accepted.

Hence, with 95% confidence, we can say the net profit margin will be greater

than 3.92%.



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Regression Analysis

We have done the regression for 10 brands. However the Regression analysis for

Provogue Industry has only been shown below. Same procedure has been

followed for the rest of the 9 brands.

REGRESSION CALCULATION FOR PROVOGUE INDUSTRIES

MODEL:

Assumptions:

a.) Linearity: We assume that the factors are linear.

b.) Independence of errors: Durbin Value=2.47 which is slightly more than 2.

Therefore the errors are slightly negatively co-related. However, It is

accepted.c.) Normality of errors: The PP graph shows that the errors are normal.

d.) Equal variance: Homoscedasticity found. Therefore accepted.

Since the equation follows all the assumptions, therefore, we go forward with the

calculations.

The calculations are done at 95% confidence level, Therefore =0.05

Calculations:

We assume the equation as: PRO NPM = 0 + (1 Pro_Emp) + (2Pro_Sell)+ errors

From the model Summary, we see that adjusted R2=.942, which indicated

that these two factors add to 94% of the value of the Pro NPM.

From the Annova table, we test the validity of the model.

H0: All Bi=0, ie: model is invalid

H1: At least 1 Bi is not equal to 0, Model is valid.

Here from the Annova table, we see the significance (p) = 0.058> =0.05

Therefore, H0 accepted and so the model is Invalid.

Significance of Independent Variables:

o H0: B1=0, Pro_Emp is not a significant value

H1: B1not equal to 0, Pro_Emp is a significant value

(p) = 0.207> , Therefore H0 accepted, hence ProEmp is not a

significant factor.



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o H0: B2=0, Pro_Sell is not a significant value

H1: B1 not equal to 0, Pro_Sell is a significant value

(p) = 0.465> , Therefore H0 accepted, hence ProSell is not a

significant factor

However, we proceed with the further calculations to show the mandated

process.

Collinearity Statistics:

We see the VIF value >10 or 20

Therefore, It is a matter of concern and hence collinear.

Getting the Value of the Coefficients from the Co-efficients table, we get

the final equation as: PRO NPM = 18.250 + (-0.592* Pro_Emp) + (0.238*Pro_Sell)

This equation tells that:

a) If there is a 1 unit change in Pro Emp and all the other factors

remaining constant, average estimated Pro NPM reduces by -0.592.

b) If there is a change of 1 unit in Pro Sell and all the other factors

remaining constant, average estimated Pro NPM increases by 0.238.

c) Also, If Pro Sell and Pro Emp are 0, still, Pro NPM is 18.25, and

therefore there are other factors also that add to the profit/loss.

The Regression data for rest of the brands is consolidated in the excel sheet

attached below.

Regression forApparel Industry.xlsx



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Annexure:

Probability Contingency Table for Garment

Categories

The data available segregates the consumption of Branded and

Unbranded garments in different exhaustive categories of Mens Wear,Womens Wear and Kids Wear. The probabilities evaluated from the data

are of the form (marginal):

o Money Spent on Branded garments

o Money spent on Un-branded garments

o Money spent on Mens garments

o

Money spent on Womens garments

o Money spent on Kids garments

The Contingency table so formed is:

Event Set (in Billion US

Dollars) Contingency Table

Catego

ry

Brand

ed

Un-

Branded

Tot

al

Catego

ry

Brand

ed

Un-

Branded

Tot

alMen 5 11 16 Men 0.125 0.275 0.4

Women 4 10 14 Women 0.1 0.250.35

Kidswear 1 9 10

Kidswear 0.025 0.225

0.25

Total 10 30 40 Total 0.25 0.75 1

The conditional probabilities for different pre-conditions are presented below:

Branded Category MarginalProbabilit Joint conditional RevisedProbability



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yMen 0.4 0.125 0.05 0.547945205

Women 0.35 0.1 0.035 0.383561644Kidswe

ar 0.25 0.025 0.00625 0.068493151

Total 0.09125

UnBranded

Category

MarginalProbability Joint conditional

RevisedProbability

Men 0.4 0.275 0.11 0.433497537Women 0.35 0.25 0.0875 0.344827586Kidswe

ar 0.25 0.225 0.05625 0.221674877Total 0.25375

MensWear

Marginal Joint

Conditional Revised Probability

Branded 0.25 0.125 0.03125 0.131578947Un-

Branded 0.75 0.275 0.20625 0.8684210530.2375

Womens Wear

Marginal Joint Conditional Revised Probability

Branded 0.25 0.1 0.025 0.117647059Un-

Branded 0.75 0.25 0.1875 0.882352941

0.2125

KidsWear

Marginal Joint Conditional Revised Probability

Branded 0.25 0.025 0.00625 0.035714286Un-

Branded 0.75 0.225 0.16875 0.9642857140.175


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