Sruti Nair – 140101179

Gaurav Srivastava-140101054

Priti Sureka –140102095

Aakash Kumar Gupta –140102001

Shrish Somani-140101169.

Rahul Bansal-140102100

BRM_INDIGO RESPONSES ANALYSIS

March 13

2015The Responses on Indigo Questionnaire floated to Sec E class was captured and transcribed & coded into SPSS to study and understand different Hypothesis. The Report describes in detail the various tests conducted on the data set and its analysis.

Group9_Sec E

IndigoResponseAnalysis_Group9

INDIGO RESPONSES HYPOTHESIS VARIATION:

BACKGROUND:

A survey was conducted for Indigo Airlines on Customer Service among students from Sec E ( PGDM 2014-16) of IMT Ghaziabad. The questionnaire covers questions to understand exactly how loyal Indigo customers are and to find out how indigo, if at all, as a service airplane lags. The responses from the survey (56 Students) was recorded and coded onto SPSS to test few Hypotheses.

FREQUENCY CROSS TABULATION:

(I) HYPOTHESIS 1: H0 – The quality of Food & Beverages offered has no effect on the Overall Experience with Indigo AirlineH1- The quality of Food & Beverages offered has an effect on the Overall Experience with Indigo Airline

A frequency cross tabulation is done at 95% level of Confidence and the results are as follows:

The Significance level is slightly above 0.05, so we can conclude that Null Hypothesis is not accepted. The Quality of Food & Beverages does have some effect on the Overall Experience with Indigo Airlines.

NOTE : The Chi Square should not have more than 20% of cells with expected count less than 5. Since this result has close to 77%, the data cannot be considered.

(I) HYPOTHESIS 2:H0 – The Waiting time at Check In has no effect on the number of times one has flown in IndigoH1- The Waiting time at Check In has effect on the number of times one has flown in Indigo

2

Chi-Square Tests

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 9.255a 4 .055

Likelihood Ratio 5.925 4 .205

Linear-by-Linear Association 3.208 1 .073

N of Valid Cases 56

a. 7 cells (77.8%) have expected count less than 5. The minimum expected count is .11.

IndigoResponseAnalysis_Group9

The Significance value 0.681 is Greater than 0.05, so we accept H0. With the current data set available, we can say that there is no relation between the Waiting time at check in and no. of times flown in Indigo Airlines.

NOTE : The Chi Square should not have more than 20% of cells with expected count less than 5. Since this result has close to 75%, the data cannot be considered.

(II) HYPOTHESIS 3:

H0 –The Overall Experience with Indigo Airlines has got no effect on the number of times flown with IndigoH1- The Overall Experience with Indigo Airlines has got effect on the number of times flown with Indigo

3

Chi-Square Tests

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 3.971a 6 .681

Likelihood Ratio 4.086 6 .665

Linear-by-Linear Association .335 1 .563

N of Valid Cases 56

a. 9 cells (75.0%) have expected count less than 5. The minimum expected count is .39.

Chi-Square Tests

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 5.730a 4 .220

Likelihood Ratio 5.196 4 .268

Linear-by-Linear Association 1.401 1 .237

N of Valid Cases 56

a. 6 cells (66.7%) have expected count less than 5. The minimum expected count is .39.

IndigoResponseAnalysis_Group9

The Significance value is 0.220 which is greater than 0.05. So, we do not reject the null Hypothesis. The Overall Experience with Indigo Airlines has No Effect on the number of times flown with Indigo Airlines.

NOTE : The Chi Square should not have more than 20% of cells with expected count less than 5. Since this result has close to 67%, the data cannot be considered.

ONE SAMPLE t TEST :

One sample t Test is performed to find out if the sample comes from a population with a specific mean. The population mean is not always known, but can be hypothesized.

For example:

1. The mean speed of the automobiles crossing NH 10 is 110 Km/hour2. The mean of the new teaching method for people struggling with grammar is equal to the

National Average Score.

But, there are a few underlying assumptions to conducting a t – Test:

(i) The dependent variable should be of interval or ratio scale.(ii) The data should be independent(iii) There should not be any significant outliers.(iv) Your dependent variable should be normally distributed.

In Indigo Airlines Questionnaire, it is not possible for one to conduct a t Test as all the available data are in nominal/ordinal form.

INDEPENDENT SAMPLE t-TEST:

Independent sample t – test compares the mean of two unrelated groups on the same dependent variable.

For example:

1. To understand if the BRM End term marks of Sec E vary with Gender. ( So, dependent sample would be the BRM Marks and 2 unrelated independent groups would be Male & Female)

But, there are few underlying assumptions:

4

IndigoResponseAnalysis_Group9

(i) The dependent variable should be in interval or ratio scale.(ii) Independent variable should contain two CATEGORICAL independent groups.(iii) There should be no relation between the observations in each group or between the

groups. (iv) There should be no significant outliers.

In Indigo Airlines Questionnaire, it is not possible for one to conduct an independent sample t test as all the available data are in nominal/ordinal form.

(eg) : Even if we consider say 2 independent groups : Ones who have registered with Indigo Website and those who have not; the dependent variable, say Overall Experience with Indigo Airline is in nominal form.

So, as we don’t know the definition of Delighted and Happy to each person and as we can’t measure in mathematical terms the exact difference between ‘Delighted’ & ‘Happy’, it is not possible to compare their means.

Hence, an independent sample t test cannot be conducted on the given data set.

PAIRED SAMPLE t- TEST:

The dependent t test compares the means between two dependent variable for the same sample.

For Example,

1. To understand the effectiveness of BRM Course on a set of students, we can compare the scores of BRM exam paper before the students attended the class and after the students attended the class to see if there is a difference.

Few of the underlying assumptions are:

(i) Your dependent variables should be on a ratio or interval level.(ii) Independent variable should consist of 2 related or matched groups ( eg: before/after)(iii) No significant outliers in the difference between the 2 related groups.(iv) The distribution of the differences between the 2 matched groups should be normally

distributed.

In our dataset for Indigo Airlines, it is not possible to conduct a paired sample t test as all the data are in nominal or ordinal scale. Also, we don’t have related or matched groups to conduct this test.

For example, if we wanted to check the effectiveness of a new Food and Beverage item in the menu, then a study before its introduction and a study after its introduction in a ratio/interval scale would be suitable for conducting a paired sample t test.

5