SUMMARIZE AND INTERPRET THE INFORMATION

Today, we have learned about following things .

1>In which category , we can classify data about retail store?

2>How to use crosstab

       a> when there is two variable?

       b> when there is three variable?

       c>where we can use cell?

       b>how to use chi-square?, 

3>how to interpret table ?

       a>which get by using crosstab(two variable)

       b>which get by using crosstab(three variable)

       c>which get by using cell

      d>which get by using chi-square

        

4>   on what basis we can reject data data?

IN WHICH CATEGORY , WE CAN CLASSIFY DATA ABOUT RETAIL THINGS?

Survey  about retail store got information about customer satisfaction .in following category . they have been divided .

        1) Strongly negative           

        2) Somewhat negative
        3) Neutral
        4) Somewhat positive
        5) Strongly positive

This is category in which researcher divide customer satisfaction .when we divide data into category , that data call nominal data, therefore , we can say that data on retail store is nominal data.we can also say this data as continuous discrete data.

HOW TO USE CROSSTAB?

   here, we use software . In which, we took data .then we went in analyze ,then into descriptive statastics, then into cross tab. In crosstab , we took two variable, then we also use crosstab in three variable .By using crosstab,we can make table of two variable or three variable .

By using cell in crosstab, we can also analyze raw or column separately .

By using chi-square , we can say probability or possibility at which event will be.

HOW TO INTERPRET DATA?

we had taken data of store in first variable .then second variable is service satisfaction . from this table , we interpret that in second store, dissatisfaction  level of customer is high.

we find out reasons , we took store as first variable then contact with employee s is second.but when we use chi-square. we found that there is no relationship between this two variable.

Then , we have taken three variable .

 1> store 

2> service satisfaction 

3> contact with employee 

when, we have found that , when there is no contact with employee , at that time, there is high possibility that service dissatisfaction level will be high, but in case that when there is contact with employee, we can not relate anything.

ON WHAT BASIS WE CAN REJECT DATA?

1>when there is cancelling effect.

2>In statistical inference, Null Hypothesis refers to a general or default position: that there is no relationship between 2 variables tested.

written by:                shyam pandule

Group Members :-      Praloy Pankaj

                                 Ruplani Saha

                                 Navdeep Singh 

                                 Navneet Singh