SURVEY CONDUCTED IN STORES

                                                                                                                          APPLIED BUSINESS STATISTICS

 SURVEY CONDUCTED IN STORES:

  Session 9&10                                               

                    In today session we conducted the survey and we got different responses from customers

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

        5) Strongly positive

                    we got more negative response for store 2 among the five stores and more positive response is for store 3 from customers who visited the stores if there is no relationship with employees...same situation was happened even when there is relationship with employees.   

    Null hypothesis:

                    A type of hypothesis used in statistics that proposes that no statistical significance exists in a set of given observations. The null hypothesis attempts to show that no variation exists between variables, or that a single variable is no different than zero. It is presumed to be true until statistical evidence nullifies it for an alternative hypothesis

                   The null hypothesis assumes that any kind of difference or significance you see in a set of data is due to chance. For example, Chuck sees that his investment strategy produces higher average returns than simply buying and holding a stock. The null hypothesis claims that there is no difference between the two average returns, and Chuck has to believe this until he proves otherwise. Refuting the null hypothesis would require showing statistical significance, which can be found using a variety of tests. If Chuck conducts one of these tests and proves that the difference between his returns and the buy-and-hold returns is significant, he can then refute the null hypothesis.

                    If significant value is less than 0.05 then null hypothesis will not be considered.Now we can said that there is relationship between services &stores.In routine business,significant value is around 0.05

    Chi square distribution:

                    Suppose that a non negative statistic T is asymptotically distributed as a chi-squared distribution with f degrees of freedom, χ²_f, as a positive number n tends to infinity. Bartlett correction T was originally proposed so that its mean is coincident with the one of χ²_f up to the order O(n⁻¹). For log-likelihood ratio statistics, many authors have shown that the Bartlett corrections are asymptotically distributed as χ²_f up to O(n⁻¹), or with errors of terms of O(n⁻²). Bartlett-type corrections are an extension of Bartlett corrections to other statistics than log-likelihood ratio statistics. These corrections have been constructed by using their asymptotic expansions up to O(n⁻¹). The purpose of the present paper is to propose some monotone transformations so that the first two moments of transformed statistics are coincident with the ones of χ²_f up to O(n⁻¹). It may be noted that the proposed transformations can be applied to a wide class of statistics whether their asymptotic expansions are available or not. A numerical study of some test statistics that are not a log-likelihood ratio statistic is described. It is shown that the proposed transformations of these statistics give a larger improvement to the chi-squared approximation than do the Bartlett corrections. Further, it is seen that the proposed approximations are comparable with the approximation based on an Edge worth expansion.

submitted by

Polisetti kartheeki(2013198)

Group members:

Pakala kalyani

Nishidh lad

Priyatam kireeti

Priyadarshi tandon