Hyper Stat Online Contents - Essay Example

This implies that the sum of all the cells will be equal to the sum of subjects. Another assumption is that chi-square is applied to a large sample of data. Usage of small samples is expected to mislead the user to come up with the wrong results. Every observation of chi-square is expected to have the same distribution. The assumption regarding cell sizes is that in a 2 by 2 table there should be five or more cells. The observations must be properly grouped. The different categories should be based on the nature of the distribution.

The hypotheses between the relationships of two values are studied by using chi-square. Since the distribution is already planned by the hypothesis, the distributions that are to be made in the cell can be expressed even without doing any calculation. Chi-square test is actually used to compare different data which are considered useful for various purposes. The comparison is made between the data that are already available with the one that will be gathered on the basis of the hypothesis. Chi-square tests whether a collected sample of data belongs to a population with a specific distribution.

Degrees of freedom or 'df' is one of the most important parameters of Chi-square distribution. Chi-square distribution tends to approach a normal distribution with an increase in the degrees of freedom. "As the df increase, the chi-square distribution approaches a normal distribution." (Hyperstat online contents). There are three possible tests in chi-square. They are testing for independence, the goodness of fit and test for homogeneity. In the first case, that is the test for independence, it is examined whether the value of a particular variable can be assessed with the help of any other variable.

Under goodness of fit analysis, the efficiency of a statistical expression to fit a given situation or observation is examined. In the third case, that is the test for homogeneity, it is examined as to whether there are similar observations for a common population in a given data. Though the formula in both cases is the same, the values are differently interpreted for arriving at a conclusion in a given scenario. F-ratio is another statistical tool used in the interpretation of data. F-ratio is mainly used to test variances.

This tests whether the variances are equal for the given independent variables. "The F-ratio is used to determine whether the variances in two independent samples are equal." (Applied statistics handbook, 2007). Therefore, f-ratio calculates the existence of any difference between the variables. It does not analyze the difference to know the reasons. That is in order to know the reason for the existence of difference certain other methods are to be applied. In the given situation the company W sells its new software products through the sales force they assigned for different regions.

Usually, the demand for different regions varies due to different factors. But without considering this variation the salespersons are expected to achieve the same sales target. In the present situation, the sales that have been achieved by the salespersons must be compared with those of the expected sales figures. Thus, the company will be able to make a clear forecast of the sales that it can possibly achieve and the salespersons can be given that as their target.