are presents with the following information in which we are presumed to answer.
We should use chi square test program to come up with numerous hypothetical
Northeast region is operating the software sold 165 and the group with no
software sold 100, The Southeast region with software sold 200 and the group
with no software sold 125. The Central region
with software sold 175 and the group with no software sold 125. The West group
with software sold 180 and the group with no software sold 130.
program is categorical variable which can have 2 possibilities either Yes, and
or No. this consider the cases whether the software is assigned or not
assigned. Performance is again a categorical variable which can have 2
possibilities either Improved or not improved.
Contingency Table is shown below.
In my Chi-Square
Calculation, the compiled the data set in contingency table. Using the
Chi-Square method, the numbers calculate as expected, differences in all the
regions of the sales group sold. The significance level of operation is: 0.05. The
calculations gives the chi-square statistical figure of is 1.7176, the degree
of freedom =3, the probability, p-value is 0.633018. Accordingly, the final development
is not significant at p < 0 .05. This variables are independent. (G F, 2014). VP of Sales at WidgeCorp, would like a trial of whether these two variables are independent or not. A nonparametric test using Chi square test is used to test the following hypothesis. Null Hypothesis Ho: the two variables which are the Software Program and Performance are independent. Alternative Hypothesis Ha: the two variables which are the Software Program and Performance are not independent. When the conclusion is statistically significant (p-value less than 0.05) it is shown that the alternative hypothesis is; otherwise (p-value greater than 0.05), the null is chosen. The relationship is not statistically significant as a result of the p-value, 0.633, is greater than 0.05. Accordingly, it is apparent that it is in favor of the null hypothesis - the two variables which are Software Program and Performance are independent. The VP of Sales at WidgeCorp, knows quite a bit about statistics, would like to know feasible null and alternative hypotheses for a nonparametric test on this data using the chi-square distribution. A nonparametric test is utilized on information that are qualitative or categorical. This is predominately is utilized when the information is does not give the right perception when observed at the mean of such variables. When looking problem, the hypothesis shows that the sales estimates are generated from the four regions are basically random and this is known as the null hypothesis. This also shows that the null hypothesis is accurate if the observed data sales for every region did not change from what was originally expected. To know this is only expected by chance. What usually correlates the null hypothesis is known as the alternative hypothesis. The null hypothesis is shown as H0 and the alternative hypothesis as H1. (C. Z. 2013). The significance level is usually the acceptable level of type I error, it is usually given as ?. Most of the time, a significance level of ? = 0 .05 is used. The significance level form part one is: less than 0.05. The P-value (that is the probability value) is the statistic value used to test the null hypothesis. If p < ? then we reject the null hypothesis. Here it is not. 0.633018 is greater than 0.05. Therefore; H0: p = <0.5, Ha: p <> 0.5
Since P is not less than
?; hence it is reasonable the state to the VP of Sales at WidgeCorp that the
null hypothesis is accurate and should be accepted. And hence state that we
should take the other correlation of the Alternative Hypothesis.