MAT119 Quantitative Business Analysis

Problem Statement

Application of one way Anova in MINITAB to solve a given task in managing Ashland MultiComm services 

Statistical Results 

Analysis of Variance 

Means

Pooled StDev = 4.10806

We can observe that the mean of system 1 is the largest while the mean of system 3 is the smallest.

The standard deviation of system 1 is also the largest and system 2 has the smallest Standard Dev.

The factor p-value is statistically significant 0.015 less than 0.05 hence we reject the null hypothesis.

Conclusion

We can see that means of different systems have significant differences, system 1 has the largest mean while system 3 has the lowest mean. On the other hand, system 1 has the largest standard deviation while system 2 has the smallest standard deviation. The p- value of is statistically significant since it is less than 0.05 significance level.

Problem Statement

In this task you are required to use one way Anova test in MINITAB worksheet to come up with the solution for managing Ashland MultiComm services

Statistical Results

3. A Chi-square test is also done to provide accurate outcome especially when handling 2xc a contingency table whereby all expected frequencies have to be large. If

the combining categories is undesirable; it is advised to use the available alternative procedures.

Conclusion

Based on the analysis provided above we can see that the p-value is 0.000 which is less than the level of significance 0.05 leading to rejection of null hypothesis. Also a Chi-square test is done in order to come up with accurate results.

Problem Statement

The task here is to use MINITAB to recreate the regression equation for the managing Ashland MultiComm Services.

Statistical Results

Regression Equation

Model Summary

1. From the regression equation y= -482 +4.483(hours) we can calculate the number of new subscribers for a month when the value of hours is provided. For instance, given the 1000 hours we can compute it as follows y= -482 + 4.483*1000= (4001). Therefore, the more the number hours the more the subscribers.

Conclusion

Using regression equation, we can compute the number of new subscriptions when the value of hours is provided. We can also note that when the number of hours is bigger, the number of subscribers tend to be bigger too. However, the prediction of new subscriptions with respect to number of hours is guided by some assumptions such as linearity and independence of errors.

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