¶ … statistical tests can provide more information than a single one, allowing for more meaningful assessments of a situation. This interaction of two statistical tests (as described below) demonstrates that in this scenario younger women are by far the most likely to be the best employees for this call center.
The Pearson r provides an answer to the question of whether or not two variables are related to each other. More than simply establishing whether a relationship exists or not, Pearson r determines how strong this relationship is and whether it is a direct or inverse relationship. In a direct relationship, if one variable goes up than so does another (or others).
For example, in general as an individual's height goes up, so does his/or her weight. In an inverse relationship, as one variable goes up another one goes down. An example of this would be: The fewer workers are assigned to construct a building the longer it will take to construct the building. Both of these relationships cited here make intuitive sense to us. We may never have considered them to be a part of the world of statistics, but we understand how different factors can be related either directly or inversely.
Pearson r can tell us how strong the relationship is between two variables. The closer to 1, the stronger the relationship is and the closer to 0 the weaker the relationship is. If the Pearson r value is positive, the relationship is direct, and if negative, the relationship is inverse. In our hypothetical case study, the r value is negative and thus the older a worker gets the less engagement they demonstrate with the customers.
The chi-squared measure indicates how likely two variables are to be independent of each other (such as a person's favorite color and their last name) or dependent on each other (such as a person's race and his/her income). In this case, the value of chi-square at less than .05 indicates that the possibility that gender and customer engagement are randomly related is less than 5%.
Important to this analysis are two concepts. A result is statistically significant if there is little chance that variables are related by chance. Substantive (or meaningful) significance addresses the ways in which a statistical finding might be meaningful to the population being examined.
statistical test (SPSS Output) is effective as far as it goes in this particular case, but the results are not as clear as what they could be. A number of variables were used to generate results that on the surface were easily defined but a more analytical approach shows that the results could be much more comprehensive. The variables included three groups; low, medium and high scores depending on
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c. Statement of the Problem i. AS9103 requirements Section 4.9.1 is a part of the AS9100 and AS9103 requirements that states that suppliers shall identify and plan installation, production as well as servicing process that affect the quality production .Under the requirements, suppliers are to achieve these objectives through specified process. Moreover, the AS9103 requirements provide a standard method to enhance a quality performance in the production and maintenance process with the