# SOLUTION: Data Analysis Report – Studypool

SOLUTION: Data Analysis Report – Studypool.

statitic discussion response

Discussion 5If I had to explaon this to someome that didnt know statyistics I would rely om something lke below- which is an exerpt from a statistics for dummies book. I find that the have suimple explanations for things that may be harder to understand.The so-called “one-way analysis of variance” (ANOVA) is used when comparing three or more groups of numbers. When comparing only two groups (A and B), you test the difference (A – B) between the two groups with a Student t test. So when comparing three groups (A, B, and C) it’s natural to think of testing each of the three possible two-group comparisons (A – B, A – C, and B – C) with a t test.But running an exhaustive set of two-group t tests can be risky, because as the number of groups goes up, the number of two-group comparisons goes up even more. The general rule is that N groups can be paired up in N(N – 1)/2 different ways, so in a study with six groups, you’d have 6×5/2, or 15 different two-group comparisons.When you do a lot of significance tests, you run an increased chance of making a Type I error — falsely concluding significance when there’s no real effect present. This type of error is also called an alpha inflation . So if you want to know whether a bunch of groups all have consistent means or whether one or more of them are different from one or more others, you need a single test producing a single p value that answers that question.The one-way ANOVA is exactly that kind of test. It doesn’t look at the differences between pairs of group means; instead, it looks at how the entire collection of group means is spread out and compares that to how much you might expect those means to spread out if all the groups were sampled from the same population (that is, if there were no true differences between the groups).The result of this calculation is expressed in a test statistic called the F ratio(designated simply as F), the ratio of how much variability there is between the groups relative to how much there is within the groups.If the null hypothesis is true (in other words, if no true difference exists between the groups), then the F ratio should be close to 1, and its sampling fluctuations should follow the Fisher F distribution, which is actually a family of distribution functions characterized by two numbers:http://www.dummies.com/education/science/biology/t…Discussion 6A statistical interaction occurs when the input variable has a different effect on the output variable, and the interactions that occur among variables look different, but their meaning is essentially the same (ICBS,2018). Simply put, two or more variables come together, and each variable shares a similar link. One variable has a more powerful influence on the other variables, and the dissimilar effects the variables produce allow researchers to thoroughly explain data when the interactions are evaluated. One variable cannot determine the output independently. All variables must be combined to comprehend the results.For example, a fictional community hospital offered an incentive for bedside nurses who achieved certification in 2014. A total of 48 nurses attained medical-surgical certification during fiscal year 2014. Three nurses attended a certification review offered by the hospital, and a total of 45 nurses did not attend the review. All nurses passed the exam regardless.In this example, the independent variable, or the number we are measuring, is the number of nurses achieving certification. The dependent variables are the nurses who attended the certification review and the nurses who did not attend the review. The sample size was 48. Overall, the incentive offer was successful as 48 nurses obtained their specialty certification, but hospital provided certification preparation courses were not cost effective. ICBS.2018. Retrieved from: http://icbseverywhere.com/blog/mini-lessons-tutori…

SOLUTION: Data Analysis Report – Studypool

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