1. How large a sample was needed for the Voss et al. (2004) study according to the power analysis? Was this the minimum sample size needed for the study or did the researchers allow for sample mortality?

Answer: After conducting a power analysis, the researchers planned a sample size of 96 patients for their study. The 96 subjects allowed for 30 subjects per group for the three study groups plus 6 subjects for sample mortality or attrition.

2. What was the sample size for the Voss et al. (2004) study? Was this sample size adequate for this study? Provide a rationale for your answer.

Answer: The sample size for this study was N = 62. The power analysis indicated that a sample of 96 was needed and the 62 subjects in the sample were less than was projected by the power analysis. However, preliminary analyses after the 62 patients were enrolled revealed significant groups differences. Since significant group differences were found, then the sample size was adequate and no Type II error occurred of saying the groups were not significantly different when they were.

3. What effect size was used in conducting the power analysis for this study? What effect size was found during data analysis and how did this effect the sample size needed for this study?

Answer: A moderate effect size of 0.33 was used to conduct the power analysis. During data analysis, the researchers indicated that significant group differences and large effect sizes were found for anxiety, pain sensation, and pain distress. Since a large effect size was found during data analysis, the sample size of 62 was adequate to detect significant group differences versus the 96 projected in the power analysis. The larger the effect size, the smaller the sample needed to detect group differences.

4. What power was used to conduct the power analysis in the Voss et al. (2004) study? What amount of error exists with this power level? Provide a rationale for your answer....

...plan describes how to use sample data to evaluate the null hypothesis. The evaluation often focuses around a single test statistic.
Analyze sample data. Find the value of the test statistic (mean score, proportion, t-score, z-score, etc.) described in the analysis plan.
Interpret results. Apply the decision rule described in the analysis plan. If the value of the test statistic is unlikely, based on the null hypothesis, reject the null hypothesis....

...FMBA SQA Final Exam
Prof. Kihoon Kim Oct. 10, 2012
F-MBA SQA Final Exam
Problem 1 2 3 4 5 Total Points 10 10 10 20 10 60 Score
Exam Rules A. B. C. D. The exam is open-book and open-note. You can use only a calculator during the exam; but you cannot use a laptop. You are NOT allowed to discuss any issues with students in the class during the exam. Any kind of cheating during the exam will result in zero score with possibly further penalties by Korea University Business...

...phone. What is the probability that exactly 5 of them own a mobile phone? a. That probability is approximately 0.005. b. That probability is approximately 0.124. c. That probability is approximately 0.279. d. That probability is approximately 0.625. 2. The operation manager of a company that manufactures shirts wants to determine whether there are diﬀerences in the quality of workmanship among the three daily shifts. She randomly selects 600 recently made shirts and carefully...

...Statistics for Business Intelligence – Hypothesis Testing
Index:
1. What is Hypothesis testing in Business Intelligence terms?
2. Define - “Statistical Hypothesis Testing” – “Inferences in Business” – and “Predictive Analysis”
3. Importance of Hypothesis Testing in Business with Examples
4. Statistical Methods to perform Hypothesis Testing in Business Intelligence
5. Identify Statistical variables required to compute Hypothesis testing....

...In today’s world, we are faced with situations everyday where Statistics can be applied. In general, Statistics is the science of collecting, organizing, and analyzing numerical data. The techniques involved in Statistics are important for the work of many professions, thus the proper preparation and theoretical background of Statistics is valuable for many successful career paths. Marketing campaigns, the realm of gambling, professional...

...2a) a) Increasing the difference between the sample mean and the original.
The z score represents the distance of each X or score from the mean.
If the distance between the sample mean and the population mean the z score will
increase.
b) Increasing the population standard deviation.
The standard deviation is the factor that is used to divide by in the equation. the bigger the SD,
then the smaller the z score.
c) Increasing the number of scores in the sample.
Should bring the...

...MBA SEMESTER 1
MB0040 – STATISTICS FOR MANAGEMENT
Assignment
Roll No.
1- Statistical survey is a scientific process of collection and analysis of numerical data used to collect information about units.
Questionnair and schedule are both methods of collecting data in statistical survey. At questionnair the questions is sent by mail to respondents to fill it and send it back. At schedule the questions is filled by the enumerator.
Questionnair is a cheaper process than...

...only use if both are interval, and sensitive to outliers, does not capture non-linear relationships, does not show the strength of relationships. Interquartile range- location measure, shows outliers. Regression- how close the value is to the line (r^2) y=a+bx a= intercept. B= slope. Intercept is y=? when x=0. Regression line explains the variation of the dependant variable, the higher the # the more accurate the regression. Regression Formula Y=a+b*x. Regression is the value...