Kimberly Khalid, Magen Adams, Maria Orozco, Michelle Sachau
January 26, 2015
As team C finishes week four of statistics 351, we have reviewed various aspects of testing a research hypothesis. Team C has also reviewed how to compare means of two or more groups and calculate the correlation between two variables. Team C has discussed some concepts that the team has struggled with and fully understood. Team C has also come to understand how this concept can be beneficial to their careers and how they may apply to improve productivity.
The steps in testing a research hypothesis
"Hypothesis testing starts with a statement, or assumption, ...view middle of the document...
The final step involves computing the test statistic, comparing it to the critical value, and making a decision to reject or not to reject the null hypothesis.
Comparing the means of two or more groups
Comparing the means of two or more groups involves the five-step hypothesis testing procedure to investigate the question. The null hypothesis would state that there is no difference in the mean values of two or more groups, and the alternative hypothesis would state that there is a difference. The level of significance would be determined by the researchers and the z or t tests would be used based on what is known about the population. The decision rule would be created based on the null and the alternative hypothesis, the level of significance, and the test statistic used (Lind, 2011)
Calculating the correlation between two variables
It is helpful when calculating the correlation between two variables to know that the two samples are independent and that the standard deviations for both populations are known. “Precise definitions of variables and the elimination of ambiguity are important in ensuring that the questions are not misinterpreted by respondents or researchers” (Wolverton, 2009). Correlation Analysis involves a group of techniques to measure the association between two variables (Lind, 2011). A scatter diagram can be used to graphically display the data to determine the relationship between the two variables by determining the coefficient of correlation. The coefficient of correlation describes the strength of the relationship between two sets of interval-scaled or ratio-scaled variables.
One of the members of Team C was able to recall some of the information that surrounded hypothesis and testing it from prior courses; however, those courses were more scientific in nature. Based on her prior knowledge, learning how to apply her understanding of terminology helped her get the main points of what was being explained. She is still struggling with how to grasp the null hypothesis and how this relates to comparing the means of two or more groups. She is hoping to be able to research this out more and has found information difficult to understand by utilizing the dictionary for difficult terms. This same team member struggled a lot with the math labs, specifically with all the calculations that are needed to obtain the correlation between two variable but was thankful that the labs enabled the students to attempt the problems numerous times in order for them to come to the correct answer. She particularly appreciates the feature that allows her to be walked through how to solve the question and relied heavily on this feature to complete her labs for this week. This team member can realize that this information is very important to be able to fully analyze data and give strong statistics that can be used to show how important programs and users are which directly relate to her work as a Project Manager. There is a saying that...