Over the last four weeks of being in the QNT/351 course our team has learned a lot about researching a hypothesis and understanding the mean. The hypothesis testing begins with the statement and the assumption that determines the population of the mean, (Lind, 2011, p.288.). There are five steps taken which include; state the hypothesis, select level of significance for it, identify the test statistics, formulate decision rule, and take a sample to arrive at a decision. On the contrary, however, McClave, 2011 states that there are seven steps including; null hypothesis, alternate hypothesis, test statistic, rejection region, assumptions, experiment and calculation of test statistic, and the conclusion. With setting a hypothesis and testing it is important to understand the mean and how to compare it amongst two or more groups. With testing the mean with multiple groups, the data collected is used to help determine the probability of a given amongst two distinct groups being analyzed which have the same or equal variances.
This info it all based off the given hypothesis and should result in proving the null hypothesis stated. When looking at the mean the distance noted between the hypothesis and the mean placed is a value given as a result of the probability of occurrence. When analyzing and testing the means of the two groups we use the variance analysis. An example of this type would be testing the means of the two groups based off statistical models and valid conclusions. Along with completing the testing of the means of the groups, we must also calculate the variance according to the distribution of the differences in means. If the standard deviation is known we can calculate the z-score by using the difference of the variance and the mean. In calculating the correlation between the two variables we see that it is crucial to know the characteristics of the coefficient. According to Lind, 2011 pages 386-389 we can calculate the coefficient of correlation. The coefficient of correlation can help determine the cause as well as effect amongst the two variables. Calculating these numbers is crucial as the results give information which help determine the level of significance of the given hypothesis.
Lind, D. A., Marchal, W. G., & Wathen, S. A. (2011). Basic Statistics for Business and Economics (7th ed.). New York, NY: McGraw-Hill/Irwin. McClave, J. T., Benson, P. G., & Sincich, T. (2011). Statistics for Business and Economics (11th ed.). Boston, MA: Pearson Education, Inc.