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## QNT351 Reflection

by: tophomework Notetaker

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# QNT351 Reflection

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QNT351 Reflection
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Date Created: 11/14/15
Team Re ection Nicolle Pack Jesus Felix John Turrieta Marissa Eliares QNT 35 1 February 2 2015 Jeffrey Duncan Team Re ection In week four Team C read about research hypotheses the relationship between two variables and how to calculate it and how to compare the means of two or more groups Another major topic was the importance of significance levels and how they relate to the accuracy of the hypotheses This team re ection will demonstrate what we learned and what we struggled with as a team as well as individuals Hypothesis Testing A hypothesis is a forecast about the outcome of a study These forecasts are formulated on the basis of theories and research questions There are five steps to testing a hypothesis The first step is the formulation of a theory that would include the level of measurement for the variable the method used to obtain a sample the nature of the population distribution and the size of the sample The second step is to state the concentration of the relationship and to start the actual research create a hypothesis that challenges the null hypothesis and set the alpha that will help conclude the significance level The third step deciding what sampling method to use In the fourth step the statistic is tested by using a formula to generate a result The fifth and final step is where the results are interpreted If the alpha level chosen in step two is less than the testing results we can assume that the null hypothesis is false and must be rejected McClave 2011 Comparing Means The purpose of comparing the means of two or more groups is to determine the probability of a result given two uniquely independent data sets with equal variances This ties directly into developing and ultimately testing hypotheses39 in an attempt to prove or disprove the null hypothesis The distance between the hypothesis and the mean directly corresponds to the probability of occurrence Variable Correlation Calculating the correlation between two variables demonstrates how the change in one variable can create an equivalent change in the second variable When there are two variables contributing to a common cause it is proposed that there is a high level of correlation between those two variables An example would be where the value of sample quotrquot is used to calculate the correlation between two discrete variables The correlation can be used to measure the linear relationship between two variables X and y A numerical descriptive measure of the linear relationship between X and y is produced by the coefficient of the correlation McClave 2011 The coefficient of correlation indicates if there is a cause and effect relationship between the two variables Calculating the correlation between two variables is needed when trying to determine the level of significance of the hypothesis Calculating the correlation between two variables will demonstrate the probability of whether or not the null hypothesis will be accepted Dif culties Our team has found the general topic of statistics to be quite daunting Some of us find numbers hard to work with especially when dealing with formulas However we have found that the information discussed in this class has been interesting While understanding the basic populations and samples theory the one topic most of us struggle with is probability We understand that it is relatable to a chance or to luck but how to calculate the probabilities was a difficult topic to grasp The z test and t test formulas and how to get the results is confusing to some of us as well Part of what is confusing is the use of symbols like u 3 and O to denote various variables given in a sample test Some of us were able to understand the one tail and two tail theories since finding the z score was fairly easy However some of the team missed the correlation between the curve and the sample test and how we would move the results from the survey into a curve Most of the team found comparing the means of two groups easy especially with the help of the MegaStat program However measuring the central tendencies and plotting those tendencies was a little more problematic Conclusion Statistics has proven to be an interesting yet challenging topic for the team As we look at populations and samples along with the different variables and ways to translate the data collected it seems quite evident that the topic of probability is the hardest to understand Significance levels appear to tell us how the hypothesis will work out but it too can be difficult to calculate In the end statistics can provide invaluable data to any business as long as it is gathered and interpreted correctly References McClave J T Benson P G amp Sincich T 2011 Statistics for Business and Economics 11th edBoston MA Pearson Education Inc Retrieved from httpsecampusphoeniXeducontenteBookLibraryZcontenteReaderaspx

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