Attachment # 00011812 - MBA_5652-18O-7_Research_Methods_Unit_VI_Scholarly_Activity.docx
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Unit VI Scholarly ActivityMBA 5652-18O-7, Research Methods Patrese Williams 191570Columbia Southern UniversityData Analysis: Hypothesis TestingThis part of the paper seeks to establish the hypothesis test for independent sample t test, dependent paired sample t test and the one way ANOVA.Independent Samples t -TestH04: The mean for group A prior training scores is not significantly different from group B revised Training scores.Ha4: The mean for group A prior training scores is significantly different from group B revised Training scores.t-Test: Two-Sample Assuming Unequal Variances Group A Prior Training ScoresGroup B Revised Training ScoresMean69.7903225884.77419355Variance122.00449526.96456901Observations6262Hypothesized Mean Difference0Df87t Stat-9.666557191P(T<=t) one-tail9.69914E-16t Critical one-tail1.662557349P(T<=t) two-tail1.93983E-15t Critical two-tail1.987608282 Two-Sample T-test assuming for Unequal variances The point mean of the scores for group A Prior Training is 69.79032258 while the point mean for the scores of group B revised training is84.77419355.The t statistic for the two tailed test is 1.987608282, while the p-value for the two tailed t test is 1.93982E-15<0.05, thus we reject the null hypothesis and conclude that the mean for group A prior training scores is significantly different from group B revised Training scores at 95% level of significance. The assumptions for the hypothesis to hold is that the sample should have been drawn from the population using a random technique. The data for the samples should also be independent (Schönbrodt, et al 2017). The assumptions for this test are met since the data follows a normal distribution.Dependent Samples (Paired Samples) t TestH04: The mean lead blood level for pre-exposure is not significantly different from the mean lead blood level for post-exposure.Ha4: The mean lead blood level for pre-exposure is significantly different from the mean lead blood level for post-exposure.t-Test: Paired Two Sample for Means Pre-Exposure μg/dLPost-Exposure μg/dLMean32.8571428633.28571429Variance150.4583333155.5Observations4949Pearson Correlation0.992236043Hypothesized Mean Difference0df48t Stat-1.929802563P(T<=t) one-tail0.029776357t Critical one-tail1.677224196P(T<=t) two-tail0.059552714t Critical two-tail2.010634758 Paired Sample t test The point mean of the scores for pre-exposure blood lead level is 32.85714286 while the point mean for the post-exposure blood lead level is 33.2857429The t statistic for the one tailed test is 1.677224196, while the p-value for the one tailed t test is 0.029776357<0.05, thus we reject the null hypothesis and conclude that the mean lead blood level for pre-exposure is significantly different from the mean lead blood level for post-exposure. at 95% level of significance. The assumptions for the hypothesis to hold is that the sample should have been drawn from the population using a random technique (Schönbrodt, et al 2017). The data for the samples should also be independent.One-Way ANOVAH04: The mean returns on investment for Air, Soil, Water and Training are significantly differentHa4: The mean returns on investment for Air, Soil, Water and Training are not significantly different.One-Way ANOVA The point values for the test are 8.9, 9.1, 7.0, and 5.4. The p-value for the test at 0.05 level of significance is is 1.76E-06 while the critical value is 2.72494392. Since the P-value is less than the critical value, we fail to reject the null hypothesis and conclude that the mean returns on investment for Air, Soil, Water and Training are significantly different. The assumptions for the test are; the populations from which the data was drawn follows a normal distribution. The scale of measurement for the data should be either ratio or scale (Goos, & Meintrup, 2016). The data for the variables in this study represents revenue on investment and thus follow ratio scale.ReferencesCreswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.Goos, P., & Meintrup, D. (2016). Statistics with JMP: Hypothesis Tests, ANOVA and Regression. Schönbrodt, F. D., Wagenmakers, E. J., Zehetleitner, M., & Perugini, M. (2017). Sequential hypothesis testing with Bayes factors: Efficiently testing mean differences. Psychological Methods, 22(2), 322.
Solution: resarch paper assignment