RSCH FPX 7864 Assessment 4 Data Analysis and Application Template

RSCH FPX 7864 Assessment 4 Data Analysis and Application Template

Name

Capella university

RSCH-FPX 7864 Quantitative Design and Analysis

Prof. Name

Date

Data Analysis and Application Template

Data Analysis Plan

There were two variables in the modern evaluation: “section” and “Quiz3.” The variable “section” refers to outstanding class sections simultaneously because the variable “Quiz3” is non-prevent and shows the correct solutions on Quiz 3. RSCH FPX 7864 Assessment 4 Data Analysis and research question behind the evaluation is stated as follows: “Is there a general difference in the average overall performance on Quiz Three across different class sections?

” The null speculation suggests no enormous difference in Quiz Three overall performance at some undefined point in the future of beauty sections, while the opportunity speculation indicates a large difference. Levene’s examination for equality of variances tests the idea of homogeneity in the data for the assessment of variance (ANOVA). The check returned an F statistic value of two.898, where the degrees of freedom for the numerator (df1) was 2.000 and the degrees of freedom for the denominator (df2) was 102.000. The p-cost had earlier been 0.060. The p-charge value of 0.060 tells us there is no evidence against the concept of homogeneity.

Results & Interpretation

Post Hoc Comparisons – section

The F test result shows a huge difference in most of the three beauty sections on Quiz Three (F = 23.521, p < .001). Therefore, the null hypothesis is rejected, suggesting differences in quiz marks in some of the sections. Submit-hoc tests using the Tukey procedure screen significant differences between sections one and two (recommend difference = zero.939, p = zero.021) and between sections two and three (recommend difference = -1.606, p <.001). However, no significant difference is observed between sections one and three (recommend difference = -zero.667, p = zero.159).

Conclusion

The test scores show a huge difference in quiz rankings among the three sections of splendour. UP-hoc tests stage up-hoc screen individual differences within sections, highlighting versions within common performance. While sections 1 and 2 and 2 and 3 cover enormously, no prime-quality variation exists within sections 1 and 3. Those results confirm established overall performance discrepancies throughout the elegance sections for the quiz.

Statistical Conclusions

The ANOVA test between quiz three normal performance across 3 class sections revealed huge versions. Post-hoc comparisons revealed varying scores across sections. Section 1 performed better than Phase 2, and Section 2 performed better than Section 3. Those findings can be useful resources in evaluating schooling strategies and curriculum diagram effectiveness, informing resource allocation and academic interventions.

Application

ANOVA software is used to determine whether or not the skills of different pattern groups are significantly different. ANOVA in nursing may be shortened to view the influence of different doses of an antidepressant on sorrow scores. For instance, patients may be divided into groups receiving certain doses (e.g., 50mg, 100mg, 150mg, 200mg), and despair scores measured via a questionnaire such as the PHQ-9. Such information is required by highbrow fitness nurses to be able to find the optimum dose of antidepressants when treating symptoms of depression.

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