RSCH-FPX7864-Armstrong-Venice-Assessment-2-1

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Data Analysis and Application Template Venice Armstrong Capella University RSCH-FPX7864 Dr. Raphael October 21, 2023 1

Data Analysis and Application Template Data Analysis Plan The four variables used in this analysis are Quiz score, GPA, Total grade, and final exam score. The variables Quiz 1, GPA, Total, and Final, are all continuous variables. Continuous variables are variables that take on numerical values and have infinite values that they can take on. What is the relationship between total and final scores in a specific academic setting? The null hypothesis for this research question is that there is no correlation between total and final scores in the given setting. The alternative hypothesis is there is a significant correlation between total and final scores in the given setting. This could indicate that changes in one variable are associated with changes in the other variable. Are high quiz scores correlated to high GPAs? The null hypothesis states that there is no correlation between GPA and quiz scores, meaning that there is no relationship between a student's academic performance and their quiz performance. The alternative hypothesis suggests that there is a significant correlation between GPA and quiz scores, implying that students with higher GPAs tend to perform better on quizzes. Testing Assumptions Descriptive Statistics quiz1 GPA total final Skewness -0.851 -0.220 -0.757 -0.341 Std. Error of Skewness 0.236 0.236 0.236 0.236 Kurtosis 0.162 -0.688 1.146 -0.277 Std. Error of Kurtosis 0.467 0.467 0.467 0.467 2

Based on the data the skewness values for the variables "quiz1," "GPA," Total", and “Final” are -0.851, -0.220, -0.757, and -0.341 respectively. A skewness value less than zero indicates that the distribution is negatively skewed, meaning it has a longer tail on the left side. However, the absolute values of the skewness are relatively small, suggesting that the deviations from the normal distribution are not substantial. The kurtosis values for the variables "quiz1," "GPA," "Total, and “Final” are 0.162, -0.688, 1.146, and -0.277 respectively. A kurtosis value of 0 indicates normally distributed data, while positive values indicate heavier tails and negative values indicate lighter tails. Here the kurtosis values indicate that the data for two variables have heavier tails compared to a normal distribution. It can be concluded that the assumptions of normality were violated for the variables "quiz1," and "Total" as the skewness and kurtosis values deviate from what would be expected in a normal distribution. However, the kurtosis values for the variables, “GPA’ and “Final”, indicate lighter tails compared to a normal distribution which signifies a violation of the assumption of normality. Results & Interpretation Variabl e Quiz1 GPA Total Final 1. quiz1 Pearson's r — p-value — 2. GPA Pearson's r 0.152 — p-value 0.121 — 3. total Pearson's r 0.797*** 0.318*** — 3

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Variabl e Quiz1 GPA Total Final p-value < .001 < .001 — 4. final Pearson's r 0.499*** 0.379*** 0.875*** — p-value < .001 < .001 < .001 — * p < .05, ** p < .01, *** p < .001 The correlation between quiz 1 and GPA is not significant, with a Pearson's r coefficient of 0.152 (p = 0.121). This suggests that there is no strong relationship between the performance in Quiz 1 and the overall GPA. Therefore, we fail to reject the null hypothesis and conclude that there is no significant correlation between quiz 1 and GPA. The correlation between quiz 1 and total is 0.797 (p < 0.001). reject the null hypothesis The correlation results between quiz 1 and final is 0.499 (p < 0.001). reject the null hypothesis. The correlation between total and GPA 0.318 (p < 0.001) reject null hypothesis. Additionally, there is a positive but weaker correlation between the GPA and the final score, with a Pearson's r coefficient of 0.379 (p < .001) reject the null hypothesis. final and total score, with a Pearson's r coefficient of 0.875 (p < .001). reject the null hypothesis -GPA and the final score, but it is not as strong as the correlation between the total score and the final score. S tatistical Conclusions 4

In analyzing the limitations of the statistical test and possible alternative explanations for the results, it is crucial to consider the sample size, the specific population under study, and the potential presence of confounding variables. Additionally, other factors such as study habits, personal motivation, or external influences can also impact the final scores, and these should be considered. Application In the field of Registered Nursing, correlations can be used to explore relationships. between different variables and outcomes. Two variables that could be studied include nurse- patient ratios and patient outcomes. Understanding the correlation between nurse-patient ratios and patient outcomes can provide valuable insights into the impact of staffing levels on patient care quality and safety. Additionally, studying the relationship between nurse education level and patient outcomes can help identify the importance of higher education in improving patient care. By examining these correlations, Registered Nurses can make evidence-based decisions to improve patient outcomes and enhance the overall quality of care they provide. 5

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