BHA FPX4106 Assessment 4 Introduction to Managing Health Care Information

RSCH FPX7864 Assessment 2 Correlation Application and Interpretation

Data Analysis Plan

RSCH FPX7864 Assessment 2 Correlation Application and Interpretation: Statistical analysis plays a pivotal role in academic research across various disciplines. Data analysis enables researchers to extract meaningful patterns and trends from raw data, thereby shedding light on complex phenomena. The purpose of this paper is to examine four key variables: Final Grade, Grade Point Average (GPA), Quiz 1 score, and Total Grade. The primary objective is to analyze the dataset compiled by the researchers, including demographic information, formative assessment performance, and exam results across three-course modules. The central research question explores the potential relationship between students’ final grades and their Grade Point Averages (GPA). Both final grades and GPA are continuous variables subject to different quantification methods, as opposed to discrete variables with finite value ranges. For the study, GPA is the predictor variable, while student gender identity is a nominal variable. An alpha of 0.05 has been set, with a sample of 105 participants selected for analysis.

Research Question

The research question centers around establishing null and alternative hypotheses to investigate the potential correlation between total grade and final grade. Specifically, the analysis seeks to address the following inquiries:

  • Is there an observable association between students’ final grades and their total grades?
  • Do these variables display a noteworthy and statistically significant correlation?

Null Hypothesis (H0)

There is no empirical evidence of a linear correlation between total grade and final grade in this study.

The Alternative Hypothesis (HA)

The alternative hypothesis for the study proposes that there is a strong potential for a linear association between students’ overall grade point average (GPA) and their final grade in the course, as stated in the alternative hypothesis. The current inquiry, along with the null and alternative hypotheses, is framed to examine the correlation between GPA and performance on Quiz 1. RSCH FPX7864 Assessment 2 Correlation Application and Interpretation

Research Question

The research question guiding the study concerns whether there is a statistically significant relationship between students’ GPA and their scores on Quiz 1.

The Null Hypothesis (Ho)

the null hypothesis states that there is no discernible linear correlation between a student’s GPA and their performance on Quiz 1. It implies no straightforward relationship between these two variables. However, the alternative hypothesis contends that there is compelling evidence of a linear correlation between students’ GPA and their grades on Quiz 1.

The Alternative Hypothesis (HA)

Empirical data substantiates the existence of a linear correlation between the academic performance metric denoted as students’ GPA and the scores obtained in the initial quiz “Quiz 1.”

Testing Assumption

Descriptive Statistics

The analysis examined the skewness and kurtosis values for GPA and final exam scores to characterize their distributions and evaluate the normality assumption. Skewness measures asymmetry in distribution, with negative values indicating leftward skew and positive values reflecting rightward skew. Kurtosis assesses peakedness relative to a normal distribution, with negative values suggesting a flatter distribution and positive values indicating more pronounced peaks.

RSCH FPX7864 Assessment 2 Correlation Application and Interpretation

The GPA showed a skewness of -0.220 and a kurtosis of -0.688, indicating mild negativity. Final exam scores exhibited a skewness of -0.341 and kurtosis of -0.277, also reflecting mild negativity (Siegel & Wagner, 2022). These values fall within acceptable ranges for skewness and kurtosis, supporting the normality assumption. The measurements do not contradict the assumption of normality (Siegel & Wagner, 2022). More comprehensive normality diagnostic tests, like normal probability plots or the Shapiro-Wilk test, are needed to validate normality fully. The descriptive statistics indicate the data follows a normal distribution, as skewness values between -0.5 and 0.5 align with expectations for a normal distribution when skewness is between -1 and 1, which confirms conformity to a normal distribution and suggests valid parameter comparisons given appropriate use. Additionally, accurate representation of both tails of the bell curve further supports normality (Shu & Ye, 2023).

Results and Interpretation

 Correlation Between the Variables

A correlation matrix analysis was conducted to evaluate the interrelationships among four principal variables: GPA, Quiz 1 score, total course grades, and final grades (RSCH FPX7864 Assessment 2 Correlation Application and Interpretation). The statistical techniques implemented provide substantial evidentiary support for the proposition that a positive association exists between higher final grades and superior academic achievement, as corroborated by the findings from the correlation matrix. Moreover, the evidence substantiates the rejection of the null hypothesis. Notably, the predetermined threshold for statistical significance, denoted Alpha (α), was established at 0.05.

The Pearson correlation coefficient (r) obtained in the study was a robust 0.88, while the corresponding p-value was a remarkably low 0.001. The result indicates a strong, positive linear relationship between final grades and total grades, suggesting that as total grades increase, final grades exhibit a proportional increase. The magnitude of the association is further emphasized by the correlation coefficient of 0.88. Therefore, these results definitively demonstrate a substantial linear association between final grades and total grades, leading to the rejection of the null hypothesis in favor of the alternative hypothesis (Aubinet, 2023).

The analysis revealed a relatively weak association between GPA and Quiz 1 performance, evidenced by a p-value of 0.121, correlation coefficient of 0.152, and sample size of 105 participants, conferring 103 degrees of freedom. These findings imply that a stronger correlation was anticipated between GPA and Quiz 1. With a sample of 105 individuals, it is crucial to note that the obtained p-value of 0.121 exceeds the more stringent 0.05 significance threshold. These results indicate the absence of a statistically significant relationship between GPA and Quiz 1, precluding the dismissal of the null hypothesis (H0) based on the evidence. It is vital to stress that these conclusions are contingent on the particular parameters and criteria applied in the study, especially the selected alpha level. Thus, additional research may be warranted to corroborate these findings. Get RSCH FPX7864 Assessment 2 Correlation Application and Interpretation

Statistical Conclusions

In RSCH FPX7864 Assessment 2 Correlation Application and Interpretation A positive correlation was found between Final Grade and Total Grade, indicating a tendency for higher Final Grades to be linked with stronger overall performance. However, GPA and Quiz 1 were only related insignificantly, suggesting no meaningful association between these two factors. The conclusions drawn from the analysis are contingent on the results of the statistical tests utilized and the a priori alpha level of 0.05 selected. Based on these parameters, there are reasonable grounds to reject the null hypothesis of no correlation between Final Grade and Total Grade and conclude that a significant linear relationship exists. The correlation between GPA and Quiz 1 is negligible, implying no statistically significant association between these variables.

RSCH FPX7864 Assessment 2

The statistical test has certain limitations, and alternative explanations may exist for the results. Such constraints may include issues with sample size, sampling bias, measurement error, or the impact of confounding variables. Increasing the sample size beyond its current n=105 may be necessary to detect more subtle relationships or ensure adequate representation of the target population. Additionally, unmeasured variables may be present that account for the observed correlations (Jörg Rahnenführer et al., 2023). Furthermore, the chosen alpha of 0.05 can increase Type I errors or may be inappropriate given the research question. Thus, further studies using larger samples, randomized sampling techniques, and control of confounding variables would be needed to validate and generalize these findings.


Correlations are a fundamental tool in medical research, especially when investigating the relationships between variables that may affect patient outcomes, diagnostic precision, or treatment effectiveness in diseases like cardiac or diabetes. In the context of these medical conditions, various variables can be explored for their correlations, such as lifestyle factors, genetic predisposition, treatment methods, and patient outcomes (Waleed Noori Hussein et al., 2023).

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For instance, studying the correlation between dietary habits and blood sugar levels can help identify crucial links that inform dietary recommendations for diabetes management. Similarly, in the case of cardiac conditions, examining the correlation between physical activity levels and heart health can provide insights that guide exercise regimens for patients. By analyzing the links between specific genetic markers and the likelihood of developing these conditions, healthcare professionals and researchers can gain a deeper understanding of the genetic components that contribute to disease risk. The analysis can lead to the development of personalized treatment and prevention strategies, ensuring that patients receive the most effective interventions based on their genetic profiles (Khawaja et al., 2023). The utilization of correlations in these medical fields not only enhances our understanding of these diseases but also empowers healthcare providers to deliver more targeted and precise care to patients.


Aubinet, M. (2023, January 1). 4 – The known unknowns: Measurement techniques (A. L. Hiscox, Ed.). ScienceDirect; Academic Press.

Jörg Rahnenführer, Riccardo De Bin, Benner, A., Ambrogi, F., Lusa, L., Anne‐Laure Boulesteix, Migliavacca, E., Binder, H., Michiels, S., Sauerbrei, W., & McShane, L. M. (2023). Statistical analysis of high-dimensional biomedical data: A gentle introduction to analytical goals, common approaches and challenges. BioMed Central  Medicine, 21(1).

Khawaja, M., Siddiqui, R., Virani, S. S., Amos, C. I., Bandyopadhyay, D., Virk, H., Alam, M., Hani Jneid, & Chayakrit Krittanawong. (2023). Integrative genetic approach facilitates precision strategies for acute myocardial infarction. Genes, 14(7).

Shu, X., & Ye, Y. (2023). Knowledge discovery: Methods from data mining and machine learning. Social Science Research, 110.

Siegel, A. F., & Wagner, M. R. (2022, January 1). Chapter 3 – Histograms: Looking at the distribution of data (A. F. Siegel & M. R. Wagner, Eds.). ScienceDirect; Academic Press.

Waleed Noori Hussein, Zainab Muzahim Mohammed, & Almnaseer, Z. A. (2023). Data analysis methods for evaluating cardiovascular disease in patients. Measurement: Sensors, 25.

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