RSCH FPX 7864 Assessment 2 Correlation Application and Interpretation

RSCH FPX 7864 Assessment 2 Correlation Application and Interpretation

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Debra

Capella University

RSCH FPX 7864

Prof.

Dec, 2024

Data Analysis Plan

RSCH FPX 7864 Assessment 2 Correlation Application and Interpretation the Quiz 1 large modern-day performance and GPA, and is interested in understanding what explains any inclined correlation exhibited. Examining measures of skewness and kurtosis assists the study in confirming whether variables exhibit normal distributions in order to determine the features of the data pattern.

Data Analysis Plan

Variables in the Analysis

• Quiz1 (sort of correct solutions): non-prevent
• GPA (previous grade difficulty commonplace): non-forestall
• substantial (stylish range of variables attained in beauty): non-forestall
• very last (very last test: extensive type of exact answers): non-forestall.

Total-Final Correlation

• Research Question: Is there a gigantic correlation between the whole variety of things attained in beauty and the last test score?
• Null Hypothesis (H₀): There can be no huge correlation between the enormous amount of things learned and acquired in elegance and the final test score.
• Alternate Hypothesis (HA): There can be an enormous correlation between most of the total number of items learned and acquired in elegance and the final test.

GPA-Quiz 1 Correlation

• Research Question: Is there a gigantic correlation between GPA and correct answers for Quiz 1?
• Null Hypothesis (H₀): There may not be a high correlation between GPA and the right responses for Quiz 1.
• Alternate Hypothesis (HA): There would be a high-quality Correlation between GPA and the size of the correct answers on Quiz1.

RSCH FPX 7864 Assessment 2 Correlation Application and Interpretation

Testing Assumption

The normality test screened skewness and kurtosis of the four most significant variables: renowned GPA, very last tests, Quiz 1, and renowned beauty variables. Skewness and kurtosis measures of -2 and +2 are typically assumed to represent a normal distribution. GPA skewness was also previously found to be -zero.851, a minor left skew in that the lower the GPA, the more typical it is in comparison to higher GPAs. The equal was as fast as it increasingly got established in the large class scores, whose skewness was 0.757, reflecting a minimal left skew (Iakovlev & Utochkin, 2023). Scores on the final exam and Quiz 1 reflected skewness of -0.341 and -0.220, respectively, which are minimal from symmetry. For kurtosis, the values were 0.162 for Quiz 1, -0.688 for GPA, 1.146 for fashionable factors, and -0.277 for the final exam. The terrible kurtosis of GPA and the very last exam indicate a flat distribution, and the stunning kurtosis of trendy factors indicates a more peaked top (Iakovlev & Utochkin, 2023). since measures of skewness and kurtosis of all variables are in the range of -2 to +2, the data are according to the normality assumption, and thus its software implementation is valid in inferential statistical analysis.

Results and Interpretation

The correlations between the four variables, GPA, Quiz 1, massive beauty grades, and final examination marks, have been investigated through a Pearson correlation matrix. The discovery found an unprecedentedly high-quality immoderate correlation between very last exam marks and preferred marks, Pearson correlation coefficient (r) = 0.88, degrees of freedom (df) = 103, and the wildly great p-value hundreds loads a good deal lower than 0.001. The finding r(103) = 0.88, p < .001, signifies rejection of the null hypothesis (H₀), with statistical significance of the relationship having been established prior to this. By chance, the correspondence between GPA and Quiz 1 previously was as fast as soon was awful. The Pearson statistic of Correlation (r) was as quickly once as soon as zero.152, p-price zero.121, and 103 levels of freedom, previously being as quickly as soon as stated as r(103) = zero.152, p = zero.121. because the price was greater than 0.05, the null speculation once as soon as rapidly was no longer discredited, meaning that GPA no longer predicts Quiz 1 normal performance significantly in the dataset. Even with a sample of 100 and five, the implications indicate an over-differentiation: conventional grades are alternatively predictive of high final exam success, while at the same time, GPA has little relationship with Quiz 1 average number one general performance.

Statistical Conclusions

They have a look at examined four variables using the method of mission, a Pearson correlation test for GPA with Quiz 1 ranks, and traditional beauty grades and final examination scores. The GPA and final examination rating distribution were near-normal, with very low, horrific kurtosis and skewness values. The overall elegance scores and last test score correlations have also been primarily exemplary, as the Pearson correlation measure provided 0.88 against the background, the rate at the same time as staying p < .001 significant [r(103) = 0.88, p < .001]. The study identifies that high cumulative grade average performance enhances improved exam results that significantly predict the very last test average standard overall performance (Garren & Osborne, 2021). The GPA to Quiz 1 score date previously used to be as soon as quickly as quickly as, but sensitive because the detail coefficient became 0.152 and the p-value became zero.121 [r(103) = 0.152, p = 0.121]. The reason the p-fee is better than the 0.05 statistical significance level indicates that GPA cannot forecast male or female quiz results based on the tremendously excessive sample duration (Garren & Osborne, 2021). The dimension of findings is closer to a dramatic disparity in the correlation voltage, which observes that tools of contemporary easy daily common overall performance are probably another precise indicator of educational achievement over GPA through the method itself. 

Limitations

One such critical restriction is the pattern period; if the pattern period is small enough, it will lower the energy of the check, increasing the likelihood of failing to detect a recent impact and limiting the overall ability of the results (Janse et al., 2021). There is a certain strange difficulty in the guise of statistical sensitivity of the sample size; statistically huge results are achievable even with big samples, although the sensible significance is extremely small. University college students may misinterpret the data due to the fact that the measuring instrument is sensitive (Janse et al., 2021).

Statistical inference is nonsense at the same moment; test assumptions like normality and independence fail to hold. The results that are obtained are not accurate in defining the right relationships between study variables simultaneously because there can be a violation of the findings’ assumptions (Janse et al., 2021). The validity of research results is based on the management of extraneous and unknown variables that would determine correlations among study variables (Janse et al., 2021). The variables ought to provide a reason for correlations instead of the direct agency hypothesized.

Application

Thus, RSCH FPX 7864 Assessment 2 Correlation Application and Interpretation myology that depends on an assessment of correlations to determine the link between muscle agencies and muscle electricity. The two variables are of great significance for muscle assessment, particularly in sarcopenia conditions, where with rising age, there is muscle atrophy and energy decline. Significant muscle mass has a great impacts on both outstanding because more muscle mass enhances electricity production. In-depth facts of the relationship are beneficial to clinical assessment, sarcopenia evaluation, and treatment, as well as to affected men or women-based comprehensive rehabilitation programs for muscle atrophy disorders (Hayashida et al., 2024). The physically preferred stylish common performance of the elderly in big things lies at the cost on which their muscles become weak because of age due to functional and clothing declines. The understanding of age-related correlations between strength and muscle parameters allows developers to develop age-adjusted exercise physical video games and rehabilitation programs for better guarding of muscle groups among older adults (Trombetti et al., 2021). The established relationships each characterize the individual or woman of muscle wasting with increasing age and present the facts required to organize redress for better mobility and a more wonderful life.

References 

Garren, S. T., & Osborne, K. M. (2021). Robustness of t-test based on skewness and kurtosis. Journal of Advances in Mathematics and Computer Science, 9(3), 102–110. https://doi.org/10.9734/jamcs/2021/v36i230342 

Hayashida, I., Tanimoto, Y., Takahashi, Y., Kusabiraki, T., & Tamaki, J. (2024). Correlation between muscle strength and muscle mass, and their association with walking speed, in community-dwelling elderly Japanese individuals. Public Library of Sciences ONE, 9(11), 5–7. https://doi.org/10.1371/journal.pone.0111810 

Iakovlev, A. U., & Utochkin, I. S. (2023). Ensemble averaging: What can we learn from skewed feature distributions? Journal of Vision, 23(1), 5–16. https://doi.org/10.1167/jov.23.1.5 

Janse, R. J., Hoekstra, T., Jager, K. J., Zoccali, C., Tripepi, G., Dekker, F. W., & van Diepen, M. (2021). Conducting correlation analysis: Important limitations and pitfalls. Clinical Kidney Journal, 14(11), 2332–2337. https://doi.org/10.1093/ckj/sfab085 

Mittal, R. (2019). Multivariate regression predictive modeling in analyzing student performance: A data mining approach. Journal of Computational and Theoretical Nanoscience, 16(10), 3–7. https://doi.org/10.1166/jctn.2019.8526 

Trombetti, A., Reid, K. F., Hars, M., Herrmann, F. R., Pasha, E., Phillips, E. M., & Fielding, R. A. (2021). Age-associated declines in muscle mass, strength, power, and physical performance: Impact on fear of falling and quality of life. Osteoporosis International, 27(2), 463–471. https://doi.org/10.1007/s00198-015-3236-5

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