# RSCH FPX 7864 Assessment 2 Correlation Application and Interpretation ## Data Analysis Plan

RSCH FPX 7864 Assessment 2 Correlation Application and Interpretation

The main variables in this study are the Final grade, the GPA, the Quiz1 score, and the Total grade. The purpose of this research is to synthesize the information recorded by teachers throughout all three modules of the program, including student demographics, performance on formative assessments, and test results. The question we’ll be trying to answer in this study is whether or not students’ final grades tend to correlate with their grade point average. Sometimes referred to as “quantitative variables,” continuous variables may be measured in a variety of ways (Sneyd et al., 2022). Unlike discrete variables, continuous variables may take on an endless range of values. The ultimate is the response variable; GPA is the predictor variable; the student’s sexual identity is the nominal variable; and GPA is absolute and proportional. Given an alpha of 0.05, the specified sample size (N) is 105. The study’s research question, null and alternate hypotheses for total-final correlation are shown below.

• Research question: Is there any significant relationship between the total grade and the final grade of the students?
• The null hypothesis (Ho) is: There is absence of a linear correlation between the total and final grades of students.
• The alternative hypothesis (HA) is: There is a linear correlation between the total and final grades of the students.

The study’s research question, null hypothesis and alternate hypothesis for gpa-quiz1 correlation are shown below.

• Research question: Is there any significant relationship between the gpa and quiz1 of the students?
• The null hypothesis (Ho) is: There is absence of a linear correlation between the gpa and quiz1 grades of students.
• The alternative hypothesis (HA) is: There is a linear correlation between the gpa and quiz1 grades of the students.

Testing Assumption

Figure 1: Descriptive Statistics

## RSCH FPX 7864 Assessment 2 Correlation Application and Interpretation

Above, the descriptive statistics table shows the skewness and kurtosis values for both the GPA and the final. Overall grade point average skewness is -0.220, kurtosis is -0.688, final exam skewness is -0.341, and kurtosis is -0.277. The GPA and the final are almost identically symmetrical, with skewness values between -1 and 1. The distribution of both GPA and final marks is almost symmetrical since both sets of data fall within the range of -0.5 to 0.5. Additionally, according to Sneyd et al. (2022), the descriptive statistics demonstrate that the research data follows a normal distribution. Therefore, the results conform to the expectations for normally distributed data. After looking at the data from the descriptive statistics, it’s clear that the study’s presuppositions were correct. Each parameter’s dependency on another makes it possible for the data to be used separately and compared (Sneyd et al., 2022). Thus, if the data is utilized properly, this is correct. In order to prove that the normal distribution concept is true, the analysis relies on the descriptive statistics

sheet, which shows that both ends of the bell curve hold true for the data.

## Results and Interpretation

RSCH FPX 7864 Assessment 2 Correlation Application and Interpretation

Figure 2: Correlation between variables

## RSCH FPX 7864 Assessment 2 Correlation Application and Interpretation

Given the potential influence of several factors, a number of research topics may develop as a result of analyzing statistical data. Is there a correlation between better grades and higher final scores? In addition, it is a subject worthy of investigation for the sake of this essay. This is a dilemma for the study’s null hypothesis, which holds that there is no correlation between higher GPA and better outcomes. Contrarily, the null hypothesis rejects this idea and instead proposes that higher GPAs correlate with better outcomes. This finding confirms the relationship between the two variables. Following that, alpha will settle in at .05.

The given correlation matrix reveals a minimal association between GPA and Quiz 1. The degree of independence (103), p-value (0.121), and correlation coefficient (0.152) all support this. With a sample size of 105 and an output p-value of .121 at the more stringent 0.05 threshold of significance, it is clear that we fail to reject the null hypothesis that there is no relationship between GPA and Quiz1.

The magnitude correlation in the aforementioned matrix table is highest between the end variables and the total variables. A linear relationship may be inferred from the substantial r = .88 Pearson correlation coefficient, with a p = .001, and a 103-degrees-of-freedom test. According to the tabular data and the chosen alpha level (0.05), the investigation would reject the null hypothesis. Despite the fact that there is a correlation between the final parameters and the overall features, we reject the null hypothesis (Siegel & Wagner, 2022).

## Statistical Conclusions

RSCH FPX 7864 Assessment 2 Correlation Application and Interpretation

When the correlation coefficient returns a positive number, it indicates that the value of Y increases proportionally to the change in X. (Siegel & Wagner, 2022). Here are my thoughts on the study’s findings, with an emphasis on my evaluations of the items in the inter-correlation grid table. It is important to check the assumptions upon which any descriptive studies, including correlations, are based before drawing any conclusions from them. In null hypothesis, X and Y variables are assumed to be dependent on one another. The correlation study between the students’ cumulative GPA and their final grade provides strong evidence against the null hypothesis. The findings are consistent with the alternative hypothesis; however, correlation analysis does have certain restrictions.

While there was a strong correlation between final grades and total grade, there was no correlation between GPA and Quiz1. However, the inability to determine whether the relationship is linear or nonlinear highlights a problem with the .05 threshold of significance from this viewpoint (Siegel & Wagner, 2022). Although this has its drawbacks, it does show which aspects are related to one another and which are not.

## Application

RSCH FPX 7864 Assessment 2 Correlation Application and Interpretation

The importance of correlations in psychological research cannot be overstated. Correlational studies are widely used in the fields of neuroscience and Integrated Personality Psychology due to the difficulty of duplicating or analyzing certain events in a controlled laboratory setting (Dickhaus, 2022). Instead of focusing on finding relationships between factors, researchers may instead gather data directly from individuals and customers. The data and analysis obtained allow academics to offer suggestions and play roles in establishing the effectiveness of linkages between additional components (Dickhaus, 2022). Correlation may have a big effect on shaping an individual’s capacity for productivity. In addition, it may be used to get insight into the complexities of a person’s personality. According to Dickhaus (2022), in ABA treatment, correlation is essential when advising a learner or patient on instructional strategy.

## RSCH FPX 7864 Assessment 2 Correlation Application and Interpretation

### References

Dickhaus, T. (2022). Correlation coefficients of bivariate normal distributions. Lectures on Dependency, 9-18.

https://doi.org/10.1007/978-3-030-96932-5_2

Siegel, A. F., & Wagner, M. R. (2022). Correlation and regression. Practical Business Statistics, 313-370.

https://doi.org/10.1016/b978-0-12-820025-4.00011-7

Sneyd, J., Fewster, R. M., & McGillivray, D. (2022). Continuous random variables. Mathematics and Statistics for Science, 799-828.

https://doi.org/10.1007/978-3-031-05318-4_39