CA 2 - Worksheet Revised beth

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CUNY Queensborough Community College *

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Medicine

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Feb 20, 2024

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CA # 2 Summer 2023 ANOVA and Correlation and Regression The emotion, frustration, is most often associated with the public display of aggression. Moreover, frustration and disappointment are often experienced when applying for acceptance to Colleges and Universities. The Socioeconomic Disadvantage (S.E.D) scale, developed at the University of California, Davis, School of Medicine, was designed to evaluate applicants based on their life circumstances, including family income and parents’ education. The aim of this scale is to increase the levels of diversity at institutions such as UC Davis’s School of Medicine. The scale is used in the Admissions process. During, and after, the admission process, in addition to dealing with feelings of Frustration/Aggression, applicants also experience feelings of Personal Relative Deprivation (P.R.D), which is conceptualized as being prevented from achieving a goal. The following fictional data, presented below, should be used to answer the following questions. 1.What role did Feedback (0 = Rejection; 1 = Acceptance; 3 = Pending) play in feelings of Frustration/Aggression during the admission process? Use a One-Way ANOVA to analyze the data. Include ANOVA Summary Table in your submission. Descriptive Statistics Descriptive Statistics Aggression 0 1 2 Valid 12 13 11 Missing 0 0 0 Mean 38.667 34.769 36.000 Std. Deviation 5.852 11.330 8.258 Minimum 29.000 13.000 20.000 Maximum 46.000 48.000 45.000 ANOVA ANOVA - Aggression Cases Sum of Squares df Mean Square F p Feedback 97.915 2 48.957 0.622 0.543 Residuals 2598.974 33 78.757 1

ANOVA - Aggression Cases Sum of Squares df Mean Square F p Note. Type III Sum of Squares According to the results, there was no statistically significant relationship between aggressive feedback. Hence, emotions of Aggression throughout the Admissions Process are Not Significantly Influenced by Feedback. 2. What happens when your parents’ highest level of education (1= H.S.; 2 = College; 3 = Graduate) is as an additional factor in your Frustration/Aggression at the admission process? There were no statistically significant differences between the main effects of education and aggressive behavior. Furthermore, there was no statistically significant association between education and aggressive behavior. As a result, the highest level of education attained by parents has little bearing on Frustration/Aggression throughout the Admissions Process. 3.Use a Two-Way ANOVA to analyze the data. Include ANOVA Summary Table in your submission. a. How are S.E.D, Personal Perception of Deprivation, Family Income, and Aggression (inter)related to/with the Admission Process? Correlation Pearson's Correlations Variable S. E. D Per.Rel. Dep. Incom e Aggression 1. S. E. D Pearson's r — p-value — 2. Per. Rel. Dep. Pearson's r -0.174 — p-value 0.309 — 3. Income Pearson's r -0.548 0.383 — p-value < .001 0.021 — 4. Aggression Pearson's r -0.030 0.109 0.045 — p-value 0.861 0.526 0.795 — S.E.D, Personal Perception of Deprivation, Family Income, and Aggression (inter)related to/with the Admission Process 2

3. Use Regression , then Classical, Correlation to analyze the relationships among the above- mentioned four variables. Select “Flag significant correlations. Are there any significant correlations? What do these significant correlations show? Correlation Pearson's Correlations Pearson's r p S. E. D - Per. Rel. Dep. -0.174 0.309 S. E. D - Income -0.548*** < .001 S. E. D - Aggression -0.030 0.861 Per. Rel. Dep. - Income 0.383* 0.021 Per. Rel. Dep. - Aggression 0.109 0.526 Income - Aggression 0.045 0.795 * p < .05, ** p < .01, *** p < .001 It shows the correlations the correlations between Aggression and the other variables are not statistically significant. The significant correlations (p < 0.5) are not observed among the mentioned variables. b. Use the same open data set to run two separate Regressions. Use S.E.D, and P.R.D, as dependent variables, and Income as Covariate. Paste the Coefficient Tables below. Linear Regression Model Summary - S. E. D Mode l R R² Adjusted R² RMSE H ₀ 0.000 0.00 0 0.000 23.982 H ₁ 0.548 0.30 0 0.280 20.355 ANOVA Model Sum of Squares df Mean Square F p H ₁ Regression 6042.281 1 6042.281 14.583 < .001 Residual 14087.275 34 414.332 Total 20129.556 35 Note. The intercept model is omitted, as no meaningful information can be shown. 3

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