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Data Analysis: Hypothesis Testing
\nUse the Sun Coast Remediation data set to conduct a correlation analysis, simple regression analysis, and multiple regression analysis using the correlation tab, simple regression tab, and multiple regression tab respectively. The statistical output tables should be cut and pasted from Excel directly into the final project ( help with nursing paper writing from experts with MSN & DNP degrees) document. For the regression hypotheses, display and discuss the predictive regression equations if the models are statistically significant. Delete instructions and examples highlighted in yellow before submitting this assignment.
\nCorrelation: Hypothesis Testing
\nRestate the hypotheses from Unit II here.
\nExample:
\nHo1: There is no statistically significant relationship between height and weight.
\nHa1: There is a statistically significant relationship between height and weight.
\nEnter data output results from Excel Toolpak here.<\/p>\n
Interpret and explain the correlation analysis results below the Excel output. Your explanation should include: r, r2, alpha level, p value, and rejection or acceptance of the null hypothesis and alternative hypothesis.
\nExample:
\nThe Pearson correlation coefficient of r = .600 indicates a moderately strong positive correlation. This equates to an r2 of .36, explaining 36% of the variance between the variables.
\nUsing an alpha of .05, the results indicate a p value of .023 < .05. Therefore, the null hypothesis is rejected, and the alternative hypothesis is accepted that there is a statistically significant relationship between height and weight.
\nNote: Excel data analysis Toolpak does not automatically calculate the p value when using the correlation function. As a workaround, the data should also be run using the regression function. The Multiple R is identical to the Pearson r in simple regression, R Square is shown, and the p value is generated. Be sure to show your results using both the correlation function and simple regression function.
\nSimple Regression: Hypothesis Testing
\nRestate the hypotheses from Unit II here.
\nHo2:
\nHa2:
\nEnter data output results from Excel Toolpak here.
\n\tInterpret and explain the simple regression analysis results below the Excel output. Your explanation should include: multiple R, R squared, alpha level, ANOVA F value, accept or reject the null and alternative hypotheses for the model, statistical significance of the x variable coefficient, and the regression model as an equation with explanation.
\nMultiple Regression: Hypothesis Testing
\nRestate the hypotheses from Unit II here.
\nHo3:
\nHa3:
\nEnter data output results from Excel Toolpak here.
\n\tInterpret and explain the simple regression analysis results below the Excel output. Your explanation should include multiple R, R squared, alpha level, ANOVA F value, accept or reject the null and alternative hypotheses for the model, statistical significance of the x variable coefficients, and the regression model as an equation with explanation.<\/p>\n
References
\nInclude references here using hanging indentations. Remember to remove this example.
\nCreswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE.<\/p>\n","protected":false},"excerpt":{"rendered":"
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