Correlation and Regression Analysis

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Data Analysis: Hypothesis Testing

Use 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 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.

Correlation: Hypothesis Testing

Restate the hypotheses from document uploaded (ref:

29005302WK093921NResearchObjective.docx)

Example:

Ho1: There is no statistically significant relationship between height and weight.

Ha1: There is a statistically significant relationship between height and weight.

Enter data output results from Excel Toolpak here.

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.

Example:

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The 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.

Using 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.

Note: 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.

Simple Regression: Hypothesis Testing

Restate the hypotheses from document uploaded (ref:

Ho2:

Ha2:

Enter data output results from Excel Toolpak here.

Interpret 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.

Multiple Regression: Hypothesis Testing

Restate the hypotheses from from document uploaded (ref:

29005302WK093921NResearchObjective.docx)

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Ho3:

Ha3:

Enter data output results from Excel Toolpak here.

Interpret 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.

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References

Include references here using hanging indentations. Remember to remove this example.

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed

methods approaches (5th ed.). SAGE.