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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 Unit II here.

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:

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 Unit II here.

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 Unit II here.

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.

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.

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4

Insert Title Here

Insert Your Name Here

Insert University Here

Course Name Here

Instructor Name

Date

Data Analysis: Hypothesis Testing

Use the Sun Coast Remediation data set to conduct an independent samples t test, dependent samples (paired samples) t test, and ANOVA using the independent samples tab, paired samples tab, and ANOVA tab in the Sun Coast data file. The statistical output tables should be cut and pasted from Excel directly into the final project document. Delete instructions and examples highlighted in yellow before submitting this assignment.

Independent Samples t Test: Hypothesis Testing

Restate the hypotheses from Unit II here.

Ho4:

Ha4:

Provide data output results from Excel Toolpak here.

Interpret and explain the independent samples t test results below the Excel output here. Include alpha level, p value, and accept or reject the null and alternative hypotheses.

Example:

Ho4: There is no statistically significant difference in mean values for the DV between Group A (IV1) and Group B (IV2).

Ha4: There is a statistically significant difference in mean values for the DV between Group A (IV1) and Group B (IV2).

The results indicate that the mean values are lower for Group A; however, the results also indicate a p value of .37627 > .05. Therefore, the null hypothesis is accepted that there is no statistically significant difference in mean values of the DV between Group A (IV1) and Group B (IV2).

Dependent Samples (Paired Samples) t Test: Hypothesis Testing

Restate the hypotheses from Unit II here.

Ho5:

Ha5:

Provide data output results from Excel Toolpak here.

Interpret and explain the dependent t test results below the Excel output here. Include alpha level, p value, and accept or reject the null and alternative hypotheses.

ANOVA: Hypothesis Testing

Restate the hypotheses from Unit II here.

Ho6:

Ha6:

Provide data output results from Excel Toolpak here.

Interpret and explain the ANOVA results below the Excel output here. Include alpha level, p value, and accept or reject the null and alternative hypotheses.

References

Include references here using hanging indentations.

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE.