Correlation and Regression Analysis
Instructions
Correlation and Regression Analysis Using Sun Coast Data Set
Using the Sun Coast data set, perform a correlation analysis, simple regression analysis, and multiple regression analysis, and interpret the results.
Please follow the Unit Scholarly Activity template to complete your assignment.
You will utilize Microsoft Excel ToolPak for this assignment.
Example:
Correlation Analysis
Restate the hypotheses.
Provide data output results from Excel Toolpak.
Interpret the correlation analysis results
Simple Regression Analysis
Restate the hypotheses.
Provide data output results from Excel Toolpak.
Interpret the simple regression analysis results
Multiple Regression Analysis
Restate the hypotheses.
Provide data output results from Excel Toolpak.
Interpret the multiple regression analysis results.
The title and reference pages do not count toward the page requirement for this assignment. This assignment should be no less than two pages in length, follow APA-style formatting and guidelines, and use references and citations as necessary.
Data analysis: Correlation and Regression
Name
University
Course
Instructor
Date
Data Analysis: Hypothesis Testing
Hypothesis testing is a type of inferential statistics that helps a researcher make conclusions about a population using sample data. This technique tests a claim about a population parameter based on representative sample data (Emmert-Streib & Dehmer, 2019). This project used the Sun Coast Remediation dataset to carry out correlation, simple regression, and multiple regression hypothesis testing to test relationships between variables. The analysis was carried out using the data analysis Toolpak in Excel.
Correlation: Hypothesis Testing
Correlation is used to test associations between two variables. This project conducted correlation analysis to establish the relationship between employees’ health and particulate matter size. The data used was from one hundred and three job sites. The hypotheses were formulated as follows:
H01: There is no statistically significant relationship between mean sick days among the employees and the number of microns
Ha1: There is a statistically significant relationship between mean sick days among the employees and the number of microns
The table below shows the results of the analysis from Excel
Microns
Mean Annual sick days
Microns
1
Mean Annual sick days
-0.715984185
1
Table SEQ Table \* ARABIC 1: Correlation Table
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.71598
R Square
0.51263
Adjusted R Square
0.50781
Standard Error
1.32778
Observations
103
ANOVA
df
SS
MS
F
Significance F
Regression
1
187.29532
187.29532
106.23618
1.891E-17
Residual
101
178.06390
1.76301
Total
102
365.35922
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
10.0814
0.31516
31.98865
1.169E-54
9.45626
10.70663
microns
-0.5224
0.05068
-10.30709
1.891E-17
-0.62291
-0.42184
Table SEQ Table \* ARABIC 2: Regression Table
Table 1 above shows a Pearson correlation coefficient of r = -0.7160. This indicates a strong negative correlation between the two variables: a decrease in the size of the microns of particulate matter increases the mean sick days. This equates to an r2 of 0.5126, explaining 51.26% of the variance between the two variables. The results indicate a p-value of 1.169 x 10-54, which is less than the alpha level of 0.05. Therefore, the null hypothesis is rejected, and the ...
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