(in bold black is my 1st half of my answers that the teacher confirmed is correct. I just need the 2nd half.)

2. Interpret the results of the data analysis by doing the following:

b. Discuss the significance of the independent variable(s) with support from your linear regression analysis results.

The significance is my P-value. My p-value for my program participation rate (x) is 1.6E-10.

xxXX my teacher says the last thing i need to add to this answer is that i need to compare to the P-value of 0.05. I dont know how . XXxx

c. Create the linear equation and explain its purpose using your analysis results.

My linear equation is this y=-0.0891x +5.24. The purpose of a linear equation is create the estimated regression line.

xxXX my teacher says the last thing i need to add to this answer is that i need to predict XXxx

xxXX lastly she gave me a hint with these sample paragraphs :

• herefore, thep-value is less than 0.05 and this is a significant result (the null is rejected and there is a significant relationship betweenXandY).
• A p-value measures the probability of obtaining the observed results, assuming that the null hypothesis is true.The lower the p-value, the greater the statistical significance of the observed difference. P-value can serve as an alternative toor in addition topreselected confidence levels for hypothesis testing XXxx

------- so i just need the 2nd part of the answer on 2 b and 2c ---------

If you needed to look at it ......

-------------below is the entire paper i have so far : -------------

C207 Task 1 Paper

Western Governors University

1. Is this program worth the cost?A business question that could be answered from this would be:

Does the program participation SIGNIFICANTLY affect the attrition rate)?

B. 1....eachof the following:

The independent variable(s) would be the wellness program participation (%), aka thex axis.

• The dependent variable would be the nurse attrition rate % which is the Y axis.
• The type of datawould be the attrition rate % of nurses leaving and the % people enrolling into the program % .I have percentages and they are ratio data.
• The quantity of data would be 36 months of data points representing each month.

2. For the chart title, legend, axis titles, &data intervals.... please see my attached excel file for the line fit plot. The line fit plot is the graphical display.

C.1.

Please see my attached excel file for the summary output table.

2. I feel like regression analysis seems appropriatehere because the data contains a single independent and dependent variable. You can assess the relationship between the program and attrition rate due to the continuous data points over a 3 year period.

D. 1

NULL: There is no significant relationship between the nurse attrition rate and nurse participation.

2.a

R squared is my goodness of fit. In my analysis, I found the r-squared

to be this .7042. R-squared is a measure of how closely the data in a

regression line fit the data in the sample. Because it is between 0 and 1, my result is closer to 1 so it's a better fit. If the r-squared were closer to 0, it would indicate that the regression line did not fit the data at all. Because it is closer to 1, this would mean the better.

2.b

The significance is my P-value. My p-value for my program participation rate (x) is 1.6E-10.

e is shorthand scientific notation, short for x10^. So, your p-value is 1.727 x 10^-5, or .00001727. If the test you used to derive this p-value was used correctly, then it means there is a 0.001727% chance that data as or more extreme than what you observed was generated under the null hypothesis, suggesting that the alternative hypothesis is a better explanation for your data. If the test you used to derive this value was not applied correctly,

COMPARE P-VALUE TO 0.05!

c. My linear equation is this y=-0.0891x +5.24. The purpose of a linear equation is create the estimated regression line.

PREDICT

3.A limitation of this research that could affect a recommended

course of action could be because this zeros in on a few

observations over a given period. For example, what if the

reasons for attrition included other factors such as lack of career

growth, the lack of custom or even rotating shift schedules, or even

the pay? In today's times, the financial crunch is felt everywhere from

the grocery store to the gas pump. For example, cost of living

adjustment raises have been necessary but not all jobs have offered

them. These are all possible factors not reflected in this research that

could affect the course of action because they could be external to

the wellness program.

4. It appears that the wellness program is having a positive effect

over time as the attrition rate % appears to be going down. We should

increase the participation (x) because we want the attrition rate % (Y)

to go down. If we have more participants in the wellness program, we

should see better results. It would be reasonable to keep the program

going.