# Inferential Statistics

Inferential Statistics
Tiffany Stoner
QNT/561
November 10th, 2014
Alfred Siu

Inferential Statistics
Research Question
What can Parkside Lab do to avoid a mass of amounts of late payments while implementing the new system of ICD 10?
Data for the Independent and Dependent Variable
The data includes unpaid invoices from random healthcare providers.   The data incudes a total of 73 unpaid invoice 49 of the invoice are from health care providers still using the ICD 9. The remaining 23 invoices are from health care providers using the newly implemented ICD 10.
Appropriate tool to test Hypothesis
The T test is the tool used. It is a tool that can compare the mean of both ICD 9 and ICD 10.   This tool can shows us where the average unpaid invoices are for each coding.   In addition, it will tell us the difference between the two and we can see how the ICD 10 is doing compared to the ICD 9.   It will be a two tailed test since we are trying to find if the two mean are different from each other.   See Appendix A. The T test equals -7.750.
Confidence Interval
The hypothesis test with a 95% confidence interval. The confidence interval is from - 56. 343 to - 33.274.   Looking at the difference of the two sample means which is -44. 81. This number lies in the range of the confidence interval.   We can be confident that there is a 95% chance the difference between the two mean lies in the range.   The t test of -7.750 also lies in the ranges.   See Appendix A.

Results
Inferential statistic is taking a small sample of a population and trying to make assumption of the population as a whole. Though the hypothesis test we are testing the data of the unpaid ICD 9 invoices and ICD 10 invoices, then comparing them to each other. This is so we can see the difference in the mean and test the uncertainty of the coding through the confidence interval. The results show that the difference of the means at -44. 81 and the t test of -7. 750 both are inside of the...