Statistical Reasoning for Health Sciences: Confidence Intervals & Sampling Methods, Lab Reports of Mathematics

Download Statistical Reasoning for Health Sciences: Confidence Intervals & Sampling Methods and more Lab Reports Mathematics in PDF only on Docsity!

lOMoARcPSD| Week 7 Assignment: Lab XXXXXX MATH225N-10470: Statistical Reasoning for the Health Sciences XXXXXXX

The two articles listed below were sourced using the link provided by the Chamberlain Library.  First Article: Confidence Intervals, Part 1  Second Article: Confidence Intervals, Part 2 A confidence interval (CI) is the range of values thought to encompass the population value under study (El-Masri, 2012). This indicates that the true mean of the population is within the lower and higher boundaries of the confidence interval (CI) if a sample of data were to be taken, plotted on a table or graph, averaged, and the CI determined. The range of confidence can be 90%, 95%, or 99%. If the CI is large, such as the 90% CI, this may indicate that insufficient data were collected or that you are not as certain that the sample of data you have is truly representative of the mean. Since there is less room for mistakes when the CI is narrow, it is more accurately able to represent the population mean because it is so near to the sample set's actual mean value. It is more typical to utilize 95% CI in the health sciences. Other studies choose to calculate its statistical significance using the mean of two CIs. As a result, rather than focusing only on two distinct confidence intervals and their boundaries, they would take the mean of two CI; if the CI ranges overlap, the difference in the mean value is not statistically significant. However, the mean value is statistically significant if there is no overlap. Sampling Method Used The systematic approach of sampling was employed for this study. The participants' height data were determined using this procedure. Advantages

A parameter's point estimate is a single value estimate. A sample mean, for example, is a point estimate of the population mean. A parameter's predicted range of values is provided by an interval estimate. The most typical kind of interval estimate is one with a confidence interval. So, in this case, the point of estimate for the average height of all the people is 68.20.

The true mean of the measured heights has a 95% confidence interval, and according to the spreadsheet above, the lower limit is 66.979573 inches, and the upper limit is 69. inches. The genuine mean of the 20 heights falls between 66.979573 inches and 69. inches, and I can be 95% positive for this. In other words, the heights of the 20 individuals fall inside the range for 95% of them.

References El-Masri, M. (2012). Coonfidence intervals: part 1. Canadian Nurse. 108(2). 8. https://web-b- ebscohostcom.chamberlainuniversity.idm.oclc.org/ehost/pdfviewer/pdfviewer? vid=3&sid=bdee4f5c-ce36-4b26-8400-70337d1b0dd9%40sessionmgr