Question

Initial Post Instructions

Thinking of the many variables tracked by hospitals and doctors' offices, confidence intervals could be created for population parameters (such as means or proportions) that were calculated from many of them. Choose a topic of study that is tracked (or that you would like to see tracked) from your place of work. Discuss the variable and parameter (mean or proportion) you chose, and explain why you would use these to create an interval that captures the true value of the parameter of patients with 95% confidence.

Consider the following:

How would changing the confidence interval to 90% or 99% affect the study? Which of these values (90%, 95%, or 99%) would best suit the confidence level according to the type of study chosen? How might the study findings be presented to those in charge in an attempt to affect change at the workplace?

Answer 1

ANSWER::

The below dataset is taken from a medical website. The data consists Standing/Walking time (minutes per day) spent by Two types of people (Lean and Obese)

Lean	Obese
516.10	262.24
612.93	469.76
314.21	371.14
589.64	410.67
575.87	345.38
543.39	421.53
676.19	353.65
552.66	272.34
379.83	415.63
505.70	423.36

From the given data:

	n	Mean	Std Dev	Std Error
Lean	10	526.65	107.70	34.06
Obese	10	374.57	67.50	21.34

Let Group 1 be Lean and Group 2 be Obese

Assuming, the population std deviation is not same.

Alpha = 0.05,

Zcritical = 1.96

95 % CI for Diff of Means = (x1-x2) +/- ZCritical * (s12/n1 + s22/n2)1/2 = {73.30, 230.86}

By Changing the confidence interval to 90%, the confidence interval will become narrower as ZCritical for 90% is 1.64

By Changing the confidence interval to 99%, the confidence interval will widen as ZCritical for 99% is 2.57

99% CI would be best suitable for the study as there will be 99% probability that the true population mean will lie in the interval.

Null and Alternate Hypothesis

H0: µ1 = µ2

H0: µ1 > µ2

Alpha = 0.05

Test Statistic

t = (x1-x2)/(s12/n1 + s22/n2)1/2

= 3.784

P-value = TDIST(3.784,10+10-2,1) = 0.000679785

Result/Conclusion

Since the p-value is less than 0.05, we reject the null hypothesis ie Lean people spend more time standing/walking than Obese people.

Hence, if obsess people want to reduce their weight, they should spend more time walking

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