![]() L one, I'm gonna use both columns here, L one and L two but L one is the first data set and you see actually is the the exact same. Um and if you go to stat and then I'm gonna go ahead and say edit now. So I'm using a T- 84, but you can use whatever technology or the formulas if you want. So I'm using technology here just to save some time. and we're asked to find a 95% confidence interval for that Data set. So we're giving a simple data set where the population mean is 50 and the standard deviation for the population is 10. And we're gonna look at how outliers affect certain datasets whenever sample sizes are different. What effect does an outlier have on a confidence interval when the data set is large?įollowing a solution number 46. (g) Compute a $95 \%$ confidence interval for the large data set with the outlier, assuming $\sigma=10. $ Verify that this observation is an outlier. (f) Suppose the last observation, $41,$ is inadvertently entered as $14. What effect does increasing the sample size have on the confidence interval? (e) Compute a $95 \%$ confidence interval for the large data set, assuming $\sigma=10. Verify that the sample mean for the large data set is the same as the sample mean for the small data set. Suppose the following small data set represents a simple random sample from a population whose mean is 50 and standard deviation is $10. Increase the confidence level to 99% observe yourĦ months, 1 week ago paste full screen screenshot Increase the confidence level to 95% observe your ![]() Continue with a 90% Confidence Level, “# of Simulations” atġ00 and a moderate sample size between 30 and 100. (1 point) How does the confidence level affect your Increase your sample size to 1000 observe the width of yourĮ. Increase the sample size to something between 30 and 100 Choose a smaller sample size between 2 and 10 observe the (1 point) How does sample size affect your confidenceĬontinue with a 90% Confidence Level and “# of Simulations” at Optional: Perform the previous steps using confidence levels 95%ĭ. Consider the placement of the sample mean in the sampling Of the intervals that do contain the parameter? Where are their sample means with respect to the sample means Is there a common feature from the intervals that do not Sample mean (dot in center of interval) to see it’s value and the The intervals that don’tĬontain the true mean are indicated in red. (1 point) What type of sample will fail to capture the true Theoretically,ĩ0% of the sample means we obtain should result in an interval thatĬontains the true parameter. (1 point) Increase “# of Simulations” to 1000. (1 point) Does your 90% confidence interval contain the trueī. Sample to estimate a parameter form that sample.Ī. Most similar to what we do “in the real world”. This means you are just taking 1 sample of n = 15. Change Sample Size to 15 and “# of Simulations” to 1.ģ. Start with a 90% confidence interval and the population forĢ. Getting Started: Go to the Simulation in Lesson 25 in the Week 4ġ. The goal of this simulation is to visualize and validate the Indicates how many confidence intervals obtain the true mean. Population, calculates a confidence interval for each sample and We hope we get one of the “good” intervals. Therefore, we assumeĪ 5% risk we might get an interval that does not contain the true Intervals will not contain the true parameter. This means when we perform a 95% confidence interval 5% of all The true parameter value 95% of the time. Possible samples of size n taken from the population, theĬonfidence intervals calculated based on those samples will contain The definition of a 95% confidence interval states: Out of all To be and how willing we are to risk not obtaining the parameter at WeĬhoose a confidence level based on how precise we need our estimate Standard error of the statistic and a level of confidence. AĬonfidence interval is comprised of an estimate from a sample, the The goal of aĬonfidence interval is to estimate an unknown parameter.
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