1 / 8

6.1 Confidence Intervals for the Mean (  known )

6.1 Confidence Intervals for the Mean (  known ). Key Concepts: Point Estimates Building and Interpreting Confidence Intervals Margin of Error Relationship Between Confidence Level and Precision Determining the Minimum Sample Size. 6.1 Confidence Intervals for the Mean (  known ).

jbartholomew
Télécharger la présentation

6.1 Confidence Intervals for the Mean (  known )

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 6.1 Confidence Intervals for the Mean ( known) • Key Concepts: • Point Estimates • Building and Interpreting Confidence Intervals • Margin of Error • Relationship Between Confidence Level and Precision • Determining the Minimum Sample Size

  2. 6.1 Confidence Intervals for the Mean ( known) • A simple random sample of 20 recent U.S. weddings yielded the following data on wedding costs, in dollars: • Use the data to obtain a point estimatefor the population mean wedding cost of all recent U.S. weddings. • If we assume wedding costs are normally distributed with σ = $8100, find the 95% confidence intervalfor the mean cost of all recent U.S. weddings.

  3. 6.1 Confidence Intervals for the Mean ( known) • What exactly is a point estimate? • a single value estimate for a population parameter. • If we want to estimate a population mean, the best statistic to use is the sample mean. • Since point estimates are single values, it is unlikely they will actually equal the population parameter. • It would be better to build an interval estimate for the parameter.

  4. 6.1 Confidence Intervals for the Mean ( known) • Confidence Intervals • Review the Sampling Distribution of Sample Means, the Central Limit Theorem, and the Empirical Rule. • General Form of a z-Interval Estimate for µ: • Let’s go back to the U.S. Weddings example to see how this all works.

  5. 6.1 Confidence Intervals for the Mean ( known) • What do we mean by sampling error? • Sampling error is defined as the difference between the point estimate and the population parameter. • Since we do not know the population parameter, we look for a maximum value of our sampling error. We call that maximum value the Margin of Error or the Maximum Error of our estimate. Haven’t we seen this before?

  6. 6.1 Confidence Intervals for the Mean ( known) • We can re-express our confidence interval using margin of error notation:

  7. 6.1 Confidence Intervals for the Mean ( known) • Practice working with confidence intervals: #36 p. 306 (Sodium Chloride Concentration) • Note how the higher confidence level resulted in a wider interval. The amount of precision in our estimate drops as we increase the confidence level. There is an inverse relationship between precision and level of confidence. #38 p. 306 (Repair Costs: Refrigerators)

  8. 6.1 Confidence Intervals for the Mean ( known) • If we’re told what level of confidence to use and the maximum amount of sampling error that will be tolerated in a study, how do we determine the appropriate sample size? Note: if σ is unknown, we can estimate it using the sample standard deviation, s, as long as n ≥ 30. #52 p. 308 (Water Dispensing Machine)

More Related