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8.1b h.w: pg 482: 13, 17, 19 – 24, 27, 31, 33

Confidence Intervals Target Goal: I can use normal calculations to construct confidence intervals . I can interpret a confidence interval in context. 8.1b h.w: pg 482: 13, 17, 19 – 24, 27, 31, 33. Rate your confidence 0 – 100%. What does it mean to be within 10 pounds?.

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8.1b h.w: pg 482: 13, 17, 19 – 24, 27, 31, 33

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  1. Confidence IntervalsTarget Goal: I can use normal calculations to construct confidence intervals. I can interpret a confidence interval in context. 8.1b h.w: pg 482: 13, 17, 19 – 24, 27, 31, 33

  2. Rate your confidence0 – 100% What does it mean to be within 10 pounds? How confident (%) are you that you can ... Guess my weight within 10 pounds? . . . within 5 pounds? . . . within 1 pounds? What happened to your level of confidence as the interval became smaller?

  3. How do we construct confidence intervals? • If the sampling model of the sample mean is approximately normal, we can use normal calculations to construct confidence intervals.

  4. For a 95% confidence interval, we want the interval corresponding to the middle 95% of the normal curve. • For a 90% confidence interval, we want the interval corresponding to the middle 90% of the normal curve. • And so on…

  5. Suppose we want to find a 90% confidence interval for a standard normal curve. If the middle 90% lies within our interval, then the remaining 10% lies outside our interval. Because the curve is symmetric, there is 5% below the interval and 5% above the interval.

  6. Critical Value • Find the z-values with area 5% below and 5% above. • Thesez-values are denoted because they come from the standard normal curve, they are centered at mean 0. • We use to remind you this is a critical value not a standardized z-score that has been calculated form data!

  7. If we are using the standard normal curve, we want to find the interval using z-values. Confidence levelTail Areainvnorm (1-tail area) 90% 95% 99% 90% 95% 99% .05 .05 .025 .025 .005 .005 -1.645 1.645 -1.96 1.96 -2.576 2.576 .05 1.645 invnorm (.95) .025 1.96 invnorm (.975) .005 2.576 invnorm (.995) Find rest.

  8. Upper p Critical Value • is called the upper p critical value, with probability p lying to its right under the standard normal curve. • To findp, we find the complement of C and divide it in half, or find . • Remember that z-valuestell us how many standard deviations we are above or belowthe mean.

  9. We want to “catch” the central probability C under the normal curve. • To construct a 95% confidence interval, we want to find the values 1.96 standard deviationbelow the mean and 1.96 standard deviations above the mean, or . • Using our sample data, this is assuming the population is at least 10n (10% condition).

  10. Constructing a Level C Confidence Interval • Using our sample data, we want to find C I = • The point estimate for μ is . • The margin of error is • Note that the margin of error is a positive number, it is not an interval.

  11. Constructing a Level C Confidence Interval • State:Identify the population of interest and the parameter you want to draw a conclusion about at what level. • Plan:Choose the appropriate inference procedure. Verify the conditions for using the selected procedure. • Do: If the conditions are met, carry out the inference procedure.

  12. C I = CI = est + - margin of error • Conclude:Interpret the results in the context of the problem

  13. Ex: Video Screen Tension Assume σ = 43 mV. Construct a90 % confidence intervalfor the mean tension μ of all the screens produced on this day. • Refer to data in notes. State :Identify the population of interest and the parameter you want to draw a conclusion about at what level. • The population is all of the video terminals produced on this given day. • We want to estimate parameter μ, the meantension for all of these screens at a 90% level of confidence.

  14. Plan:Choose the appropriate inference procedure. Verify the conditions for using the selected procedure. • Since we know standard deviation, we should use a one sample z confidence interval for the population to estimate μ. Ck: 1) SRS 2) approx. normal ? Graph data: STATPLOT: normal probability plot :enter data and use last graph choice.

  15. The normal probability plot is roughly linear so we have no reason to doubt the normality of the population from which the data came.

  16. Do:If the conditions are met, carry out the inference procedure. 306.3 n = 20, • Find for 90% confidence level: • Draw picture: • C I = • C I = • = (290.5, 322.1)

  17. Conclude:Interpret the results in the context of the problem. • We are 90%confident that the true mean tension in the entire batch of video terminals produced that day is between 290.5 and 322.1 mV.

  18. Calculating a Confidence Interval Confidence Intervals: The Basics • Properties of Confidence Intervals: • The “margin of error” is the (critical value) • (standard dev. of statistic) • The user chooses the confidence level, and the margin of error follows from this choice. • The critical value depends on the confidence level and the sampling distribution of the statistic. • Greater confidence requires a larger critical value • The standard deviation of the statistic depends on the sample size n • The margin of error gets smaller when: • The confidence level decreases • The sample size n increases

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