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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Chapter 7 Statistical Intervals Based on a Single Sample

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Confidence Intervals An alternative to reporting a single value for the parameter being estimated is to calculate and report an entire interval of plausible values – a confidence interval (CI). A confidence level is a measure of the degree of reliability of the interval.

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. 7.1 Basic Properties of Confidence Intervals

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. 95% Confidence Interval If after observing X 1 = x 1,…, X n = x n, we compute the observed sample mean, then a 95% confidence interval for can be expressed as

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Other Levels of Confidence 0

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Other Levels of Confidence A confidence interval for the mean of a normal population when the value of is known is given by

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Sample Size The general formula for the sample size n necessary to ensure an interval width w is

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Deriving a Confidence Interval Let X 1,…, X n denote the sample on which the CI for the parameter is to be based. Suppose a random variable satisfying the following properties can be found: 1. The variable depends functionally on both X 1,…,X n and 2. The probability distribution of the variable does not depend on or any other unknown parameters.

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Let denote this random variable. In general, the form of h is usually suggested by examining the distribution of an appropriate estimator For any between 0 and 1, constants a and b can be found to satisfy Deriving a Confidence Interval

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Deriving a Confidence Interval Now suppose that the inequalities can be manipulated to isolate lower confidence limit For a CI. upper confidence limit

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. 7.2 Large-Sample Confidence Intervals for a Population Mean and Proportion

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Large-Sample Confidence Interval If n is sufficiently large, the standardized variable has approximately a standard normal distribution. This implies that is a large-sample confidence interval for with level

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Confidence Interval for a Population Proportion p with level Lower(–) and upper(+) limits:

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Large-Sample Confidence Bounds for Upper Confidence Bound: Lower Confidence Bound:

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. 7.3 Intervals Based on a Normal Population Distribution

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Normal Distribution The population of interest is normal, so that X 1,…, X n constitutes a random sample from a normal distribution with both

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. t Distribution When is the mean of a random sample of size n from a normal distribution with mean the rv has a probability distribution called a t distribution with n – 1 degrees of freedom (df).

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Properties of t Distributions Let t v denote the density function curve for v df. 1. Each t v curve is bell-shaped and centered at 0. 2. Each t v curve is spread out more than the standard normal (z) curve.

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Properties of t Distributions 3. As v increases, the spread of the corresponding t v curve decreases. 4. As, the sequence of t v curves approaches the standard normal curve (the z curve is called a t curve with df =

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. t Critical Value Let = the number on the measurement axis for which the area under the t curve with v df to the right of is called a t critical value.

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Pictorial Definition of 0

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Confidence Interval Let and s be the sample mean and standard deviation computed from the results of a random sample from a normal population with mean The

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Prediction Interval A prediction interval (PI) for a single observation to be selected from a normal population distribution is The prediction level is

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Tolerance Interval Let k be a number between 0 and 100. A tolerance interval for capturing at least k% of the values in a normal population distribution with a confidence level of 95% has the form

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. 7.4 Confidence Intervals for the Variance and Standard Deviation of a Normal Population

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Normal Population Let X 1,…, X n be a random sample from a normal distribution with parameters Then the rv has a chi-squared probability distribution with n – 1 df.

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Chi-squared Critical Value Let, called a chi-squared critical value, denote the number of the measurement axis such that of the area under the chi-squared curve with v df lies to the right of

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Notation Illustrated

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Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Confidence Interval confidence interval for for the variance of a normal population has lower limit upper limit For a confidence interval for, take the square root of each limit above.

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