# Statistical Intervals. Chapter 7 Stat 4570/5570 Material from Devore s book (Ed 8), and Cengage

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1 7 Statistical Intervals Chapter 7 Stat 4570/5570 Material from Devore s book (Ed 8), and Cengage

2 Confidence Intervals The CLT tells us that as the sample size n increases, the sample mean X is close to normally distributed with expected value µ and standard deviation Standardizing X by first subtracting its expected value and then dividing by its standard deviation yields the standard normal variable How big does our sample need to be if the underlying population is normally distributed? 2

3 Basic Properties of Confidence Intervals Because the area under the standard normal curve between 1.96 and 1.96 is.95, we know: This is equivalent to: which can be interpreted as the probability that the interval includes the true mean µ is 95%. 3

4 Basic Properties of Confidence Intervals The interval is thus called the 95% confidence interval for the mean. This interval varies from sample to sample, as the sample mean varies. So the interval itself is a random interval. 4

5 Basic Properties of Confidence Intervals The CI interval is centered at the sample mean X and extends 1.96 to each side of X. The interval s width is 2 (1.96), which is not random; only the location of the interval (its midpoint X) is random. 5

6 Basic Properties of Confidence Intervals For a given sample, the CI can be expressed either as or as A concise expression for the interval is x ± 1.96, where gives the left endpoint (lower limit) and + gives the right endpoint (upper limit). 6

7 Interpreting a Confidence Level We started with an event (that the random interval captures the true value of µ) whose probability was.95 It is tempting to say that µ lies within this fixed interval with probability µ is a constant (unfortunately unknown to us). It is therefore incorrect to write the statement P(µ lies in (a, b)) = since µ either is in (a,b) or isn t. Basically, µ is not random (it s a constant), so it can t have a probability associated with its behavior. 7

8 Interpreting a Confidence Level Instead, a correct interpretation of 95% confidence relies on the long-run relative frequency interpretation of probability. To say that an event A has probability.95 is to say that if the same experiment is performed over and over again, in the long run A will occur 95% of the time. So the right interpretation is to say that in repeated sampling, 95% of the confidence intervals obtained from all samples will actually contain µ. The other 5% of the intervals will not. 8

9 Interpreting a Confidence Level Example: the vertical line cuts the measurement axis at the true (but unknown) value of µ. One hundred 95% CIs (asterisks identify intervals that do not include µ). 9

10 Interpreting a Confidence Level According to this interpretation, the confidence level is not a statement about any particular interval, eg (79.3, 80.7). Instead it pertains to what would happen if a very large number of like intervals were to be constructed using the same CI formula. 10

11 Other Levels of Confidence Probability of 1 α is achieved by using z α/2 in place of 1.96 P( z α/2 Z < z α/2 ) = 1 α 11

12 Other Levels of Confidence A 100(1 α)% confidence interval for the mean µ when the value of σ is known is given by or, equivalently, by The formula for the CI can also be expressed in words as Point estimate ± (z critical value) (standard error). 12

13 Example A sample of 40 units is selected and diameter measured for each one. The sample mean diameter is mm, and the standard deviation of measurements is 0.1mm. Let s calculate a confidence interval for true average hole diameter using a confidence level of 90%. What is the width of the interval? What about the 99% confidence interval? What are the advantages and disadvantages to a wider confidence interval? 13

14 Sample size computation For each desired confidence level and interval width, we can determine the necessary sample size. Example: A response time is normally distributed with standard deviation 25 milliseconds. A new system has been installed, and we wish to estimate the true average response time µ for the new environment. Assuming that response times are still normally distributed with σ = 25, what sample size is necessary to ensure that the resulting 95% CI has a width of (at most) 10? 14

15 Unknown mean and variance We now have: - a CI for the mean µ of a normal distribution - a large-sample CI for µ for any distribution with a confidence level of 100(1 α)% is: A practical difficulty is the value of σ, which will rarely be known. Instead we work with the standardized variable Where the sample standard deviation S has replaced σ. 15

16 Unknown mean and variance Previously, there was randomness only in the numerator of Z by virtue of, the estimator. In the new standardized variable, both value from one sample to another. and S vary in Thus the distribution of this new variable should be wider than the Normal to reflect the extra uncertainty. This is indeed true when n is small. However, for large n the subsititution of S for σ adds little extra variability, so this variable also has approximately a standard normal distribution. 16

17 A Large-Sample Interval for µ Proposition If n is sufficiently large, the standardized variable has approximately a standard normal distribution. This implies that is a large-sample confidence interval for µ with confidence level approximately 100(1 α)%. (7.8) This formula is valid regardless of the population distribution. 17

18 A Large-Sample Interval for µ Generally speaking, n > 40 will be sufficient to justify the use of this interval. This is somewhat more conservative than the rule of thumb for the CLT because of the additional variability introduced by using S in place of σ. 18

19 Small sample intervals for the mean The CLT cannot be invoked when n is small Need to do something else when n<40 When n<40, we have to: Make a specific assumption about the form of the population distribution and Derive a CI tailored to that assumption. Example: Develop a CI for µ when the population is described by a gamma distribution, another interval for the case of a Weibull distribution, etc. 19

20 t Distributions 20

21 Intervals Based on a Normal Population Distribution The result on which inferences are based introduces a new family of probability distributions called t distributions. When is the mean of a random sample of size n from a normal distribution with mean µ, the rv has a probability distribution called a t distribution with n 1 degrees of freedom (df). 21

22 Properties of t Distributions Figure below illustrates some members of the t-family 22

23 Properties of t Distributions Properties of t Distributions Let t ν denote the t distribution with ν df. 1. Each t ν curve is bell-shaped and centered at Each t ν curve is more spread out than the standard normal (z) curve. 3. As ν increases, the spread of the corresponding t ν curve decreases. 4. As ν, the sequence of t ν curves approaches the standard normal curve (so the z curve is the t curve with df = ). 23

24 Properties of t Distributions Let t α,ν = the number on the measurement axis for which the area under the t curve with ν df to the right of t α,ν is α; t α,ν is called a t critical value. For example, t.05,6 is the t critical value that captures an upper-tail area of.05 under the t curve with 6 df 24

25 Tables of t Distributions The probabilities of t curves are found in a similar way as the normal curve. Example: obtain t.05,15 25

26 The One-Sample t Confidence Interval 26

27 The One-Sample t Confidence Interval Let and s be the sample mean and sample standard deviation computed from the results of a random sample from a normal population with mean µ. Then a 100(1 α)% confidence interval for µ is or, more compactly 27

28 Example cont d A dataset on the modulus of material rupture (psi): There are 30 observations. The sample mean is The sample standard deviation is

29 Intervals Based on Nonnormal Population Distributions The one-sample t CI for µ is robust to small or even moderate departures from normality unless n is very small. By this we mean that if a critical value for 95% confidence, for example, is used in calculating the interval, the actual confidence level will be reasonably close to the nominal 95% level. If, however, n is small and the population distribution is non-normal, then the actual confidence level may be considerably different from the one you think you are using when you obtain a particular critical value from the t table. 29

30 A Confidence Interval for a Population Proportion 30

31 A Confidence Interval for a Population Proportion Let p denote the proportion of successes in a population. A random sample of n individuals is to be selected, and X is the number of successes in the sample. X can be thought of as a sum of all X i s, where 1 is added for every success that occurs and a 0 for every failure, so X X n = X). Thus, X can be regarded as a binomial rv with mean np and. If both np 10 and n(1-p) 10, X has approximately a normal distribution. 31

32 A Confidence Interval for a Population Proportion The natural estimator of p is = X / n, fraction of successes. Since is the sample mean, (X X n )/ n has approximately a normal distribution. We know that, E( ) = p (unbiasedness) and. The standard deviation involves the unknown parameter p. Standardizing by subtracting p and dividing by then implies that And the CI is 32

33 One-Sided Confidence Intervals 33

34 One-Sided Confidence Intervals (Confidence Bounds) The confidence intervals discussed thus far give both a lower confidence bound and an upper confidence bound for the parameter being estimated. In some circumstances, an investigator will want only one of these two types of bounds. For example, a psychologist may wish to calculate a 95% upper confidence bound for true average reaction time to a particular stimulus, or a reliability engineer may want only a lower confidence bound for true average lifetime of components of a certain type. 34

35 One-Sided Confidence Intervals (Confidence Bounds) Because the cumulative area under the standard normal curve to the left of is.95, Manipulating the inequality inside the parentheses to isolate µ on one side and replacing rv s by calculated values gives the inequality µ > 1.645s/ The expression on the right is the desired lower confidence bound. 35

36 One-Sided Confidence Intervals (Confidence Bounds) Starting with P( < Z).95 and manipulating the inequality results in the upper confidence bound. A similar argument gives a one-sided bound associated with any other confidence level. Proposition A large-sample upper confidence bound for µ is and a large-sample lower confidence bound for µ is 36

37 Confidence Intervals for Variance of a normal population 37

38 Confidence Intervals for the Variance of a Normal Population Let X 1, X 2,, X n be a random sample from a normal distribution with parameters µ and σ 2. Then has a chi-squared ( 2 ) probability distribution with n 1 df. We know that the chi-squared distribution is a continuous probability distribution with a single parameter v, called the number of degrees of freedom, with possible values 1, 2, 3,

39 Confidence Intervals for the Variance of a Normal Population The graphs of several 2 probability density functions are 39

40 Confidence Intervals for the Variance of a Normal Population The chi-squared distribution is not symmetric, so tables contain values of both for α near 0 and 1 40

41 Confidence Intervals for the Variance of a Normal Population As a consequence Or equivalently Substituting the computed value s 2 into the limits gives a CI for σ 2 Taking square roots gives an interval for σ. 41

42 Confidence Intervals for the Variance of a Normal Population A 100(1 α)% confidence interval for the variance σ 2 of a normal population has lower limit and upper limit A confidence interval for σ has lower and upper limits that are the square roots of the corresponding limits in the interval for σ 2. 42

43 Example The data on breakdown voltage of electrically stressed circuits are: breakdown voltage is approximately normally distributed. 43

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