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Ance is known ,18-24, Ance is unknown ,18-24 – HP 50g Graphing Calculator User Manual

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Page 18-24

Θ The parameter α is known as the significance level. Typical values of α are

0.01, 0.05, 0.1, corresponding to confidence levels of 0.99, 0.95, and
0.90, respectively.

Confidence intervals for the population mean when the
population variance is known

Let

⎯X be the mean of a random sample of size n, drawn from an infinite

population with known standard deviation

σ. The 100(1-α) % [i.e., 99%, 95%,

90%, etc.], central, two-sided confidence interval for the population mean

μ is

(

⎯X−z

α/2

⋅σ/√n , ⎯X+z

α/2

⋅σ/√n ), where z

α/2

is a standard normal variate that

is exceeded with a probability of

α /2. The standard error of the sample

mean,

⎯X, is ⋅σ/√n.

The one-sided upper and lower 100(1-

α) % confidence limits for the population

mean

μ are, respectively, X+z

α

⋅σ/√n , and ⎯X−z

α

⋅σ/√n . Thus, a lower, one-

sided, confidence interval is defined as (-

∞ , X+z

α

⋅σ/√n), and an upper, one-

sided, confidence interval as (X

−z

α

⋅σ/√n,+∞). Notice that in these last two

intervals we use the value z

α

, rather than z

α/2

.

In general, the value z

k

in the standard normal distribution is defined as that

value of z whose probability of exceedence is k, i.e., Pr[Z>z

k

] = k, or Pr[Z

k

]

= 1 – k. The normal distribution was described in Chapter 17.

Confidence intervals for the population mean when the
population variance is unknown

Let

⎯X and S, respectively, be the mean and standard deviation of a random

sample of size n, drawn from an infinite population that follows the normal
distribution with unknown standard deviation

σ. The 100⋅(1−α) % [i.e., 99%,

95%, 90%, etc.] central two-sided confidence interval for the population mean

μ, is (⎯X− t

n-1,

α/2

⋅S /√n , ⎯X+ t

n-1,

α/2

⋅S/√n ), where t

n-1,

α/2

is Student's t

variate with

ν = n-1 degrees of freedom and probability α/2 of exceedence.

The one-sided upper and lower 100

⋅ (1-α) % confidence limits for the

population mean

μ are, respectively,

X + t

n-1,

α/2

⋅S/√n , and ⎯X− t

n-1,

α/2

⋅S /√n.