1. Confidence Intervals – Introduction
A point estimate provides no information about the precision and
reliability of estimation.
For example, the sample mean is a point estimate of the
population mean μ but because of sampling variability, it is virtually
never the case that
A point estimate says nothing about how close it might be to μ.
An alternative to reporting a single sensible value for the parameter
being estimated it to calculate and report an entire interval of
plausible values – a confidence interval (CI). 1

2. Confidence level
A confidence level is a measure of the degree of reliability of a confidence interval. It is denoted as 100(1-α)%.
The most frequently used confidence levels are 90%, 95% and 99%.
A confidence level of 100(1-α)% implies that 100(1-α)% of all
samples would include the true value of the parameter estimated.
The higher the confidence level, the more strongly we believe that
the true value of the parameter being estimated lies within the
interval.
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3. CI for μ When σ is Known
Suppose X1, X2,…,Xn are random sample from N(μ, σ2) where μ is unknown and σ is known.
A 100(1-α)% confidence interval for μ is,
Proof:
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4. Example
The National Student Loan Survey collected data about the amount
of money that borrowers owe. The survey selected a random sample
of 1280 borrowers who began repayment of their loans between four
to six months prior to the study. The mean debt for the selected
borrowers was $18,900 and the standard deviation was $49,000.
Find a 95% for the mean debt for all borrowers.
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5. Width and Precision of CI
The precision of an interval is conveyed by the width of the interval.
If the confidence level is high and the resulting interval is quite
narrow, the interval is more precise, i.e., our knowledge of the value
of the parameter is reasonably precise.
A very wide CI implies that there is a great deal of uncertainty
concerning the value of the parameter we are estimating.
The width of the CI for μ is ….
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6. Important Comment
Confidence intervals do not need to be central, any a and b that solve
define 100(1-α)% CI for the population mean μ.
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7. One Sided CI
CI gives both lower and upper bounds for the parameter being estimated.
In some circumstances, an investigator will want only one of these
two types of bound.
A large sample upper confidence bound for μ is
A large sample lower confidence bound for μ is
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8. CI for μ When σ is Unknown
Suppose X1, X2,…,Xn are random sample from N(μ, σ2) where both
μ and σ are unknown.
If σ2 is unknown we can estimate it using s2 and use the tn-1 distribution.
A 100(1-α)% confidence interval for μ in this case, is …
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9. Large Sample CI for μ
Recall: if the sample size is large, then the CLT applies and we have
A 100(1-α)% confidence interval for μ, from a large iid sample is
If σ2 is not known we estimate it with s2.
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10. Example – Binomial Distribution
Suppose X1, X2,…,Xn are random sample from Bernoulli(θ)
distribution. A 100(1-α)% CI for θ is….
Example…
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