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# Lecture 14 - PowerPoint PPT Presentation

Lecture 14. Chapter 7 Statistical Intervals Based on a Single Sample. What is a confidence interval (CI)?

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### Lecture 14

Chapter 7

Statistical Intervals Based on a Single Sample

• What is a confidence interval (CI)?

The point estimate provides only a single value estimate for a population parameter. It does not provide any information on how “good” the estimate is, or how close it is to the real value of the parameter.

A confidence interval provides an interval along with a certain confidence probability value (1) for a population parameter. A CI indicates the percentage of the CIs formed from a number of different samples of same size that would contain the real value of the estimated population parameter.

For example:

Let the the 95% confidence interval for the mean product length, calculated using a certain sample, is [24,29]. This means that if we kept taking similar samples to which we calculated the above CI from, about 95% of the CIs that we form will contain the real value of the mean length. Also, since (1) = 0.95,  = 0.05

• Types of CIs

• There are three types of CIs:

• Two-sided CI L    U

• Lower one-sided CI L    

• Upper one-sided CI -    U

• There are three types of continuous random variables.

• Nominal-the-best (NTB)  L    U

• Larger-the-best (LTB)  L    

• Smaller-the-best (STB)  -    U

• Most common CIs are 95%, 99%, and 90% CIs.

• Some Examples

• Nominal-the-better

• Clearance, chemical content (pH level), etc.

• We would like the value to be between two comsumer specification limits (LSL = m -  and USL = m + ).

• Larger-the-best

• We would like the value to be larger than a consumer lower specification limit LSL only.

• Smaller-the-better

• Amount of error, time delay, monetary loss, etc.

• We would like the value to be smaller than a consumer upper specification limit USL only.

• In general, to develop a parametric CI for a parameter , the sampling distribution (SMD) of its point estimator must be known and then used appropriately.

• Further, to obtain a lower one-sided CI, the value of  should generally be placed at the upper tail of a distribution and vice a versa for an upper one-sided CI.

WHEN THE VARIANCE IS KNOWN

WHEN THE VARIANCE IS UNKNOWN

• In the previous example, the population variance was assumed to be known. However, in reality it is very likely that the real value of the variance will not be known.

• As the central limit theorem states, the sample average has approximately a normal distribution, whatever the parent distribution is, and:

• However, since the value of  is not known, we will use the unbiased estimator S instead of . The standardized variable

• follows approximately the standard normal distribution only if n > 40. Therefore, we will use the standard normal distribution only if n > 40, but not if n < 40.

follows a t (Student’s t) distribution with (n-1) degrees of freedom (df). The df is denoted by .

• The t distribution is always more spread out than the standard normal distribution, which accounts for the added variability due to the uncertainty about the real value of the population variance.

• For n > 40, the t distribution becomes practically equal to the standard normal distribution.