1. Estimation Using a Single Sample
Chapter 9
Estimation Using a Single Sample

2. Point Estimation
A point estimate of a population characteristic is a single number that is based on sample data and represents a plausible value of the characteristic.

3. Example
A sample of 200 students at a large university is selected to estimate the proportion of students that wear contact lens. In this sample 47 wear contact lens.
Let p = the true proportion of all students at this university that wear contact lens. Consider “success” being a student wears contact lens.

4. Example A sample of weights of 34 male freshman students was obtained.
If one wanted to estimate the true mean of all male freshman students, you might use the sample mean as a point estimate for the true mean.

5. Example
After looking at a histogram and boxplot of the data (below) you might notice that the data seems reasonably symmetric with a outlier, so you might use either the sample median or a sample trimmed mean as a point estimate.
260
220
180
140

6. Bias
A statistic with mean value equal to the value of the population characteristic being estimated is said to be an unbiased statistic. A statistic that is not unbiased is said to be biased.

7. Criteria
Given a choice between several unbiased statistics that could be used for estimating a population characteristic, the best statistic to use is the one with the smallest standard deviation.

8. Large-sample Confidence Interval for a Population Proportion
A confidence interval for a population characteristic is an interval of plausible values for the characteristic. It is constructed so that, with a chosen degree of confidence, the value of the characteristic will be captured inside the interval.

9. Confidence Level
The confidence level associated with a confidence interval estimate is the success rate of the method used to construct the interval.

10. Recall

11. Some considerations

12. Some considerations

13. The 95% Confidence Interval

14. Example
For a project, a student randomly sampled 182 other students at a large university to determine if the majority of students were in favor of a proposal to build a field house. He found that 75 were in favor of the proposal.
Let p = the true proportion of students that favor the proposal.

15. Example - continued

16. The General Confidence Interval
The general formula for a confidence interval for a population proportion p when
1. p is the sample proportion from a random sample , and
2. The sample size n is large (np  10 and np(1-p)  10) is

17. Finding a z Critical Value
Finding a z critical value for a 98% confidence interval.
2.33
Looking up the cumulative area or in the body of the table we find z = 2.33

18. Some Common Critical Values
Confidence
level
z critical
value
80%
1.28
90%
1.645
95%
1.96
98%
2.33
99%
2.58
99.8%
3.09
99.9%
3.29

19. Terminology

20. Terminology
The bound on error of estimation, B, associated with a 95% confidence interval is
(1.96)(standard error of the statistic).
The bound on error of estimation, B, associated with a confidence interval is
(z critical value)·(standard error of the statistic).

21. Sample Size
The sample size required to estimate a population proportion p to within an amount B with 95% confidence is
The value of p may be estimated by prior information. If no prior information is available, use p = 0.5 in the formula to obtain a conservatively large value for n.
Generally one rounds the result up to the nearest integer.

22. Sample Size Calculation Example
If a TV executive would like to find a 95% confidence interval estimate within 0.03 for the proportion of all households that watch NYPD Blue regularly. How large a sample is needed if a prior estimate for p was 0.15.
We have B = 0.03 and the prior estimate of p = 0.15
A sample of 545 or more would be needed.

23. Sample Size Calculation Example revisited
Suppose a TV executive would like to find a 95% confidence interval estimate within 0.03 for the proportion of all households that watch NYPD Blue regularly. How large a sample is needed if we have no reasonable prior estimate for p.
We have B = 0.03 and should use p = 0.5 in the formula.
The required sample size is now 1068.
Notice, a reasonable ball park estimate for p can lower the needed sample size.

24. Another Example The required sample size is 4032.
A college professor wants to estimate the proportion of students at a large university who favor building a field house with a 99% confidence interval accurate to If one of his students performed a preliminary study and estimated p to be 0.412, how large a sample should he take.
We have B = 0.02, a prior estimate p = and we should use the z critical value 2.58 (for a 99% confidence interval)
The required sample size is 4032.

25. One-Sample z Confidence Interval for m

26. One-Sample z Confidence Interval for m
If n is small (generally n < 30) but it is reasonable to believe that the distribution of values in the population is normal, a confidence interval for m (when s is known) is
Notice that this formula works when s is known and either
1. n is large (generally n  30) or
2. The population distribution is normal (any sample size.

27. Example
Find a 90% confidence interval estimate for the true mean fills of catsup from this machine.

28. Example I (continued) 645 . 1 36 n , 228 018 6 x = s
018 6 x = s
Z critical value is 1.645
90% Confidence Interval
(5.955, 6.081)

29. Unknown  - Small Size Samples [All Size Samples]

30. t Distributions

31. t Distributions

32. t distribution

33. t Distributions Continued
Notice: As df increase, t distributions approach the standard normal distribution.
Since each t distribution would require a table similar to the standard normal table, we usually only create a table of critical values for the t distributions.

35. One-Sample t Procedures

36. Confidence Interval Example
Ten randomly selected shut-ins were each asked to list how many hours of television they watched per week. The results are
Find a 90% confidence interval estimate for the true mean number of hours of television watched per week by shut-ins.

37. Confidence Interval Example Continued

38. Confidence Interval Example Continued
To calculate the confidence interval, we had to make the assumption that the distribution of weekly viewing times was normally distributed. Consider the following normal plot of the 10 data points.

39. Confidence Interval Example Continued