1. STA 291 Spring 2010
Lecture 12
Dustin Lueker

2. Central Limit Theorem
For random sampling, as the sample size n grows, the sampling distribution of the sample mean, , approaches a normal distribution
Amazing: This is the case even if the population distribution is discrete or highly skewed
Central Limit Theorem can be proved mathematically
Usually, the sampling distribution of is approximately normal for n≥30
We know the parameters of the sampling distribution
STA 291 Spring 2010 Lecture 12

3. Central Limit Theorem (Binomial Version)
For random sampling, as the sample size n grows, the sampling distribution of the sample proportion, , approaches a normal distribution
Usually, the sampling distribution of is approximately normal for np≥5, nq≥5
We know the parameters of the sampling distribution
STA 291 Spring 2010 Lecture 12

4. Example
Assume the population mean is 15 with a standard deviation of 7.
What is the probability of getting a sample mean greater than 13.5 from a sample of size 36?
STA 291 Spring 2010 Lecture 12

5. Example
Assume the true proportion of UK students who will fill out an NCAA tournament bracket is .62.
What is the probability of finding 15 people out of a sample of 20 that are going to fill out an NCAA tournament bracket?
STA 291 Spring 2010 Lecture 12

6. Statistical Inference: Estimation
Inferential statistical methods provide predictions about characteristics of a population, based on information in a sample from that population
Quantitative variables
Usually estimate the population mean
Mean household income
For qualitative variables
Usually estimate population proportions
Proportion of people voting for candidate A
STA 291 Spring 2010 Lecture 12

7. Two Types of Estimators
Point Estimate
A single number that is the best guess for the parameter
Sample mean is usually a good guess for the population mean
Interval Estimate
Point estimator with error bound
A range of numbers around the point estimate
Gives an idea about the precision of the estimator
The proportion of people voting for A is between 67% and 73%
STA 291 Spring 2010 Lecture 12

8. Point Estimator
A point estimator of a parameter is a sample statistic that predicts the value of that parameter
A good estimator is
Unbiased
Centered around the true parameter
Consistent
Gets closer to the true parameter as the sample size gets larger
Efficient
Has a standard error that is as small as possible (made use of all available information)
STA 291 Spring 2010 Lecture 12

9. Unbiased
An estimator is unbiased if its sampling distribution is centered around the true parameter
For example, we know that the mean of the sampling distribution of equals μ, which is the true population mean
Thus, is an unbiased estimator of μ
Note: For any particular sample, the sample mean may be smaller or greater than the population mean
Unbiased means that there is no systematic underestimation or overestimation
STA 291 Spring 2010 Lecture 12

10. Biased
A biased estimator systematically underestimates or overestimates the population parameter
In the definition of sample variance and sample standard deviation uses n-1 instead of n, because this makes the estimator unbiased
With n in the denominator, it would systematically underestimate the variance
STA 291 Spring 2010 Lecture 12

11. Efficient
An estimator is efficient if its standard error is small compared to other estimators
Such an estimator has high precision
A good estimator has small standard error and small bias (or no bias at all)
The following pictures represent different estimators with different bias and efficiency
Assume that the true population parameter is the point (0,0) in the middle of the picture
STA 291 Spring 2010 Lecture 12

12. Bias and Efficient
Note that even an unbiased and efficient estimator does not always hit exactly the population parameter.
But in the long run,
it is the best estimator.
STA 291 Spring 2010 Lecture 12

13. Point Estimators of the Mean and Standard Deviation
Sample mean is unbiased, consistent, and (often) relatively efficient for estimating μ
Sample standard deviation is almost unbiased for estimating population standard deviation
No easy unbiased estimator exists
Both are consistent
STA 291 Spring 2010 Lecture 12