2 Learning ObjectivesKnow the mean and standard deviation for a sampling distribution of meansUnderstand the central limit theorem and how it applies to sampling distributionsKnow the difference between the CLT and the law of large numbersUse the characteristics of sampling distributions of sample means to solve statistical problems
3 Parametersthe mean and standard deviation (μ and σ) of the POPULATION!!!Statisticsthe mean and standard deviation ( ) of the SAMPLE!!!
4 Suppose that (x bar)is the mean of an SRS of size n drawn from a large population with mean μ and the standard deviation σ .Then the mean of the sampling distribution of is: μThe standard deviation is:
5 **** The behavior of in repeated samples is very similar to that of the sample proportion . is an unbiased estimator of the population mean µ2. The values of are less spread for larger samples. Their standard deviation decreases at a rate 1/√n.3. Only use for the standard deviation of when the population is at least 10 times as large as the sample
6 Ex: The height of young women varies approximately according to N(64 Ex: The height of young women varies approximately according to N(64.5, 2.5) distribution. If we measure the height of an SRS of 10 young women, the sampling distribution of their sample mean height will have a mean μ= and standard deviation s=___.μ= 64.5s= (2.5/√10)=0.79 inches
8 Sampling Distribution of a Sample Mean: Draw an SRS of size n from a population that has the normal distribution with mean _μ_ and standard deviation _σ_. Then the sample mean _μ_ has the normal distributionN(μ , σ/√n )Applet: pling_dist/index.html
9 Central Limit Theorem (CLT) Draw an SRS of size n from any population with mean µ and standard deviation σ. When n large, the sampling distribution of the sample mean (x bar) is close to the normal distribution N(μ , σ/√n ) with mean µ and standard deviation σ/√n.
10 Ex: The time that a technician requires to perform preventive maintenance on an air- conditioning unit is governed by the exponential distribution whose density curve is approximately normal. The mean time is µ=1hour and the standard deviation is σ=1 hour.Your company decides to operate 70 units. What is the probability that their average maintenance time exceeds 50 minutes?
14 How does the CLT differ from the Law of Large Numbers? -Law of large numbers: Larger sample size, closer your sample mean gets to your true mean.-CLT: Larger sample size, closer your sample gets to N(μ , σ/√n ) .