lecture09.Distribution of the Sample Mean I - Looking at...

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Lecture 9Looking at DataChapter 5.1 The sampling Distribution of a Sample Mean
AgendaAnnouncementsQuiz #5 Normal CalculationLecture 8 cont. - Backwards Normal Problems- Check normalityLecture 9 - Sampling distribution-Central Limit Theorem
AnnouncementsExam 1 on Monday, 2/23.HW #3: The normal distribution Open Monday, 2/2.Due Monday, 2/16 HW #4: Sampling distribution Open Monday, 2/9.Due Thursday, 2/19 Grad #1: regrade request deadline Thursday, 2/12.Lab #5: Normal Distribution on Friday, 2/13.
Chapter 5.1Sampling Distribution
A parameteris a number that describes the population. In practice, the value of a parameter is unknown, because we cannot examine the entire population. We write μ(the Greek letter mu) for the mean of a population. This is a fixed parameter that is unknown. A statisticis a number that can be computed from the sample data without making use of any unknown parameters. In practice, we use a statistic to estimate an unknown parameter. The mean of the sampleis the familiar 𝑋, the average of the observations in the sample. This is a statistic that would almost certainly take a different value if we chose another sample from the same population. The sample mean from a sample or an experiment is an estimate of the mean μ of the underlying population. Recap for a moment
We have been looking at the distribution of the values of a variable and the density curve that best fit that distribution. Now we are going to look at the distribution of values of a particular variable, the statistic.Sampling distribution: The sampling distribution of a statistic is the distribution of values taken by the statistic in all possible samples of the same size for the same populationRemember: Our final aim estimate the value of a parameter from the entire population.Recap for a moment
How are we going to proceed?

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