VCU STAT 210 - Lecture24 (54 pages)

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Lecture24



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Lecture24

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Pages:
54
School:
Virginia Commonwealth University
Course:
Stat 210 - Basic Practice of Statistics
Basic Practice of Statistics Documents

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STAT 210 Lecture 24 Introduction to Confidence Intervals October 23 2017 Practice Problems Pages 187 and 188 Relevant problems VII 1 VII 2 VII 3 and VII 4 Recommended problems VII 1 VII 2 VII 3 VII 4 Additional Reading and Examples Read pages 185 and 186 Top Hat 2 Inference Statistical inference involves using statistics computed from data collected in a sample to make statements inferences about unknown population parameters Two types of statistical inference are estimation of parameters using confidence intervals and statistical tests about parameters In this chapter we learn the basic concepts associated with confidence intervals and with statistical tests Inference The first step in any inference procedure is to state the practical question that needs to be answered This involves specifying the population of interest and then the specific parameter that inferences need to be made about Motivating Example Suppose the population is all students at this university There are two parameters p the proportion of all students at this university who have children m mean IQ of all students at this university Motivating Example Suppose the population is all students at this university There are two parameters p the proportion of all students at this university who have children m mean IQ of all students at this university Does anyone know the proportion of all students at this university who have children Motivating Example Suppose the population is all students at this university There are two parameters p the proportion of all students at this university who have children m mean IQ of all students at this university Does anyone know the mean IQ of all students at this university Motivating Example Suppose the population is all students at this university There are two parameters p the proportion of all students at this university who have children m mean IQ of all students at this university Since data for all students is not known it is likely not possible to determine the value of either parameter This is when statistical inference comes into action Motivating Example Suppose the population is all students at this university If the parameter is p the proportion of all students at this university who have children and if data for all students is not available what could we do Motivating Example Suppose the population is all students at this university If the parameter is p the proportion of all students at this university who have children and if data for all students is not available what could we do 1 Select a sample from the population 2 Collect data for students in the sample 3 Compute the proportion of the students in the sample that have children Being from the sample this is a statistic Top Hat Motivating Example Suppose the population is all students at this university The parameter is p the proportion of all students at this university who have children and is unknown From our sample of students have children So the proportion of the sample



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