Chap 3 &amp; 4 lecture

# Chap 3 &amp;amp; 4 lecture - CHAPTER 3 Collecting Data...

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CHAPTER 3 Collecting Data Descriptive statistics – numerical and graphical summaries that characterize a dataset (Ch 2) Inferential statistics – sample data used to make conclusions about a broader range of individuals than just those who are observed The Fundamental Rule for Using Data for Inference: Data can be used to make inference about a much larger group if the sample can be considered to be representative with regard to the question of interest For example, if I want to estimate the average height of female college students in the US, then I need to select a sample that is representative of this population. Bias – What Can Go Wrong? a. Selection bias – the sample is selected in a way that does not include the whole population b.Nonresponse bias – those selected to participate do not respond c. Response bias – participants provide incorrect information

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Accuracy Margin of error – a measurement of accuracy Conservative Margin of Error = (1 / √n ) * 100% Confidence Interval – an interval of values that estimates an unknown population value Approximate 95% CI = sample proportion ± 1 / √n Choosing a Sample Size 1. Determine the margin of error desired 2. Solve for n in MoE = 1 / √n Methods of Selecting a Sample Simple random sample – every unit of the population has the same chance of being selected for the sample For example, I could put the name of every PSU student in a hat and then randomly select 1000 names.
Stratified Sampling – population divided into groups and then a simple random sample is taken from each group For example, I could divide the PSU student population into freshmen, sophomores, juniors, seniors, and graduates, and then take a random sample of 500 from each group. Cluster sampling – population divided into groups and a random sample of groups is selected, use all the individuals in those chosen groups For example, I could divide the PSU student population into dorm-dwellers, on-campus apartment dwellers, off-campus apartment dwellers, off-campus house dwellers, fraternity dwellers, and others. Then, I take a random sample of 2 groups and use their responses. Systematic sampling – population list provided and every k th individual in the entire population For example, I could generate a list of all enrolled students at PSU and sample every 100 th name. What Else Can Go Wrong?

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## This note was uploaded on 02/03/2010 for the course STAT 200 at Pennsylvania State University, University Park.

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Chap 3 &amp;amp; 4 lecture - CHAPTER 3 Collecting Data...

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