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  1. Introduction
  2. Graphing Distributions
  3. Summarizing Distributions
  4. Describing Bivariate Data
  5. Probability
  6. Research Design
  7. Normal Distribution
  8. Advanced Graphs
  9. Sampling Distributions

  10. Estimation
    1. Contents
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    2. Introduction
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         Video
    3. Degrees of Freedom
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         Video
    4. Characteristics of Estimators
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         Video
    5. Bias and Variability Simulation
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    6. Confidence Intervals
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         Video
    7. Confidence Intervals Intro
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         Video
    8. Confidence Interval for Mean
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         Video
    9. t distribution
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         Video
    10. Confidence Interval Simulation
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    11. Difference between Means
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         Video
    12. Correlation
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         Video
    13. Proportion
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    14. Statistical Literacy
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    15. Exercises
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  11. Logic of Hypothesis Testing
  12. Tests of Means
  13. Power
  14. Regression
  15. Analysis of Variance
  16. Transformations
  17. Chi Square
  18. Distribution Free Tests
  19. Effect Size
  20. Case Studies
  21. Calculators
  22. Glossary
 

Proportion

Author(s)

David M. Lane

Prerequisites

Introduction to the Normal Distribution, Normal Approximation to the Binomial, Sampling Distribution of the Mean, Sampling Distribution of a Proportion, Confidence Intervals, Confidence Interval on the Mean

Learning Objectives
  1. Estimate the population proportion from sample proportions
  2. Apply the correction for continuity
  3. Compute a confidence interval

A candidate in a two-person election commissions a poll to determine who is ahead. The pollster randomly chooses 500 registered voters and determines that 260 out of the 500 favor the candidate. In other words, 0.52 of the sample favors the candidate. Although this point estimate of the proportion is informative, it is important to also compute a confidence interval. The confidence interval is computed based on the mean and standard deviation of the sampling distribution of a proportion. The formulas for these two parameters are shown below:

μp = π

Since we do not know the population parameter π, we use the sample proportion p as an estimate. The estimated standard error of p is therefore

We start by taking our statistic (p) and creating an interval that ranges (Z.95)(sp) in both directions, where Z.95 is the number of standard deviations extending from the mean of a normal distribution required to contain 0.95 of the area (see the section on the confidence interval for the mean). The value of Z.95 is computed with the normal calculator and is equal to 1.96. We then make a slight adjustment to correct for the fact that the distribution is discrete rather than continuous.

sp is calculated as shown below:

To correct for the fact that we are approximating a discrete distribution with a continuous distribution (the normal distribution), we subtract 0.5/N from the lower limit and add 0.5/N to the upper limit of the interval. Therefore the confidence interval is


Lower limit: 0.52 - (1.96)(0.0223) - 0.001 = 0.475
Upper limit: 0.52 + (1.96)(0.0223) + 0.001 = 0.565

0.475 ≤ π ≤ 0.565

Since the interval extends 0.045 in both directions, the margin of error is 0.045. In terms of percent, between 47.5% and 56.5% of the voters favor the candidate and the margin of error is 4.5%. Keep in mind that the margin of error of 4.5% is the margin of error for the percent favoring the candidate and not the margin of error for the difference between the percent favoring the candidate and the percent favoring the opponent. The margin of error for the difference is 6.36%, the square root of 2 times the margin of error for the individual percent. Keep this in mind when you hear reports in the media; the media often get this wrong.

 



prop.test(260,500,correct=TRUE)

1-sample proportions test with continuity correction

data: 260 out of 500, null probability 0.5
X-squared = 0.722, df = 1, p-value = 0.3955
alternative hypothesis: true p is not equal to 0.5
95 percent confidence interval:
0.4752277 0.5644604
sample estimates:
p
0.52
Please answer the questions:
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