Interval estimation for proportions

3,546 views

Published on

Statistics Assignment - Interval Estimation for Proportions

Published in: Education
3 Comments
6 Likes
Statistics
Notes
  • this is very professional presentation how can i get it please - if you can send it to me
    jamalbaathar@yahoo.com
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
  • can u send me this presentation i need it fr assignment shahzeb_21@yahoo.com
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
  • hi i like this presenation very much and can be fruitful for my study please send me the copy my email is r4raj_77@hotmail.com
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
No Downloads
Views
Total views
3,546
On SlideShare
0
From Embeds
0
Number of Embeds
4
Actions
Shares
0
Downloads
0
Comments
3
Likes
6
Embeds 0
No embeds

No notes for slide

Interval estimation for proportions

  1. 1. Interval Estimation for Proportions<br />
  2. 2. Inference Process<br />Population<br />Estimates, Tests and Conclusions<br />Sample Statistics<br />Sample<br /> x p<br />
  3. 3. Statistical Estimation<br />Estimate<br />Interval estimate<br />Point estimate<br /><ul><li> confidence interval for mean
  4. 4. confidence interval for proportion
  5. 5. sample mean
  6. 6. sample proportion</li></li></ul><li>Point Estimate v/s Interval Estimate<br />An estimate of a population parameter may be expressed in two ways: <br />Point estimate. A point estimate of a population parameter is a single value of a statistic. For example, the sample mean x is a point estimate of the population mean μ. Similarly, the sample proportion p is a point estimate of the population proportion P.<br />Interval estimate. An interval estimate is defined by two numbers, between which a population parameter is said to lie. For example, a < x < b is an interval estimate of the population mean μ. It indicates that the population mean is greater than a but less than b<br />
  7. 7. Point Estimate v/s Interval Estimate<br />Interval Estimate = Point Estimate +/- Margin of Error<br />Interval Estimate = p +/- Margin of Error<br />The point estimate is a sample statistic used to estimate the population parameter. For instance, the sample proportion p is a point estimator of the population proportion p.<br /> Because the point estimator does not provide information about how close the estimate is to the population parameter, statisticians prefer to use an interval estimate which provides information about the precision of the estimate.<br />
  8. 8. Properties<br />p is based on large sample condition that both <br />np and nq are 5 or more.<br />
  9. 9. Normal Approximation of the sampling distribution of p<br />Sampling distribution of p<br />sp = p(1-p)<br />_____<br />n<br />p<br />p<br />
  10. 10. Example 1<br />WE School conducted a class survey to check if they were confident about their Statistics presentations. The survey was conducted on 200 students and it found that 80 students were confident about their Statistics presentations.<br />Thus the point estimate of the proportion of the population of students who were confident about their presentation is 80/200 = 0.4<br />Using 95% confidence interval, we have<br />
  11. 11. Contd….<br />Therefore,<br /> = 0.4 – 1.96 * 0.034641 < p < 0.4 + 1.96 * 0.034641<br /> = 0.4 - 0.067896 < p < 0.4 + 0.067896 <br /> = 0.332104 < p < 0.467896 <br />Thus the margin of error is 0.067896 and the 95% confidence interval estimate of the population proportion is 0.332104 to 0.467896. <br />Using percentages, the survey results enable us to state that with 95% confidence between 33.21% and 46.78% of all students are confident about their Statistics presentations.<br />
  12. 12. Example 2<br />The Peacock Cable Television Company thinks that 40% of their customers have more outlets wired than they are paying for.<br />A random sample of 400 houses reveals that 110 of the houses have excessive outlets.<br />Construct a 99% confidence interval for the true proportion of houses having too many outlets. <br />
  13. 13. Contd…..<br />Do you feel the company is accurate in its belief about the proportion of customers who have more outlets wired than they are paying for? <br />X = number of houses that have excessive outlets<br /> = 110,<br />n = 400,<br />and the confidence level = .99.<br />Thus, <br />
  14. 14. Contd….<br />.495<br />.005<br />1 - a = .99<br />a = .01<br />
  15. 15. Contd….<br />A 99% confidence interval for the true proportion of houses having too many outlets is given by <br />
  16. 16. Contd….<br />Interpretation: We are 99% confident that the true proportion of houses having too many outlets is between .2175 and .3325.<br />Note: We do not believe that 40% of customers have more outlets wired than they are paying for because we are 99% confident that the true proportion is in the interval 21.75% to 33.25% and 40% isnot in that interval.<br />
  17. 17. Thank you !<br /><ul><li>VenkateshEswar
  18. 18. AdityaMahagaonkar
  19. 19. Priya Bhattacharya
  20. 20. ParagDhake
  21. 21. TanviShenoy</li>

×