1 / 22

Pertemuan 16 Pendugaan Parameter

Pertemuan 16 Pendugaan Parameter. Matakuliah : I0134 – Metoda Statistika Tahun : 2005 Versi : Revisi. Learning Outcomes. Pada akhir pertemuan ini, diharapkan mahasiswa akan mampu : Mahasiswa dapat menghitung penduga selang dari rataan, proporsi dan varians. Outline Materi.

emmanuel-hansen
Télécharger la présentation

Pertemuan 16 Pendugaan Parameter

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Pertemuan 16Pendugaan Parameter Matakuliah : I0134 – Metoda Statistika Tahun : 2005 Versi : Revisi

  2. Learning Outcomes Pada akhir pertemuan ini, diharapkan mahasiswa akan mampu : • Mahasiswa dapat menghitung penduga selang dari rataan, proporsi dan varians.

  3. Outline Materi • Selang nilai tengah (rataan) • Selang beda nilai tengah (rataan) • Selang proporsi dan beda proporsi • Selang varians dan proporsi varians

  4. Interval Estimation • Interval Estimation of a Population Mean: Large-Sample Case • Interval Estimation of a Population Mean: Small-Sample Case • Determining the Sample Size • Interval Estimation of a Population Proportion  [--------------------- ---------------------] [--------------------- ---------------------] [--------------------- ---------------------]

  5. Interval Estimation of a Population Mean:Large-Sample Case • Sampling Error • Probability Statements about the Sampling Error • Constructing an Interval Estimate: Large-Sample Case with  Known • Calculating an Interval Estimate: Large-Sample Case with  Unknown

  6. Sampling Error • The absolute value of the difference between an unbiased point estimate and the population parameter it estimates is called the sampling error. • For the case of a sample mean estimating a population mean, the sampling error is Sampling Error =

  7. Interval Estimate of a Population Mean:Large-Sample Case (n> 30) • With  Known where: is the sample mean 1 - is the confidence coefficient z/2 is the z value providing an area of /2 in the upper tail of the standard normal probability distribution s is the population standard deviation n is the sample size

  8. Interval Estimate of a Population Mean:Large-Sample Case (n> 30) • With  Unknown In most applications the value of the population standard deviation is unknown. We simply use the value of the sample standard deviation, s, as the point estimate of the population standard deviation.

  9. Interval Estimation of a Population Mean:Small-Sample Case (n < 30) with  Unknown • Interval Estimate where 1 - = the confidence coefficient t/2 = the t value providing an area of /2 in the upper tail of a t distribution with n - 1 degrees of freedom s = the sample standard deviation

  10. Contoh Soal: Apartment Rents • Interval Estimation of a Population Mean: Small-Sample Case (n < 30) with  Unknown A reporter for a student newspaper is writing an article on the cost of off-campus housing. A sample of 10 one-bedroom units within a half-mile of campus resulted in a sample mean of $550 per month and a sample standard deviation of $60. Let us provide a 95% confidence interval estimate of the mean rent per month for the population of one-bedroom units within a half-mile ofcampus. We’ll assume this population to be normally distributed.

  11. Contoh Soal: Apartment Rents • t Value At 95% confidence, 1 -  = .95,  = .05, and /2 = .025. t.025 is based on n - 1 = 10 - 1 = 9 degrees of freedom. In the t distribution table we see that t.025 = 2.262.

  12. Estimation of the Difference Between the Means of Two Populations: Independent Samples • Point Estimator of the Difference between the Means of Two Populations • Sampling Distribution • Interval Estimate of Large-Sample Case • Interval Estimate of Small-Sample Case

  13. Sampling Distribution of • Properties of the Sampling Distribution of • Expected Value • Standard Deviation where: 1 = standard deviation of population 1 2 = standard deviation of population 2 n1 = sample size from population 1 n2 = sample size from population 2

  14. Interval Estimate of 1 - 2:Large-Sample Case (n1> 30 and n2> 30) • Interval Estimate with 1 and 2 Known where: 1 -  is the confidence coefficient • Interval Estimate with 1 and 2 Unknown where:

  15. Contoh Soal: Par, Inc. • 95% Confidence Interval Estimate of the Difference Between Two Population Means: Large-Sample Case, 1 and 2 Unknown Substituting the sample standard deviations for the population standard deviation: = 17 + 5.14 or 11.86 yards to 22.14 yards. We are 95% confident that the difference between the mean driving distances of Par, Inc. balls and Rap, Ltd. balls lies in the interval of 11.86 to 22.14 yards.

  16. Interval Estimate of 1 - 2:Small-Sample Case (n1 < 30 and/or n2 < 30) • Interval Estimate with  2 Known where:

  17. Contoh Soal: Specific Motors • 95% Confidence Interval Estimate of the Difference Between Two Population Means: Small-Sample Case = 2.5 + 2.2 or .3 to 4.7 miles per gallon. We are 95% confident that the difference between the mean mpg ratings of the two car types is from .3 to 4.7 mpg (with the M car having the higher mpg).

  18. Inferences About the Difference Between the Proportions of Two Populations • Sampling Distribution of • Interval Estimation of p1 - p2 • Hypothesis Tests about p1 - p2

  19. Sampling Distribution of • Expected Value • Standard Deviation • Distribution Form If the sample sizes are large (n1p1, n1(1 - p1), n2p2, and n2(1 - p2) are all greater than or equal to 5), the sampling distribution of can be approximated by a normal probability distribution.

  20. Interval Estimation of 2 • Interval Estimate of a Population Variance where the values are based on a chi-square distribution with n - 1 degrees of freedom and where 1 -  is the confidence coefficient.

  21. Interval Estimation of 2 • Chi-Square Distribution With Tail Areas of .025 .025 .025 95% of the possible 2 values 2 0

  22. Selamat Belajar Semoga Sukses.

More Related