DS212_Chap7_Sampling_and_Sampling_Dist

# DS212_Chap7_Sampling_and_Sampling_Dist - C hapte 7 S pling...

This preview shows pages 1–10. Sign up to view the full content.

1 Chapter 7: Sampling and Sampling Distributions Sada Soorapanth Spring 2010

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
2 Statistical Inference x Population Parameters μ , σ 2 , p μ = population mean σ 2 = population variance p = population proportion = sample average s 2 = sample variance = sample proportion x Sample Statistics p s x , , 2 Collect samples Estimate population parameters Sample Statistics p s x , , 2 Sample Statistics p s x , , 2 p
3 Reasons for Sampling Sampling can save time and money. For given resources, sampling can broaden the scope of the data set. Because the research process is sometimes destructive, the sample can save product. If accessing the population is impossible; sampling is the only option.

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
4 Samples Provide Estimates of Population A tire manufacturer developed a new tire designed to provide an increase in mileage over the firm’s current line of tires. To estimate the mean number of miles provided by the new tires, the manufacturer selected a sample of 120 new tires for testing. The test results provided a sample mean of 36,500 miles. Hence, an estimate of the mean tire mileage for the population of new tires was 36,500 miles
5 Random Versus Nonrandom Sampling Random sampling Every unit of the population has the same probability of being included in the sample. A chance mechanism is used in the selection process. Eliminates bias in the selection process Also known as probability sampling Nonrandom Sampling Every unit of the population does not have the same probability of being included in the sample. Open to selection bias Not appropriate data collection methods for most statistical methods Also known as nonprobability sampling

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
6 Random Sampling Techniques Simple Random Sampling Stratified Random Sampling Systematic Random Sampling Cluster (or Area) Sampling
7 Simple Random Sampling from a Finite Population A simple random sample of size n from a finite population of size N is a sample selected such that each possible sample has the same probability of being selected Steps 1. Number each unit in the population from 1 to N. 2. Use a random number table or a random number generator to select n distinct numbers between 1 and N, inclusively.

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
8 Simple Random Sample (Finite Population): Step 1: Numbered Population 01 Alaska Airlines 02 Alcoa 03 Ashland 04 Bank of America 05 BellSouth 06 Chevron 07 Citigroup 08 Clorox 09 Delta Air Lines 10 Disney 11 DuPont 12 Exxon Mobil 13 General Dynamics 14 General Electric 15 General Mills 16 Halliburton 17 IBM 18 Kellog 19 KMart 20 Lowe’s 21 Lucent 22 Mattel 23 Mead 24 Microsoft 25 Occidental Petroleum 26 JCPenney 28 Ryder 29 Sears 30 Time Warner N = 30 n = 6
9 Simple Random Sample (Finite Population): Step 2: Random Number Table 9 9 4 3 7 8 7 9 6 1 4 5 7 3 7 3 7 5 5 2 9 7 9 6 9 3 9 0 9 4 3 4 4 7 5 3 1 6 1 8 5 0 6 5 6 0 0 1 2 7 6 8 3 6 7 6 6 8 8 2 0 8 1 5 6 8 0 0 1 6 7 8 2 2 4 5 8 3 2 6 8 0 8 8 0 6 3 1 7 1

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

## This note was uploaded on 09/10/2011 for the course DS 212 taught by Professor Saltzman during the Spring '08 term at S.F. State.

### Page1 / 36

DS212_Chap7_Sampling_and_Sampling_Dist - C hapte 7 S pling...

This preview shows document pages 1 - 10. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online