Ch 4 Stat inferences - INDE 321: Quality Control Instructor...

Info iconThis preview shows pages 1–11. Sign up to view the full content.

View Full Document Right Arrow Icon

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

View Full DocumentRight Arrow Icon

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

View Full DocumentRight Arrow Icon

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

View Full DocumentRight Arrow Icon

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

View Full DocumentRight Arrow Icon

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

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: INDE 321: Quality Control Instructor : Linda Boyle, University of Washington Dept. of Industrial and Systems Engineering Dept. of Civil and Environmental Engineering Chapter 4 Statistical inferences Chapter 4: Inferences about Process Quality In real life, we infer something about the population using a random sample Descriptive statistics describe the sample Sampling distributions describe the distribution of a sample . useful for hypothesis testing and parameter estimations. Sampling Distributions Sampling from a Normal Distribution Chi-square ( 2 ) distribution t distribution F distribution Sampling from Bernoulli Distribution The sum sum of a sample from a Bernoulli process has a binomial distribution Sampling from Poisson Distribution Chapter 4 Two categories of statistical inferences: 1. Parameter Estimation 2. Hypothesis Testing Parameter Estimation Parameters are values representing the population. e.g., , 2 (The population mean and variance, respectively). Parameters in reality are often unknown and must be estimated. Statistics are estimates of parameters. e.g., (The sample mean and variance, respectively). 2 S , x Hypothesis Testing A statistical hypothesis is a statement about the parameters of a probability distribution. Single Sample Null hypothesis Alternative hypothesis 1 : H : H = Statistical Inference (Single Sample) Example: An automobile manufacturer claims a particular automobile can average 35 mpg (highway). Suppose we are interested in testing this claim. We will sample 25 cars under identical conditions and calculate average mpg for sample. Before data collection, decide the following if sample average is less than 33 mpg or more than 37 mpg we will reject the makers claim (critical values) Statistical Inference (Single Sample) Example (continued) H : = 35 H 1 : 35 From the sample of 25 cars, the average mpg was found to be 31.5 . What is your conclusion? Statistical Inference - Definitions Significance Level The level of significance , determines the size of the rejection region. It is also known as the probability of a Type I error (want this to be small) Type I error- rejecting the null hypothesis when it is true. How small? Usually want 10 . or 05 . Statistical Inference - Definitions Types of Error Type I error- rejecting the null hypothesis when it is true. Pr(Type I error) = . Sometimes called the producers risk . Probability that a good lot will be rejected Type II error- not rejecting the null hypothesis when it is false. Pr(Type II error) = . Sometimes called the consumers risk ....
View Full Document

This note was uploaded on 04/26/2010 for the course INDE 321 taught by Professor Boyle during the Winter '10 term at University of Washington.

Page1 / 55

Ch 4 Stat inferences - INDE 321: Quality Control Instructor...

This preview shows document pages 1 - 11. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online