Chapter 2 Simple Comparative Experiments

Slides:



Advertisements
Similar presentations
Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Chapter 9 Inferences Based on Two Samples.
Advertisements

Statistics Review – Part II Topics: – Hypothesis Testing – Paired Tests – Tests of variability 1.
BPS - 5th Ed. Chapter 241 One-Way Analysis of Variance: Comparing Several Means.
CHAPTER 2 Building Empirical Model. Basic Statistical Concepts Consider this situation: The tension bond strength of portland cement mortar is an important.
CmpE 104 SOFTWARE STATISTICAL TOOLS & METHODS MEASURING & ESTIMATING SOFTWARE SIZE AND RESOURCE & SCHEDULE ESTIMATING.
Inferential Statistics
Confidence Interval and Hypothesis Testing for:
Comparing Two Population Means The Two-Sample T-Test and T-Interval.
Some Basic Statistical Concepts
10-1 Introduction 10-2 Inference for a Difference in Means of Two Normal Distributions, Variances Known Figure 10-1 Two independent populations.
9-1 Hypothesis Testing Statistical Hypotheses Statistical hypothesis testing and confidence interval estimation of parameters are the fundamental.
Sampling Distributions
Chapter 3 Experiments with a Single Factor: The Analysis of Variance
Analysis of Variance Chapter 3Design & Analysis of Experiments 7E 2009 Montgomery 1.
4-1 Statistical Inference The field of statistical inference consists of those methods used to make decisions or draw conclusions about a population.
EEM332 Design of Experiments En. Mohd Nazri Mahmud
Chapter 2Design & Analysis of Experiments 7E 2009 Montgomery 1 Chapter 2 –Basic Statistical Methods Describing sample data –Random samples –Sample mean,
Chapter 11: Inference for Distributions
Inferences About Process Quality
Chapter 9 Hypothesis Testing.
5-3 Inference on the Means of Two Populations, Variances Unknown
Hypothesis Testing and T-Tests. Hypothesis Tests Related to Differences Copyright © 2009 Pearson Education, Inc. Chapter Tests of Differences One.
Chapter 9 Title and Outline 1 9 Tests of Hypotheses for a Single Sample 9-1 Hypothesis Testing Statistical Hypotheses Tests of Statistical.
Statistical Inference for Two Samples
Chapter 24: Comparing Means.
AM Recitation 2/10/11.
Variance-Test-1 Inferences about Variances (Chapter 7) Develop point estimates for the population variance Construct confidence intervals for the population.
II.Simple Regression B. Hypothesis Testing Calculate t-ratios and confidence intervals for b 1 and b 2. Test the significance of b 1 and b 2 with: T-ratios.
1/2555 สมศักดิ์ ศิวดำรงพงศ์
ISE 352: Design of Experiments
Statistika & Rancangan Percobaan
5-1 Introduction 5-2 Inference on the Means of Two Populations, Variances Known Assumptions.
1 Design of Engineering Experiments Part 2 – Basic Statistical Concepts Simple comparative experiments –The hypothesis testing framework –The two-sample.
Introduction to Statistical Inference Chapter 11 Announcement: Read chapter 12 to page 299.
Topics: Statistics & Experimental Design The Human Visual System Color Science Light Sources: Radiometry/Photometry Geometric Optics Tone-transfer Function.
Chapter 9 Hypothesis Testing and Estimation for Two Population Parameters.
10-1 Introduction 10-2 Inference for a Difference in Means of Two Normal Distributions, Variances Known Figure 10-1 Two independent populations.
Chapter 11 Inference for Distributions AP Statistics 11.1 – Inference for the Mean of a Population.
9-1 Hypothesis Testing Statistical Hypotheses Definition Statistical hypothesis testing and confidence interval estimation of parameters are.
Statistical Decision Making. Almost all problems in statistics can be formulated as a problem of making a decision. That is given some data observed from.
Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Chapter 9 Inferences Based on Two Samples.
1-1 Copyright © 2015, 2010, 2007 Pearson Education, Inc. Chapter 24, Slide 1 Chapter 24 Paired Samples and Blocks.
Psychology 301 Chapters & Differences Between Two Means Introduction to Analysis of Variance Multiple Comparisons.
1 Chapter 2: Simple Comparative Experiments (SCE) Simple comparative experiments: experiments that compare two conditions (treatments) –The hypothesis.
1 10 Statistical Inference for Two Samples 10-1 Inference on the Difference in Means of Two Normal Distributions, Variances Known Hypothesis tests.
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide
Design of Engineering Experiments Part 2 – Basic Statistical Concepts
Inference for Regression Simple Linear Regression IPS Chapter 10.1 © 2009 W.H. Freeman and Company.
4 Hypothesis & Testing. CHAPTER OUTLINE 4-1 STATISTICAL INFERENCE 4-2 POINT ESTIMATION 4-3 HYPOTHESIS TESTING Statistical Hypotheses Testing.
McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter 8 Hypothesis Testing.
DOX 6E Montgomery1 Design of Engineering Experiments Part 2 – Basic Statistical Concepts Simple comparative experiments –The hypothesis testing framework.
Chapter 10 The t Test for Two Independent Samples
Copyright (C) 2002 Houghton Mifflin Company. All rights reserved. 1 Understandable Statistics S eventh Edition By Brase and Brase Prepared by: Lynn Smith.
© Copyright McGraw-Hill 2004
Statistical Inference Statistical inference is concerned with the use of sample data to make inferences about unknown population parameters. For example,
Comparing Means Chapter 24. Plot the Data The natural display for comparing two groups is boxplots of the data for the two groups, placed side-by-side.
Chapter 9 Inferences Based on Two Samples: Confidence Intervals and Tests of Hypothesis.
Hypothesis Testing. Suppose we believe the average systolic blood pressure of healthy adults is normally distributed with mean μ = 120 and variance σ.
1 Design and Analysis of Experiments (2) Basic Statistics Kyung-Ho Park.
Hypothesis Tests u Structure of hypothesis tests 1. choose the appropriate test »based on: data characteristics, study objectives »parametric or nonparametric.
Statistical Inference for the Mean Objectives: (Chapter 8&9, DeCoursey) -To understand the terms variance and standard error of a sample mean, Null Hypothesis,
Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Chapter 7 Inferences Concerning Means.
Statistical Decision Making. Almost all problems in statistics can be formulated as a problem of making a decision. That is given some data observed from.
Two-Sample Hypothesis Testing
Statistical Quality Control, 7th Edition by Douglas C. Montgomery.
Chapter 4. Inference about Process Quality
This Week Review of estimation and hypothesis testing
Chapter 2 Simple Comparative Experiments
9 Tests of Hypotheses for a Single Sample CHAPTER OUTLINE
DESIGN OF EXPERIMENT (DOE)
Presentation transcript:

Chapter 2 Simple Comparative Experiments

2.1 Introduction Consider experiments to compare two conditions Simple comparative experiments Example: The strength of portland cement mortar Two different formulations: modified v.s. unmodified Collect 10 observations for each formulations Formulations = Treatments (levels)

The data (Table 2.1) Observation (sample), j Modified Mortar (Formulation 1) Unmodified Mortar (Formulation 2) 1 16.85 17.50 2 16.40 17.63 3 17.21 18.25 4 16.35 18.00 5 16.52 17.86 6 17.04 17.75 7 16.96 18.22 8 17.15 17.90 9 16.59 17.96 10 16.57 18.15

Dot diagram: Form 1 (modified) v.s. Form 2 (unmodified) unmodified (17.92) > modified (16.76)

Hypothesis testing (significance testing): a technique to assist the experiment in comparing these two formulations.

2.2 Basic Statistical Concepts Run = each observations in the experiment Error = random variable Graphical Description of Variability Dot diagram: the general location or central tendency of observations Histogram: central tendency, spread and general shape of the distribution of the data (Fig. 2-2)

Box-plot: minimum, maximum, the lower and upper quartiles and the median

Probability Distributions Mean, Variance and Expected Values

2.3 Sampling and Sampling Distribution Random sampling Statistic: any function of the observations in a sample that does not contain unknown parameters Sample mean and sample variance Properties of sample mean and sample variance Estimator and estimate Unbiased and minimum variance

Degree of freedom: Random variable y has v degree of freedom if E(SS/v) = σ2 The number of independent elements in the sum of squares The normal and other sampling distribution: Sampling distribution Normal distribution: The Central Limit Theorem Chi-square distribution: the distribution of SS t distribution F distribution

2.4 Inferences about the Differences in Means, Randomized Designs Use hypothesis testing and confidence interval procedures for comparing two treatment means. Assume a completely randomized experimental design is used. (a random sample from a normal distribution)

2.4.1 Hypothesis Testing Compare the strength of two different formulations: unmodified v.s. modified Two levels of the factor yij : the the jth observation from the ith factor level, i=1, 2, and j = 1,2,…, ni

Model: yij = μi + ε ij yij ~ N( μ i, σi2) Statistical hypotheses: Test statistic, critical region (rejection region) Type I error, Type II error and Power The two-sample t-test:

Values of t0 that are near zero are consistent with the null hypothesis Values of t0 that are very different from zero are consistent with the alternative hypothesis t0 is a “distance” measure-how far apart the averages are expressed in standard deviation units Notice the interpretation of t0 as a signal-to-noise ratio

So far, we haven’t really done any “statistics” We need an objective basis for deciding how large the test statistic t0 really is In 1908, W. S. Gosset derived the reference distribution for t0 … called the t distribution Tables of the t distribution - text, page 640

A value of t0 between –2. 101 and 2 A value of t0 between –2.101 and 2.101 is consistent with equality of means It is possible for the means to be equal and t0 to exceed either 2.101 or –2.101, but it would be a “rare event” … leads to the conclusion that the means are different Could also use the P-value approach

The P-value is the risk of wrongly rejecting the null hypothesis of equal means (it measures rareness of the event) The P-value in our problem is P = 3.68E-8

Checking Assumptions in the t-test: Equal-variance assumption Normality assumption Normal Probability Plot: y(j) v.s. (j – 0.5)/n

Estimate mean and variance from normal probability plot: Mean: 50 percentile Variance: the difference between 84th and 50th percentile Transformations

2.4.2 Choice of Sample Size Type II error in the hypothesis testing Operating Characteristic curve (O.C. curve) Assume two population have the same variance (unknown) and sample size. For a specified sample size and , larger differences are more easily detected To detect a specified difference , the more powerful test, the more sample size we need.

2.4.3 Confidence Intervals The confidence interval on the difference in means General form of a confidence interval: The 100(1-) percent confidence interval on the difference in two means

2.5 Inferences about the Differences in Means, Paired Comparison Designs Example: Two different tips for a hardness testing machine 20 metal specimens Completely randomized design (10 for tip 1 and 10 for tip 2) Lack of homogeneity between specimens An alternative experiment design: 10 specimens and divide each specimen into two parts.

The statistical model: μ i is the true mean hardness of the ith tip, β j is an effect due to the jth specimen, ε ij is a random error with mean zero and variance σi2 The difference in the jth specimen: The expected value of this difference is

Testing μ 1 = μ 2 <=> testing μd = 0 The test statistic for H0: μd = 0 v.s. H1: μd ≠ 0 Under H0, t0 ~ tn-1 (paired t-test) Paired comparison design Block (the metal specimens) Several points: Only n-1 degree of freedom (2n observations) Reduce the variance and narrow the C.I. (the noise reduction property of blocking)

2.6 Inferences about the Variances of Normal Distributions Test the variances Normal distribution Hypothesis: H0: σ 2 = σ02 v.s. H1: σ 2 ≠ σ02 The test statistic is Under H0, The 100(1-) C.I.:

Hypothesis: H0: σ12 = σ22 v.s. H1: σ12 ≠ σ22 The test statistic is F0 = S12/ S22 , and under H0, F0 = S12/ S22 ~ Fn1-1,n2-1 The 100(1-) C.I.: