IV.3 Designs to Minimize Variability Background Background An Example An Example –Design Steps –Transformations –The Analysis A Case Study A Case Study.

Slides:



Advertisements
Similar presentations
You have data! What’s next? Data Analysis, Your Research Questions, and Proposal Writing Zoo 511 Spring 2014.
Advertisements

Statistics Review – Part II Topics: – Hypothesis Testing – Paired Tests – Tests of variability 1.
Multiple Comparisons in Factorial Experiments
Normal Distributions: Finding Probabilities
Errors in Chemical Analyses: Assessing the Quality of Results
The Essentials of 2-Level Design of Experiments Part I: The Essentials of Full Factorial Designs The Essentials of 2-Level Design of Experiments Part I:
Regression Analysis Once a linear relationship is defined, the independent variable can be used to forecast the dependent variable. Y ^ = bo + bX bo is.
Chapters 3 Uncertainty January 30, 2007 Lec_3.
Introduction to Inference Estimating with Confidence Chapter 6.1.
IV.4.1 IV.4 Signal-to-Noise Ratios o Background o Example.
Statistical Treatment of Data Significant Figures : number of digits know with certainty + the first in doubt. Rounding off: use the same number of significant.
Chapter 9 Hypothesis Testing.
Correlation and Regression Analysis
1. Normal Curve 2. Normally Distributed Outcomes 3. Properties of Normal Curve 4. Standard Normal Curve 5. The Normal Distribution 6. Percentile 7. Probability.
Chapter 5: Continuous Random Variables
Continuous Probability Distributions A continuous random variable can assume any value in an interval on the real line or in a collection of intervals.
Relationships Among Variables
Statistical Analysis. Purpose of Statistical Analysis Determines whether the results found in an experiment are meaningful. Answers the question: –Does.
1 Doing Statistics for Business Doing Statistics for Business Data, Inference, and Decision Making Marilyn K. Pelosi Theresa M. Sandifer Chapter 11 Regression.
V. Rouillard  Introduction to measurement and statistical analysis ASSESSING EXPERIMENTAL DATA : ERRORS Remember: no measurement is perfect – errors.
Introduction to Linear Regression and Correlation Analysis
Answering questions about life with statistics ! The results of many investigations in biology are collected as numbers known as _____________________.
Darts anyone? A study in probability Sean Macduff ETEC 442 June 23, 2005.
QNT 531 Advanced Problems in Statistics and Research Methods
1 Least squares procedure Inference for least squares lines Simple Linear Regression.
Statistics and Quantitative Analysis Chemistry 321, Summer 2014.
The Scientific Method. The Scientific Method The Scientific Method is a problem solving-strategy. *It is just a series of steps that can be used to solve.
The Normal Distribution. Bell-shaped Density The normal random variable has the famous bell-shaped distribution. The most commonly used continuous distribution.
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.
The Scientific Method Formulation of an H ypothesis P lanning an experiment to objectively test the hypothesis Careful observation and collection of D.
Normal Curve 64, 95, 99.7.
Modular 11 Ch 7.1 to 7.2 Part I. Ch 7.1 Uniform and Normal Distribution Recall: Discrete random variable probability distribution For a continued random.
Chapter 6: Random Errors in Chemical Analysis CHE 321: Quantitative Chemical Analysis Dr. Jerome Williams, Ph.D. Saint Leo University.
Measures of central tendency are statistics that express the most typical or average scores in a distribution These measures are: The Mode The Median.
Lesson 12-2 Exponential & Logarithmic Functions
Significant Figures Always record data as accurately as you can (as many sig. figs. as method allows) The last digit of the value that you record should.
§ 5.3 Normal Distributions: Finding Values. Probability and Normal Distributions If a random variable, x, is normally distributed, you can find the probability.
Statistics Chapter 6 / 7 Review. Random Variables and Their Probability Distributions Discrete random variables – can take on only a countable or finite.
Barnett/Ziegler/Byleen Finite Mathematics 11e1 Chapter 11 Review Important Terms, Symbols, Concepts Sect Graphing Data Bar graphs, broken-line graphs,
Holt McDougal Algebra 2 Significance of Experimental Results How do we use tables to estimate areas under normal curves? How do we recognize data sets.
The Essentials of 2-Level Design of Experiments Part I: The Essentials of Full Factorial Designs The Essentials of 2-Level Design of Experiments Part I:
1 Module One: Measurements and Uncertainties No measurement can perfectly determine the value of the quantity being measured. The uncertainty of a measurement.
Discrete and Continuous Random Variables. Yesterday we calculated the mean number of goals for a randomly selected team in a randomly selected game.
The Essentials of 2-Level Design of Experiments Part I: The Essentials of Full Factorial Designs The Essentials of 2-Level Design of Experiments Part I:
© 2010 Pearson Prentice Hall. All rights reserved 7-1.
Smith/Davis (c) 2005 Prentice Hall Chapter Fifteen Inferential Tests of Significance III: Analyzing and Interpreting Experiments with Multiple Independent.
1 7.5 CONTINUOUS RANDOM VARIABLES Continuous data occur when the variable of interest can take on anyone of an infinite number of values over some interval.
Quality Control: Analysis Of Data Pawan Angra MS Division of Laboratory Systems Public Health Practice Program Office Centers for Disease Control and.
Chapter 7: The Distribution of Sample Means. Frequency of Scores Scores Frequency.
Chapter 11: Measurement and data processing Objectives: 11.1 Uncertainty and error in measurement 11.2 Uncertainties in calculated results 11.3 Graphical.
Chapter 6: Random Errors in Chemical Analysis. 6A The nature of random errors Random, or indeterminate, errors can never be totally eliminated and 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.
Cell Diameters and Normal Distribution. Frequency Distributions a frequency distribution is an arrangement of the values that one or more variables take.
Chapter 4 Basic Estimation Techniques
How to display data clearly & effectively
Random Variables and Probability Distribution (2)
Inverse Transformation Scale Experimental Power Graphing
Normal Probability Distributions
Analyzing the Association Between Categorical Variables
The Essentials of 2-Level Design of Experiments Part I: The Essentials of Full Factorial Designs Developed by Don Edwards, John Grego and James Lynch.
10-5 The normal distribution
Chapter 7: The Distribution of Sample Means
ENM 310 Design of Experiments and Regression Analysis Chapter 3
Criteria for tests concerning standard deviations.
The Essentials of 2-Level Design of Experiments Part I: The Essentials of Full Factorial Designs Developed by Don Edwards, John Grego and James Lynch.
The Essentials of 2-Level Design of Experiments Part I: The Essentials of Full Factorial Designs Developed by Don Edwards, John Grego and James Lynch.
IV.3 Designs to Minimize Variability
Objectives 6.1 Estimating with confidence Statistical confidence
Presumptions Subgroups (samples) of data are formed.
Presentation transcript:

IV.3 Designs to Minimize Variability Background Background An Example An Example –Design Steps –Transformations –The Analysis A Case Study A Case Study

Background Accuracy/Precision Factors Can Affect Response Variable by Either Factors Can Affect Response Variable by Either –Changing Its Average Value (Accuracy) –Changing Its Variation (Precision) or –BOTH

Background Example 4 - Example I.2.3 Revisited Which Factors Affect Which Factors Affect –Accuracy? –Precision?

Background Analysis for Changes in Variability For studying Variability, we can use ALL the designs, ALL the ideas that we used when studying changes in mean response level. For studying Variability, we can use ALL the designs, ALL the ideas that we used when studying changes in mean response level. However, However, –Smaller Variability is ALWAYS better –We MUST work with replicated experiments –We will need to transform the response s

Example 5 Mounting an Integrated Circuit on Substrate Figure 5 - Factor Level Lochner and Matar - Figure 5.11 Response: bond strength Response: bond strength

Example 5 - Design Steps Selecting the Design Figure 6 - The Experimental Design Lochner and Matar - Figure Select an appropriate experimental design 1. Select an appropriate experimental design

Example 5 - Design Steps Replication and Randomization 2. Determine number of replicates to be used 2. Determine number of replicates to be used –Consider at Least 5 (up to 10) –In Example 5: 5 replicates, 40 trials 3. Randomize order of ALL trials 3. Randomize order of ALL trials –Replicates Run Sequentially Often Have Less Variation Than True Process Variation –This May Be Inconvenient!

Example 5 - Design Steps Collecting the Data Figure 7 - The Data Lochner and Matar - Figure Perform experiment; record data 4. Perform experiment; record data 5. Group data for each factor level combination and calculate s. 5. Group data for each factor level combination and calculate s.

Example 5 - Design Steps The Analysis 6. Calculate logarithms of standard deviations obtained in 5. Record these. 6. Calculate logarithms of standard deviations obtained in 5. Record these. 7. Analyze log s as the response. 7. Analyze log s as the response.

Transformations Why transform s? If the data follow a bell-shaped curve, then so do the cell means and the factor effects for the means. However, the cell standard deviations and factor effects of the standard deviations do not follow a bell-shaped curve. If the data follow a bell-shaped curve, then so do the cell means and the factor effects for the means. However, the cell standard deviations and factor effects of the standard deviations do not follow a bell-shaped curve. If we plot such data on our normal plotting paper, we would obtain a graph that indicates important or unusual factor effects in the absence of any real effect. The log transformation ‘normalizes’ the data. If we plot such data on our normal plotting paper, we would obtain a graph that indicates important or unusual factor effects in the absence of any real effect. The log transformation ‘normalizes’ the data.

Transformations Distributions and Normal Probability Plots of s 2 and Log(s 2 )

Example 5 - Analysis Figure 8 - Response Table for Mean Lochner and Matar - Figure 5.14 yABCABACBCD Standard Order Bond Strength Adhesive Type Conductor Material Cure Time IC Post Coating Sum Divisor Effect

Example 5 - Analysis Figure 9 - Response Table for Log(s) Lochner and Matar - Figure 5.15 yABCABACBCD Standard OrderLog(s) Adhesive Type Conductor Material Cure Time IC Post Coating Sum Divisor Effect

Example 5 - Analysis Figure 10 - Effects Normal Probability Plot for Mean What Factor Settings Favorably Affect the Mean? What Factor Settings Favorably Affect the Mean?

Example 5 - Analysis Figure 11 - Effects Normal Probability Plot for Log(s) Lochner and Matar - Figure 5.16 What Factor Settings Favorably Affect Variability? What Factor Settings Favorably Affect Variability?

Example 5 - Interpretation Silver IC post coating increases bond strength and decreases variation in bond strength. Silver IC post coating increases bond strength and decreases variation in bond strength. Adhesive D2A decreases variation in bond strength. Adhesive D2A decreases variation in bond strength. 120-minute cure time increases bond strength. 120-minute cure time increases bond strength.

Case Study 1 Filling Weight of Dry Soup Mix - Factors and Response

Case Study 1 Filling Weight of Dry Soup Mix - Effects Table Interpret This Data Interpret This Data –Determine the Important Effects –Do the Interaction Tables and Plots for Significant Interactions Interpret This Data Interpret This Data –Determine the Important Effects –Do the Interaction Tables and Plots for Significant Interactions