When the Lord created the world and people to live in it - an enterprise which, according to modern science, took a very long time - I could well imagine that He reasoned as follows:
IOE/Stat 265, Fall 2009 Lecture #1: Role of Statistics in Engineering
Ga

Slide 16, Lec20
IOE/Stat 265 Fall 2009 IOE/Stat 265, Fall 2009 Lecture #20A: Addendum
Textbook Example 8.13 Small Sample Binomial Hypothesis Test Sample Binomial Hypothesis Test
8-3 Test Population Proportion 8-X Test Normal Variance (or Std Dev) Test Nor

Two Sample Mean Tests Summary Sample Mean Tests Summary IOE/Stat IOE/Stat 265, Fall 2009 Lecture Lecture #21: Hypothesis Tests for Two Means
9-1 9-2 9-3 9-4 9-5 Differences in Means w/Variances Known Differences in Means w/Variances Unknown Paired t -Test

Case 4. Analysis of Paired Data Analysis of Paired Data
IOE/Stat IOE/Stat 265, Fall 2009 Lecture #22 #22 Paired t Test
9-1 9-2 9-3 9-4 9-5 Differences in Means w/Variances Known Differences in Means w/Variances Unknown Paired t -Test Paired Differences Be

IOE/Stat IOE/Stat 265, Fall 2009 Lecture #23: Hypothesis Testing for Two Variances and Two Proportions
Case 5: Tests for 2 Proportions (Section 9-4) Case 6: Tests for 2 Variances (S (Section 9-5)
1
Case 5: Tests of Two Proportions 5: Tests of Two Proport

warning
Examples of failures
Hyatt
3 mile island
http:/www.wowpage.com/tmi/
3mile island
3mile island
3mile island
Chernobyl
Shuttle
Apollo 13: the successful failure
Compatibility

Visual System: The Eye
Anatomy
Optical Instrument: a camera Accommodation: adjustment of the lens power by control of the ciliary muscle (control of focus) Aperture: adjustment of the pupil diameter by control of radial muscles and a sphincter muscle (con

Visual Presentation of Visual Presentation of Information
Photometry Visual acuity acuity Recommendations
I. Photometry
Luminous flux Rate at which light energy is emitted from a source
1 lumen =
1 W 683
with a = 555nm
for a light with a wave length of

Human vibration: Understanding the effects and Using appropriate methods to prevent disorders
Examples of vibration exposure
An Overview of Human Vibration
Introduction A bit of History What is a Vibration? Definition Physical characteristics Analysis Mea

Signal Detection Theory
"Detection represents a source of uncertainty near the threshold of Perception"
isics Disadvantages Of Classical
Not good enough to evaluate observer's performance and/or behavior
Forced choice : when signal was supposed below t

Design of Controls
Extension of Power & Horse Power Control: An Interface Match human physical and cognitive capability & task requirements Problem: Adequacy In A Context Reduce Physical And Cognitive Workload
Design of Controls: Basic Criteria
1. Accessi

REVIEW Exam 2 Bring calculator (nothing in memory related to course)
Closed book (everything you need is supplied)
Format: TF, SA, MC, Computation
MTM Concept: decomposition in subtasks (elemental motions) simple computation using tables (provided if nece

Review exam 3
Exam time 12:00 -1:30 PM on December 10
Close book Bring calculator: NOTHING related to class in memory even if exam is open book.
FORMAT: True/False, Short Answer, fill in the blanks, COMPUTATIONS, Open ended Content: last section of course

Short Version of Chapter 8 Version of Chapter
IOE/Stat 265, Fall 2009 Lecture #19: #19 Hypothesis Tests for One Mean
8-1 8-2 8-3 8-4 8-5 Hypotheses & Test Procedures Tests about a Population Mean Tests about a Population Proportion Tests about Population

7-3 Prediction Interval (PI) Single Future Value Future Value
IOE/Stat 265 Fall 2009 IOE/Stat 265, Fall 2009 Lecture Lecture #18: Prediction and Tolerance Intervals
Fi conside the best point estimate of single First consider the best point estimate of a s

IOE/Stat IOE/Stat 265, Fall 2009 Lecture #17: Statistical Intervals (for Variances and Proportions) Based on a Single Sample Sa
7.1 7.2 7.3 7.4 Basic Properties of Confidence Intervals Larger Sample Intervals for Means and Proportions Intervals Based on N

Chapter Outline
IOE/Stat 265, Fall 2009 Lecture #3: Probability Concepts
2-1 Sample Spaces & Events
Random Experiments Sample Spaces Events Axioms of Probability Interpretations of Probability Properties of Probability Combinations/Permutations
2
2-2 In

2.2 Additive Rule of Probability
IOE/Mfg/Stats 265, Win 2009 Lecture #4: Conditioning and Independence
More Learning Outcomes: 5. Calculate and interpret conditional probabilities. 6. Determine independence of events and use to calculate probabilities. 7.

IOE/Stat 265, Fall 2009 Lecture #5: Bayes Rules!
Independence
Two events A and B are independent if P(A|B) = P(A)
This implies that the occurrence of event B doesnt have an effect on the occurrence of event A If P(A|B) P(A) , then A and B are dependent.
S

Ch 3: Learning Objectives
IOE/Stat 265, Fall 2009 Lecture #6: Discrete Distributions
3-1 3-2 3.3 3.4 3.5 3.6 Discrete Random Variables Probability Distributions for Discrete Random Variables Expected Values Binomial Probability Distribution Hypergeometric

3-5 Hypergeometric Distribution
IOE/Stat 265, Fall 2009 Lecture #8: Poisson Distribution (and Hypergeometric)
Ch. 3.5-3.6 For a finite population of N items of which M are successes", a sample of n independent observations is drawn without replacement. Th

Ch 4: Learning Objectives 4: Learning Objectives
IOE/Stat 265, Fall 2009 Lecture #9: Continuous Distributions
Ch 4-1 4-4 Determine probabilities from PDF CDF Determine probabilities from PDF, CDF. Determine PDF from CDF and vice-versa Calculate Means, Var

IOE/Stat IOE/Stat 265, Fall 2009 Lecture #10: (Uniform), Normal, and Exponential Distributions
Ch 4-3 4-4
Continuous Uniform Distribution Uniform Distribution
A random variable X has a continuous uniform random variable has continuous uniform distribution

IOE/Stat IOE/Stat 265, Fall 2009 Lecture #11: Gamma, Weibull, and Weibull, Normal, LogNormal, and Beta Distributions
Ch. 4 - 4, 5
Gamma Distribution Properties Distribution Properties
Gamma family Gamma family represents a variety of skewed variety of ske

IOE/S 265 IOE/Stat 265, Fall 2009 2009 Lecture Lecture #12: Joint, Marginal, and Conditional Probability Probability
5-1 5-2 5-3 5-4 5-5 Jointly Distributed Random Variables Expected Values, Covariance & Correlation Statistics and Their Distributions Th D