Stat 3411 Fall 2012 Exam 1 The questions will cover material from assignments, classroom examples, posted lecture notes, and the text reading material corresponding to the points covered in class. If there is a concept in the book that is not covered in the posted lecture notes, in class, or on assignments, I won't ask you anything about that topic. The questions will be chosen so that the required arithmetic and other work is minimal in order to fit questions into 55 minutes. Plotting, if any, you are asked to do will be very quick and simple. o See problem 6 on the posted chapter 3 sample exam questions. Make use of information posted on the class web site, http://www.d.umn.edu/~rregal/stat3411.html Stat 3411 Syllabus Notes NIST Engineering Statistics Handbook National Institute of Standards and Technology Assignments Data Sets, Statistics Primer, and Examples Project Exam Information Sample Exam Questions From the Notes link: Chapters 1 and 2 1.1 Engineering Statistics: What and Why 1.2 Basic Terminology 1.3 Measurement: Its Importance and Difficulty 1.4 Mathematical Models, Reality, and Data Analysis 2.1 General Principles in the Collection of Engineering Data 2.2 Sampling in Enunerative Studies 2.3 Principles for Effective Experimentation 2.4 Some Common Experimental Designs 2.5 Collecting Engineering Data Outline Notes Guitar Strings: Use of Experimental Design Sticker Adhesion: Use of Models Testing Golf Clubs Observational and Experimental Studies: Ford Explorer Rollovers Excel: Factorial Randomization Table B1: Random Digits The first two chapters concerned basic ideas that go into designing studies and collecting data. The posted outline and notehttp://www.d.umn.edu/~rregal/stat3411_lectures.html summarize some of this information including some points that were not mentioned explicitly in class. Know definitions of terms. For example Observational vs experimental study. Enumerative vs analytical study Population vs sample Univariate vs multivariate data Repeated measures/ paired data Blinding Factorial study, factor, level Response variable, managed variable, experimental variable Concomitant variable, extraneous variable, blocking variable, blocks Randomization Replication Completely randomized design, randomized block design Know advantages and disadvantages of different options. For example - What is the advantage of experimental studies vs observational studies? - What are advantages of randomized block designs vs completely randomized designs? Chapter 3 3.1 Elementary Graphical and Tabular Summaries 3.2 Quantiles and Related Graphical Tools 3.3 Standard Numerical Summaries Outline Notes NIST Box Plots Some Distribution Plots Thermoplastics: Run Chart, Histogram, Boxplot Excel: Plots of Zinc Data Section 3.2.3 Normal Plots NIST Normal Plots Standard Normal Quantiles Normal Plot: Trichloroethylene Plume Normal Plots: Injection Molding For this exam you will not need to find the sample mean, variance and standard deviation simply on your calculator. On future exams, you will need to be able to do this. You will not have to construct any plots yourself. Be able to interpret plots discussed in class, posted notes, or assigned on homework. For example - Dot diagram - Scatter diagram - Box plot - Normal plot - Stem and leaf diagram - Histogram - Run chart Fitting Lines and Surfaces: Chapter 4 4.1 Fitting a Line by Least Squares 4.2 Fitting Curves and Surfaces by Least Squares 4.4 Transformations and Choice of Measurement Scale Section 4.1 Ceramic Regression Board Shrinkage Residual Plots and Ln Transform Section 4.2 Asphalt Quadratic Lake Superior Light Effect of Assumptions: Exponential Fit Textile Example We have done a bit from section 4.4 with logarithmic transformations, but we will continue to do more with this section. Anything I ask you about transformed data will be something that was on homework or covered in classroom examples. I will not ask you to construct any residual or normal plots. Be able to interpret plots and other JMP output. Be able to say what you would do as next steps in analysis. o For example . What residual plots need to be done? . Which variable has curvature that we need to include in the model? . What would you do to improve the model and improve the fit of the model? Know conditions/assumptions that need to be satisfied for this type of analysis? Know how you would check these assumptions/conditions. Understand issues with extrapolation. Be able to find numerical answers similar to questions assignments. o For example . Find R2 from sums of squares. What is the effect of increasing X by 1 unit? By 10 units? Decreasing X by 5 units? Be able to do simple calculation of residuals and other summaries. o For example . The first part of assignment 3 and part (A). Problem 1 on the posted sample exam questions for chapter 4. Sample Exam Questions http://www.d.umn.edu/~rregal/oldexams_stat3411.html Chapters 1 and 2 Questions: Not problem 7 Chapter 3 Questions: Not problems 2 or 7 Sections 4.1, 4.2, 4.4 Questions: Only part (a) of problem 2, not problem 2, only part (a) of problem 8, nor problem 11 Replace Excel with JMP in Problem 12, not problem 17 .