l7_anova4 - Analysis of Variance ANOVA 16.881 Robust System...

Info icon This preview shows pages 1–9. Sign up to view the full content.

View Full Document Right Arrow Icon
Analysis of Variance ANOVA Robust System Design 16.881 Session #7 MIT
Image of page 1

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

View Full Document Right Arrow Icon
Proposed Schedule Changes • Switch lecture No quiz – Informal (ungraded) presentation of term project ideas Read Phadke ch. 7 -- Construction Orthogonal Arrays – Quiz on ANOVA – Noise experiment due Robust System Design 16.881 Session #7 MIT
Image of page 2
Learning Objectives • Introduce hypothesis testing • Introduce ANOVA in statistic practice • Introduce ANOVA as practiced in RD • Compare to ANOM • Get some practice applying ANOVA in RD • Discuss / compare / contrast Robust System Design 16.881 Session #7 MIT
Image of page 3

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

View Full Document Right Arrow Icon
Hypothesis Testing A technique that uses sample data from a population to come to reasonable conclusions with a certain degree of confidence Robust System Design 16.881 Session #7 MIT
Image of page 4
Hypothesis Testing Terms Null Hypothesis (H o ) -- The hypothesis to be tested (accept/reject) Test statistic -- A function of the parameters of the experiment on which you base the test Critical region -- The set of values of the test statistic that lead to rejection of H o Robust System Design 16.881 Session #7 MIT
Image of page 5

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

View Full Document Right Arrow Icon
Hypothesis Testing Terms (cont.) Level of significance ( α ) -- A measure of confidence that can be placed in a result not merely being a matter of chance p value -- The smallest level of significance at which you would reject H o Robust System Design 16.881 Session #7 MIT
Image of page 6
Robust System Design Session #7 MIT 16.881 Comparing the Variance of Two Samples • Null Hypothesis -- H o : • Test Statistic -- • Acceptance criteria -- • Assumes independence & normal dist. r = 2 1 σ σ F 1 r 2 Var X1 ( Var X2 ( . 2 1 5 . 0 ) 2 , 1 , ( α < d d F pF ) )
Image of page 7

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

View Full Document Right Arrow Icon
F Distribution • Three arguments – d1 (numerator DOF) – d2 (denominator DOF) – x (cutoff) F(x,d1,d2) Γ d1 d2 2 d1 d1 2 .
Image of page 8
Image of page 9
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern