'Variance' presentation slideshows

Information Theoretic Learning

Information Theoretic Learning. Jose C. Principe Yiwen Wang Computational NeuroEngineering Laboratory Electrical and Computer Engineering Department University of Florida www.cnel.ufl.edu principe@cnel.ufl.edu. Acknowledgments. Dr. Deniz Erdogmus My students: Puskal Pokharel

1.59k views • 116 slides

Biostatistics

1.26k views • 71 slides

Biometrical Models and Introduction to Genetic Analysis

Biometrical Models and Introduction to Genetic Analysis. Pak Sham, University of Hong Kong 4 th March 2019 The 2019 International Workshop on Statistical Genetics Methods for Human Complex Traits. What is biometrical genetics?.

917 views • 58 slides

Discussion of Stanford University Budget Process & Systems

862 views • 56 slides

Summarizing Variation

Summarizing Variation. Michael C Neale PhD Virginia Institute for Psychiatric and Behavioral Genetics Virginia Commonwealth University. Overview. Mean Variance Covariance Not always necessary/desirable. Computing Mean. Formula E (x i )/N Can compute with Pencil Calculator SAS SPSS

856 views • 48 slides

General Linear Models

961 views • 47 slides

Analyzing & Interpreting Data

1.6k views • 42 slides

Discrete Probability Distributions

772 views • 32 slides

Enrico Infante* EUROSTAT, Unit G3: Short-Term Statistics; Tourism

629 views • 20 slides

Review

Review. Poisson Random Variable P [X = i ] = e - ? ? i / i! i.e. the probability that the number of events is i E [ X ] = ? for Poisson Random Variable ? X 2 = ? . Signal to Noise Ratio. Object we are trying to detect. ?I. I. Background. Definitions:.

337 views • 19 slides

Statistics fun and exciting Workshop

316 views • 19 slides

Importance of Statistics in Psychology

Higher Education Academy Psychology Learning and Teaching Conference, Bath, July 2008 Development of an interactive visual workspace to aid the intuitive understanding of ANOVA (Analysis of Variance) Richard Stephens & Sol Nte School of Psychology. Importance of Statistics in Psychology.

987 views • 18 slides

Chapter 4 Return and Risk

Chapter 4 Return and Risk. The objectives of this chapter are to enable you to: Understand and calculate returns as a measure of economic efficiency Understand the relationships between present value and IRR and YTM

450 views • 18 slides

540 views • 18 slides

CHAPTER 7

606 views • 17 slides

LECTURE 19: FOUNDATIONS OF MACHINE LEARNING

309 views • 14 slides

Body Shape Perception in Healthy and Eating-Disordered Women

Deanna Puttonen University of British Columbia Research Paper by Uher et al. Body Shape Perception in Healthy and Eating-Disordered Women. Background. Dissatisfaction with body shape and size is the rule, not the exception Eating disorders: Anorexia Bulimia Distorted body perceptions.

566 views • 12 slides

Teaching Survey Sampling Theory using R

Teaching Survey Sampling Theory using R. Michael D. Larsen George Washington University UseR 2010 poster session, 7/21/10. Uses of R in the course. Data analysis; exploring data Programming complex formulas Simulation of properties of estimators

901 views • 12 slides

Maximum likelihood estimators

Maximum likelihood estimators. Example: Random data X i drawn from a Poisson distribution with unknown ??? We want to determine ?? For any assumed value of ?? the probability density at X=X i is: Likelihood of full set of measurements for any given ?? is:

729 views • 11 slides

- Statistical Analysis 2 -

Topic 9 - Statistical Analysis 2 - T-test & Z-test Deal with only one metric question Test the hypothesis about the mean Whether to use T-test or Z-test? Depend on the sample size If n>30, use Z-test If n<30, use T-test Determine whether the test is one-tailed or two-tailed

1.84k views • 10 slides