'Variance' diaporamas de présentation

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Analyzing & Interpreting Data

Analyzing & Interpreting Data

Analyzing & Interpreting Data Assessment Institute Summer 2005 Categorical vs. Continuous Variables Categorical Variables Examples  S tudent’s major, enrollment status, gender, ethnicity; also whether or not the student passed the cutoff on a test Continuous Variables

By jacob
(848 views)

- Statistical Analysis 2 -

- 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

By jana
(1162 views)

Importance of Statistics in Psychology

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.

By Roberta
(650 views)

Body Shape Perception in Healthy and Eating-Disordered Women

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.

By mike_john
(440 views)

Chapter 4 Return and Risk

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

By Mia_John
(247 views)

Biometrical Models and Introduction to Genetic Analysis

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?.

By liam
(266 views)

Summarizing Variation

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

By arleen
(364 views)

Information Theoretic Learning

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

By Albert_Lan
(389 views)

General Linear Models

General Linear Models

General Linear Models. The theory of general linear models posits that many statistical tests can be solved as a regression analysis, including t-tests and ANOVA’s.

By Pat_Xavi
(212 views)

Discussion of Stanford University Budget Process & Systems

Discussion of Stanford University Budget Process & Systems

Discussion of Stanford University Budget Process & Systems . September 6, 2005. Dr. Andrew Harker Director of Budget Management aharker@stanford.edu 650.725.0666. Outline. Stanford Financial Overview Stanford Financial Management Changing Role of the “Budget”

By Albert_Lan
(217 views)

Teaching Survey Sampling Theory using R

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

By JasminFlorian
(716 views)

Biostatistics

Biostatistics

Biostatistics. Unit 5 Samples Needs to be completed. 12/24/13. Sampling distributions. Sampling distributions are important in the understanding of statistical inference. 

By mahala
(411 views)

Discrete Probability Distributions

Discrete Probability Distributions

Chapter 4. Discrete Probability Distributions. Probability Distributions. § 4.1. x. x. 0. 0. 2. 2. 4. 4. 6. 6. 8. 8. 10. 10. Random Variables. A random variable x represents a numerical value associated with each outcome of a probability distribution.

By abe
(155 views)

LECTURE 19: FOUNDATIONS OF MACHINE LEARNING

LECTURE 19: FOUNDATIONS OF MACHINE LEARNING

LECTURE 19: FOUNDATIONS OF MACHINE LEARNING. Objectives: Occam’s Razor No Free Lunch Theorem Minimum Description Length Bias and Variance Jackknife Bootstrap

By dana
(150 views)

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

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

New innovative 3-way ANOVA a-priori test for direct vs. indirect approach in Seasonal Adjustment. Workshop on Seasonal Adjustment – Luxembourg, 6 March 2012. Enrico Infante* EUROSTAT, Unit G3: Short-Term Statistics; Tourism. Dario Buono * EUROSTAT, Unit B1: Quality, Research and Methodology.

By march
(402 views)

CHAPTER 7

CHAPTER 7

CHAPTER 7. Flexible Budgets, Direct-Cost Variances, and Management Control. Basic Concepts. Variance – difference between an actual and an expected (budgeted) amount Management by Exception – the practice of focusing attention on areas not operating as expected (budgeted)

By parson
(424 views)

Review

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:.

By annice
(144 views)

Maximum likelihood estimators

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:

By vui
(383 views)

Today’s Lecture

Today’s Lecture

Today’s Lecture. One more test for normality Shapiro-Wilk Test Testing variances Equality of Variance via the F-Distribution Levene’s Test for Equality of Variances. Reference Material. Shapiro and Wilk, 1965. Biometrika (52:3 and 4) pgs. 591-611. Burt and Barber, page 325

By lenci
(249 views)

Statistics fun and exciting Workshop

Statistics fun and exciting Workshop

Statistics fun and exciting Workshop. What’s going to be covered. Diagrams Data Summary and Presentation Binomial distribution Engineering/Statistics Toolbox Z-test Type 2 Error T-test C 2 Test. Dot Diagram. Box Plot. Q 3. Q 2. Q 1. x = 1550. IQR. 1.5 IQR. 1050. 1.5 IQR. 1070.

By thy
(111 views)

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