'Normal distribution' presentation slideshows

Research Methods & Design in Psychology

Lecture 3 Descriptives & Graphing Lecturer: James Neill Research Methods & Design in Psychology Overview Univariate descriptives & graphs Non-parametric vs. parametric Non-normal distributions Properties of normal distributions Graphing relations b/w 2 and 3 variables

1.85k views • 87 slides

Research Methods & Design in Psychology

Lecture 3 Descriptives & Graphing Lecturer: James Neill. Research Methods & Design in Psychology. Overview. Univariate descriptives & graphs Non-parametric vs. parametric Non-normal distributions Properties of normal distributions Graphing relations b/w 2 and 3 variables.

1.23k views • 87 slides

Moment Generating Functions

Moment Generating Functions. The Uniform distribution from a to b. Continuous Distributions. The Normal distribution (mean m , standard deviation s ). The Exponential distribution. Weibull distribution with parameters a and b . The Weibull density, f ( x ). ( a = 0.9, b = 2).

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NEW MODELS FOR HIGH AND LOW FREQUENCY VOLATILITY

NEW MODELS FOR HIGH AND LOW FREQUENCY VOLATILITY. Robert Engle NYU Salomon Center Derivatives Research Project. FORECASTING WITH GARCH. DJ RETURNS. DOW JONES SINCE 1990. Dependent Variable: DJRET Method: ML - ARCH (Marquardt) - Normal distribution Date: 01/13/05 Time: 14:30

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The Rotary Youth Exchange Experience: Culture Shock and Reverse Culture Shock

The Rotary Youth Exchange Experience: Culture Shock and Reverse Culture Shock. YEO Pre-Convention Meeting Montreal, 2010 Dennis White and Justin Burnett .

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ERLPM Workshop Statistical Analysis

650 views • 39 slides

Continuous Probability Distributions

Continuous Probability Distributions. Continuous Random Variables and Probability Distributions. Random Variable: Y Cumulative Distribution Function (CDF): F ( y )=P( Y ? y ) Probability Density Function (pdf): f ( y )=d F ( y )/d y Rules governing continuous distributions:

756 views • 37 slides

Continuous Probability Distributions

Continuous Probability Distributions. Chapter 7. GOALS. Understand the difference between discrete and continuous distributions. Compute the mean and the standard deviation for a uniform distribution . Compute probabilities by using the uniform distribution.

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CPSC 531:Input Modeling

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About the Exam

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Chapter 5: Continuous Random Variables

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DMI Modeling Systems And Plans For CEEH Activities

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Hypotheses Testing

Hypotheses Testing Example 1 We have tossed a coin 50 times and we got k = 19 heads Should we accept/reject the hypothesis that p = 0.5 (the coin is fair) Null versus Alternative Null hypothesis (H 0 ): p = 0.5

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Survey Methodology Sampling error and sample size

Survey Methodology Sampling error and sample size. EPID 626 Lecture 4. Lecture overview. Finish discussion of nonprobability sampling Discuss sampling error Discuss sample sizes Some practical exercises. Nonprobability sampling designs.

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Linear Discriminant Analysis and Its Variations

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Materials for Lecture 08

Materials for Lecture 08. Chapters 4 and 5 Chapter 16 Sections 3.2-3.7.3 Lecture 08 Bernoulli . xlsx Lecture 08 Normality Test.xls Lecture 08 Simulation Model with Simetar.xlsx Lecture 08 Normal.xls Lecture 08 Simulate a Reg Model.xls. Stochastic Simulation.

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Starting point for generating other distributions

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Numbers

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Confidence Intervals for Population Means

Confidence Intervals for Population Means. BUSA 2100, Sections 8.1, 8.2. Estimation Process. Point estimates are single numbers, e.g. X-bar = $35,000. Point estimates should be close to the true population mean (or proportion), but are almost never exactly equal.

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Approximations to Probability Distributions: Limit Theorems

Approximations to Probability Distributions: Limit Theorems. Sequences of Random Variables. Interested in behavior of functions of random variables such as means, variances, proportions For large samples, exact distributions can be difficult/impossible to obtain

514 views • 15 slides