'Normal distribution' presentation slideshows

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

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

C. Energy Environment Health. DMI Modeling Systems And Plans For CEEH Activities. Gross, A. Baklanov, U. S. Korsholm, J. H. Sørensen, A. Mahura & A. Rasmussen. Content:. Off-Line Air Pollution Modeling On-Line Air Pollution Modeling

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

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.

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.

32 nd Eastern Region Annual Airports Conference. ERLPM Workshop Statistical Analysis. Carl Steinhauer Consultant. Limit # of samples Statistical Analysis Estimate average and % within limits. Analysis % Taller than 5’-5” % between 5’-5” and 6’-5” Average Height. Overview.

Starting point for generating other distributions. Normal Distribution. Commonly used – processes where many random variables are added results in normal distribution. Lognormal Distribution. Perhaps not as commonly recognized or used as the

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:

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.

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).

Numbers. Numbers mean different things in different situations. Consider three answers that appear to be identical but are not. “What number were you wearing in the race?” “5” What place did you finish in ?” “5” How many minutes did it take you to finish?” “5”. Number Scales. Nominal Scale

CPSC 531:Input Modeling. Instructor: Anirban Mahanti Office: ICT 745 Email: mahanti@cpsc.ucalgary.ca Class Location: TRB 101 Lectures: TR 15:30 – 16:45 hours Class web page: http://pages.cpsc.ucalgary.ca/~mahanti/teaching/F05/CPSC531

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

Chapter 5: Continuous Random Variables. Where We’ve Been. Using probability rules to find the probability of discrete events Examined probability models for discrete random variables. Where We’re Going. Develop the notion of a probability distribution for a continuous random variable

Linear Discriminant Analysis and Its Variations. Abu Minhajuddin CSE 8331. Department of Statistical Science Southern Methodist University April 27, 2002. Plan…. The Problem Linear Discriminant Analysis Quadratic Discriminant Analysis Other Extensions Evaluation of the Method

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.

About the Exam. No cheat sheet Bring a calculator You may NOT use the calculator on your phone or iPad or computer or other media device Short essay answers Math problems to be solved Know all materials covered including last Thursday’s lecture on simulation

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.

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