PPT-Data: Normal Distribution

J C F Gauss Central Limit Theorem Symmetry around μ μ is the confluence of the mean median and mode of the data μ splits the data in half Pareto Principle 2080 Rule 20 of . Explaining the Normal Distribution. Preliminaries: How to describe data. Discussion Question: How do we describe data in Statistics?. In this course, the way we describe data is by using the C.U.S.S. Method. We look at these characteristics:. Reading: . Chapter 11, Sections 12-1 and 12-2 . of Applied Hydrology. 04/04/2006. 2. Probability. A measure of how likely an event will occur. A number expressing the ratio of favorable outcome to the all possible outcomes . Reading: Applied Hydrology Sections 12-2 to 12-6. 2. Frequency analysis for extreme events . If you know T, you can find y. T. , and once y. T. is know, x. T. can be computed by . Q. Find a flow (or any other event) that has a return period of T years. Reading: . Chapter 11, Sections 12-1 and 12-2 . of Applied Hydrology. 04/04/2006. 2. Probability. A measure of how likely an event will occur. A number expressing the ratio of favorable outcome to the all possible outcomes . Objectives:. For variables with relatively normal distributions:. Students should know the approximate percent of observations in a set of data that will fall between the mean and ± 1 . sd. , 2 . sd. Distributions. Definition. Many sets of data fit what is called a Normal Distribution: EG.  . Examples when the Normal distribution arises. Looking at the national averages for NCEA.. When measuring heights, weights, arm spans, hand spans . 2.1 Density Curves and the Normal Distributions. 2.2 Standard Normal Calculations. 2. Histogram for Strength of Yarn Bobbins. X. X. X. X. X. X. X. X. X. X. X. X. X. X. X. X. X. X. X. X. X. X. X. X. Bobbin #1: 17.15 g/tex. @UWE_JT9. @. dave_lush. Scientific . Practice. The Binomial Distribution. This distribution can be seen when the outcomes have discrete values…. eg. rolling dice. Assumptions…. Fixed . number of . Distributions. Lecture Presentation Slides. Macmillan Learning ©. 2017. Chapter 1. Looking at Data—. Distributions. Introduction. 1.1 Data. 1.2 Displaying Distributions with Graphs. 1.3 Describing Distributions with Numbers. DSQR Training. Basic Statistical Concepts. Ted Fisher/Fred Nunez. Corporate Quality . Goals. At the end of this section you be able to use and identify;. Continuous data. Discrete data. Parameters describing the center. 2. Frequency analysis for extreme events . If you know T, you can find y. T. , and once y. T. is know, x. T. can be computed by . Q. Find a flow (or any other event) that has a return period of T years. It is also known as the Gaussian distribution and the bell curve. .. The general form of its probability density function is-. Normal Distribution in . Statistics. The normal distribution is the most important probability distribution in statistics because it fits many natural phenomena. . Dehaish. Outlines. Normal distribution. Standard normal distribution . Find probability when known z score . Find z score from known areas . Conversion to Standard normal distribution.. Sampling distribution of sample mean . Normal random variables. The Normal distribution is by far the most important and useful probability distribution in statistics, with many applications in economics, engineering, astronomy, medicine, error and variation analysis, etc. The Normal distribution is often called the bell curve, due to its distinctive shape..