'Bell shaped' diaporamas de présentation

Review

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Continuous Probability Distributions

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Chapter 6. Continuous Random Variables

Chapter 6. Continuous Random Variables. Reminder: Continuous random variable takes infinitely many values Those values can be associated with measurements on a continuous scale (without gaps or interruptions). Example: Uniform Distribution.

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Chapter 4 Continuous Random Variables and Probability Distributions

PROBABILITY (6MTCOAE205). Chapter 4 Continuous Random Variables and Probability Distributions. Probability Distributions. Probability Distributions. Ch. 3. Discrete Probability Distributions. Continuous Probability Distributions. Ch. 4. Binomial. Uniform. Hypergeometric. Normal.

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An Introduction to Things to Come!

An Introduction to Things to Come!. Administrivia. The details on the class can be found here: www.stanford.edu/class/hrp259 Class participation plus lots of little assignments and 2 exams determines your final grade.

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Biostatistics in Practice

Biostatistics in Practice. Session 2: Summarization of Quantitative Information. Peter D. Christenson Biostatistician http://gcrc. LABioMed.org /Biostat. Topics for this Session. Experimental Units Independence of Measurements Graphs: Summarizing Results Graphs: Aids for Analysis

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Things gone bye.

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Chapter 1

Chapter 1. Picturing Distributions with Graphs. Statistics. Statistics is a science that involves the extraction of information from numerical data obtained during an experiment or from a sample. It involves:. the design of the experiment or sampling procedure,

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CHAPTER 6 Statistical Analysis of Experimental Data

CHAPTER 6 Statistical Analysis of Experimental Data. Table 6.1 shows the results of a set of 60 measurements of air temperature in the duct. These temperature data are observed values of a random variable.

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4. Probability Distributions

4. Probability Distributions. Probability : With random sampling or a randomized experiment, the probability an observation takes a particular value is the proportion of times that outcome would occur in a long sequence of observations.

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Biostatistics in Practice

Biostatistics in Practice. Session 2: Summarization of Quantitative Information. Youngju Pak Biostatistician http://research.LABioMed.org/Biostat. Topics for this Session. Experimental Units Independence of Measurements Graphs: Summarizing Results Graphs: Aids for Analysis

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16 Mathematics of Normal Distributions

16 Mathematics of Normal Distributions. 16.1 Approximately Normal Distributions of Data 16.2 Normal Curves and Normal Distributions 16.3 Standardizing Normal Data 16.4 The 68-95-99.7 Rule 16.5 Normal Curves as Models of Real-Life Data Sets 16.6 Distribution of Random Events

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Some probability distribution The Normal Distribution

Some probability distribution The Normal Distribution. Objectives. Introduce the Normal Distribution Properties of the Standard Normal Distribution Introduce the Central Limit Theorem. Normal Distribution. Why are normal distributions so important?

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Probability Distributions

Probability Distributions. Random Variable A numerical outcome of a random experiment Can be discrete or continuous Generically, x Probability Distribution The pattern of probabilities associated with all of the random variables for a specific experiment Can be a table, formula, or graph

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Biostatistics in Practice

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Density Curves Normal Distribution Area under the curve

Density Curves Normal Distribution Area under the curve. Learning Objectives. By the end of this lecture, you should be able to: Describe what is meant by a density curve Be able to identify normal (bell-shaped), skewed, bimodal, uniform distributions from a density curve

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Normal Random Variables

Normal Random Variables. In the class of continuous random variables, we are primarily interested in NORMAL random variables. These are a continuous random variable with a bell-shaped distribution. These normal or bell-shaped variables occur often in nature. Example: Heights of Males

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The Normal Distribution

The Normal Distribution. The Area under the curve. The area under the curve represents everything: 100%. The mean is in the middle. 50% of the data is below the mean. 50% of the data is above the mean. Remember that the mean=median=mode!. Within one standard deviation. P(-1<Z<1) = 68%.

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STA 291 Fall 2009

STA 291 Fall 2009. Lecture 6 Dustin Lueker. Measures of Variation. Statistics that describe variability Two distributions may have the same mean and/or median but different variability Mean and Median only describe a typical value, but not the spread of the data Range Variance

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aspoitalia

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