'Sample variance' diaporamas de présentation

STATISTICS

STATISTICS. Summarizing, Visualizing and Understanding Data. I. Populations, Variables, and Data. Populations and Samples.

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Describing distributions with numbers

Describing distributions with numbers. William P. Wattles Psychology 302. 1. Measuring the Center of a distribution. Mean The arithmetic average Requires measurement data Median The middle value Mode The most common value. 2. Measuring the center with the Mean. 3. Our first formula.

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Descriptive Data Summarization (Understanding Data)

Descriptive Data Summarization (Understanding Data). Remember: 3VL. NULL corresponds to UNK for unknown u or unk also represended by NULL

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Descriptive Statistics

Descriptive Statistics. 2. Elementary Statistics Larson Farber. Section 2.1. Frequency Distributions and Their Graphs. 102 124 108 86 103 82 71 104 112 118 87 95 103 116 85 122 87 100 105 97 107 67 78 125 109 99 105 99 101 92. Frequency Distributions.

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Descriptive Statistics

Descriptive Statistics. Measures of Central Tendency. Why? What? And How. Remember, data reduction is key Are the scores generally high or generally low? Where the center of the distribution tends to be located Three measures of central tendency Mode Median Mean

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Fractal Financial Market Analysis

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Lecture 3

Lecture 3. Properties of Summary Statistics: Sampling Distribution. Main Theme . How can we use math to justify that our numerical summaries from the sample are good summaries of the population?. Lecture Summary.

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Chi-square and F Distributions

Chi-square and F Distributions. Children of the Normal. Questions. What is the chi-square distribution? How is it related to the Normal? How is the chi-square distribution related to the sampling distribution of the variance?

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Statistics 303

Statistics 303. Chapter 12 ANalysis Of VAriance. ANOVA: Comparing Several Means. The statistical methodology for comparing several means is called analysis of variance, or ANOVA. In this case one variable is categorical. This variable forms the groups to be compared.

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Correlation

Correlation. Covariance. The variance of a variable X provides information on the variability of X . The covariance of two variables X and Y provides information on the related variability of X and Y together.

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7.5 The Variance and Standard Deviation

7.5 The Variance and Standard Deviation. Variance of Probability Distribution Spread Standard Deviation Unbiased Estimate Sample Variance and Standard Deviation Alternative Definitions Chebychev's Inequality. Variance of Probability Distribution.

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Describing Data: Numerical Measures

Describing Data: Numerical Measures. Chapter 3. GOALS. Calculate the arithmetic mean, weighted mean, median, mode, and geometric mean . Explain the characteristics, uses, advantages, and disadvantages of each measure of location .

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Completing the ANOVA

Completing the ANOVA. From the Summary Statistics. Necessary Information. It is possible to complete the Analysis of Variance table for simple regression from the summary statistics. You need the correlation coefficient, the sample size, and the sample variance for the response variable, y.

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Sampling Distributions and Forward Inference

Sampling Distributions and Forward Inference. By simulating the process of drawing random samples of size N from a population with a specific mean and variance, we can learn (a) how much error we can expect on average and

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Summation Algebra Proofs

Summation Algebra Proofs. James H. Steiger. Sum of Deviation Scores. Prove that, in any list of numbers, the sum of the deviation scores is zero, that is,. Linear Transformation Rule for Means.

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Measures of Variability

Measures of Variability. Objective: Students should know what a variance and standard deviation are and for what type of data they typically used . Measures of Variability. Single values that summarize the degree of variability in a set of quantitative data Most common are:

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Properties of Estimators

Properties of Estimators. Parameters:. Describe the population. Statistics:. Describe samples. But we use it to estimate population parameters. Sufficiency Un-biased Resistance Efficiency. Sample Variance as an Unbiased estimator. UNbiased. Biased. Example population:. y: 1, 2, 3.

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Thawed Plasma Expiration Joint Commission Standards

Thawed Plasma Expiration Joint Commission Standards. TAC October 2011. Joint Commission Standards. Federal regulation 21 CFR 606.122 (m) Requires the infusion of thawed plasma within six hours of thawing

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