'Square root transformation' presentation slideshows

Assumptions of multiple regression

Assumptions of multiple regression. Assumption of normality Transformations Assumption of linearity Assumption of homoscedasticity Script for testing assumptions Practice problems. Assumptions of Normality, Linearity, and Homoscedasticity.

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Assumption of linearity

Assumption of linearity. Assumption of linearity Strategy for solving problems Producing outputs for evaluating linearity Assumption of linearity script Sample Problems. Assumption of linearity.

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Testing Multivariate Assumptions

Testing Multivariate Assumptions.

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Computing Transformations

Computing Transformations. Transforming variables Transformations for normality Transformations for linearity. Transformations: Transforming variables to satisfy assumptions.

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Kiyomi Shirakawa Hitotsubashi University / National Statistics Center Yutaka Abe

Creating Synthetic Microdata for Higher Educational Use in Japan : Reproduction of Distribution Type based on the Descriptive Statistics. Kiyomi Shirakawa Hitotsubashi University / National Statistics Center Yutaka Abe Hitotsubashi University Shinsuke Ito Chuo University. Outline.

499 views • 33 slides

Assumption of normality

Assumption of normality. Assumption of normality Transformations Assumption of normality script Practice problems. Assumption of Normality. Many of the statistical methods that we will apply require the assumption that a variable or variables are normally distributed.

595 views • 30 slides

Assumption of normality

Assumption of normality. Assumption of normality Transformations Assumption of normality script Practice problems. Assumption of Normality. Many of the statistical methods that we will apply require the assumption that a variable or variables are normally distributed.

544 views • 27 slides

A Further Look at Transformations

A Further Look at Transformations. The goal of transformations is to change the numeric scale that we use to represent the variable so that the values on the transformed scale more closely approximate the desired normal distribution or linear relationship with another variable.

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Lecture 9

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Transforming the data

Transforming the data. Modified from: Gotelli and Allison 2004. Chapter 8; Sokal and Rohlf 2000 Chapter 13. What is a transformation?. It is a mathematical function that is applied to all the observations of a given variable

298 views • 22 slides

More on data transformations

More on data transformations. No recipes, but some advice. If the primary problem is non-linearity, look at a scatter plot of the data to suggest plausible transformations. It is possible to use transformations other than ln(x) and ln(y). Try fitting. if the trend in your.

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Quantitative Data Display: Part II

Quantitative Data Display: Part II. 8/30/11. Timeplots (AKA Time Series). A time series or timeplot plots a variable measured on interval or ratio scale against time.

256 views • 12 slides

Chapter 15

Chapter 15. Strategies When Population Distributions are Not Normal: Data Transformations and Rank-Order Tests. Assumptions in the Standard Hypothesis-Testing Procedures. Populations follow a normal curve Populations have equal variances Ceiling and floor effects. Data Transformations.

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Homogeneity of Variance

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Homogeneity of Variance

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