Assessing Normality and Data Transformations

Role of Normality. Many statistical methods require that the numeric variables we are working with have an approximate normal distribution.For example, t-tests, F-tests, and regression analyses all require in some sense that the numeric variables are approximately normally distributed.. Standardized normal distribution with empirical rule percentages..

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Reliability

Reliability. Consistency in testing. Types of variance. Meaningful variance Variance between test takers which reflects differences in the ability or skill being measured Error variance

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Abnormal Psychology: Concepts of Normality

Abnormal Psychology: Concepts of Normality. Part I. Concepts of normality and abnormality. It is not any easy task to define what is normal and what is abnormal .

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Assessing Normality

Assessing Normality. Are my data normally distributed?. Evidence for Normality. Mean and median should not be too different. Histogram or stem-and-leaf plot should look symmetric. Box plot should look symmetric. Normal probability plot should roughly be a straight line.

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Effect of Violations of Normality

Effect of Violations of Normality. On the Correlation Coefficient t-Test. Edgell and Noon, 1984. Is the t-test for correlation coefficients robust to violations of its assumptions?. Overview. Review t-test of the Correlation Coefficient Violations Bivariate Normal Assumption

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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.

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Assessing Normality

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Normality volumetric Calculations

Normality volumetric Calculations.

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OUTLIER, HETEROSKEDASTICITY,AND NORMALITY

OUTLIER, HETEROSKEDASTICITY,AND NORMALITY. Robust Regression HAC Estimate of Standard Error Quantile Regression. Robust regression analysis. alternative to a least squares regression model when fundamental assumptions are unfulfilled by the nature of the data

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Normality?

Compare one or two populations. Experimental Design?. Independent Samples. Blocked Samples. Normality?. Non-normal distribution. Normal distribution. Number of populations?. Number of populations?. Compare two ore more populations. see Page 2. Kruskal-Wallis Test. Kruskal-Wallis Test.

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Approaches to Normality

Approaches to Normality. What is normal?. Normality v Abnormality. Normal is … Often defined as a pattern of thoughts, feelings or behaviour that conforms to a usual, typical or expected standard depending on the approach Abnormal …

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Assessing Normality

Assessing Normality. When is the data Normal enough?. Use the 68-95-99.7 Rule. Plot the data and check for the shape to be……. Determine if ____ % of the data is within 1 standard deviation Determine if 95% of the data is within ___ standard deviations

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Assessing Normality

Assessing Normality. Assessing Normality

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Testing and Fixing for Normality

Testing and Fixing for Normality. Gui Shichun. What is meant by normality?.

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Reliability

Reliability. Consistent Dependable Replicable Stable. Threats to reliability. Construction Administration (tester, testee, environment) Scoring Interpretation. Two Administrations. Test/retest same test - same group Parallel/Alternate forms different tests - same group.

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Assessing Normality and Data Transformations

Assessing Normality and Data Transformations. Role of Normality. Many statistical methods require that the numeric variables we are working with have an approximate normal distribution .

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Normality and Skewness of Genetic Evaluations

Normality and Skewness of Genetic Evaluations. Effects of Skewness. Number of extreme bulls Rankings are ok within countries Problems when EBVs with differing skewness merged across countries Linear vs non-linear models Economic contribution to index

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Normality

Normality. Dr. Steve Badger. Number of moles of solute. Molarity =. Liter of solution. Number of moles of solute. Molality =. Kg of solvent. Concentrations of Solutions. A brief review of molarity & molality. Preparing a Solution of Known Molarity.

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Assessing Normality and Data Transformations

Many statistical methods require that the numeric variables we are working with have an approximate normal distribution. For example, t-tests, F-tests, and regression analyses all require in some sense that the numeric variables are approximately normally distributed.

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Testing Normality

Testing Normality. Many test procedures that we have developed rely on the assumption of Normality. There are many test for Normality of data. One uses the normal to provide cell probabilities for the chi-square goodness-of-fit test. A “better” test is based on the Normal Probability Plot.

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Data Transformation For Normality

Data Transformation For Normality. An assumption of our analysis is that the data is normally distributed If the data is not normally distributed, then you must do a transformation to get normal data: Log(Y), 1/Y, SQRT(Y).

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Equivalent Weights and Normality

Equivalent Weights and Normality. Normality - the number of equivalents (equivalent weights) of solute per liter of solution. Normality ( N ) = # eq. / L of solution Molarity ( M ) = # moles/ L of solution

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