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Linearity - Reasons for Screening Data
Many of the technics of standard statistical analysis are based on the assumption that the relationship, if any, between variables is linear. Measures of linear relationship such as the Pearson r, cannot detect any nonlinear relationship between variables.
In analyses that are somehow related to predicted values of variables, the analysis of linearity is primarily conducted by evaluating the residual plots. More specifically, this is done by looking at the standardized residual plots with residuals for each observation appearing on the horizontal axis and their standardized values along the vertical axis.
A second more crude method of assessing linearity is accomplished by inspecting the bivariate scatterplots. If the variables being analyzed are, both normally distributed and linearly related, then the resulting scatterplot would be of elliptical shape.
Latin square is an experimental design targeted at removing from the experimental error the variation from two extraneous sources so that a more sensitive test of the treatment ef
Profile plots is a technique of representing the multivariate data graphically. Each of the observation is represented by a diagram comprising of a sequence of equispaced vertical
Linearity - Reasons for Screening Data Many of the technics of standard statistical analysis are based on the assumption that the relationship, if any, between variables is li
The scatter plot of SRES1 versus totexp demonstrates that there is non-linear relationship that exists as most of the points are below and above zero. The scatter plot show that th
The model which arises in the context of estimating the size of the closed population where individuals within the population could be identified only during some of the observatio
Range is the difference between the largest and smallest observations in the data set. Commonly used as an easy-to-calculate measure of the dispersion in the set of observations b
The Null Hypothesis - H0: There is autocorrelation The Alternative Hypothesis - H1: There is no autocorrelation Rejection Criteria: Reject H0 (n-s)R 2 > = (1515 - 4) x (0.
Principal factor analysis is the method of factor analysis which is basically equivalent to a principal components analysis performed on reduced covariance matrix attained by repl
Prevalence : The measure of the number of people in a population who have a certain disease at a given point in time. It c an be measured by two methods, as point prevalence and p
Omitted covariates is a term generally found in the connection with regression modelling, where the model has been incompletely specified by not including significant covariates.
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