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Discriminant analysis (DA) helps to determine which variables discriminate between two or more naturally occurring groups. Mathematically equivalent to MANOVA, it ' is extensively used when a set of explanatory variables are used to predict the group to which a given unit belongs (which is a nominal dependent variables). It cornhines the explanatory variables in order to create the largest F when the groups are used as a fixed factor in an ANOVA.
The model is constructed with a set of observations for which the classes are known. The set of observations are sometimes referred to as the training set.
Normal Distribution Meaning: According to ya Lun Chou There perfectly smooth and symmetrical curve, resulting from the expansion of the binomial (p+q) n when n approac
A rightist incumbent (player I) and a leftist challenger (player C) run for senate. Each candidate chooses among two possible political platforms: Left or Right. The rules of the g
Evaluate Gross Reproduction Rate: From the data given below compute : i) General Fertility Rate ii) Specific Fertility Rate iii) Total Fertility Rate iv)
Statistical Keys To do statistical operations we must first set the calculator on SD mode [SD stands for "standard deviation" which is the usual st
Multi stage or Cluster Random sampling Under this method, the random selection is made of primary, intermediate and final units from a given population. The area of investigat
Simple Linear Regression One calculate of the risk or volatility of an individual stock is the standard deviation of the total return (capital appreciation plus dividends) over
regression line drawn as Y=C+1075x, when x was 2, and y was 239, given that y intercept was 11. calculate the residual
Steps in ANOVA The three steps which constitute the analysis of variance are as follows: To determine an estimate of the population variance from the variance that exi
Correspondence analysis is an exploratory technique used to analyze simple two-way and multi-way tables containing measures of correspondence between the rows and colulnns of an
Factor analysis (FA) explains variability among observed random variables in terms of fewer unobserved random variables called factors. The observed variables are expressed in
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