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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.
1. Suppose you are estimating the imports (from both the U.S. mainland and foreign countries) of fuels and petroleum products in Hawaii (the dependent variable). The values of the
what does it mean by moving average?
(a) The Horton's initial infiltration capacity for a catchment is 204 mm/h and the constant infiltration value at saturation is 60 mm/h. For a rainfall in excess of 204 mm/h mainta
Consider the sample of 60 package design ratings given in the table below. A Sample of Package Design Ratings (Composite S
a 100 squash balls are bounce from height of 100 inches with average height 30 inch with standard deviation 3/4 inch. a ball is fast if bounce above 32 inch. what is chance of gett
1. Calculate the mean and mode of: Central size 15 25 35 45 55 65 75 85 Frequencies 5 9 13 21 20 15 8 3 The following data shows the monthly expenditure of 80 students of
Main stages of Statistical Inquiry The following are the various stages of a statistical inquiry (1) Planning the Inquiry: First of all we have to assess the problem und
Using the raw measurement data presented below, calculate the t value for independent groups to determine whether or not there exists a statistically significant difference between
MARKS IN LAW :10 11 10 11 11 14 12 12 13 10 MARKS IN STATISTICS :20 21 22 21 23 23 22 21 24 23 MARKS IN LAW:13 12 11 12 10 14 14 12 13 10 MARKS IN STATISTICS:24 23 22 23 22 22 24 2
Henry Kaiser suggested a rule for selecting a number of components m less than the number needed for perfect reconstruction: set m equal to the number of eigenvalues greater than I
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