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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.
The total number of overtime hours (in 1000s) worked in a large steel mill was recorded for 16 quarters, as shown below. Year Quarter Overtime hour
In the early 1990s researchers at The Ohio State University studied consumer ratings of six fast-food restaurants: Borden Burger, Hardee's, Burger King, McDonald's, Wendy's, and Wh
The cost of living index number on a some data was 200. From the base period, the percentage enhances in prices were-Rent Rs 60, clothing Rs 250, Fuel and Light Rs 150 and Miscella
I have 5 observations that i must plug into spss. I need an example of 1. I do not know if you are familiar with SPSS but I am going to ask anyway. Subject 1 is a Hispanic male who
Consider the linear transformation (a) Find the image of (3 , -2 , 2) under T. (b) Does the vector (5, 3) belong to the range of T? (c) Determine the matrix of the transf
Question Following the general methodology used by econometricians as explained in the session for week 1 (eight steps), explain how you would proceed to determine if a good com
Examining the Population Variance Business decision making does not limit itself to setting up the hypothesis to test for the equality of more than two means or proportions sim
Large Sample Test for Proportion A random sample of size n (n > 30) has a sample proportion p of members possessing a certain attribute (success). To test the hypothesis that t
how to get statistical quality control assignment in brief?
The 4 assumptions of regression: 1. Variables are normally distributed 2. Linear relationship between the independent and dependent variables 3. Homosced
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