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As one of the oldest multivariate statistical methods of data reduction, Principal Component Analysis (PCA)simplifies a dataset by producing a small number of derived variables that are uncorrelated and that account for most of the variation in the original data set. Eventually, the derived variables are combinations of the original variables. For example, it might be ?hat students take 10 examinations and some students do well in one exam whilst other students do better in another. It is difficult to compare one student with another when we have marks from 10 examinations to consider. One obvious way of comparing students is to calculate tlie mean score. This is a constructed combination of the existing variables,. However. we may get a more useful comparison of overall performances by considering other constructed combinations of the 10 exam marks. The PCA is one way of constructing such combinations, doing so in such ewakas to account for as much as possible of the variation in the original data. One can then compare students' performance by considering this much sn~aller number of variables.
The first step in this case is to ensure that you are adequately clear on the General Linear Model and its relationship to both ANOVA and regression. The distinction is approxim
Scatter Diagram The first step in correlation analysis is to visualize the relationship. For each unit of observation in correlation analysis there is a pair of numerical value
Advantages of Sampling Why should we settle on a sample instead of studying the entire population? Sampling has the following advantages over a census (study of the entire pop
The file Midterm Data.xls has a tab labeled "National Grid vs. Alcoa" which presents historical price data for two stocks. Using the National Grid price as the X-value and the Al
A study was designed to investigate the effects of two variables - (1) a student's level of mathematical anxiety and (2) teaching method - on a student's achievement in a mathemati
Cluster Sampling Here the population is divided into clusters or groups and then Random Sampling is done for each cluster. Cluster Sampling differs from Stratified Sampl
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
Suppose that before the minimum wage law change, the underlying mean number of part-time employees per Burger King Restaurant in New Jersey was 20.3. It was thought that the increa
Grouped Data For calculating mode from a frequency distribution, the following formula Mode = L mo + x W where,
The following data give the repair costs (in RM) for 30 randomly selected cars from a list of cars involved in collisions. a) By using RM 1 as the lower limit of the first
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