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Probability and Statistics Research Project Probability and Statistics Research Project Name: Lakeisha M. Henderson ID: @02181956 Spring 2007 Abstract Table of Contents
Bank The conclusion of research and statistics is very evident in managerial decision-making. Statistical methods such as descriptive or nonparametric statistics,
(3) basic probability concepts, (4) normal distribution, (5) confidence intervals and (6) inferential statistics. Issue and Opportunity Identification USA World Bank
FOR INTRODUCTION The impact of research and statistics is very apparent in managerial decision-making. Things like descriptive or nonparametric statistics, probability
provided room for errors of margin. All the marketing executives need to become familiar with probability and statistics, especially as it relates to sampling and
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Probability and Statistics Research Project
Name: Lakeisha M. Henderson
ID: @02181956
Spring 2007
Abstract
Table of Contents
Principle Component Analysis (PCA)
Definition
.4
Uses of PCA
5
Illustrative Example of PCA
5
Method to Determine PCA
..6
Basic Analysis of Variance (ANOVA)
Purpose and Definition of ANOVA
12
Illustrative Example of ANOVA
.12
Risk Based Design Concepts
Definition
.15
Predictions and Relation to Risk Based Designs
.15
Principle Components Analysis (PCA)
Definition:
Principal Components Analysis is a method that reduces data dimensionality by
performing a covariance analysis between factors. As such, it is suitable for data sets
in multiple dimensions. It is a way of identifying patterns in data, and expressing the data in such a way as to highlight their similarities and differences. Since patterns in data can be hard to find in data of high dimension, where the luxury of graphical representation is not available, PCA is a powerful tool for analyzing data. The other main advantage of PCA is that once you have found these patterns in the data, and you compress the data, i.e. by reducing the number of dimensions, without much loss of information.
Technically speaking, PCA is an orthogonal linear transformation that transforms the data to a new coordinate system such that the greatest variance by any projection of the data comes to lie on the first coordinate (called the first...
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