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Date Submitted: 05/16/2010 11:51 AM
(Comprehensive Seminar)
Functional Area 1- Quantitative Methods
22) Time series Analysis, Regression/correlation analysis
23) * Regression/Correlation Analysis: Techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. That analysis helps us understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed.
The applications: Trend analysis
- Regression Analysis, Oct. 11, 2009, http://en.wikipedia.org/wiki/Regression_analysis
* Time series Analysis: methods that attempt to understand such time series, often either to understand the underlying context of the data points (Where did they come from? What generated them?), or to make forecasts (predictions).
The applications: Economic Forecasting, Sales Forecasting, Stock Market Analysis, Inventory Studies
- Time series Analysis, Oct. 9, 2009, http://en.wikipedia.org/wiki/Time_series
- NIST/SEMATECH e-Handbook of Statistical Methods, 2006, http://www.itl.nist.gov/div898/handbook/, date.
24) Coefficient of correlation: a measure of how well trends in the predicted values follow trends in past actual values. It is a measure of how well the predicted values from a forecast model "fit" with the real-life data.
The mathematical formula for computing r is:
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The value of r is such that -1 < r < +1.
Positive correlation: If x and y have a strong positive linear correlation, r is close to +1. An r value of exactly +1 indicates a perfect positive fit.
Negative correlation: If x and y have a strong negative linear correlation, r is close to -1. An r value of exactly -1 indicates a perfect negative fit.
No correlation: If there is no linear correlation or a weak linear correlation, r is close to 0. A value near zero means that there is a random, nonlinear relationship between the...