Egwald Statistics: Econometrics, Probability, and Stochastic Processes
by
Elmer G. Wiens
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A. Multiple regression analysis package.
Run regressions online. Easy to learn. Easy to use.
a. Linear multiple regression.
1. Example: Predicting Canadian Elections using the equation:
C = ß0 + ß1 * E + ß2 * OQ + ß3 * P + ß4 * BC
where
C = Percent of seats of Canada taken by the winning party
E = Percent of seats of Eastern Canada taken by the winning party
OQ = Percent of seats of Ontario and Quebec taken by the winning party
P = Percent of seats of Prairie Provinces taken by the winning party
BC = Percent of seats of B.C. taken by the winning party
Regression equation using the data for 19492004: Predicting Canadian Elections. These 18 elections yield the equation:
C = 17.321 + 0.13 * E + 0.857 * OQ + 0.117 * P + 0.113 * BC
(Each regression coefficient is statistically significant)
2. Do your own regression study.
b. Restricted linear regression.
1. Example: Cobb Douglas Production Function
2. Do your own restricted regression study
Check out the derivation of the regression model.
Statistics, multiple regression, and numerical analysis references.
B. Probability and Stochastic Processes.
Learn key concepts used in statistics and regression analysis.
a. Probability
1. Graphs of the normal and gamma probability distributions.
2. Graphs of the chisquare, Studentt, and F probability distributions.
3. Graphs of the sample mean and variance distributions.
4. Hypotheses testing of means and variances.
b. Stochastic Processes
1. Random Walk.
C. Background and References.
1. Linear Algebra background to least squares (multiple regression).
2. Statistics and probability theory
references.
Links
The Math Forum Internet Mathematics Library has an extensive list of sites for probability and statistics. http://mathforum.org/library/topics/probstat.
Study econometrics in the Department of Economics of the Katholieke Universiteit Leuven, Belgium.
