Regression Modeling

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A01=Michael Panik
advanced data analysis
ARIMA Procedure
Author_Michael Panik
Bayesian estimation approaches
Category=UFM
Coeff Var
density
DF Parameter Estimate Standard Error
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
error
estimates
function
Fuzzy Linear Regression Model
homoscedasticity
maximum likelihood
Model Statement
nonparametric modeling techniques
OLS Estimate
OLS Estimator
OLS Regression
OLS Residual
ordinary least squares
parameter
probability
proc
Proc Print Data
Proc Reg Data
quantile regression methods
Reg Procedure Model
Regression Model
regression modeling
regression techniques for scientific research
Regressors X1
Reject H0
robust regression
Root MSE
sas
SAS Code
SAS Dataset
SAS System
Smoothing Parameter
Source DF Sum
spatial regression analysis
standard
statistical inference
system
Test H0
time series modeling
Variable DF Parameter Estimate
Variable DF Parameter Estimate Standard
WLS Estimator

Product details

  • ISBN 9781420091977
  • Weight: 1700g
  • Dimensions: 178 x 254mm
  • Publication Date: 30 Apr 2009
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs.

The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression, L1 and q-quantile regression, regression in a spatial domain, ridge regression, semiparametric regression, nonlinear least squares, and time-series regression issues. For most of the regression methods, the author includes SAS procedure code, enabling readers to promptly perform their own regression runs.

A Comprehensive, Accessible Source on Regression Methodology and ModelingRequiring only basic knowledge of statistics and calculus, this book discusses how to use regression analysis for decision making and problem solving. It shows readers the power and diversity of regression techniques without overwhelming them with calculations.

Panik, Michael

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