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Multiple Linier Regression / Regresi linier berganda

Posted by dhin pada 26 April 2009

Multiple Linear Regression / Regresi Linier Berganda

regresi-berganda 1

Free Download

1. Multiple Linier Regression (Notes_11, GEOS 585A, Spring 2007)
2. Multiple Linear Regression and Logistic Regression (Patrick Monahan        Ph.D. Division of Biostatistics Department of Medicine).
3. Multiple Linear Regression: Cloud Seeding.

4. Multiple Linier Regression Methode.
5. Multiple Linear Regression Using the Matrix Capabilities of the TI-85.
6. Multiple Linier Regression

Introduction
Model regresi merupakan suatu model yang menggambarkan hubungan antara variabel bebas dengan variabel respon. Model regresi linier yang terdiri dari satu variabel bebas dengan satu variabel respon disebut model regresi linier sederhana (Rousseeuw, 1984). Dalam berbagai bidang sering kali model regresi linier sederhana tidak cukup mewakili untuk menyatakan data yang ada kedalam model regresi, dengan demikian diperlukan perluasan model yang meliputi lebih dari satu variabel bebas dan satu variabel respon yang disebut dengan model regresi linier berganda.

Multiple linear regression (MLR) is a method used to model the linear relationship between a dependent variable and one or more independent variables. The dependent variable is sometimes also called the predictand, and the independent variables the predictors. MLR is based on least squares: the model is fit such that the sum-of-squares of differences of observed and predicted values is minimized. MLR is probably the most widely used method in dendroclimatology for developing models to reconstruct climate variables from tree-ring series. Typically, a climatic variable is defined as the predictand and tree-ring variables from one or more sites are defined as predictors. The model is fit to a period – the calibration period – for which climatic and tree-ring data overlap. In the process of fitting, or estimating, the model, statistics are computed that summarize the accuracy of the regression model for the calibration period. The performance of the model on data not used to fit the model is usually checked in some way by a process called validation. Finally, tree-ring data from before the calibration period are substituted into the prediction equation to get a reconstruction of the predictand. The reconstruction is a “prediction” in the sense that the regression model is applied to generate estimates of the predictand variable outside the period used to fit the data. The uncertainty in the reconstruction is summarized by confidence intervals, which can be computed by various alternative ways. Regression has long been used in dendroclimatology for reconstructing climate variables from tree rings. A few examples of dendroclimatic studies using linear regression are reconstruction of annual precipitation in the Pacific Northwest (Graumlich 1987), reconstruction of runoff of the White River, Arkansas (Cleaveland and Stahle 1989), reconstruction of an index of the El Nino Southern Oscillation (Michaelsen 1989), and reconstruction of a drought index for Iowa (Cleaveland and Duvick 1992).

3 Tanggapan to “Multiple Linier Regression / Regresi linier berganda”

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