Data Mining Using SAS Applications, 1st Edition by George Fernandez (auth.)

By George Fernandez (auth.)

Show description

Read or Download Data Mining Using SAS Applications, 1st Edition PDF

Similar mathematical & statistical books

Computation of Multivariate Normal and t Probabilities (Lecture Notes in Statistics)

This e-book describes lately built tools for actual and effective computation of the mandatory likelihood values for issues of or extra variables. It comprises examples that illustrate the likelihood computations for various functions.

Excel 2013 for Environmental Sciences Statistics: A Guide to Solving Practical Problems (Excel for Statistics)

This is often the 1st e-book to teach the features of Microsoft Excel to educate environmentall sciences facts effectively.  it's a step by step exercise-driven consultant for college students and practitioners who have to grasp Excel to resolve useful environmental technological know-how problems.  If knowing information isn’t your most powerful go well with, you're not in particular mathematically-inclined, or while you're cautious of pcs, this can be the precise ebook for you.

Lectures on the Nearest Neighbor Method (Springer Series in the Data Sciences)

This article offers a wide-ranging and rigorous evaluation of nearest neighbor equipment, some of the most very important paradigms in computing device studying. Now in a single self-contained quantity, this booklet systematically covers key statistical, probabilistic, combinatorial and geometric rules for figuring out, studying and constructing nearest neighbor tools.

Recent Advances in Modelling and Simulation

Desk of Content01 Braking strategy in vehicles: research of the Thermoelastic Instability PhenomenonM. Eltoukhy and S. Asfour02 Multi-Agent platforms for the Simulation of Land Use swap and coverage InterventionsPepijn Schreinemachers and Thomas Berger03 Pore Scale Simulation of Colloid DepositionM.

Extra resources for Data Mining Using SAS Applications, 1st Edition

Sample text

Bles used in modeling is an important data mining requirement. The variables can be generally classified into continuous or categorical. Preparing Data for Data Mining • 17 Continuous variables are numeric variables that describe quantitative attributes of the cases and have a continuous scale of measurement. Means and standard deviations are commonly used to quantify the central tendency and dispersion. Total sales per customers and total manufacturing costs per products are examples of interval scales.

The variables can be generally classified into continuous or categorical. Preparing Data for Data Mining • 17 Continuous variables are numeric variables that describe quantitative attributes of the cases and have a continuous scale of measurement. Means and standard deviations are commonly used to quantify the central tendency and dispersion. Total sales per customers and total manufacturing costs per products are examples of interval scales. An interval-scale target variable is a requirement for multiple regression and neural net modeling.

A box plot shows the distribution pattern and the central tendency of the data. The line between the lowest adjacent limit and the bottom of the box represents one fourth of the data. One fourth of the data fall between the bottom of the 1. 5 A box plot illustrating the distribution pattern among the TRAINING, VALIDATION, and TEST samples for the continuous variable NETSALES generated by running the SAS macro RANSPLIT. box and the median, and another one fourth between the median and the top of the box.

Download PDF sample

Rated 4.19 of 5 – based on 21 votes