Skip to content

Data Analysis Using the Method of Least Squares Extracting the Most Information from Experiments

Spend $50 to get a free movie!

ISBN-10: 3540256741

ISBN-13: 9783540256748

Edition: 2006

Authors: John Wolberg

List price: $54.99
Blue ribbon 30 day, 100% satisfaction guarantee!
Out of stock
what's this?
Rush Rewards U
Members Receive:
Carrot Coin icon
XP icon
You have reached 400 XP and carrot coins. That is the daily max!


The preferred method of data analysis of quantitative experiments is the method of least squares. Often, however, the full power of the method is overlooked and very few books deal with these techniques at the level of detail that the subject deserves. The purpose of Data Analysis Using the Least-Square Methods is to fill this gap and include the type of information required to help scientists and engineers apply the method to the problems of interest in their special field. In addition, graduate students in science and engineering doing work of experimental nature can benefit from this book. Particularly, both linear and non-linear least squares, the use of experimental error estimates for…    
Customers also bought

Book details

List price: $54.99
Copyright year: 2006
Publisher: Springer Berlin / Heidelberg
Publication date: 12/16/2005
Binding: Paperback
Pages: 250
Size: 5.98" wide x 9.02" long x 0.25" tall
Weight: 0.990
Language: English

Quantitative Experiments
Dealing with Uncertainty
Statistical Distributions
The normal distribution
The binomial distribution
The Poisson distribution
The x[superscript 2] distribution
The t distribution
The F distribution
Parametric Models
Basic Assumptions
Systematic Errors
Nonparametric Models
Statistical Learning
The Method of Least Squares
The Objective Function
Data Weighting
Obtaining the Least Squares Solution
Uncertainty in the Model Parameters
Uncertainty in the Model Predictions
Treatment of Prior Estimates
Applying Least Squares to Classification Problems
Model Evaluation
Selecting the Best Model
Variance Reduction
Linear Correlation
Using the Model for Extrapolation
Out-of-Sample Testing
Analyzing the Residuals
Candidate Predictors
Using the F Distribution
Nonlinear Correlation
Rank Correlation
Designing Quantitative Experiments
The Expected Value of the Sum-of-Squares
The Method of Prediction Analysis
A Simple Example: A Straight Line Experiment
Designing for Interpolation
Design Using Computer Simulations
Designs for Some Classical Experiments
Choosing the Values of the Independent Variables
Some Comments about Accuracy
General Purpose Nonlinear Regression Programs
The NIST Statistical Reference Datasets
Nonlinear Regression Convergence Problems
Linear Regression: a Lurking Pitfall
Multi-Dimensional Models
Software Performance
The Regress Program
Kernel Regression
Kernel Regression Order Zero
Kernel Regression Order One
Kernel Regression Order Two
Nearest Neighbor Searching
Kernel Regression Performance Studies
A Scientific Application
Applying Kernel Regression to Classification
Group Separation: An Alternative to Classification
Generating Random Noise
Approximating the Standard Normal Distribution