Statistical analysis and data display : an intermediate course with examples in S-plus, R, and SAS / Richard M. Heiberger and Burt Holland.
By: Heiberger, Richard M
.
Contributor(s): Holland, Burt
.
Material type:
BookSeries: Springer texts in statistics.Publisher: New York : Springer, c2004Description: xxiv, 729 p. : ill. ; 24 cm. + hbk.ISBN: 0387402705 (hbk.).Subject(s): S-Plus | SAS (Computer file)| Item type | Current library | Call number | Copy number | Status | Barcode | |
|---|---|---|---|---|---|---|
| General lending | MTU Bishopstown Library Lending | 519.50285 (Browse shelf(Opens below)) | 1 | Available | 00099185 |
Enhanced descriptions from Syndetics:
This book provides a concise overview of modern statistical topics at an elementary level. Assuming an understanding of basic calculus & a previous statistical course, it will serve as a reference book for applied statisticians in many quantitative areas who are interested in stat methods
Includes bibliographical references (pages 709-719) and index.
Introduction and motivation -- Data and statistics -- Statistics concepts -- Graphs -- Introductory inference -- One-way analysis of variance -- Multiple comparisons -- Linear regression by least squares -- Multiple regression - More than one predictor -- Multiple regression - Dummy variables and contrasts -- Multiple regression - Regression diagnostics -- Two-way analysis of variance -- Design of experiments - Factorial designs -- Design of experiments - complex designs -- Bivariate statistics - discrete data -- Nonparametrics -- Logistic regression -- Time series analysis.
Table of contents provided by Syndetics
- Introduction and Motivation
- Data and Statistics
- Statistics Concepts
- Graphs
- Introductory Inference
- One-Way Analysis of Variance, ANOVA
- Multiplenbsp;Comparisons
- Linear Regression by Least Squares
- Multiple Regression - More Than One Predictor
- Multiple Regression - Dummy Variables and Contrasts
- Multiple Regression - Regression Diagnostics
- Two-Way Analysis of Variance
- Design of Experiments - Factorial Designs
- Design of Experiments - More Complex Designs
- Bivariate Statistics - Discrete Data
- Nonparametrics
- Logistic Regression
- Time Series Analysis