Learning regression analysis by simulation / KunioTakezawa.
Material type:
TextPublication details: Tokyo : Springer, 2014.Description: xii, 300 p. ; illISBN: - 9784431543206 (hard cover : alk. paper)
- 23 T136 000SA.06
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 000SA.06 T136 (Browse shelf(Opens below)) | Available | 135407 |
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| 000SA.06 P192 Growth curve modeling : | 000SA.06 P769 Model-free prediction and regression : | 000SA.06 Sa163 Statistical inference for models with multivariate t-distributed errors / | 000SA.06 T136 Learning regression analysis by simulation / | 000SA.061 R873 Graphical models for categorical data / | 000SA.062 Ag277 Foundations of linear and generalized linear models / | 000SA.062 C435 Some nonparametric hybrid predictive models: asymptotic properties and applications/ |
Includes index.
1 Linear Algebra--
2 Distributions and Tests--
3. Simple Regression--
4. Multiple regression--
5 Akaike's Information Criterion (AIC) and the Third Variance--
6. Linear Mixed Model--
Index.
The standard approach of most introductory books for practical statistics is that readers first learn the minimum mathematical basics of statistics and rudimentary concepts of statistical methodology. They then are given examples of analyses of data obtained from natural and social phenomena so that they can grasp practical definitions of statistical methods. Finally they go on to acquaint themselves with statistical software for the PC and analyze similar data to expand and deepen their understanding of statistical methods. This book, however, takes a slightly different approach, using sim.
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