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Applied Linear Regression Models - Hardcover, by Kutner Michael H; - Good l

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Item specifics

Condition
Good: A book that has been read but is in good condition. Very minimal damage to the cover including ...
Book Title
Applied Linear Regression Models
ISBN
9780072386912
Subject Area
Mathematics, Business & Economics
Publication Name
Applied Linear Regression Models
Publisher
McGraw-Hill Higher Education
Item Length
9.4 in
Subject
Probability & Statistics / Regression Analysis, Statistics
Publication Year
2003
Type
Textbook
Format
Hardcover
Language
English
Item Height
1.2 in
Author
John Neter, Michael H. Kutner, William Wasserman, Chris J. Nachtsheim
Item Weight
19.4 Oz
Item Width
7.5 in
Number of Pages
720 Pages
Category

About this product

Product Identifiers

Publisher
McGraw-Hill Higher Education
ISBN-10
0072386916
ISBN-13
9780072386912
eBay Product ID (ePID)
2363320

Product Key Features

Number of Pages
720 Pages
Language
English
Publication Name
Applied Linear Regression Models
Subject
Probability & Statistics / Regression Analysis, Statistics
Publication Year
2003
Type
Textbook
Subject Area
Mathematics, Business & Economics
Author
John Neter, Michael H. Kutner, William Wasserman, Chris J. Nachtsheim
Format
Hardcover

Dimensions

Item Height
1.2 in
Item Weight
19.4 Oz
Item Length
9.4 in
Item Width
7.5 in

Additional Product Features

Edition Number
4
Intended Audience
College Audience
LCCN
2003-044224
Dewey Edition
21
Illustrated
Yes
Dewey Decimal
519.5/36
Table Of Content
Part1 Simple Linear Regression 1 Linear Regression with One Predictor Variable 2 Inferences in Regression and Correlation Analysis 3 Diagnostics and Remedial Measures 4 Simultaneous Inferences and Other Topics in Regression Analysis 5 Matrix Approach to Simple Linear Regression Analysis Part 2 Multiple Linear Regression 6 Multiple Regression I 7 Multiple Regression II 8 Building the Regression Model I: Models for Quantitative and Qualitative Predictors 9 Building the Regression Model II: Model Selection and Validation 10 Building the Regression Model III: Diagnostics 11 Remedial Measures and Alternative Regression Techniques 12 Autocorrelation in Time Series Data Part 3 Nonlinear Regression 13 Introduction to Nonlinear Regression and Neural Networks 14 Logistic Regression, Poisson Regression, and Generalized Linear Models
LC Classification Number
QA278.2.N46 2004

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