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Applied Logistic Regression Analysis
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Bibliographic Detail
Publisher
Sage Pubns
Publication date
August 1, 1995
Binding
Paperback
Book category
Adult Non-Fiction
ISBN-13
9780803957572
ISBN-10
0803957572
Dimensions
0.50 by 5.50 by 8.50 in.
Weight
0.25 lbs.
Availability§
Out of Print
Original list price
$16.95
Other format details
sci/tech
§As reported by publisher
Amazon.com says people who bought this book also bought:
Understanding Regression Analysis: An Introductory Guide (Quantitative Applications in the Social Sciences) | Regression Diagnostics | Regression With Dummy Variables | Interaction Effects in Logistic Regression | Interaction Effects in Multiple Regression | Logistic Regression | Multiple Regression
Understanding Regression Analysis: An Introductory Guide (Quantitative Applications in the Social Sciences) | Regression Diagnostics | Regression With Dummy Variables | Interaction Effects in Logistic Regression | Interaction Effects in Multiple Regression | Logistic Regression | Multiple Regression
Summaries and Reviews
Amazon.com description: Product Description:
Emphasizing the parallels between linear and logistic regression, Scott Menard explores logistic regression analysis and demonstrates its usefulness in analyzing dichotomous, polytomous nominal, and polytomous ordinal dependent variables. The book is aimed at readers with a background in bivariate and multiple linear regression.
Editions
Paperback
2 edition from Sage Pubns (October 9, 2001)
9780761922087 | details & prices | 111 pages | 5.75 × 8.75 × 0.50 in. | 0.35 lbs | List price $22.00
The price comparison is for this edition
from Sage Pubns (August 1, 1995)
9780803957572 | details & prices | 5.50 × 8.50 × 0.50 in. | 0.25 lbs | List price $16.95
About: Emphasizing the parallels between linear and logistic regression, Scott Menard explores logistic regression analysis and demonstrates its usefulness in analyzing dichotomous, polytomous nominal, and polytomous ordinal dependent variables.
About: Emphasizing the parallels between linear and logistic regression, Scott Menard explores logistic regression analysis and demonstrates its usefulness in analyzing dichotomous, polytomous nominal, and polytomous ordinal dependent variables.
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