๐”– Scriptorium
โœฆ   LIBER   โœฆ

๐Ÿ“

Linear Probability, Logit, and Probit Models (Quantitative Applications in the Social Sciences)

โœ Scribed by John H. Aldrich, Forrest D. Nelson


Publisher
Sage Publications, Inc
Year
1984
Tongue
English
Leaves
106
Category
Library

โฌ‡  Acquire This Volume

No coin nor oath required. For personal study only.

โœฆ Synopsis


Ordinary regression analysis is not appropriate for investigating dichotomous or otherwise "limited" dependent variables, but this volume examines three techniques -- linear probability, probit, and logit models -- which are well-suited for such data. It reviews the linear probability model and discusses alternative specifications of non-linear models. Using detailed examples, Aldrich and Nelson point out the differences among linear, logit, and probit models, and explain the assumptions associated with each.

โœฆ Table of Contents


Local Disk......Page 0
cover......Page 1
cover-0......Page 2
cover-1......Page 3
page_1......Page 4
page_2......Page 5
page_3......Page 6
page_4......Page 8
page_5......Page 10
page_6......Page 11
page_7......Page 12
page_9......Page 13
page_10......Page 14
page_11......Page 15
page_12......Page 16
page_13......Page 17
page_14......Page 18
page_15......Page 19
page_16......Page 20
page_17......Page 22
page_18......Page 24
page_19......Page 25
page_20......Page 26
page_21......Page 27
page_22......Page 28
page_23......Page 29
page_24......Page 30
page_25......Page 31
page_26......Page 32
page_27......Page 33
page_28......Page 34
page_29......Page 35
page_30......Page 36
page_31......Page 37
page_32......Page 38
page_33......Page 39
page_34......Page 40
page_35......Page 41
page_36......Page 42
page_37......Page 43
page_38......Page 44
page_39......Page 45
page_40......Page 46
page_41......Page 47
page_42......Page 48
page_43......Page 49
page_44......Page 50
page_45......Page 51
page_46......Page 52
page_47......Page 53
page_48......Page 54
page_49......Page 55
page_50......Page 56
page_51......Page 57
page_52......Page 58
page_53......Page 59
page_54......Page 60
page_55......Page 61
page_56......Page 62
page_57......Page 63
page_58......Page 64
page_59......Page 65
page_60......Page 66
page_61......Page 67
page_62......Page 68
page_63......Page 70
page_64......Page 72
page_65......Page 74
page_66......Page 75
page_67......Page 76
page_68......Page 77
page_69......Page 78
page_70......Page 79
page_71......Page 80
page_72......Page 81
page_73......Page 83
page_74......Page 84
page_75......Page 85
page_76......Page 86
page_77......Page 88
page_78......Page 89
page_79......Page 90
page_80......Page 91
page_81......Page 92
page_82......Page 93
page_83......Page 94
page_85......Page 95
page_86......Page 96
page_87......Page 98
page_88......Page 99
page_89......Page 100
page_90......Page 101
page_91......Page 102
page_93......Page 103
page_94......Page 105
page_95......Page 106


๐Ÿ“œ SIMILAR VOLUMES


Interpreting Probability Models: Logit,
โœ Tim F. Liao ๐Ÿ“‚ Library ๐Ÿ“… 1994 ๐Ÿ› Sage Publications, Inc ๐ŸŒ English

What is the probability that something will occur, and how is that probability altered by a change in an independent variable? To answer these questions, Tim Futing Liao introduces a systematic way of interpreting commonly used probability models. Since much of what social scientists study is measur

Ordinal Log-Linear Models (Quantitative
โœ Masako Ishii-Kuntz ๐Ÿ“‚ Library ๐Ÿ“… 1994 ๐Ÿ› Sage Publications, Inc ๐ŸŒ English

What log-linear models can social scientists use to examine categorical variables whose attributes may be logically rank-ordered? In this book, the author presents a technique that is often overlooked but highly advantageous when dealing with such ordered variables as social class, political ideolog

Applied Logistic Regression Analysis (Qu
โœ Scott William Menard ๐Ÿ“‚ Library ๐Ÿ“… 1995 ๐Ÿ› Sage Publications, Inc ๐ŸŒ English

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 a