Data Analysis Using Regression and Multilevel/Hierarchical Models
This book originated as lecture notes for a course in regression and multilevel modeling, offered by the statistics department at Columbia University and attended by graduate students and postdoctoral researchers in social sciences (political science, economics, psychology, education, business, s...
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| Ngôn ngữ: | English |
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Cambridge University Press
2013
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oai:scholar.dlu.edu.vn:DLU123456789-355752014-01-19T23:45:28Z Data Analysis Using Regression and Multilevel/Hierarchical Models Gelman, Andrew Hill, Jennifer Analysis Regression This book originated as lecture notes for a course in regression and multilevel modeling, offered by the statistics department at Columbia University and attended by graduate students and postdoctoral researchers in social sciences (political science, economics, psychology, education, business, social work, and public health) and statistics. The prerequisite is statistics up to and including an introduction to multiple regression. Advanced mathematics is not assumed—it is important to understand the linear model in regression, but it is not necessary to follow the matrix algebra in the derivation of least squares computations. It is useful to be familiar with exponents and logarithms, especially when working with generalized linear models. 2013-09-17T01:38:53Z 2013-09-17T01:38:53Z 2006 Book 978-0-511-26878-6 https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/35575 en application/pdf Cambridge University Press |
| institution |
Thư viện Trường Đại học Đà Lạt |
| collection |
Thư viện số |
| language |
English |
| topic |
Analysis Regression |
| spellingShingle |
Analysis Regression Gelman, Andrew Hill, Jennifer Data Analysis Using Regression and Multilevel/Hierarchical Models |
| description |
This book originated as lecture notes for a course in regression and multilevel modeling,
offered by the statistics department at Columbia University and attended
by graduate students and postdoctoral researchers in social sciences (political science,
economics, psychology, education, business, social work, and public health)
and statistics. The prerequisite is statistics up to and including an introduction to
multiple regression.
Advanced mathematics is not assumed—it is important to understand the linear
model in regression, but it is not necessary to follow the matrix algebra in the
derivation of least squares computations. It is useful to be familiar with exponents
and logarithms, especially when working with generalized linear models. |
| format |
Book |
| author |
Gelman, Andrew Hill, Jennifer |
| author_facet |
Gelman, Andrew Hill, Jennifer |
| author_sort |
Gelman, Andrew |
| title |
Data Analysis Using Regression and
Multilevel/Hierarchical Models |
| title_short |
Data Analysis Using Regression and
Multilevel/Hierarchical Models |
| title_full |
Data Analysis Using Regression and
Multilevel/Hierarchical Models |
| title_fullStr |
Data Analysis Using Regression and
Multilevel/Hierarchical Models |
| title_full_unstemmed |
Data Analysis Using Regression and
Multilevel/Hierarchical Models |
| title_sort |
data analysis using regression and
multilevel/hierarchical models |
| publisher |
Cambridge University Press |
| publishDate |
2013 |
| url |
https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/35575 |
| _version_ |
1819782587602698240 |