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1. |
Record Nr. |
UNINA9910791526303321 |
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Titolo |
Australia [[electronic resource]] |
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Pubbl/distr/stampa |
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[S.l.], : Maps.com, c1999 |
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Descrizione fisica |
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1 online resource (1 map) |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale cartografico a stampa |
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Livello bibliografico |
Monografia |
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2. |
Record Nr. |
UNINA9910791456503321 |
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Autore |
Chunawalla S. A |
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Titolo |
Foundations of advertising [[electronic resource] ] : theory & practice / / S.A. Chunawalla, K.C. Sethia |
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Pubbl/distr/stampa |
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Mumbai, : Himalaya Pub. House, 2008 |
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ISBN |
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1-64287-583-X |
1-282-80370-0 |
9786612803703 |
1-4416-7611-2 |
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Edizione |
[Rev. ed.] |
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Descrizione fisica |
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1 online resource (749 p.) |
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Altri autori (Persone) |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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COVER ; CONTENTS; Celebrating 100 Years of Indian Advertising; Advertising World; Fundamentals of Advertising: I; Fundamentals of Advertising: II; Types of Advertising; Advertising as a Career; Advertising Business; Campaign Planning and Creativity in Advertising; Advertising Budget; Media Selection, Planning, Scheduling & Strategy; |
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Print Media: Newspapers and Magazines; Electronic Media: TV: The New Golden Goose; Electronic Media: Radio; Media of the New Millennium - Internet; Outdoor and Transit Media; Direct Marketing (DM), Cinema and Miscellaneous Media; Message Design and Development |
Advertising AppealsAdvertisement Copy for Print Media; Copywriting for Radio, TV and Outdor Media; Advertisement Art - The Layout of Advertisements; Advertising Art - Preparation of a Layout: I; Advertising Art - Preparation of a Layout: II; Glamour of Advertising: Models, Fashion Shows, VJs, DJs, RJs and MCs; Print Ad Production; TV Commercial/ Ad Film Production; Production of Animation Films; Fundamentals of Computer Graphics; Ad Effectiveness; Marketing Research; Advertisement Stalwarts; Advertisement and Society; Case Studies; Index |
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3. |
Record Nr. |
UNINA9910364955903321 |
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Autore |
Christensen Ronald |
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Titolo |
Advanced Linear Modeling : Statistical Learning and Dependent Data / / by Ronald Christensen |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
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ISBN |
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Edizione |
[3rd ed. 2019.] |
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Descrizione fisica |
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1 online resource (618 pages) : illustrations |
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Collana |
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Springer Texts in Statistics, , 2197-4136 |
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Disciplina |
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Soggetti |
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Probabilities |
Mathematics - Data processing |
Statistics |
Probability Theory |
Computational Mathematics and Numerical Analysis |
Statistical Theory and Methods |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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1. Nonparametric Regression -- 2. Penalized Estimation -- 3. Reproducing Kernel Hilbert Spaces -- 4. Covariance Parameter |
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Estimation -- 5. Mixed Models and Variance Components -- 6. Frequency Analysis of Time Series -- 7. Time Domain Analysis -- 8. Linear Models for Spacial Data: Kriging -- 9. Multivariate Linear Models: General. 10. Multivariate Linear Models: Applications -- 11. Generalized Multivariate Linear Models and Longitudinal Data -- 12. Discrimination and Allocation -- 13. Binary Discrimination and Regression -- 14. Principal Components, Classical Multidimensional Scaling, and Factor Analysis -- A Mathematical Background -- B Best Linear Predictors -- C Residual Maximum Likelihood -- Index -- Author Index. |
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Sommario/riassunto |
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Now in its third edition, this companion volume to Ronald Christensen’s Plane Answers to Complex Questions uses three fundamental concepts from standard linear model theory—best linear prediction, projections, and Mahalanobis distance— to extend standard linear modeling into the realms of Statistical Learning and Dependent Data. This new edition features a wealth of new and revised content. In Statistical Learning it delves into nonparametric regression, penalized estimation (regularization), reproducing kernel Hilbert spaces, the kernel trick, and support vector machines. For Dependent Data it uses linear model theory to examine general linear models, linear mixed models, time series, spatial data, (generalized) multivariate linear models, discrimination, and dimension reduction. While numerous references to Plane Answers are made throughout the volume, Advanced Linear Modeling can be used on its own given a solid background in linear models. Accompanying R code for the analyses is available online. |
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