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This workshop will provide an overview of the statistical test choices, variable creation and transformation, and output interpretation using SPSS. Hands-on lab exercises will demonstrate how to execute basic data transformations and analyses using SPSS. Descriptive statistics, chi-square, means comparisons (t-test and one-way ANOVA) and correlations will be covered. We will spend about one hour covering basic information about SPSS and getting ready for data entry in a lecture format and two hours in the computer lab with hands-on exercises in using SPSS. Topics: Preparing your data (lecture) Creating a codebook – variables, names, labels, missing values Entering data in SPSS (lecture and lab exercises) Using SPSS and importing from EXCEL Modifying and organizing your data (lab exercises) Compute, recode, and count transformations Descriptive statistics (lab exercises) Frequencies, descriptives, and simple graphs Statistics for single dependent variables (lab exercises) Chi square Correlation T-Test One Way ANOVA
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This workshop will provide an overview of the statistical test choices, variable creation and transformation, and output interpretation using SPSS. Hands-on lab exercises will demonstrate how to execute basic data transformations and analyses using SPSS. Descriptive statistics, chi-square, means comparisons (t-test and one-way ANOVA) and correlations will be covered. We will spend about one hour covering basic information about SPSS and getting ready for data entry in a lecture format and two hours in the computer lab with hands-on exercises in using SPSS. Topics: Preparing your data (lecture) Creating a codebook – variables, names, labels, missing values Entering data in SPSS (lecture and lab exercises) Using SPSS and importing from EXCEL Modifying and organizing your data (lab exercises) Compute, recode, and count transformations Descriptive statistics (lab exercises) Frequencies, descriptives, and simple graphs Statistics for single dependent variables (lab exercises) Chi square Correlation T-Test One Way ANOVA
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Spss student outline course
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EXAMEN PARCIAL DE OFIMATICA
Cue 005453
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reyzonjhariel
Descriptive abstracts A descriptive abstract indicates the type of information found in the work. It makes no judgements about the work, nor does it provide results or conclusions of the research. It does incorporate key words found in the text and may include the purpose, methods, and scope of the research. Essentially, the descriptive abstract describes the work being abstracted. Some people consider it an outline of the work, rather than a summary. Descriptive abstracts are usually very short—100 words or less
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REGIONE LOMBARDIA - Promozione e sviluppo dell'export
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How Thomas applicant/employee questionnaires ensure that you start an interview with as much information as you used to have when you finished one, applicable to both recruitment and development.
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DASH is a realization of the PGAS (partitioned global address space) model in the form of a C++ template library without the need for a custom PGAS (pre-)compiler. We present the DASH NArray concept, a multidimensional array abstraction designed as an underlying container for stenciland dense numerical applications. After introducing fundamental programming concepts used in DASH, we explain how these have been extended by multidimensional capabilities in the NArray abstraction. Focusing on matrix-matrix multiplication in a case study, we then discuss an implementation of the SUMMA algorithm for dense matrix multiplication to demonstrate how the DASH NArray facilitates portable efficiency and simplifies the design of efficient algorithms due to its explicit support for locality-based operations. Finally, we evaluate the performance of the SUMMA algorithm based on the NArray abstraction against established implementations of DGEMM and PDGEMM. In combination with mechanisms for automatic optimization of logical process topology and domain decomposition, our implementation yields highly competitive results without manual tuning, significantly outperforming Intel MKL and PLASMA in node-level use cases as well as ScaLAPACK in highly distributed scenarios.
A Multidimensional Distributed Array Abstraction for PGAS (HPCC'16)
A Multidimensional Distributed Array Abstraction for PGAS (HPCC'16)
Menlo Systems GmbH
Developed by María Jesús Campos Fernández, teacher of Geography, History and Art in a bilingual section in IES Parque de Lisboa (Alcorcón, Madrid, Spain) learningfromgeography.wikispaces.com learningfromhistory.wikispaces.com
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Il bando intende sostenere l'adozione di nuovi modelli di business per la promozione dell'export da parte delle micro, piccole e medie imprese e reti d'imprese promuovendo la realizzazione di progetti articolati per sviluppare e consolidare il business nei mercati target.
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DASH is a realization of the PGAS (partitioned global address space) model in the form of a C++ template library without the need for a custom PGAS (pre-)compiler. We present the DASH NArray concept, a multidimensional array abstraction designed as an underlying container for stenciland dense numerical applications. After introducing fundamental programming concepts used in DASH, we explain how these have been extended by multidimensional capabilities in the NArray abstraction. Focusing on matrix-matrix multiplication in a case study, we then discuss an implementation of the SUMMA algorithm for dense matrix multiplication to demonstrate how the DASH NArray facilitates portable efficiency and simplifies the design of efficient algorithms due to its explicit support for locality-based operations. Finally, we evaluate the performance of the SUMMA algorithm based on the NArray abstraction against established implementations of DGEMM and PDGEMM. In combination with mechanisms for automatic optimization of logical process topology and domain decomposition, our implementation yields highly competitive results without manual tuning, significantly outperforming Intel MKL and PLASMA in node-level use cases as well as ScaLAPACK in highly distributed scenarios.
A Multidimensional Distributed Array Abstraction for PGAS (HPCC'16)
A Multidimensional Distributed Array Abstraction for PGAS (HPCC'16)
Menlo Systems GmbH
Developed by María Jesús Campos Fernández, teacher of Geography, History and Art in a bilingual section in IES Parque de Lisboa (Alcorcón, Madrid, Spain) learningfromgeography.wikispaces.com learningfromhistory.wikispaces.com
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RODRIGUEZ Expressió oral 3/2/14 SIGNIFICAT: Desesperar-se per alguna cosa. EXEMPLE: El meu germà s’estira els cabells perquè ha perdut el Barça.
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