SPSS for Applied Sciences

2013 ◽  
Author(s):  
Cole Davis
Keyword(s):  
Applied Sciences ◽  
Data Analysis ◽  
Computer Science ◽  
Statistical Tests ◽  
Farm Management ◽  
Data Sets ◽  
Statistical Software ◽  
Sports Science ◽  
Different Types ◽  
Test User

This book offers a quick and basic guide to using SPSS and provides a general approach to solving problems using statistical tests. It is both comprehensive in terms of the tests covered and the applied settings it refers to, and yet is short and easy to understand. Whether you are a beginner or an intermediate level test user, this book will help you to analyse different types of data in applied settings. It will also give you the confidence to use other statistical software and to extend your expertise to more specific scientific settings as required. The author does not use mathematical formulae and leaves out arcane statistical concepts. Instead, he provides a very practical, easy and speedy introduction to data analysis, offering examples from a range of scenarios from applied science, handling both continuous and rough-hewn data sets. Examples are given from agriculture, arboriculture, biology, computer science, ecology, engineering, farming and farm management, hydrology, medicine, ophthalmology, pharmacology, physiotherapy, spectroscopy, sports science, audiology and epidemiology.

scholarly journals The Multispecies Coalescent Model Outperforms Concatenation Across Diverse Phylogenomic Data Sets

2020 ◽  
Vol 69 (4) ◽  
pp. 795-812 ◽  
Author(s):  
Xiaodong Jiang ◽  
Scott V Edwards ◽  
Liang Liu
Keyword(s):  
Data Analysis ◽  
Model Validation ◽  
Bayesian Model ◽  
Statistical Tests ◽  
Gc Content ◽  
Data Sets ◽  
Gene Trees ◽  
Coalescent Model ◽  
Multispecies Coalescent ◽  
Substitution Models

Abstract A statistical framework of model comparison and model validation is essential to resolving the debates over concatenation and coalescent models in phylogenomic data analysis. A set of statistical tests are here applied and developed to evaluate and compare the adequacy of substitution, concatenation, and multispecies coalescent (MSC) models across 47 phylogenomic data sets collected across tree of life. Tests for substitution models and the concatenation assumption of topologically congruent gene trees suggest that a poor fit of substitution models, rejected by 44% of loci, and concatenation models, rejected by 38% of loci, is widespread. Logistic regression shows that the proportions of GC content and informative sites are both negatively correlated with the fit of substitution models across loci. Moreover, a substantial violation of the concatenation assumption of congruent gene trees is consistently observed across six major groups (birds, mammals, fish, insects, reptiles, and others, including other invertebrates). In contrast, among those loci adequately described by a given substitution model, the proportion of loci rejecting the MSC model is 11%, significantly lower than those rejecting the substitution and concatenation models. Although conducted on reduced data sets due to computational constraints, Bayesian model validation and comparison both strongly favor the MSC over concatenation across all data sets; the concatenation assumption of congruent gene trees rarely holds for phylogenomic data sets with more than 10 loci. Thus, for large phylogenomic data sets, model comparisons are expected to consistently and more strongly favor the coalescent model over the concatenation model. We also found that loci rejecting the MSC have little effect on species tree estimation. Our study reveals the value of model validation and comparison in phylogenomic data analysis, as well as the need for further improvements of multilocus models and computational tools for phylogenetic inference. [Bayes factor; Bayesian model validation; coalescent prior; congruent gene trees; independent prior; Metazoa; posterior predictive simulation.]

scholarly journals Topics in data analysis using R in extreme value theory

2013 ◽  
Vol 10 (1) ◽  
Author(s):  
Helena Penalva ◽  
Manuela Neves
Keyword(s):  
Applied Sciences ◽  
Data Analysis ◽  
Open Source Software ◽  
Extreme Value Theory ◽  
Extreme Values ◽  
Value Theory ◽  
Extreme Value ◽  
Extreme Value Statistics ◽  
Data Sets ◽  
Statistics Of Extremes

The statistical Extreme Value Theory has grown gradually from the beginning of the 20th century. Its unquestionable importance in applications was definitely recognized after Gumbel's book in 1958, Statistics of Extremes. Nowadays there is a wide number of applied sciences where extreme value statistics are largely used. So, accurately modeling extreme events has become more and more important and the analysis requires tools that must be simple to use but also should consider complex statistical models in order to produce valid inferences. To deal with accurate, friendly, free and open-source software is of great value for practitioners and researchers. This paper presents a review of the main steps for initializing a data analysis of extreme values in R environment. Some well documented packages are briefly described and two data sets will be considered for illustrating the use of some functions.

scholarly journals USING SPSS FOR RESEARCH AND DATA ANALYSIS

2019 ◽  
Vol 32 (3) ◽  
pp. 363-368
Author(s):  
Agron Alili ◽  
Dejan Krstev
Keyword(s):  
Data Analysis ◽  
Software Package ◽  
Science Research ◽  
Data File ◽  
Health Science ◽  
Environmental Research ◽  
Statistical Software ◽  
Health Science Research ◽  
The Social ◽  
Different Types

There is no question that business, education, and all fields of science have come to rely heavily on the computer. This dependence has become so great that it is no longer possible to understand social and health science research without substantial knowledge of statistics and without at least some rudimentary understanding of statistical software. The number and types of statistical software packages that are available continue to grow each year. In this paper we have chosen to work with SPSS, or the Statistical Package for the Social Sciences. SPSS was chosen because of its popularity within both academic and business circles, making it the most widely used package of its type. SPSS is also a versatile package that allows many different types of analyses. transformations, and forms of output - in short, it will more than adequately serve our purposes. The SPSS software package is continually being updated and improved, and so with each major revision comes a new version of that package. In this paper, we will describe and use the most recent version of SPSS, called SPSS for Windows, in order to use this text for data analysis, your must have access to the SPSS for Windows software package.The capability of SPSS is truly astounding. The package enables you to obtain statistics ranging from simple descriptive numbers to complex analyses of multivariate matrices. You can plot the data in histograms, scatterplots, and other ways. You can combine files, split files, and sort files. You can modify existing variables and create new ones. In short, you can do just about anything you'd ever want with a set of data using this software package. A number of specific SPSS procedures are relevant to the kinds of statistical analyses covered in an introductory level statistics or research methods course typically found in the social and health sciences, natural sciences, or business. Yet, we will touch on just a fraction of the many things that SPSS can do. Our aim is to help то become familiar with SPSS, and we hope that this introduction will both reinforce our understanding of statistics and lead us to see what a powerful tool SPSS is, how it can actually help you better understand your data, how it can enable you to test hypotheses that were once too difficult to consider, and how it can save our incredible amounts of time as well as reduce the likelihood of making errors in data analyses. We show how to create a data file and generate an output file. We also discuss how to name and save the different types of files created in the three main SPSS windows. This paper will present a software presentation from a survey on socio-economic and environmental research.

scholarly journals Incorporating Computer Programming & Data Science into a Guided Inquiry-Based Undergraduate Ecology Lab

2019 ◽  
Vol 81 (9) ◽  
pp. 649-657 ◽  
Author(s):  
Jennifer Rahn ◽  
Dana Willner ◽  
James Deverick ◽  
Peter Kemper ◽  
Margaret Saha
Keyword(s):  
Biological Sciences ◽  
Data Analysis ◽  
Computer Science ◽  
Data Science ◽  
Computer Programming ◽  
Biological Diversity ◽  
Data Sets ◽  
Complex Data ◽  
Complex Data Sets ◽  
Basic Ideas

The biological sciences are becoming increasingly reliant on computer science and associated technologies to quickly and efficiently analyze and interpret complex data sets. Introducing students to data analysis techniques is a critical part of their development as well-rounded, scientifically literate citizens. As part of a collaborative effort between the Biology and Computer Science departments at William & Mary, we sought to develop laboratory exercises that would introduce basic ideas of data analysis while also exposing students to Python, a commonly used computer programming language. We accomplished this by developing exercises within the interactive Jupyter Notebook platform, an open-source application that allows Python code to be written and executed as discrete blocks in real time. Students used the developed Jupyter Notebook to analyze data collected as part of a multiweek ecology field experiment aimed at determining the effect of white-tailed deer on aspects of biological diversity. These inquiry-based laboratory exercises generated scientifically relevant data and gave students a chance to experience and participate in ongoing scientific research while demonstrating the utility of computer science in the scientific process.

scholarly journals Exploratory Data Analysis using Python

2019 ◽  
Vol 8 (12) ◽  
pp. 4727-4735
Keyword(s):  
Data Analysis ◽  
Recommendation System ◽  
Exploratory Data Analysis ◽  
Object Oriented ◽  
Data Sets ◽  
Great Role ◽  
Interactive Programming ◽  
Different Types ◽  
Exploratory Data ◽  
Python Programming

Data need to be analyzed so as to produce good result. Using the result decision can be taken. For example recommendation system, ranking of the page, demand fore casting, prediction of purchase of the product. There are some leading companies where the review of the customer plays a great role to analyze the factor which influences the review rating. We have used exploratory data analysis (EDA) where data interpretations can be done in row and column format. We have used python for data analysis. it is object oriented ,interpreted and interactive programming language. it is open source with rich sets of libraries like pandas, MATplotlib, seaborn etc. We have used different types of charts and various types of parameter to analyze Amazon review data sets which contains the reviews of electronic data items. We have used python programming for the data analysis

scholarly journals Data Evaluation and Statistical Analysis of Functional Observational Battery Data Using a Linear Models Approach

1989 ◽  
Vol 8 (1) ◽  
pp. 157-169 ◽  
Author(s):  
John P. Creason
Keyword(s):  
Statistical Analysis ◽  
Linear Models ◽  
Statistical Tests ◽  
Ease Of Use ◽  
Data Evaluation ◽  
Behavioral Characteristics ◽  
Statistical Research ◽  
Statistical Software ◽  
Different Types ◽  
Functional Observational Battery

Statistical analysis of functional observational battery (FOB) data presents special problems in that there are three different types of data collected (continuous, count, and categorical), all of which are measured in a repeated manner across time. Initial measurements are made before any treatment is applied, and proper use of these individual control values must be determined. A coherent structure for the analysis of such data is laid out, and examples of applications are presented. Rationale for the approaches used are described. Behavioral characteristics of the statistical tests are summarized for one FOB experiment to show that the tests indeed perform properly. The availability and ease of use of the SAS statistical software employed, including the key analysis procedures PROC CATMOD and PROC GLM, and of the SASGRAPH graphics procedures and their importance to data evaluation in the FOB are fully described. Cautions about these procedures and further statistical research and development needs are summarized.

DTI data analysis: application of fiber tracking to group averaged data sets

2010 ◽  
Vol 41 (01) ◽  
Author(s):  
HP Müller ◽  
A Unrath ◽  
A Riecker ◽  
AC Ludolph ◽  
J Kassubek
Keyword(s):  
Data Analysis ◽  
Fiber Tracking ◽  
Data Sets ◽  
Analysis Application

Medical Diagnosis from a Clinician’s Point of View

1978 ◽  
Vol 17 (01) ◽  
pp. 28-35
Author(s):  
F. T. De Dombal
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Computer Science ◽  
Medical Diagnosis ◽  
Information Science ◽  
Point Of View ◽  
Joint Research ◽  
The World

This paper discusses medical diagnosis from the clinicians point of view. The aim of the paper is to identify areas where computer science and information science may be of help to the practising clinician. Collection of data, analysis, and decision-making are discussed in turn. Finally, some specific recommendations are made for further joint research on the basis of experience around the world to date.

Market of Insurance in Russia: Major Tendencies and Problems of Development

2006 ◽  
pp. 115-127
Author(s):  
T Natkhov
Keyword(s):  
Data Analysis ◽  
Statistical Data ◽  
Statistical Data Analysis ◽  
Medium Term ◽  
Insurance Sector ◽  
Different Types ◽  
Basic Characteristics

The article considers recent tendencies in the development of the market of insurance in Russia. On the basis of statistical data analysis the most urgent problems of the insurance sector are formulated. Basic characteristics of different types of insurance are revealed, and measures on perfection of the insurance institution in the medium term are proposed.

Automated Emission Data Registration and Segmentation for IC Analysis

2016 ◽  
Author(s):  
Franco Stellari ◽  
Peilin Song
Keyword(s):  
Data Analysis ◽  
Registration Accuracy ◽  
Emission Data ◽  
Data Registration ◽  
Different Types ◽  
Test Chips

Abstract In this paper, the development of advanced emission data analysis methodologies for IC debugging and characterization is discussed. Techniques for automated layout to emission registration and data segmentations are proposed and demonstrated using both 22 nm and 14 nm SOI test chips. In particular, gate level registration accuracy is leveraged to compare the emission of different types of gates and quickly create variability maps automatically.

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