Professional IOS Programming with Swift by Michael Dippery

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Professional IOS Programming with Swift

by Michael Dippery

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This book is designed to be a user's guide for students and other interested readers to perform statistical data analysis with IBM SPSS, which is a major statistical software package used extensively in academic, government and business settings. IBM SPSS has a user-friendly point-and-click interface and a robust selection of statistical and data analytic procedures. This book addresses the needs, level of sophistication and interest in introductory statistical methodology on the part of undergraduate students as well as more advanced students in social and behavioral science, business, health-related and education programs. Each chapter covers a particular statistical procedure and has the following format: an example problem or analysis goal together with a data set; IBM SPSS analysis with step-by-step analysis setup and accompanying screen shots and IBM SPSS output with screen shots and narrative on how to read or interpret the results of the analysis. Getting Started with IBM SPSS Obtaining, Editing and Saving Statistical Output Manipulating Data Descriptive Statistics Procedures Simple Data Transformations Evaluating Score Distribution Assumptions Bivariate Correlation Regressing (Predicting) Quantitative Variables Regressing (Predicting) Categorical Variables Survival Analysis Reliability as a Gauge of Measurement Quality Analysis of Structure Evaluating Causal (Predictive) Models T Test Univariate Group Differences: Anova and Ancova Multivariate Group Differences: Manova and Discriminant Function Analysis Multidimensional Scaling Cluster Analysis Nonparametric Procedures for Analyzing Frequency Data Preface Part 1 Getting Started with IBM SPSS Chapter 1 Introduction to IBM SPSS Chapter 2 Entering Data in IBM SPSS Chapter 3 Importing Data from Excel to IBM SPSS Part 2 Obtaining, Editing and Saving Statistical Output Chapter 4 Performing Statistical Procedures in IBM SPSS Chapter 5 Editing Output Chapter 6 Saving and Copying Output Part 3 Manipulating Data Chapter 7 Sorting and Selecting Cases Chapter 8 Splitting Data Files Chapter 9 Merging Data from Separate Files Part 4 Descriptive Statistics Procedures Chapter 10 Frequencies Chapter 11 Descriptive Chapter 12 Explore Part 5 Simple Data Transformations Chapter 13 Standardizing Variables to Z Scores Chapter 14 Recoding Variables Chapter 15 Visual Binning Chapter 16 Computing New Variables Chapter 17 Transforming Dates to Age Part 6 Evaluating Score Distribution Assumptions Chapter 18 Detecting Univariate Outliers Chapter 19 Detecting Multivariate Outliers Chapter 20 Assessing Distribution Shape: Normality, Skewness and Kurtosis Chapter 21 Transforming Data to Remedy Statistical Assumption Violations Part 7 Bivariate Correlation Chapter 22 Pearson Correlation Chapter 23 Spearman Rho and Kendall Tau-B Rank-Order Correlations Part 8 Regressing (Predicting) Quantitative Variables Chapter 24 Simple Linear Regression Chapter 25 Centering the Predictor Variable in Simple Linear Regression Chapter 26 Multiple Linear Regression Chapter 27 Hierarchical Linear Regression Chapter 28 Polynomial Regression Chapter 29 Multilevel Modeling Part 9 Regressing (Predicting) Categorical Variables Chapter 30 Binary Logistic Regression Chapter 31 ROC Analysis Chapter 32 Multinomial Logistic Regression Part 10 Survival Analysis Chapter 33 Survival Analysis: Life Tables Chapter 34 The Kaplan--Meier Survival Analysis Chapter 35 Cox Regression Part 11 Reliability as a Gauge of Measurement Quality Chapter 36 Reliability Analysis: Internal Consistency Chapter 37 Reliability Analysis: Assessing Rater Consistency Part 12 Analysis of Structure Chapter 38 Principal Components and Factor Analysis Chapter 39 Confirmatory Factor Analysis Part 13 Evaluating Causal (Predictive) Models Chapter 40 Simple Mediation Chapter 41 Path Analysis Using Multiple Regressions Chapter 42 Path Analysis Using Structural Equation Modeling Chapter 43 Structural Equation Modeling Part 14 T Test Chapter 44 One-Sample T Test Chapter 45 Independent-Samples T Test Chapter 46 Paired-Samples T Test Part 15 Univariate Group Differences: Anova and Ancova Chapter 47 One-Way Between-Subjects Anova Chapter 48 Polynomial Trend Analysis Chapter 49 One-Way Between-Subjects Ancova Chapter 50 Two-Way Between-Subjects Anova Chapter 51 One-Way Within-Subjects Anova Chapter 52 Repeated Measures Using Linear Mixed Models Chapter 53 Two-Way Mixed Anova Part 16 Multivariate Group Differences: Manova and Discriminant Function Analysis Chapter 54 One-Way Between-Subjects Manova Chapter 55 Discriminant Function Analysis Chapter 56 Two-Way Between-Subjects Manova Part 17 Multidimensional Scaling Chapter 57 Multidimensional Scaling: Classical Metric Chapter 58 Multidimensional Scaling: Metric Weighted Part 18 Cluster Analysis Chapter 59 Hierarchical Cluster Analysis Chapter 60 K-Means Cluster Analysis Part 19 Nonparametric Procedures for Analyzing Frequency Data Chapter 61 Single-Sample Binomial and Chi-Square Tests: Binary Categories Chapter 62 Single-Sample (One-Way) Multinominal Chi-Square Tests Chapter 63 Two-Way Chi-Square Test of Independence Chapter 64 Risk Analysis Chapter 65 Chi-Square Layers Chapter 66 Hierarchical Log linear Analysis Appendix Statistics Tables References Author Index Subject IndexISBN - 9788126556854

Pages : 308

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