Rowan University Library owns many electronic resources to assist with understanding statistical concepts. All the resources are available to Rowan students, staff and faculty.

- IBM SPSS 27IBM SPSS 27 (available from IRT at the link above) is statistical software used to solve business and research problems by means of ad-hoc analysis, hypothesis testing and predictive analytics.

- Applied Statistics with SPSS by Eelko K. R. E. HuizinghCall Number: Available onlineISBN: 1412919304Publication Date: 2007-02-28Part I: Learning to work with SPSS;

Chapter 1 - Background to SPSS for Windows;

Chapter 2 - The use of SPSS in statistical research;

Chapter 3 - From data source to data file;

Chapter 4 - Session 1: The first steps;

Chapter 5 - Session 2: Charts and computations;

Chapter 6 - Session 3: Performing statistical analyses;

Part II: Working with SPSS and Data Entry;

Chapter 7 - Designing and conducting questionnaires with Data Entry;

Chapter 8 - Creating a data file;

Chapter 9 - Computation and classification of variables

Chapter 10 - Selecting, sorting and weighting cases

Chapter 11 - Merging, aggreating and transposing data files;

Chapter 12 - Describing your data;

Chapter 13 - Charts;

Chapter 14 - Crosstables;

Chapter 15 - Analysing multiple responses;

Chapter 16 - Describing groups and testing the differences;

Chapter 17 - Analysis of variance;

Chapter 18 - Correlation and regression;

Chapter 19 - Non-parametric tests;

Chapter 20 - Customising SPSS

Accessibly written and easy to use, Applied Statistics Using SPSS is an all-in-one self-study guide to SPSS and do-it-yourself guide to statistics. This book is based around the needs of undergraduate students embarking on their own research project, and its self-help style is designed to boost the skills and confidence of those that will need to use SPSS in the course of their research project. - IBM SPSS Modeler Cookbook by Keith McCormickCall Number: Available onlineISBN: 1849685479Publication Date: 2013-01-01Chapter 1: Data Understanding

Introduction

Using an empty aggregate to evaluate sample size

Evaluating the need to sample from the initial data

Using CHAID stumps when interviewing an SME

Using a single cluster K-means as an alternative to anomaly detection

Using an @NULL multiple Derive to explore missing data

Creating an outlier report to give to SMEs

Detecting potential model instability early using the Partition node and Feature Selection

Chapter 2: Data Preparation Select

Introduction

Using the Feature Selection node creatively to remove, or decapitate, perfect predictors

Running a Statistics node on anti-join to evaluate potential missing data

Evaluating the use of sampling for speed

Removing redundant variables using correlation matrices

Selecting variable using the CHAID modeling node

Selecting variables using the Means node

Selecting variables using single-antecedent association rules

Chapter 3: Data Preparation Clean

Introduction

Binning scale variables to address missing data

Using a full data model/partial data model approach to address missing data

Imputing in-stream mean or median

Imputing missing values randomly from uniform or normal distributions

Using random imputation to match a variable's distribution

Searching for similar records using a neural network for inexact matching

Using neuro-fuzzy searching to find similar names

Producing longer Soundex codes

Chapter 4: Data Preparation Construct

Introduction

Building transformations with multiple Derive nodes

Calculating and comparing conversion rates

Grouping categorical values

Transforming high skew and kurtosis variables with a multiple Derive node

Creating flag variables for aggregation

Using Association Rules for interaction detection/feature creation

Creating time-aligned cohorts

Chapter 5: Data Preparation Integrate and Format

Introduction

Speeding up merge with caching and optimization settings

Merging a look-up table

Shuffle-down (nonstandard aggregation)

Cartesian product merge using key-less merge by key

Multiplying out using Cartesian product merge, user source, and derive dummy

Changing large numbers of variable names without scripting

Parsing nonstandard dates

Parsing and performing a conversion on a complex stream

Sequence processing

Chapter 6: Selecting and Building a Model

Introduction

Evaluating balancing with the Auto Classifier

Building models with and without outliers

This is a practical cookbook with intermediate-advanced recipes for SPSS Modeler data analysts. It is loaded with step-by-step examples explaining the process followed by the experts.If you have had some hands-on experience with IBM SPSS Modeler and now want to go deeper and take more control over your data mining process, this is the guide for you. It is ideal for practitioners who want to break into advanced analytics. - IBM SPSS Modeler Essentials by Keith McCormick; Jose Jesus Salcedo; Bowen Wei (Technical editor)Call Number: Available onlineISBN: 9781788296823Publication Date: 2017-12-26Chapter 1: Introduction to Data Mining and Predictive Analytics

Introduction to data mining

CRISP-DM overview

The data mining process (as a case study)

Chapter 2: The Basics of Using IBM SPSS Modeler

Introducing the Modeler graphic user interface

Building streams

Modeler stream rules

Help options

Chapter 3: Importing Data into Modeler

Data structure

Levels of measurement and roles

Chapter 4: Data Quality and Exploration

Data Audit node options

Chapter 5: Cleaning and Selecting Data

Selecting cases

Sorting cases

Identifying and removing duplicate cases

Reclassifying categorical values

Chapter 6: Combining Data Files

Combining data files with the Append node

Removing fields with the Filter node

Combining data files with the Merge node

Chapter 7: Deriving New Fields

Derive – Formula

Derive – Flag

Derive – Nominal

Derive – Conditional

Chapter 8: Looking for Relationships Between Fields

Relationships between categorical fields

Relationships between categorical and continuous fields

Relationships between continuous fields

Chapter 9: Introduction to Modeling Options in IBM SPSS Modeler

Classification

Association

Segmentation

Chapter 10: Decision Tree Models

Decision tree theory

CHAID theory

CHAID results

Chapter 11: Model Assessment and Scoring

Contrasting model assessment with the Evaluation phase

Get up-and-running with IBM SPSS Modeler without going into too much depth. Identify interesting relationships within your data and build effective data mining and predictive analytics solutions. IBM SPSS Modeler allows users to quickly and efficiently use predictive analytics and gain insights from your data. With almost 25 years of history, Modeler is the most established and comprehensive Data Mining workbench available.

This book takes a detailed, step-by-step approach to introducing data mining using the de facto standard process, CRISP-DM, and Modeler's easy to learn "visual programming" style. You will learn how to read data into Modeler, assess data quality, prepare your data for modeling, find interesting patterns and relationships within your data, and export your predictions. Using a single case study throughout, this intentionally short and focused book sticks to the essentials.

This book provides an overview of various popular data modeling techniques and presents a detailed case study of how to use CHAID, a decision tree model. Assessing a model's performance is as important as building it; this book will also show you how to do that. Finally, you will see how you can score new data and export your predictions. By the end of this book, you will have a firm understanding of the basics of data mining and how to effectively use Modeler to build predictive models. - Quantitative Analysis and IBM® SPSS® Statistics by Abdulkader AljandaliCall Number: Available onlineISBN: 9783319455273Publication Date: 2016-11-15Introduction to IBM SPSS Statistics

Getting Started

Data Examination and Description

Graphics and Introductory Statistical Analysis of Data

Frequencies and Crosstabulations

Coding, Missing Values, Conditional and Arithmetic Operations

Hypothesis Tests

Hypothesis Tests Concerning Means

Nonparametric Hypothesis Tests

Methods of Business Forecasting

Bivariate Correlation and Regression

Elementary Time Series Methods

Other Useful Features of IBM SPSS Statistics

Secondary Sources of Data for Business, Finance and Marketing Students

This guide is for practicing statisticians and data scientists who use IBM SPSS for statistical analysis of big data in business and finance. This is the first of a two-part guide to SPSS for Windows, introducing data entry into SPSS, along with elementary statistical and graphical methods for summarizing and presenting data. Part I also covers the rudiments of hypothesis testing and business forecasting while Part II will present multivariate statistical methods, more advanced forecasting methods, and multivariate methods. IBM SPSS Statistics offers a powerful set of statistical and information analysis systems that run on a wide variety of personal computers. The software is built around routines that have been developed, tested, and widely used for more than 20 years. - SPSS for Starters by Ton J. Cleophas; Aeilko H. ZwindermanCall Number: Available onlineISBN: 9400798792Publication Date: 2014-09-28One-Sample Continuous and Binary Data (t-Test, z-Test) (10 and 55 Patients)

Paired Continuous Data (Paired-t, Wilcoxon) (10 Patients)

Unpaired Continuous Data (Unpaired t-Tests, Mann–Whitney) (20 Patients)

Linear Regression (20 Patients)

Repeated Measures ANOVA, Friedman (10 Patients)

Mixed Models (20 Patients)

One-Way-ANOVA, Kruskall–Wallis (30 Patients)

Trend Test for Continuous Data (30 Patients)

Unpaired Binary Data (Chi-Square, Crosstabs) (55 Patients)

Logistic Regression (55 Patients)

Trend Tests for Binary Data (106 Patients)

Paired Binary (McNemar Test) (139 General Practitioners)

Multiple Paired Binary Data (Cochran’s Q Test) (139 Patients)

Cox Regression (60 Patients)

Cox Regression with Time-dependent Variables (60 Patients)

Validating Qualitative Diagnostic Tests (575 Patients)

Validating Quantitative Diagnostic Tests (17 Patients)

Reliability Assessment of Qualitative Diagnostic Tests (17 Patients)

Reliability Assessment of Quantitative Diagnostic Tests (17 Patients)

This small book addresses different kinds of datafiles, as commonly encountered in clinical research, and their data-analysis on SPSS Software. Clinical researchers currently perform basic statistics without professional help from a statistician, including t-tests and chi-square tests. With help of user-friendly software the step from such basic tests to more complex tests has become smaller, and more easy to take. - SPSS Statistics for Data Analysis and Visualization by Keith McCormick; Jesus Salcedo; Jon Peck (As told to); Jason Verlen (Foreword by); Andrew Wheeler (As told to)Call Number: Available onlineISBN: 9781119003557Publication Date: 2017-05-01Part I Advanced Statistics

Chapter 1 Comparing and Contrasting IBM SPSS AMOS with Other Multivariate Techniques

Chapter 2 Monte Carlo Simulation and IBM SPSS Bootstrapping

Chapter 3 Regression with Categorical Outcome Variables

Chapter 4 Building Hierarchical Linear Models

Part II Data Visualization

Chapter 5 Take Your Data Visualizations to the Next Level

Chapter 6 The Code Behind SPSS Graphics: Graphics Production Language

Chapter 7 Mapping in IBM SPSS Statistics

Chapter 8 Geospatial Analytics

Chapter 9 Perceptual Mapping with Correspondence Analysis, GPL, and OMS

Chapter 10 Display Complex Relationships with Multidimensional Scaling

Part III Predictive Analytics

Chapter 11 SPSS Statistics versus SPSS Modeler: Can I Be a Data Miner Using SPSS Statistics?

Chapter 12 IBM SPSS Data Preparation

Chapter 13 Model Complex Interactions with IBM SPSS Neural Networks

Chapter 14 Powerful and Intuitive: IBM SPSS Decision Trees

Chapter 15 Find Patterns and Make Predictions with K Nearest Neighbors

Part IV Syntax, Data Management, and Programmability

Chapter 16 Write More Efficient and Elegant Code with SPSS Syntax Techniques

Chapter 17 Automate Your Analyses with SPSS Syntax and the Output Management System

Chapter 18 Statistical Extension Commands

Dive deeper into SPSS Statistics for more efficient, accurate, and sophisticated data analysis and visualization SPSS Statistics for Data Analysis and Visualization goes beyond the basics of SPSS Statistics to show you advanced techniques that exploit the full capabilities of SPSS. The authors explain when and why to use each technique, and then walk you through the execution with a pragmatic, nuts and bolts example.

Coverage includes extensive, in-depth discussion of advanced statistical techniques, data visualization, predictive analytics, and SPSS programming, including automation and integration with other languages like R and Python. You'll learn the best methods to power through an analysis, with more efficient, elegant, and accurate code.

IBM SPSS Statistics is complex: true mastery requires a deep understanding of statistical theory, the user interface, and programming. Most users don't encounter all of the methods SPSS offers, leaving many little-known modules undiscovered. This book walks you through tools you may have never noticed, and shows you how they can be used to streamline your workflow and enable you to produce more accurate results. - SPSS Statistics for Dummies by Keith McCormick; Jesus Salcedo; Aaron Poh (As told to)Call Number: Available onlineISBN: 9781118989012Publication Date: 2015-06-02Part I Getting Started with SPSS

Chapter 1 Introducing SPSS

Chapter 2 Installing SPSS

Chapter 3 A Simple Statistical Analysis Example

Part II Getting Data in and out of SPSS

Chapter 4 Entering and Defining Data

Chapter 5 Opening Data Files

Chapter 6 Getting Data and Results out of SPSS

Chapter 7 More About Defining Your Data

Part III Messing with Data in SPSS

Chapter 8 The Transform and Data Menus

Chapter 9 Using Functions

Chapter 10 Manipulating Files

Part IV Graphing Data

Chapter 11 On the Menu: Graphing Choices in SPSS

Chapter 12 Building Graphs Using the Chart Builder

Part V Analyzing Data

Chapter 13 Using Descriptive Statistics

Chapter 14 Showing Relationships between Categorical Dependent and Independent Variables

Chapter 15 Showing Relationships between Continuous Dependent and Categorical Independent Variables

Chapter 16 Showing Relationships between Continuous Dependent and Independent Variables

Part VI Making SPSS Your Own: Settings, Templates, and Looks

Chapter 17 Changing Settings

Chapter 18 Editing Charts and Chart Templates

Chapter 19 Editing Tables

Part VII Programming SPSS with Command Syntax

Chapter 20 Getting Acquainted with Syntax

Chapter 21 Adding Syntax to Your Toolkit

Part VIII The Part of Tens

Chapter 22 Ten (Or So) Modules You Can Add to SPSS

Chapter 23 Ten (Or So) Useful SPSS Online Resources

Chapter 24 Ten Professional Development Projects for SPSS Users

The ultimate beginner's guide to SPSS and statistical analysis, SPSS Statistics For Dummies is the fun and friendly guide to mastering SPSS. This book contains everything you need to know to get up and running quickly with this industry-leading software, with clear, helpful guidance on working with both the software and your data.

- JMPRowan's Mathematics Department has a campus license for the statistical software JMP that is available to download for students and faculty for their personally owned systems. To obtain the license and installer for JMP Pro please follow the directions on this link.

- Statistics with JMP: Hypothesis Tests, ANOVA and Regression by Peter Goos; David MeintrupCall Number: Available onlineISBN: 9781119097150Publication Date: 2016-04-18Part One Estimators and Tests

1 Estimating Population Parameters

2 Interval Estimators

3 Hypothesis Tests

Part Two One Population

4 Hypothesis Tests for a Population Mean, Proportion, or Variance

5 Two Hypothesis Tests for the Median of a Population

6 Hypothesis Tests for the Distribution of a Population

Part Three Two Populations

7 Independent Versus Paired Samples

8 Hypothesis Tests for the Means, Proportions, or Variances of Two Independent Samples

9 A Nonparametric Hypothesis Test for the Medians of Two Independent Samples

10 Hypothesis Tests for the Means of Two Paired Samples

11 Two Nonparametric Hypothesis Tests for Paired Samples

Part Four More Than Two Populations

12 Hypothesis Tests for More Than Two Population Means: One-Way Analysis of Variance

13 Nonparametric Alternatives to an Analysis of Variance

14 Hypothesis Tests for More Than Two Population Variances

Part Five Additional Useful Tests and Procedures

15 The Design of Experiments and Data Collection

16 Testing Equivalence

17 The Estimation and Testing of Correlation and Association

18 An Introduction to Regression Modeling

19 Simple Linear Regression

Appendix A The Binomial Distribution

Appendix B The Standard Normal Distribution

Appendix C The χ2-Distribution

Appendix D Student’s t-Distribution

Appendix E The Wilcoxon Signed-Rank Test

Appendix F The Shapiro–Wilk Test

Appendix G Fisher’s F-Distribution

Appendix H The Wilcoxon Rank-Sum Test

Appendix I The Studentized Range or Q-Distribution

Appendix J The Two-Tailed Dunnett Test

Appendix K The One-Tailed Dunnett Test

Appendix L The Kruskal–Wallis Test

Appendix M The Rank Correlation Test

This book provides a first course on parameter estimation (point estimates and confidence interval estimates), hypothesis testing, ANOVA and simple linear regression. The authors approach combines mathematical depth with numerous examples and demonstrations using the JMP software.

Key features: Provides a comprehensive and rigorous presentation of introductory statistics that has been extensively classroom tested. Pays attention to the usual parametric hypothesis tests as well as to non-parametric tests (including the calculation of exact p-values). Discusses the power of various statistical tests, along with examples in JMP to enable in-sight into this difficult topic. Promotes the use of graphs and confidence intervals in addition to p-values. - Modern Industrial Statistics: with applications in R, MINITAB and JMP by Ron S. Kenett; Shelemyahu Zacks; Daniele AmbertiCall Number: Available onlineISBN: 1118763696Publication Date: 2013-12-16Part I Principles of Statistical Thinking and Analysis;

Chapter 1 The Role of Statistical Methods in Modern Industry and Services

1.1 The different functional areas in industry and services

1.2 The quality-productivity dilemma

1.3 Fire-fighting

1.4 Inspection of products

1.5 Process control

1.6 Quality by design

1.7 Information quality and practical statistical efficiency

1.8 Chapter highlights

1.9 Exercises

Chapter 2 Analyzing Variability: Descriptive Statistics

2.1 Random phenomena and the structure of observations

2.2 Accuracy and precision of measurements

2.3 The population and the sample

2.4 Descriptive analysis of sample values

2.4.1 Frequency distributions of discrete random variables

2.4.2 Frequency distributions of continuous random variables

2.4.3 Statistics of the ordered sample

2.4.4 Statistics of location and dispersion

2.5 Prediction intervals

2.6 Additional techniques of exploratory data analysis

2.6.1 Box and whiskers plot

2.6.2 Quantile plots

2.6.3 Stem-and-leaf diagrams

2.6.4 Robust statistics for location and dispersion

2.7 Chapter highlights

2.8 Exercises

Chapter 3 Probability Models and Distribution Functions

3.1 Basic probability

3.1.1 Events and sample spaces: Formal presentation of random measurements

3.1.2 Basic rules of operations with events: Unions, intersections

3.1.3 Probabilities of events

3.1.4 Probability functions for random sampling

3.1.5 Conditional probabilities and independence of events

3.1.6 Bayes formula and its application

3.2 Random variables and their distributions

3.2.1 Discrete and continuous distributions

3.2.2 Expected values and moments of distributions

3.2.3 The standard deviation, quantiles, measures of skewness and kurtosis

3.2.4 Moment generating functions

3.3 Families of discrete distribution

3.3.1 The binomial distribution

3.3.2 The hypergeometric distribution

3.3.3 The Poisson distribution

3.3.4 The geometric and negative binomial distributions

3.4 Continuous distributions

3.4.1 The uniform distribution on the interval (a, b), a < b

3.4.2 The normal and log-normal distributions

3.4.3 The exponential distribution

3.4.4 The gamma and Weibull distributions

3.4.5 The Beta distributions

3.5 Joint, marginal and conditional distributions

3.5.1 Joint and marginal distributions

3.5.2 Covariance and correlation

3.5.3 Conditional distributions

3.6 Some multivariate distributions

3.6.1 The multinomial distribution

3.6.2 The multi-hypergeometric distribution

3.6.3 The bivariate normal distribution

3.7 Distribution of order statistics

3.8 Linear combinations of random variables

3.9 Large sample approximations

3.9.1 The law of large numbers

3.9.2 The Central Limit Theorem

3.9.3 Some normal approximations

3.10 Additional distributions of statistics of normal samples

3.10.1 Distribution of the sample variance

Fully revised and updated, this book combines a theoretical background with examples and references to R, MINITAB and JMP. Topics addressed include computer-intensive data analysis, acceptance sampling, univariate and multivariate statistical process control, design of experiments, quality by design, and reliability using classical and Bayesian methods. Provides examples in R using a dedicated package called MISTAT, and also refers to MINITAB and JMP. Provides over 40 data sets representing real-life case studies. Is complemented by a comprehensive website providing an introduction to R, and installations of JMP scripts and MINITAB macros, including effective tutorials with introductory material.

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