Statistical Analysis Using IBM SPSS Statistics (V26)
Par Global KnowledgeObjectifs
• Introduction to statistical analysis
• Describing individual variables
• Testing hypotheses
• Testing hypotheses on individual variables
• Testing on the relationship between categorical variables
• Testing on the difference between two group means
• Testing on differences between more than two group means
• Testing on the relationship between scale variables
• Predicting a scale variable: Regression
• Introduction to Bayesian statistics
• Overview of multivariate procedures
Programme
Introduction to statistical analysis
• Identify the steps in the research process
• Identify measurement levels
Describing individual variables
• Chart individual variables
• Summarize individual variables
• Identify the normal distribution
• Identify standardized scores
Testing hypotheses
• Principles of statistical testing
• One-sided versus two-sided testing
• Type I, type II errors and power
Testing hypotheses on individual variables
• Identify population parameters and sample statistics
• Examine the distribution of the sample mean
• Test a hypothesis on the population mean
• Construct confidence intervals
• Tests on a single variable
Testing on the relationship between categorical variables
• Chart the relationship
• Describe the relationship
• Test the hypothesis of independence
• Assumptions
• Identify differences between the groups
• Measure the strength of the association
Testing on the difference between two group means
• Chart the relationship
• Describe the relationship
• Test the hypothesis of two equal group means
• Assumptions
Testing on differences between more than two group means
• Chart the relationship
• Describe the relationship
• Test the hypothesis of all group means being equal
• Assumptions
• Identify differences between the group means
Testing on the relationship between scale variables
• Chart the relationship
• Describe the relationship
• Test the hypothesis of independence
• Assumptions
• Treatment of missing values
Predicting a scale variable: Regression
• Explain linear regression
• Identify unstandardized and standardized coefficients
• Assess the fit
• Examine residuals
• Include 0-1 independent variables
• Include categorical independent variables
Introduction to Bayesian statistics
• Bayesian statistics and classical test theory
• The Bayesian approach
• Evaluate a null hypothesis
• Overview of Bayesian procedures in IBM SPSS Statistics
Overview of multivariate procedures
• Overview of supervised models
• Overview of models to create natural groupings
Formations de la même catégories (5)
This course focuses on reviewing concepts of data science, where participants will learn the stages of a data science project. Topics include using automated tools to prepare data for analysis, build models, evaluate models, and deploy models. To learn about these data science concepts and topics, participants will use IBM SPSS Modeler as a tool.
This course (formerly: Introduction to IBM SPSS Text Analytics for IBM SPSS Modeler (v18)) teaches you how to analyze text data using IBM SPSS Modeler Text Analytics. You will be introduced to the complete set of steps involved in working with text data, from reading the text data to creating the final categories for additional analysis. After the final model has been created, there is an example of how to apply the model to perform churn analysis in telecommunications. Topics include how to automatically and manually create and modify categories, how to edit synonym, type, and exclude dictionaries, and how to perform Text Link Analysis and Cluster Analysis with text data. Also included are examples of how to create resource tempates and Text Analysis packages to share with other projects and other users.
This course provides an application-oriented introduction to advanced statistical methods available in IBM SPSS Statistics. Students will review a variety of advanced statistical techniques and discuss situations in which each technique would be used, the assumptions made by each method, how to set up the analysis, and how to interpret the results. This includes a broad range of techniques for predicting variables, as well as methods to cluster variables and cases.
This course focuses on using analytical models to predict a categorical field, such as churn, fraud, response to a mailing, pass/fail exams, and machine break-down. Students are introduced to decision trees such as CHAID and C&R Tree, traditional statistical models such as Logistic Regression, and machine learning models such as Neural Networks. Students will learn about important options in dialog boxes, how to interpret the results, and explain the major differences between the models.
Clustering and Association Modeling Using IBM SPSS Modeler (v18.1) introduces modelers to two specific classes of modeling that are available in IBM SPSS Modeler: clustering and associations. Participants will explore various clustering techniques that are often employed in market segmentation studies. Participants will also explore how to create association models to find rules describing the relationships among a set of items, and how to create sequence models to find rules describing the relationships over time among a set of items.
