# Formation Introduction à l'analyse statistique avec IBM SPSS Statistics (v25)

Durée 2 jours
Niveau Intermédiaire
Classe à distance
Possible

Vous pouvez suivre cette formation en direct depuis votre domicile ou votre lieu de travail. Plus d'informations sur notre solution de classe à distance...

Référence 0G51AG
Éligible CPF Non
Cours officiel IBM

This course provides an application-oriented introduction to the statistical component of IBM SPSS Statistics. Students will review several statistical techniques and discuss situations in which they would use each technique, how to set up the analysis, as well as how to interpret the results.

This includes a broad range of techniques for exploring and summarizing data, as well as investigating and testing relationships. Students will gain an understanding of when and why to use these various techniques as well as how to apply them with confidence, interpret their output, and graphically display the results.

Upon completion this course, you will be able to :

• Introduction to statistical analysis
• Examine individual variables
• Test hypotheses about individual variables
• Test the relationship between categorical variables
• Test on the difference between two group means
• Test on differences between more than two group means
• Test the relationship between scale variables
• Predict a scale variable: Regression
• Introduction to Bayesian statistics
• Overview of multivariate procedures

Public :

Anyone who has worked with IBM SPSS Statistics and wants to become better versed in the basic statistical capabilities of IBM SPSS Statistics Base.
Anyone who wants to refresh their knowledge and statistical experience.

Prérequis :

Familiarity with basic concepts in statistics, such as measurement levels, mean, and standard deviation.
Familiarity with the windows in IBM SPSS Statistics either by experience with IBM SPSS Statistics (version 18 or later) or completion of the IBM SPSS Statistics Essentials (V25) course.

### Introduction to statistical analysis

Identify the steps in the research process
Principles of statistical analysis

### Examine individual variables

Identify measurement levels
Chart individual variables
Summarize individual variables
Examine the normal distribution
Examine standardized scores

### Test hypotheses about individual variables

Identify population parameters and sample statistics
Examine the distribution of the sample mean
Determine the sample size
Test a hypothesis on the population mean
Construct a confidence interval for the population mean
Tests on a single variable: One-Sample T Test, Paired-Samples T Test, and Binomial Test

### Test the relationship between categorical variables

Chart the relationship between two categorical variables
Describe the relationship: Compare percentages in Crosstabs
Test the relationship: The Chi-Square test in Crosstabs
Assumptions of the Chi-Square test
Pairwise compare column proportions
Measure the strength of the association

### Test on the difference between two group means

Compare the Independent-Samples T Test to the Paired-Samples T Test
Chart the relationship between the group variable and scale variable
Describe the relationship: Compare group means
Test on the difference between two group means: Independent-Samples T Test
Assumptions of the Independent-Samples T Test

### Test on differences between more than two group means

Describe the relationship: Compare group means
Test the hypothesis of equal group means: One-Way ANOVA
Assumptions of One-Way ANOVA
Identify differences between group means: Post-hoc tests

### Test the relationship between scale variables

Chart the relationship between two scale variables
Describe the relationship: Correlation
Test on the correlation
Assumptions for testing on the correlation
Treatment of missing values

### Predict a scale variable: Regression

What is linear regression?
Explain unstandardized and standardized coefficients
Assess the fit of the model: R Square
Examine residuals
Include 0-1 independent variables
Include categorical independent variables

### Introduction to Bayesian statistics

Bayesian statistics versus classical test theory
Explain the Bayesian approach
Evaluate a null hypothesis: Bayes Factor
Bayesian procedures in IBM SPSS Statistics

### Overview of multivariate procedures

Overview of supervised models
Overview of models to create natural groupings

## Dates et lieux

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