- Référence : 0G51AG
- Durée : 2 jours (14h)
- Lieu : Au choix. À distance ou en présentiel, à Paris ou en Régions
1490€ HT
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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 :
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.
Identify the steps in the research process
Principles of statistical analysis
Identify measurement levels
Chart individual variables
Summarize individual variables
Examine the normal distribution
Examine standardized scores
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
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
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
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
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
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
Bayesian statistics versus classical test theory
Explain the Bayesian approach
Evaluate a null hypothesis: Bayes Factor
Bayesian procedures in IBM SPSS Statistics
Overview of supervised models
Overview of models to create natural groupings
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