Description
At Sixe Engineering we have been providing official IBM training around the world for over 12 years. Get the best training from our specialists in Europe. We have important discounts and offers for two or more students.
Course details
| IBM course code: 0G51AG | Category: SPSS / SPSS Statistics |
| Delivery: Online & on-site** | Course length in days: 2 |
Target audience
• 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.
Desired Prerequisites:
- 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.
Instructors
The great majority of the IBM courses we offer are taught directly by our engineers. This is the only way we can guarantee the highest quality. We complement all the training with our own materials and laboratories, based on our experience during the deployments, migrations and courses that we have carried out during all these years.
Added value
Our courses are deeply role oriented. To give an example, the needs for technology mastery are different for developer teams and for the people in charge of deploying and managing the underlying infrastructure. The level of previous experience is also important and we take it very seriously. That is why beyond (boring) commands and tasks, we focus on solving the problems that arise in the day to day of each team. Providing them with the knowledge, competencies and skills required for each project. In addition, our documentation is based on the latest version of each product.
Agenda and course syllabus
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
Do you need to adapt this syllabus to your needs? Are you interested in other courses? Ask us without obligation.
Locations for on-site delivery
- Austria: Vienna
- Belgium: Brussels, Ghent
- Denmark: Cophenhagen
- Estonia: Tallinn
- Finland: Helsinki
- France: Paris, Marseille, Lyon
- Germany: Berlin, Munich, Cologne, Hamburg
- Greece: Athens, Thessaloniki
- Italy: Rome
- Louxemburg: Louxembourg (city)
- Netherlands: Amsterdam
- Norway: Oslo
- Portugal: Lisbon, Braga, Porto, Coimbra
- Slovakia: Bratislava
- Slovenia: Bratislava
- Spain: Madrid, Sevilla, Valencia, Barcelona, Bilbao, Málaga
- Sweden: Stockholm
- Turkey: Ankara
- United Kingdom: London
