Översikt
| Kurskod | 0K093SE | Leveranstyp | Self-paced Virtual Class |
|---|---|---|---|
| Kurslängd | 3.0 dagar | Kurstyp | |
| Listpris |
SEK 13 680,00 exkl moms
SEK 17 100,00 inkl moms |
PLEASE DO NOT MAKE TRAVEL ARRANGEMENT FOR THIS SELF PACED VIRTUAL COURSE (SPVC). ONCE YOU ARE ENROLLED IN THIS COURSE, YOU WILL NOT BE ABLE TO CANCEL YOUR ENROLLMENT.
After you receive confirmation that you are enrolled, you will be sent further instructions to access your course material and remote labs. You will then have a 30 day window in which to complete your course. Within this 30 days, you will have 336 hours of elab time. The self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours a day, but lab system access is allocated on a first-come, first-served basis. When you are not using the elab system, ensure that you suspend your elab to maximize your hours available to use the elab system. Once you have accessed the course, help is available Monday through Friday; questions will be responded to within 24 hours.
NO EXTENSIONS FOR THIS COURSE WILL BE GRANTED.
This is the self paced training version of ''Advanced Statistical Analysis Using IBM SPSS Statistics'' classroom course Advanced Statistical Analysis Using IBM SPSS Statistics is a three day self-paced training course that provides an application-oriented introduction to the advanced statistical methods available in IBM® SPSS® Statistics for data analysts and researchers. 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, as well as how to interpret the results. This includes a broad range of techniques for predicting both continuous and categorical outcomes, as well as methods to cluster cases, create statistical groupings of variables, and find similar cases using a large set of variables. Students will gain an understanding of when and why to use these various techniques as well as how to apply them with confidence and interpret their output.
Målgrupp
This advanced course is for:
- Anyone who has worked with SPSS Statistics and wants to become better versed in the more advanced statistical capabilities.
- Anyone who has a solid understanding of statistics and wants to expand their knowledge of appropriate statistical procedures and how to set them up using SPSS Statistics.
- Analysts and Modelers
Förkunskaper
You should have:
- On the job statistical experience or completion of the Introduction to Statistical Analysis Using IBM SPSS Statistics course and/or Intermediate-level statistics oriented courses.
- Knowledge of basic statistics, including linear regression.
- Knowledge of IBM SPSS Statistics Standard, IBM SPSS Statistics Professional, IBM SPSS Statistics Premium.
Nyckelområden
Factor Analysis
- Explain the basic theory of factor analysis and the steps in factor analysis
- Explain the assumptions and requirements of factor analysis
- Specify a factor analysis and interpret the output
K-Means Cluster Analysis
- Explain the basic theory of cluster analysis and the steps in doing a cluster analysis
- Explain the approach of K-Means cluster analysis
- Specify a K-Means cluster analysis and interpret the output
TwoStep Cluster Analysis
- Explain the basic approach of TwoStep cluster analysis
- Specify a TwoStep cluster analysis
- Use the Model Viewer to study and interpret the output
Binary Logistic Regression
- Explain the basic theory and assumptions of logistic regression
- Specify a logistic regression analysis
- Interpret model fit, logistic regression coefficients and model accuracy
Multinomial Logistic Regression
- Explain the basic theory of multinomial logistic regression
- Specify a multinomial logistic regression analysis
- Interpret model fit, logistic regression coefficients and model accuracy
Discriminant Analysis
- Explain the basic theory of discriminant analysis and how cases are classified
- Specify a two-group discriminant analysis and interpret the resulting output
- Complete additional analysis and validation of the discriminant model
Nearest Neighbor Analysis
- Explain the basic approach of nearest neighbor analysis
- Explain the meaning of k in the analysis and how cases are classified
- Specify a nearest neighbor analysis and interpret the resulting output in the Model Viewer
Kaplan-Meier Analysis
- Explain the general principles of survival analysis
- Specify a Kaplan-Meier analysis and interpret the resulting tabular and graphical output
- Specify a Kaplan-Meier analysis with a strata variable, and with pairwise comparisons
Cox Regression
- Explain the general principles of Cox regression
- Specify a Cox regression analysis and interpret the resulting tabular and graphical output
- Test the assumption of proportional hazards
- Specify a Cox regression with time-varying covariate for variables that don't meet the assumption of proportionality
Generalized Linear Models
- Explain the use of the exponential family of distributions and a link function and how these differential a generalized linear model from a general linear model
- Specify a Generalized Linear Model analysis and interpret the resulting output
- Check model assumptions and predictions
Linear Mixed Models
- Explain the general principles of a linear mixed model approach to data analysis
- Specify a Linear Mixed Model analysis and interpret the resulting output, building successive models of greater complexity
Hårdvarukrav
Minimum Requirements (for PC) to use SPVC
- Windows 2000, XP, Vista, and 7
- Firefox, including V3.6.6
- Internet Explorer, including V8
- browser settings include:
- Java Runtime Environment must be installed and enabled in the web browser
- Web browser must be set to enable/allow JavaScript
- Web browser Pop-up blocking mechanisms must be set to allow pop-ups from elearn.ihost.com
- Cookies must be enabled
- Flash-enabled
- ActiveX-enabled
- CITRIX, for the remote lab (instructions to install included with the SPVC)