Kursen har inget datum.

## Översikt

Kurskod | 0L019SE | Kunskapsnivå | Advanced |
---|---|---|---|

Kurslängd | 1.0 dagar | Leveranstyp | Instructor led
(Hands-on labs) |

Kurstyp | |||

Listpris | Set by Partner |

NOTE: This is a live, instructor-led online course. Do not make arrangements to travel.

IBM SPSS Education is pleased to offer you our courses in an exciting learning format, Instructor-led Online (ILO). Students are offered a similar experience to live classroom training, with the convenience of having it delivered directly to their desktop.

IBM SPSS Statistics: Advanced Topics in Regression and Discriminant Analysis (V19) is a one day instructor-led online course that provides a practical, application-oriented introduction to some of the advanced statistical methods available in IBM® SPSS® Statistics for data analysts and researchers. Students will review several 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. 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.

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Roadmaps:

## Målgrupp

This course is for:

- Anyone who has worked with IBM 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 IBM 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.
- IBM SPSS Statistics Standard, IBM SPSS Statistics Professional, IBM SPSS Statistics Premium.

## Nyckelområden

**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

**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

**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