PASW Regression

IBM SPSS Regression 18

Producator: IBM SPSS | Platforma: Windows | Gestiune si prelucrare informatii

IBM SPSS Regression 18 (formerly called SPSS Regression) enables you to apply more sophisticated models to your data using its wide range of nonlinear regression models
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Tags: data mining, data analysis, spss, regression

Link producator: IBM SPSS

Part number Denumire licenta Platforma Licenta RON cu TVA Cantitate Cos
New - PASW Regression
PASW-REG-1 PASW Regression Windows 1 User 1.189,21 RON

Puteti comanda produse atat online (selectati cantitatea si apasati butonul "Cumpara"), cat si telefonic:
03727 48020 (programul pentru comenzi telefonice: de luni pana vineri, intre orele 09:00 si 17:00).

Part number Denumire licenta Platforma Licenta RON cu TVA Cantitate Cos
New - PASW Regression
PASW-REG-3 PASW Regression Windows 3 User 3.333,12 RON

PASW-REG-5 PASW Regression Windows 5 User 5.475,84 RON

PASW-REG-10 PASW Regression Windows 10 User 10.475,52 RON

PASW-REG-25 PASW Regression Windows 25 User 24.046,08 RON

PASW-REG-50 PASW Regression Windows 50 User 41.664,00 RON

Puteti comanda produse atat online (selectati cantitatea si apasati butonul "Cumpara"), cat si telefonic:
03727 48020 (programul pentru comenzi telefonice: de luni pana vineri, intre orele 09:00 si 17:00).

Prezentare PASW Regression

IBM SPSS Regression 18 (formerly called SPSS Regression) enables you to apply more sophisticated models to your data using its wide range of nonlinear regression models. You can apply IBM SPSS Regression 18, to many disciplines, including:

• Market research: Study consumer buying habits
• Medical research: Study response to dosages through probit analysis
• Institutional research: Measure academic achievement tests
• Loan assessment: Analyze good and bad credit risks

IBM SPSS Regression 18 includes these procedures:
• Multinomial logistic regression (MLR): Predict categorical outcomes with more than two categories
• Binary logistic regression: Easily classify your data into two groups
• Nonlinear regression (NLR) and constrained nonlinear regression (CNLR): Estimate parameters of nonlinear models
• Probit analysis: Evaluate the value of stimuli using a logit or probit transformation of the proportion responding


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