SlideShare a Scribd company logo
1 of 7
Download to read offline
IBM® SPSS® Statistics Subscription:
Base, add-ons and features
Base package
The Base subscription includes the following features:
Data access and management	
• Compare two data files for compatibility	
• Data prep features: Define Variable Properties tool; Copy Data Properties
tool, Visual Bander, Identify Duplicate Cases; Date/Time wizard	
• Data Restructure wizard			
-	 Single record to multiple records			
-	 Multiple records to single record	
•	 Direct Excel data access	
• Easier importing from Excel and CSV	
•	 Export data to SAS and current versions of Excel	
• Export/insert to Database wizard	
• Import data from IBM Cognos® Business Intelligence	
• Import/export to/from Dimensions	
• Import Stata files (until V14)	
•	 Long variable names	
•	 Longer value labels	
• Multiple datasets can be run in one SPSS session	
•	 ODBC Capture—DataDirect drivers	
•	 OLE DB data access	
• Password protection	
• SAS 7/8/9 data files including compressed files)	
• Text wizard	
•	 Unicode support	
• Very long text strings
Data preparation	
•	 Automated data preparation—enhanced model viewer for automated			
data preparation	
• Validate data—streamline the process of validating data before
analyzing it	
• Anomaly detection—identify unusual cases in a multivariate setting	
•	 Optimal binning
Graphs	
•	 Auto and cross correlation graphs	
•	 Basic graphs	
• Mapping (geospatial analysis)	
• Chart gallery	
•	 Chart options	
• ChartBuilder UI for commonly used charts	
•	 Charts for multiple response variables	
• Graphics Production Language for custom charts	
• Interactive graphs—scriptable	
• Overlay and dual Y charts	
• Panelled charts	
• ROC analysis	
•	 Time series charts
Base features
1 • 2 • 3
Add-on: Custom tables and
advanced statistics
Add-on: Complex sampling
and testing
Add-on: Forecasting and
decision trees
Output	
•	 Case summaries	
• Style output	
•	 Conditional formatting	
•	 Codebook	
•	 Export charts as Microsoft Graphic Object	
•	 Export model as XML to SmartScore	
• Export to PDF	
• Export to Word/Excel/PowerPoint	
•	 HTML output	
• Improved performance for large pivot tables	
• OLAP cubes/pivot tables	
• Output management system	
•	 Output scripting	
•	 Reports summaries in rows and columns	
•	 Search and replace	
• Smart devices (tablets and phones)	
•	 Table to graph conversion	
•	 Web reports
Help features	
•	 Application examples	
• Index	
•	 Statistics coach	
•	 Tutorial	
•	 Extensions
Data editor enhancements	
• Custom attributes for user-defined metadata	
•	 Spell checker	
•	 Splitter controls	
• Variable sets for wide data	
• Variable icons
Extended programmability	
• Custom UI builder enhancements (work seamlessly with Python and R
and can be used in IBM SPSS Modeler)	
•	 New Extensions hub	
•	 Custom dialog builder for Extensions	
• Flow control or syntax jobs	
• Partial least squares regression	
• Python, .NET and Java for front-end scripting	
• SPSS equivalent of the SAS DATA STEP	
•	 Support for R algorithms and graphics	
• User-defined procedures
Base features
1 • 2 • 3
Add-on: Custom tables and
advanced statistics
Add-on: Complex sampling
and testing
Add-on: Forecasting and
decision trees
Statistics	
• ANOVA (in syntax only)	
•	 Automatic linear models	
•	 Cluster	
•	 Correlate—bivariate, partial, distances	
•	 Crosstabs	
• Define variable sets	
• Descriptive ratio statistics (PVA)	
•	 Descriptives	
• Discriminant analysis	
•	 Enhanced model viewer on two-step cluster and new nonparametrics	
•	 Explore	
• Factor analysis	
• Frequencies	
• Geo-spatial analytics (STP and GSAR) (NEW!)	
• Improved performance for frequencies, crosstabs, descriptives
(Statistics Base Server)	
•	 Matrix operations																							
• Means
• Monte Carlo simulation
• Nearest	neighbor	analysis
• New nonparametric tests
• One	way	ANOVA
• Ordinal	regression	(PLUM)
• Ordinary	least	squares	regression
• PP	plots
• QQ plots
• Ratio
• Reliability	and	ALSCAL	multidimensional	scaling
• ROC curve
• Compare ROC curves
• Rule	checking	on	secondary	SPC	charts
• Summarize	data
• T tests: paired samples, independent samples, one-samples
• Two-step	cluster:	categorical	and	continuous	data/large	data
sets
Multithreaded algorithms		
•	 SORT
Bootstrapping	
•	 Sampling and pooling	
•	 Descriptive procedures that can be bootstrapped
- Correlations/nonparametric correlations			
-	 Crosstabs			
-	 Descriptives			
-	 Examine
- Frequencies			
-	 Means
- Partial correlations			
-	 T tests
Base features
1 • 2 • 3
Add-on: Custom tables and
advanced statistics
Add-on: Complex sampling
and testing
Add-on: Forecasting and
decision trees
Base features
Add-on: Custom tables and
advanced statistics
Add-on: Complex sampling
and testing
Add-on: Forecasting and
decision trees
Add-on: Custom tables and advanced statistics
This add-on to the Base subscription includes the following features:
Custom tables 																														
• 35 descriptive statistics
• Drag and drop interface
• Inferential	statistics
• Nested tables
• Place	totals	in	any	row,	column,	or	layer
• Post	computed	categories
• Effective base for weighted sample results
• Put	multiple	variables	into	the	same	table
• Significance	tests	on	multiple	response	variables
• Significance	test	in	custom	tables	main	table
• Significance	values	for	column	means	and	column	proportion	
tests
• Specialized	multiple	response	set	tables
• False	discovery	correction	method	for	multiple	comparisons
• Syntax	converter
ble preview
Regression
• Binary	logistic	regression
• Logit response models
• Multinomial logistic regression
• Nonlinear regression
• Probit	response	analysis
• Two	stage	least	squares
• Weighted	least	squares
• Quantile regression
Advanced statistics 	
•	 Cox regression	
• General linear modeling (GLM)			
-	 General factorial
- Multivariate (MANOVA)			
-	 Repeated measures
- Variance components	
• Generalized linear models and generalized estimating equations			
-	 Gamma regression
- Poisson regression			
-	 Negative binomial	
•	 GENLOG for loglinear and logit	
• Generalized linear mixed models (GLMM) (ordinal targets included)	
• Bayesian statistics	
•	 Hierarchical loglinear models	
•	 Kaplan Meier	
• Linear mixed-level models (aka hierarchical linear models)	
•	 Survival	
• Variance component estimation
Add-on: Complex sampling and testing
This add-on to the Base subscription includes the following features:
Complex samples (CS)	
• CS Cox regression (also multithreaded)	
•	 CS descriptives	
•	 CS general linear models	
•	 CS logistic regression	
•	 CS ordinal regression	
•	 CS selection	
•	 CS tabulate	
• Sampling wizard/Analysis Plan wizard
Base features
Add-on: Custom tables and
advanced statistics
Add-on: Complex sampling
and testing
Add-on: Forecasting and
decision trees
Exact tests	
•	 Cochran’s Q test	
• Contingency coefficient	
• Cramer’s V	
• Fisher’s exact test	
• Somers’ D—symmetric and asymmetric	
• Friedman test	
•	 Gamma	
•	 Goodman and Kruskal tau	
• Jonckheere-Terpstra test	
•	 Kappa	
• Kendall’s coefficient of concordance	
•	 Kendall’s tau-b and tau-c	
•	 Kruskal-Wallis test	
•	 Likelihood ratio test	
• Linear-by-linear association test	
• Mann-Whitney U or Wilcoxon rank-sum W test	
• Marginal homogeneity test	
•	 McNemar test	
•	 Median test	
• Pearson Chi-square test	
• Pearson’s R	
• Phi	
•	 Sign test	
•	 Spearman correlation	
• Uncertainty coefficient—symmetric or asymmetric	
• Wald-Wolfowitz runs test	
•	 Wilcoxon signed-rank test
Categories	
• Correspondence analysis (ANACOR)	
• Principal components analysis for categorical data (CATPCA;
replaces PRINCALS)	
• Ridge regression, lasso, elastic net (CATREG)	
• CORRESPONDENCE	
• Nonlinear canonical correlation (OVERALS)	
•	 Multidimensional scaling for individual differences scaling with
constraints (PROXSCAL)	
• Preference scaling (PREFSCAL; multidimensional unfolding)	
• Multiple correspondence analysis
Missing values	
•	 Data patterns table	
• Imputation with means estimation or regression	
•	 Listwise and pairwise statistics	
•	 Missing patterns table	
•	 Multiple imputation of missing data	
• Pooling
Conjoint	
• Estimate utilities (CONJOINT)	
• For conjoint analysis (ORTHOPLAN)	
• PLANCARDS
Add-on: Forecasting and decision trees
This add-on to the Base subscription includes the following features:
Decision trees	
•	 C&RT		
• CHAID	
• Exhaustive CHAID	
•	 QUEST
Forecasting	
•	 Auto regressive integrated moving average	
•	 Autoregression	
•	 Expert modeler exponential smoothing methods	
• Forecast multiple series (outcomes) at once	
•	 Temporal causal modeling	
•	 Seasonal decomposition	
• Spectral analysis
Neural networks	
• Multilayer perception	
•	 Radial basis function
Direct marketing	
• Cluster analysis	
• Contact profiling	
•	 Control package test	
• Propensity to purchase	
• RFM analysis: recency, frequency, monetary	
•	 Zip code response
Base features
Add-on: Custom tables and
advanced statistics
Add-on: Complex sampling
and testing
Add-on: Forecasting and
decision trees
Base features
Add-on: Custom tables and
advanced statistics
Add-on: Complex sampling
and testing
Add-on: Forecasting and
decision trees
© Copyright IBM Corporation 2018
IBM Corporation
New Orchard Road
Armonk, NY 10504
Produced in the United States of America
November 2018
IBM, the IBM logo, ibm.com, Cognos and SPSS are trademarks
of International Business Machines Corp., registered in many
jurisdictions worldwide. Other product and service names might
be trademarks of IBM or other companies. A current list of IBM
trademarks is available on the web at “Copyright and trademark
information” at ibm.com/legal/copytrade.shtml
Microsoft, Windows, Windows NT, and the Windows logo are
trademarks of Microsoft Corporation in the United States, other
countries, or both.
Java	and	all	Java-based	trademarks	and	logos	are	trademarks	or	
registered	trademarks	of	Oracle	and/or	its	affiliates.
This document is current as of the initial date of publication and
may	be	changed	by	IBM	at	any	time.	Not	all	offerings	are	available	
in	every	country	in	which	IBM	operates.
THE	INFORMATION	IN	THIS	DOCUMENT	IS	PROVIDED	“AS	IS”	
WITHOUT	ANY	WARRANTY,	EXPRESS	OR	IMPLIED,	INCLUDING	
WITHOUT	ANY	WARRANTIES	OF	MERCHANTABILITY,	FITNESS	
FOR	A	PARTICULAR	PURPOSE	AND	ANY	WARRANTY	OR	
CONDITION	OF	NON-INFRINGEMENT.	IBM	products	are	warranted	
according to the terms and conditions of the agreements under
which	they	are	provided.
92022092USEN-01

More Related Content

Similar to IBM SPSS Statistics Subscription (월 구독) 제품 구성

Statistis, Row Counts, Execution Plans and Query Tuning
Statistis, Row Counts, Execution Plans and Query TuningStatistis, Row Counts, Execution Plans and Query Tuning
Statistis, Row Counts, Execution Plans and Query TuningGrant Fritchey
 
WhyR? Analiza sentymentu
WhyR? Analiza sentymentuWhyR? Analiza sentymentu
WhyR? Analiza sentymentuŁukasz Grala
 
DataMass Summit - Machine Learning for Big Data in SQL Server
DataMass Summit - Machine Learning for Big Data  in SQL ServerDataMass Summit - Machine Learning for Big Data  in SQL Server
DataMass Summit - Machine Learning for Big Data in SQL ServerŁukasz Grala
 
eRum2016 -RevoScaleR - Performance and Scalability R
eRum2016 -RevoScaleR - Performance and Scalability ReRum2016 -RevoScaleR - Performance and Scalability R
eRum2016 -RevoScaleR - Performance and Scalability RŁukasz Grala
 
An Easier Way to Prepare Clinical Trial Data for Reporting and Analysis
An Easier Way to Prepare Clinical Trial Data for Reporting and AnalysisAn Easier Way to Prepare Clinical Trial Data for Reporting and Analysis
An Easier Way to Prepare Clinical Trial Data for Reporting and AnalysisPerficient
 
IPC Data Analysis and Extraction
IPC Data Analysis and ExtractionIPC Data Analysis and Extraction
IPC Data Analysis and Extractionpzybrick
 
PGConf.ASIA 2019 Bali - Keynote Speech 2 - Ivan Pachenko
PGConf.ASIA 2019 Bali - Keynote Speech 2 - Ivan PachenkoPGConf.ASIA 2019 Bali - Keynote Speech 2 - Ivan Pachenko
PGConf.ASIA 2019 Bali - Keynote Speech 2 - Ivan PachenkoEqunix Business Solutions
 
Taking R Analytics to SQL and the Cloud
Taking R Analytics to SQL and the CloudTaking R Analytics to SQL and the Cloud
Taking R Analytics to SQL and the CloudRevolution Analytics
 
Building your first Analysis Services Tabular BI Semantic model with SQL Serv...
Building your first Analysis Services Tabular BI Semantic model with SQL Serv...Building your first Analysis Services Tabular BI Semantic model with SQL Serv...
Building your first Analysis Services Tabular BI Semantic model with SQL Serv...Microsoft TechNet - Belgium and Luxembourg
 
Виталий Бондаренко "Fast Data Platform for Real-Time Analytics. Architecture ...
Виталий Бондаренко "Fast Data Platform for Real-Time Analytics. Architecture ...Виталий Бондаренко "Fast Data Platform for Real-Time Analytics. Architecture ...
Виталий Бондаренко "Fast Data Platform for Real-Time Analytics. Architecture ...Fwdays
 
Apache CarbonData+Spark to realize data convergence and Unified high performa...
Apache CarbonData+Spark to realize data convergence and Unified high performa...Apache CarbonData+Spark to realize data convergence and Unified high performa...
Apache CarbonData+Spark to realize data convergence and Unified high performa...Tech Triveni
 

Similar to IBM SPSS Statistics Subscription (월 구독) 제품 구성 (20)

Statistis, Row Counts, Execution Plans and Query Tuning
Statistis, Row Counts, Execution Plans and Query TuningStatistis, Row Counts, Execution Plans and Query Tuning
Statistis, Row Counts, Execution Plans and Query Tuning
 
WhyR? Analiza sentymentu
WhyR? Analiza sentymentuWhyR? Analiza sentymentu
WhyR? Analiza sentymentu
 
DataMass Summit - Machine Learning for Big Data in SQL Server
DataMass Summit - Machine Learning for Big Data  in SQL ServerDataMass Summit - Machine Learning for Big Data  in SQL Server
DataMass Summit - Machine Learning for Big Data in SQL Server
 
Sas keyword
Sas keywordSas keyword
Sas keyword
 
eRum2016 -RevoScaleR - Performance and Scalability R
eRum2016 -RevoScaleR - Performance and Scalability ReRum2016 -RevoScaleR - Performance and Scalability R
eRum2016 -RevoScaleR - Performance and Scalability R
 
An Easier Way to Prepare Clinical Trial Data for Reporting and Analysis
An Easier Way to Prepare Clinical Trial Data for Reporting and AnalysisAn Easier Way to Prepare Clinical Trial Data for Reporting and Analysis
An Easier Way to Prepare Clinical Trial Data for Reporting and Analysis
 
Taming the shrew Power BI
Taming the shrew Power BITaming the shrew Power BI
Taming the shrew Power BI
 
IPC Data Analysis and Extraction
IPC Data Analysis and ExtractionIPC Data Analysis and Extraction
IPC Data Analysis and Extraction
 
Statistics and Indexes Internals
Statistics and Indexes InternalsStatistics and Indexes Internals
Statistics and Indexes Internals
 
Kicktag - About Kicktag & Cosmos 2014
Kicktag - About Kicktag & Cosmos 2014Kicktag - About Kicktag & Cosmos 2014
Kicktag - About Kicktag & Cosmos 2014
 
Data Warehouse
Data WarehouseData Warehouse
Data Warehouse
 
kalyani.ppt
kalyani.pptkalyani.ppt
kalyani.ppt
 
Data Warehouse
Data WarehouseData Warehouse
Data Warehouse
 
kalyani.ppt
kalyani.pptkalyani.ppt
kalyani.ppt
 
PGConf.ASIA 2019 Bali - Keynote Speech 2 - Ivan Pachenko
PGConf.ASIA 2019 Bali - Keynote Speech 2 - Ivan PachenkoPGConf.ASIA 2019 Bali - Keynote Speech 2 - Ivan Pachenko
PGConf.ASIA 2019 Bali - Keynote Speech 2 - Ivan Pachenko
 
Taking R Analytics to SQL and the Cloud
Taking R Analytics to SQL and the CloudTaking R Analytics to SQL and the Cloud
Taking R Analytics to SQL and the Cloud
 
Building your first Analysis Services Tabular BI Semantic model with SQL Serv...
Building your first Analysis Services Tabular BI Semantic model with SQL Serv...Building your first Analysis Services Tabular BI Semantic model with SQL Serv...
Building your first Analysis Services Tabular BI Semantic model with SQL Serv...
 
Cheetah:Data Warehouse on Top of MapReduce
Cheetah:Data Warehouse on Top of MapReduceCheetah:Data Warehouse on Top of MapReduce
Cheetah:Data Warehouse on Top of MapReduce
 
Виталий Бондаренко "Fast Data Platform for Real-Time Analytics. Architecture ...
Виталий Бондаренко "Fast Data Platform for Real-Time Analytics. Architecture ...Виталий Бондаренко "Fast Data Platform for Real-Time Analytics. Architecture ...
Виталий Бондаренко "Fast Data Platform for Real-Time Analytics. Architecture ...
 
Apache CarbonData+Spark to realize data convergence and Unified high performa...
Apache CarbonData+Spark to realize data convergence and Unified high performa...Apache CarbonData+Spark to realize data convergence and Unified high performa...
Apache CarbonData+Spark to realize data convergence and Unified high performa...
 

More from Jin Sol Kim 김진솔

[하시코프] Terraform 소개자료 (국문) Nov2021
[하시코프] Terraform 소개자료 (국문) Nov2021[하시코프] Terraform 소개자료 (국문) Nov2021
[하시코프] Terraform 소개자료 (국문) Nov2021Jin Sol Kim 김진솔
 
[하시코프] Consul 소개자료 (국문) Nov2021
[하시코프] Consul 소개자료 (국문) Nov2021[하시코프] Consul 소개자료 (국문) Nov2021
[하시코프] Consul 소개자료 (국문) Nov2021Jin Sol Kim 김진솔
 
[하시코프] Vault 소개자료 (국문) Nov2021
[하시코프] Vault 소개자료 (국문) Nov2021[하시코프] Vault 소개자료 (국문) Nov2021
[하시코프] Vault 소개자료 (국문) Nov2021Jin Sol Kim 김진솔
 
하시코프 Nomad 소개자료 (국문) Nov2021
하시코프 Nomad 소개자료 (국문) Nov2021하시코프 Nomad 소개자료 (국문) Nov2021
하시코프 Nomad 소개자료 (국문) Nov2021Jin Sol Kim 김진솔
 
[HashiCorp] 클라우드 낭비를 Terraform으로 최소화하는 방법 (Whitepaper)
[HashiCorp] 클라우드 낭비를 Terraform으로 최소화하는 방법 (Whitepaper)[HashiCorp] 클라우드 낭비를 Terraform으로 최소화하는 방법 (Whitepaper)
[HashiCorp] 클라우드 낭비를 Terraform으로 최소화하는 방법 (Whitepaper)Jin Sol Kim 김진솔
 
[HashiCorp] IaC로 시작하는 하이브리드 클라우드 관리 전략 with Terraform, Consul, Nomad (June 2021)
[HashiCorp] IaC로 시작하는 하이브리드 클라우드 관리 전략 with Terraform, Consul, Nomad (June 2021)[HashiCorp] IaC로 시작하는 하이브리드 클라우드 관리 전략 with Terraform, Consul, Nomad (June 2021)
[HashiCorp] IaC로 시작하는 하이브리드 클라우드 관리 전략 with Terraform, Consul, Nomad (June 2021)Jin Sol Kim 김진솔
 
ILOG CPLEX 최적화 솔루션 소개_dec2020
ILOG CPLEX 최적화 솔루션 소개_dec2020ILOG CPLEX 최적화 솔루션 소개_dec2020
ILOG CPLEX 최적화 솔루션 소개_dec2020Jin Sol Kim 김진솔
 
Cognos Analytics on Cloud 가격 정책 소개
Cognos Analytics on Cloud 가격 정책 소개 Cognos Analytics on Cloud 가격 정책 소개
Cognos Analytics on Cloud 가격 정책 소개 Jin Sol Kim 김진솔
 
IBM 온라인 주문 고객 지원 요청 방법 (2020 March)
IBM 온라인 주문 고객 지원 요청 방법 (2020 March) IBM 온라인 주문 고객 지원 요청 방법 (2020 March)
IBM 온라인 주문 고객 지원 요청 방법 (2020 March) Jin Sol Kim 김진솔
 
IBM SPSS Statistics Subscription (월 구독) 구입 방법
IBM SPSS Statistics Subscription (월 구독) 구입 방법 IBM SPSS Statistics Subscription (월 구독) 구입 방법
IBM SPSS Statistics Subscription (월 구독) 구입 방법 Jin Sol Kim 김진솔
 
IBM SPSS Statistics Subscription (월 구독) 견적서 안내
IBM SPSS Statistics Subscription (월 구독) 견적서 안내 IBM SPSS Statistics Subscription (월 구독) 견적서 안내
IBM SPSS Statistics Subscription (월 구독) 견적서 안내 Jin Sol Kim 김진솔
 
IBM Cognos Analytics 구매 가이드
IBM Cognos Analytics 구매 가이드 IBM Cognos Analytics 구매 가이드
IBM Cognos Analytics 구매 가이드 Jin Sol Kim 김진솔
 
My IBM에서 신용카드 변경하는 방법 (May 2019)
My IBM에서 신용카드 변경하는 방법 (May 2019)My IBM에서 신용카드 변경하는 방법 (May 2019)
My IBM에서 신용카드 변경하는 방법 (May 2019)Jin Sol Kim 김진솔
 

More from Jin Sol Kim 김진솔 (13)

[하시코프] Terraform 소개자료 (국문) Nov2021
[하시코프] Terraform 소개자료 (국문) Nov2021[하시코프] Terraform 소개자료 (국문) Nov2021
[하시코프] Terraform 소개자료 (국문) Nov2021
 
[하시코프] Consul 소개자료 (국문) Nov2021
[하시코프] Consul 소개자료 (국문) Nov2021[하시코프] Consul 소개자료 (국문) Nov2021
[하시코프] Consul 소개자료 (국문) Nov2021
 
[하시코프] Vault 소개자료 (국문) Nov2021
[하시코프] Vault 소개자료 (국문) Nov2021[하시코프] Vault 소개자료 (국문) Nov2021
[하시코프] Vault 소개자료 (국문) Nov2021
 
하시코프 Nomad 소개자료 (국문) Nov2021
하시코프 Nomad 소개자료 (국문) Nov2021하시코프 Nomad 소개자료 (국문) Nov2021
하시코프 Nomad 소개자료 (국문) Nov2021
 
[HashiCorp] 클라우드 낭비를 Terraform으로 최소화하는 방법 (Whitepaper)
[HashiCorp] 클라우드 낭비를 Terraform으로 최소화하는 방법 (Whitepaper)[HashiCorp] 클라우드 낭비를 Terraform으로 최소화하는 방법 (Whitepaper)
[HashiCorp] 클라우드 낭비를 Terraform으로 최소화하는 방법 (Whitepaper)
 
[HashiCorp] IaC로 시작하는 하이브리드 클라우드 관리 전략 with Terraform, Consul, Nomad (June 2021)
[HashiCorp] IaC로 시작하는 하이브리드 클라우드 관리 전략 with Terraform, Consul, Nomad (June 2021)[HashiCorp] IaC로 시작하는 하이브리드 클라우드 관리 전략 with Terraform, Consul, Nomad (June 2021)
[HashiCorp] IaC로 시작하는 하이브리드 클라우드 관리 전략 with Terraform, Consul, Nomad (June 2021)
 
ILOG CPLEX 최적화 솔루션 소개_dec2020
ILOG CPLEX 최적화 솔루션 소개_dec2020ILOG CPLEX 최적화 솔루션 소개_dec2020
ILOG CPLEX 최적화 솔루션 소개_dec2020
 
Cognos Analytics on Cloud 가격 정책 소개
Cognos Analytics on Cloud 가격 정책 소개 Cognos Analytics on Cloud 가격 정책 소개
Cognos Analytics on Cloud 가격 정책 소개
 
IBM 온라인 주문 고객 지원 요청 방법 (2020 March)
IBM 온라인 주문 고객 지원 요청 방법 (2020 March) IBM 온라인 주문 고객 지원 요청 방법 (2020 March)
IBM 온라인 주문 고객 지원 요청 방법 (2020 March)
 
IBM SPSS Statistics Subscription (월 구독) 구입 방법
IBM SPSS Statistics Subscription (월 구독) 구입 방법 IBM SPSS Statistics Subscription (월 구독) 구입 방법
IBM SPSS Statistics Subscription (월 구독) 구입 방법
 
IBM SPSS Statistics Subscription (월 구독) 견적서 안내
IBM SPSS Statistics Subscription (월 구독) 견적서 안내 IBM SPSS Statistics Subscription (월 구독) 견적서 안내
IBM SPSS Statistics Subscription (월 구독) 견적서 안내
 
IBM Cognos Analytics 구매 가이드
IBM Cognos Analytics 구매 가이드 IBM Cognos Analytics 구매 가이드
IBM Cognos Analytics 구매 가이드
 
My IBM에서 신용카드 변경하는 방법 (May 2019)
My IBM에서 신용카드 변경하는 방법 (May 2019)My IBM에서 신용카드 변경하는 방법 (May 2019)
My IBM에서 신용카드 변경하는 방법 (May 2019)
 

Recently uploaded

Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Patryk Bandurski
 
Install Stable Diffusion in windows machine
Install Stable Diffusion in windows machineInstall Stable Diffusion in windows machine
Install Stable Diffusion in windows machinePadma Pradeep
 
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024BookNet Canada
 
Snow Chain-Integrated Tire for a Safe Drive on Winter Roads
Snow Chain-Integrated Tire for a Safe Drive on Winter RoadsSnow Chain-Integrated Tire for a Safe Drive on Winter Roads
Snow Chain-Integrated Tire for a Safe Drive on Winter RoadsHyundai Motor Group
 
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptx
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptxMaking_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptx
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptxnull - The Open Security Community
 
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...HostedbyConfluent
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreternaman860154
 
SIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge GraphSIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge GraphNeo4j
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsMemoori
 
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure serviceWhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure servicePooja Nehwal
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationMichael W. Hawkins
 
How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?XfilesPro
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking MenDelhi Call girls
 
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024BookNet Canada
 
Artificial intelligence in the post-deep learning era
Artificial intelligence in the post-deep learning eraArtificial intelligence in the post-deep learning era
Artificial intelligence in the post-deep learning eraDeakin University
 
Hyderabad Call Girls Khairatabad ✨ 7001305949 ✨ Cheap Price Your Budget
Hyderabad Call Girls Khairatabad ✨ 7001305949 ✨ Cheap Price Your BudgetHyderabad Call Girls Khairatabad ✨ 7001305949 ✨ Cheap Price Your Budget
Hyderabad Call Girls Khairatabad ✨ 7001305949 ✨ Cheap Price Your BudgetEnjoy Anytime
 
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking MenDelhi Call girls
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slidespraypatel2
 
Pigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food ManufacturingPigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food ManufacturingPigging Solutions
 

Recently uploaded (20)

Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
 
Vulnerability_Management_GRC_by Sohang Sengupta.pptx
Vulnerability_Management_GRC_by Sohang Sengupta.pptxVulnerability_Management_GRC_by Sohang Sengupta.pptx
Vulnerability_Management_GRC_by Sohang Sengupta.pptx
 
Install Stable Diffusion in windows machine
Install Stable Diffusion in windows machineInstall Stable Diffusion in windows machine
Install Stable Diffusion in windows machine
 
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
 
Snow Chain-Integrated Tire for a Safe Drive on Winter Roads
Snow Chain-Integrated Tire for a Safe Drive on Winter RoadsSnow Chain-Integrated Tire for a Safe Drive on Winter Roads
Snow Chain-Integrated Tire for a Safe Drive on Winter Roads
 
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptx
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptxMaking_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptx
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptx
 
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreter
 
SIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge GraphSIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge Graph
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial Buildings
 
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure serviceWhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day Presentation
 
How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
 
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
 
Artificial intelligence in the post-deep learning era
Artificial intelligence in the post-deep learning eraArtificial intelligence in the post-deep learning era
Artificial intelligence in the post-deep learning era
 
Hyderabad Call Girls Khairatabad ✨ 7001305949 ✨ Cheap Price Your Budget
Hyderabad Call Girls Khairatabad ✨ 7001305949 ✨ Cheap Price Your BudgetHyderabad Call Girls Khairatabad ✨ 7001305949 ✨ Cheap Price Your Budget
Hyderabad Call Girls Khairatabad ✨ 7001305949 ✨ Cheap Price Your Budget
 
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slides
 
Pigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food ManufacturingPigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food Manufacturing
 

IBM SPSS Statistics Subscription (월 구독) 제품 구성

  • 1. IBM® SPSS® Statistics Subscription: Base, add-ons and features Base package The Base subscription includes the following features: Data access and management • Compare two data files for compatibility • Data prep features: Define Variable Properties tool; Copy Data Properties tool, Visual Bander, Identify Duplicate Cases; Date/Time wizard • Data Restructure wizard - Single record to multiple records - Multiple records to single record • Direct Excel data access • Easier importing from Excel and CSV • Export data to SAS and current versions of Excel • Export/insert to Database wizard • Import data from IBM Cognos® Business Intelligence • Import/export to/from Dimensions • Import Stata files (until V14) • Long variable names • Longer value labels • Multiple datasets can be run in one SPSS session • ODBC Capture—DataDirect drivers • OLE DB data access • Password protection • SAS 7/8/9 data files including compressed files) • Text wizard • Unicode support • Very long text strings Data preparation • Automated data preparation—enhanced model viewer for automated data preparation • Validate data—streamline the process of validating data before analyzing it • Anomaly detection—identify unusual cases in a multivariate setting • Optimal binning Graphs • Auto and cross correlation graphs • Basic graphs • Mapping (geospatial analysis) • Chart gallery • Chart options • ChartBuilder UI for commonly used charts • Charts for multiple response variables • Graphics Production Language for custom charts • Interactive graphs—scriptable • Overlay and dual Y charts • Panelled charts • ROC analysis • Time series charts Base features 1 • 2 • 3 Add-on: Custom tables and advanced statistics Add-on: Complex sampling and testing Add-on: Forecasting and decision trees
  • 2. Output • Case summaries • Style output • Conditional formatting • Codebook • Export charts as Microsoft Graphic Object • Export model as XML to SmartScore • Export to PDF • Export to Word/Excel/PowerPoint • HTML output • Improved performance for large pivot tables • OLAP cubes/pivot tables • Output management system • Output scripting • Reports summaries in rows and columns • Search and replace • Smart devices (tablets and phones) • Table to graph conversion • Web reports Help features • Application examples • Index • Statistics coach • Tutorial • Extensions Data editor enhancements • Custom attributes for user-defined metadata • Spell checker • Splitter controls • Variable sets for wide data • Variable icons Extended programmability • Custom UI builder enhancements (work seamlessly with Python and R and can be used in IBM SPSS Modeler) • New Extensions hub • Custom dialog builder for Extensions • Flow control or syntax jobs • Partial least squares regression • Python, .NET and Java for front-end scripting • SPSS equivalent of the SAS DATA STEP • Support for R algorithms and graphics • User-defined procedures Base features 1 • 2 • 3 Add-on: Custom tables and advanced statistics Add-on: Complex sampling and testing Add-on: Forecasting and decision trees
  • 3. Statistics • ANOVA (in syntax only) • Automatic linear models • Cluster • Correlate—bivariate, partial, distances • Crosstabs • Define variable sets • Descriptive ratio statistics (PVA) • Descriptives • Discriminant analysis • Enhanced model viewer on two-step cluster and new nonparametrics • Explore • Factor analysis • Frequencies • Geo-spatial analytics (STP and GSAR) (NEW!) • Improved performance for frequencies, crosstabs, descriptives (Statistics Base Server) • Matrix operations • Means • Monte Carlo simulation • Nearest neighbor analysis • New nonparametric tests • One way ANOVA • Ordinal regression (PLUM) • Ordinary least squares regression • PP plots • QQ plots • Ratio • Reliability and ALSCAL multidimensional scaling • ROC curve • Compare ROC curves • Rule checking on secondary SPC charts • Summarize data • T tests: paired samples, independent samples, one-samples • Two-step cluster: categorical and continuous data/large data sets Multithreaded algorithms • SORT Bootstrapping • Sampling and pooling • Descriptive procedures that can be bootstrapped - Correlations/nonparametric correlations - Crosstabs - Descriptives - Examine - Frequencies - Means - Partial correlations - T tests Base features 1 • 2 • 3 Add-on: Custom tables and advanced statistics Add-on: Complex sampling and testing Add-on: Forecasting and decision trees
  • 4. Base features Add-on: Custom tables and advanced statistics Add-on: Complex sampling and testing Add-on: Forecasting and decision trees Add-on: Custom tables and advanced statistics This add-on to the Base subscription includes the following features: Custom tables • 35 descriptive statistics • Drag and drop interface • Inferential statistics • Nested tables • Place totals in any row, column, or layer • Post computed categories • Effective base for weighted sample results • Put multiple variables into the same table • Significance tests on multiple response variables • Significance test in custom tables main table • Significance values for column means and column proportion tests • Specialized multiple response set tables • False discovery correction method for multiple comparisons • Syntax converter ble preview Regression • Binary logistic regression • Logit response models • Multinomial logistic regression • Nonlinear regression • Probit response analysis • Two stage least squares • Weighted least squares • Quantile regression Advanced statistics • Cox regression • General linear modeling (GLM) - General factorial - Multivariate (MANOVA) - Repeated measures - Variance components • Generalized linear models and generalized estimating equations - Gamma regression - Poisson regression - Negative binomial • GENLOG for loglinear and logit • Generalized linear mixed models (GLMM) (ordinal targets included) • Bayesian statistics • Hierarchical loglinear models • Kaplan Meier • Linear mixed-level models (aka hierarchical linear models) • Survival • Variance component estimation
  • 5. Add-on: Complex sampling and testing This add-on to the Base subscription includes the following features: Complex samples (CS) • CS Cox regression (also multithreaded) • CS descriptives • CS general linear models • CS logistic regression • CS ordinal regression • CS selection • CS tabulate • Sampling wizard/Analysis Plan wizard Base features Add-on: Custom tables and advanced statistics Add-on: Complex sampling and testing Add-on: Forecasting and decision trees Exact tests • Cochran’s Q test • Contingency coefficient • Cramer’s V • Fisher’s exact test • Somers’ D—symmetric and asymmetric • Friedman test • Gamma • Goodman and Kruskal tau • Jonckheere-Terpstra test • Kappa • Kendall’s coefficient of concordance • Kendall’s tau-b and tau-c • Kruskal-Wallis test • Likelihood ratio test • Linear-by-linear association test • Mann-Whitney U or Wilcoxon rank-sum W test • Marginal homogeneity test • McNemar test • Median test • Pearson Chi-square test • Pearson’s R • Phi • Sign test • Spearman correlation • Uncertainty coefficient—symmetric or asymmetric • Wald-Wolfowitz runs test • Wilcoxon signed-rank test Categories • Correspondence analysis (ANACOR) • Principal components analysis for categorical data (CATPCA; replaces PRINCALS) • Ridge regression, lasso, elastic net (CATREG) • CORRESPONDENCE • Nonlinear canonical correlation (OVERALS) • Multidimensional scaling for individual differences scaling with constraints (PROXSCAL) • Preference scaling (PREFSCAL; multidimensional unfolding) • Multiple correspondence analysis Missing values • Data patterns table • Imputation with means estimation or regression • Listwise and pairwise statistics • Missing patterns table • Multiple imputation of missing data • Pooling Conjoint • Estimate utilities (CONJOINT) • For conjoint analysis (ORTHOPLAN) • PLANCARDS
  • 6. Add-on: Forecasting and decision trees This add-on to the Base subscription includes the following features: Decision trees • C&RT • CHAID • Exhaustive CHAID • QUEST Forecasting • Auto regressive integrated moving average • Autoregression • Expert modeler exponential smoothing methods • Forecast multiple series (outcomes) at once • Temporal causal modeling • Seasonal decomposition • Spectral analysis Neural networks • Multilayer perception • Radial basis function Direct marketing • Cluster analysis • Contact profiling • Control package test • Propensity to purchase • RFM analysis: recency, frequency, monetary • Zip code response Base features Add-on: Custom tables and advanced statistics Add-on: Complex sampling and testing Add-on: Forecasting and decision trees
  • 7. Base features Add-on: Custom tables and advanced statistics Add-on: Complex sampling and testing Add-on: Forecasting and decision trees © Copyright IBM Corporation 2018 IBM Corporation New Orchard Road Armonk, NY 10504 Produced in the United States of America November 2018 IBM, the IBM logo, ibm.com, Cognos and SPSS are trademarks of International Business Machines Corp., registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of IBM trademarks is available on the web at “Copyright and trademark information” at ibm.com/legal/copytrade.shtml Microsoft, Windows, Windows NT, and the Windows logo are trademarks of Microsoft Corporation in the United States, other countries, or both. Java and all Java-based trademarks and logos are trademarks or registered trademarks of Oracle and/or its affiliates. This document is current as of the initial date of publication and may be changed by IBM at any time. Not all offerings are available in every country in which IBM operates. THE INFORMATION IN THIS DOCUMENT IS PROVIDED “AS IS” WITHOUT ANY WARRANTY, EXPRESS OR IMPLIED, INCLUDING WITHOUT ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND ANY WARRANTY OR CONDITION OF NON-INFRINGEMENT. IBM products are warranted according to the terms and conditions of the agreements under which they are provided. 92022092USEN-01