IBM SPSS Statistics SPSS Stats...¢  2016-03-23¢  IBM SPSS Statistics overview IBM SPSS Statistics software

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  • IBM SPSS Statistics Product capabilities overview 3.3.2016

  • Analytics

    © 2016 IBM Corporation2

    Session agenda

    IBM SPSS Statistics positioning4

    IBM SPSS Statistics overview1

    IBM SPSS Statistics usage2

    Example use cases3

  • Analytics

    © 2016 IBM Corporation3

    Session agenda

    IBM SPSS Statistics positioning4

    IBM SPSS Statistics overview1

    IBM SPSS Statistics usage2

    Example use cases3

  • Analytics

    © 2016 IBM Corporation4

    IBM SPSS Statistics overview

    Statistical approach involves

    – forming a theory about a possible

    relationship

    – converting it to a hypothesis

    – testing that hypothesis using statistical

    methods

    It is a manual, user-driven, top-down

    approach to data analysis

    Data mining involves

    – the interrogation of the data

    – determined by the method and

    goal, rather than by the user

    It is a data-driven, self-organizing,

    bottom-up approach to data analysis that

    works on very large data sets

    Statistics Approach Modeling Approach

    Both approaches drive predictive analytics

    IBM SPSS Statistics IBM SPSS Modeler

    “Statistical Modeling: The Two Cultures,” Leo Breiman, Statistical Science, 2001, Vol.16 (3), pp.199-231.

  • Analytics

    © 2016 IBM Corporation5

    IBM SPSS Statistics overview

    • Hypothesis testing, advanced

    statistical analysis

    • Top-down approach

    • Spreadsheet-like look & feel

    • General environment for predictive

    analytics and statistical analysis

    • Well-suited for ad-hoc analysis

    – Core descriptive statistical capabilities

    – Advanced statistical functions

    – Many types of regression

    – Charting and mapping capabilities

    – Tabular analysis & output

  • Analytics

    © 2016 IBM Corporation6

    IBM SPSS Statistics overview

    Quickly understand large and complex

    datasets using advanced statistical

    procedures ensuring high accuracy to

    drive quality decision-making

    Reveal deeper insights and provide

    better confidence intervals via

    visualizations

    Solve complex business and

    reseach questions via means of

    statistical analysis and assumption

    validation

    Programmability for advanced users that

    leverages common statistical programming

    languages in the market (Python, R)

  • Analytics

    © 2016 IBM Corporation7

    IBM SPSS Statistics overview

    IBM SPSS Statistics software is used by a variety of customers to solve industry specific business issues to drive quality

    decision-making. Methods like forecasting, analyzing trends and assumption validation can provide a robust, user friendly

    platform to understand your data and solve complex business and research problems.

    IBM SPSS Statistics helps organizations like Lloyds TSB, Kent State University and the IRS have expanded their markets,

    improved research outcomes while ensuring regulatory compliance and managed risk (evaluate programs, prevent crimes

    and assess loan risks).

    Organizations have a core need to understand data and the statistical analyses applied to that data, and to quickly and

    accurately analyze and interpret data to drive decision-making. In a variety of industries and applications, there is a need to

    confirm (or deny) the existence of trends in data. IBM SPSS Statistics is the world’s leading statistical software used to

    solve such business and research problems by means of ad-hoc analysis, hypothesis testing and predictive analytics.

  • Analytics

    © 2016 IBM Corporation8

    Session agenda

    IBM SPSS Statistics positioning4

    IBM SPSS Statistics overview1

    IBM SPSS Statistics usage2

    Example use cases3

  • Analytics

    © 2016 IBM Corporation9

    IBM SPSS Statistics usage  SPSS Statistics is typically used by a statistician, who knows the technique to use (regression, decision tree, correlations,

    etc.), and will:

     1) Build descriptions from the data, e.g.

    • How many customers are male, or from a certain age group

    • Average amount per transaction

    • How many students passed last year

    • How many goals scored per match, on average

    • What information is in my data?

     2) Build inferences from the data

    • Based on 1500 voters polled, who will win election?

    • Based on past student retention rates, how many will finish the year?

    • Based on revenue per quarter over X years, how much we can expect to bring in this quarter?

    • Base on average points per game, what will the team (or player) score in the next game?

    • Are our assumptions correct?

    • Based on a sample of a population, what does the rest of the population look like?

    • Based on a sample in time, what is the likely outcome in the future?

    • What things actually have an affect on the results?

  • Analytics

    © 2016 IBM Corporation10

    IBM SPSS Statistics bundles

    Statistics Base

    Advanced Statistics

    Custom Tables

    Regression

    Standard Statistics Base

    Advanced Statistics

    Custom Tables

    Regression

    Data Preparation

    Missing Values

    Categories

    Decision Trees

    Forecasting

    Statistics Base

    Advanced Statistics

    Custom Tables

    Regression

    Data Preparation

    Missing Values

    Categories

    Decision Trees

    Forecasting

    Bootstrapping

    Conjoint

    Exact Tests

    Neural Networks

    Direct Marketing

    Complex Samples

    Viz Designer

    Amos

    Sample Power

    Professional Premium

  • Analytics

    © 2016 IBM Corporation11

    Session agenda

    IBM SPSS Statistics positioning4

    IBM SPSS Statistics overview1

    IBM SPSS Statistics usage2

    Example use cases3

  • Analytics

    © 2016 IBM Corporation12

    CRI now makes smarter clinical decisions that help patients and improve performance

    The need

    To advance clinical practice in the mental health sector,

    Centerstone Research Institute (CRI) wanted to use emergent

    analytics technologies to bridge the gap between researchers

    and healthcare providers.

    The solution

    With the implementation of IBM predictive analytics software,

    CRI built a framework that is able to predict which services are

    likely to work best for which individual.

    Real business results

     Provided a potential 42% improvement in patient outcomes

     Enabled cost savings of 58% in the cost per unit of outcome

    change

     Bridged the gap between researchers and physicians,

    transforming the way mental health services are provided

    Solution components:

     IBM SPSS Statistics

     IBM SPSS Modeler

    "With our expertise in Big Data management, and IBM’s leading-edge analytics technologies, we’re well positioned to help

    shift the paradigm for mental health services."

    —Tom Doub, CEO, Centerstone Research

    Institute

    http://www- 03.ibm.com/software/businesscasestudies/ us/en/corp?synkey=J053479Y94979F29

    Centerstone Research Institute

    (CRI) is a not-for-profit research

    organization dedicated to improving

    the quality and effectiveness of care

    for individuals with mental health and

    addiction disorders.

    Headquarters:

    • Bloomington, Indiana

    Patients served per year:

    • 70, 000

    Research Partners:

     Harvard University Medical Center

     Vanderbilt University

     Indiana University

     Northwestern University

     University of Illinois of Chicago

     Meharry Medical College

     Janssen Pharmaceuticals

     Telesage, Inc.

    http://www-03.ibm.com/software/businesscasestudies/us/en/corp?synkey=J053479Y94979F29

  • Analytics

    © 2016 IBM Corporation13

    Fondazione IRCCS Istituto Nazionale dei Tumori (INT) personalized cancer care

    The need

    Fondazione INT, a leading cancer and research center in Milan, wanted to improve patient care by

    tailoring treatment approaches to specific individuals. The institute needed the ability to analyze

    past treatments and cases, and combine that information with the patient’s personal statistics and

    disease profile, to create a fact-based treatment plan for each patient. In addition, being able to analyze

    overall outcome data would help the institute provide more cost-effective, efficient care for its patients.

    The solution

    The IBM solution proactively shows the physician statistics on similar clinical cases, possible

    alternative treatments and predicted outcomes for each. Knowing this, physicians can ensure that

    patients receive only the procedures they need.

    Real business results

     Avoids unnecessary treatment (estimated to be up to 60% of all treatment)

     Increases the chances of successful outcomes by creating personalized