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SPS-100 exam Dumps Source : IBMSPSSSTATL1P - IBM SPSS Statistics flat 1

Test Code : SPS-100
Test denomination : IBMSPSSSTATL1P - IBM SPSS Statistics flat 1
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: 70 true Questions

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IBM IBMSPSSSTATL1P - IBM SPSS

Monster Merger: IBM Buys SPSS For Approx. $1.2 Billion In cash Deal | killexams.com true Questions and Pass4sure dumps

IBM is buying analytics software and solutions company SPSS in an complete cash transaction at a value of $50/share – a forty two percent top class to Monday’s closing fee of $35.09 on Nasdaq – leading to a complete cash consideration within the merger of approximately $1.2 billion. The acquisition is domain to SPSS shareholder approval, regulatory clearances and other closing situations, and is expected to shut later within the 2d half of 2009.

massive Blue talked about the acquisition of the publicly-held Chicago company become anticipated to fortify its tips-agenda initiative, which helps businesses recall information and circle it into a strategic asset. IBM shares fell sixty seven cent to $116.ninety six in pre-market trading, while SPSS shares jumped 41 percent to $49.fifty nine.

A message from SPSS Chairman, CEO & President Jack Noonan:

We and IBM view this as a extremely-complementary movement from each a technology and a market station viewpoint.

We’re enthusiastic about this agreement and the positive prospects that SPSS and IBM can achieve together in continuing to lead an traffic that they helped shape. The depth and breadth of IBM’s components, its client and market gain can simplest raise their capacity to tackle the transforming into market for Predictive Analytics.

In becoming a member of with IBM, they can increase Predictive Analytics as a competitive erudition for groups and groups international. They discern this as a transformative adventure if you want to accelerate the adoption of Predictive Analytics.

Between now and the acquisition close, they are able to continue to assist their valued clientele and partners in commonplace vogue. current contracts will continue to be in impact, account relationships and assist infrastructure will continue to be unchanged.

Predictive analytics application captures and analyzes information about individuals’s attributes, attitudes and behaviors to profit a complete figuring out of expected future behaviors, so groups could obtain smarter decisions for more desirable company results.

IBM says it will proceed to aid and enhance SPSS technologies whereas allowing clients to recall potential of its personal product portfolio. SPSS will become a piece of the assistance management division in the utility group enterprise unit, led via Ambuj Goyal, time-honored manager, IBM tips management.

replace – In different information: IBM additionally bought Ounce Labs, a provider of enterprise supply code protection testing and intends to integrate it into its Rational utility company. pecuniary phrases had been not disclosed.


IBM is rushing up SPSS with Apache Spark | killexams.com true Questions and Pass4sure dumps

IBM nowadays announced that it is improving several of its present application items with the Apache Spark open-supply facts processing engine, including the SPSS predictive analytics software.

IBM purchased SPSS for $1.2 billion in 2009. SPSS itself begun at Stanford school in 1968 and is customary in records lessons at universities.

IBM is chiefly incorporating Spark into SPSS Modeler and SPSS Analytic Server, a spokesman advised VentureBeat in an e mail.

Spark is too being applied to IBM’s BigInsights, Streams, and DataWorks utility, in keeping with a press release.

The Sparkification of IBM comes after large Blue introduced an immense commitment to the open-source software in June. on the time, IBM introduced Spark as a carrier on its Bluemix cloud; that carrier is now often available.

IBM has made greater than 60 contributions to Spark on the grounds that the June announcement, in keeping with the statement.


a glance on the IBM SPSS Modeler and IBM SPSS data analytics tools | killexams.com true Questions and Pass4sure dumps

IBM's SPSS predictive analytics tools involve IBM SPSS Modeler and IBM SPSS records. SPSS Modeler provides...

records mining and textual content evaluation utility, while SPSS statistics is an integrated household of items. each tools enable users to build predictive models and execute different analytics initiatives.

The IBM SPSS Modeler goals clients who believe puny or no programming knowledge. users are offered with a drag-and-drop consumer interface, enabling them to build predictive models and operate other data analytics. Modeler can apply several techniques and algorithms to assist the user find counsel hidden in the information. The device can additionally back in integrating and consolidating every kindly of information sets from dispersed statistics sources throughout the organization.

The IBM SPSS statistics suite is an integrated set of items geared toward extra expert facts analysts. SPSS facts addresses the finished analytical manner, from planning to statistics collection, evaluation, reporting and deployment.

IBM SPSS Modeler aspects

edition 18 offers right here facets:

  • more than 30 groundwork computing device gaining erudition of algorithms.
  • Extensions that deliver endured advancements to be used with open supply items, equivalent to R and Python.
  • better assist for a number of multithreaded analytical algorithms, together with Random bushes, Tree-AS, Generalized Linear Engine, Linear-AS, Linear champion Vector computing device and Two-Step-AS clustering.
  • The means to eschew loads of Python and Spark laptop getting to know, as well as other Python analytics libraries natively in Modeler with out requiring using the Analytic Server, as was required in the musty version.
  • SPSS Modeler bundles are deployed on premises, and SPSS Modeler Gold is available as a cloud providing. The customer entrance conclusion of SPSS Modeler runs beneath windows and macOS, whereas the server piece runs on Unix, Linux and home windows.

    IBM SPSS Modeler gives here versions:

  • SPSS Modeler personal: A single-person computer product.
  • SPSS Modeler skilled: A desktop product that works with IBM SPSS Analytic Server, providing better scalability and efficiency and enabling purposes for exhaust across an organization.
  • SPSS Modeler top rate: This version comprises superior algorithms and capabilities, akin to text analytics, entity analytics and convivial community analysis, that enhance model accuracy with unstructured statistics.
  • SPSS Modeler Gold: This edition provides analytical selection administration, collaboration and deployment capabilities. SPSS Modeler Gold is too attainable as a cloud providing.
  • IBM SPSS statistics features

    SPSS statistics version 24 includes here modern elements:

  • The aptitude to access greater than one hundred extensions, enabling clients to recall talents of free libraries written in R, Python and SPSS syntax.
  • The IBM SPSS Extension Hub to browse, download, update, remove and generally exploit extensions.
  • a huge upgrade to the customized Dialog Builder, enabling clients to more effectively build and install their personal extensions. Enhancements involve modern controls and modern homes for existing controls and several different improvements to the consumer interface.
  • improvements that permit clients to more without difficulty and right away import and export data into SPPS statistics.
  • improvements to the customized Tables module, including modern statistical functionality and client-requested elements.
  • IBM SPSS statistics offers the following three versions (each and every with extra modules):

  • SPSS statistics universal tools give superior statistical techniques that champion linear and nonlinear statistical fashions, as well as predictive simulation modeling, which bills for unclear inputs, geospatial analytics and customised tables.
  • SPSS information skilled materiel aid information instruction, lacking values and data validity, resolution trees, and forecasting.
  • SPSS data premium adds advanced analytical concepts, together with structural equation modeling, in-depth sampling evaluation and testing. This bundle additionally includes methods that target direct advertising and excessive-conclusion charts and graphs.
  • Pricing for the SPSS Modeler and SPSS records predictive analytics materiel vary counting on the bundle alternatives, the variety of clients and the license period. SPSS information is now purchasable as a subscription alternative or a perpetual license. IBM presents free trials of each IBM SPSS Modeler and IBM SPSS facts.

    subsequent Steps

    Why the time age unstructured records is a misnomer

    How massive data is changing information modeling ideas

    massive records systems pose modern challenges to records governance

    connected materials View more

    SPS-100 IBMSPSSSTATL1P - IBM SPSS Statistics flat 1

    Study lead Prepared by Killexams.com IBM Dumps Experts


    Killexams.com SPS-100 Dumps and true Questions

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    SPS-100 exam Dumps Source : IBMSPSSSTATL1P - IBM SPSS Statistics flat 1

    Test Code : SPS-100
    Test denomination : IBMSPSSSTATL1P - IBM SPSS Statistics flat 1
    Vendor denomination : IBM
    : 70 true Questions

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    Emergency department visits by uninsured patients drop in Illinois after Medicaid expansion | killexams.com true questions and Pass4sure dumps

    ORLANDO, Fla. - modern research adds to a growing cadaver of evidence suggesting that the expansion of Medicaid under the Affordable reliance Act (ACA) helped low-income families reliance less on emergency department visits for medical care.

    The study abstract, "Effect of Affordable reliance Act Medicaid Expansion on Emergency Department Visits by Uninsured Patients in Illinois 2009-2015," will be presented on Friday, Nov. 2, at the American Academy of Pediatrics (AAP) 2018 National Conference & Exhibition in Orlando, Fla.

    In Illinois, according to the abstract, Medicaid expansion was associated with a significant drop in uncompensated reliance in emergency department by patients without insurance between 2009 and 2015. The ACA extended Medicaid coverage eligibility to adults with incomes up to 133 percent of the federal poverty level.

    "As a result of the ACA, 850,000 people in Illinois gained health insurance during the study period," said abstract author Peter Nguyen, MD, a Pediatric Emergency Medicine Fellow at Michigan condition University's Helen Devos Children's Hospital who previously studied in Illinois. "During the very time, they saw a significant drop in emergency department visits by patients without insurance."

    Researchers analyzed emergency department visits using data from the Healthcare Cost and Utilization Project and found there were a total of 31.2 million emergency department visit in Illinois during the study period. Of these, 4.1 million were visits by uninsured patients.

    Uninsured emergency department visits in the condition peaked right before ACA implementation, Dr. Nguyen said, when they accounted for 16 percent the visits. Post-ACA implementation, there was a 40 percent reduction in uninsured emergency department visits.

    "This study adds to the growing cadaver of evidence that the ACA can be beneficial in promoting patient-centered care, shifting it from the emergency department to a patient's medical home for ongoing, comprehensive care. This is a healthier situation for everyone," Dr. Nguyen said.

    Dr. Nguyen said that proposed policy changes may late the reduction in uncompensated emergency department visits. "With increasing skepticism about the long-term artery for the ACA, enrollment has started to decline," he said. "So, it will be nosy to discern whether we'll discern a surge again in uninsured emergency department visits."

    Dr. Nguyen will present an abstract of the study, available below, between 3:15 and 3:30 p.m. ET on Friday, Nov. 2, in Orlando Ballroom L of the Orange County Convention heart in Orlando, FL.

    Please note: only the abstract is being presented at the meeting. In some cases, the researcher may believe more data available to partake with media, or may be preparing a longer article for submission to a journal.

    ###

    The American Academy of Pediatrics is an organization of 67,000 primary reliance pediatricians, pediatric medical subspecialists and pediatric surgical specialists dedicated to the health, safety and well-being of infants, children, adolescents and youthful adults. For more information, visit http://www. aap. org. Reporters can access the meeting program and other apropos meeting information through the AAP meeting website at http://www. aapexperience. org/

    Abstract Title:

    Effect of Affordable reliance Act Medicaid Expansion on Emergency Department Visits by Uninsured Patients in Illinois 2009-2015

    Peter Nguyen

    Introduction: Medicaid expansion was one of the key provisions of the Affordable reliance Act (ACA) designed to reduce the number of people without health insurance. The ACA extended Medicaid coverage to complete adult citizens with annual incomes up to 133% of the federal poverty level. Purpose: Their study objective was to study the repercussion of ACA on Uninsured Emergency Department Visits (UiEDV). Methods: They downloaded aggregated EDV from the Healthcare Cost and Utilization Project (HCUP) website and extracted condition of Illinois EDV and sorted the data by year and quarter. They looked for trends in the quarterly UiEDV data and calculated summary statistics to determine the variance and mean. They used generalized linear modeling (Poisson and a customized negative binomial regression (NB)) to model UiEDV using quarterly UiEDV matter as a subject variable and pre and post ACA implementation periods as independent factors. They used log as the link function, and the software calculated the estimated parameter. From the model, they derived the Incidence Rate Ratio (IRR) and its corresponding 95% self-possession Interval (95%CI); they compared both models using Goodness of apt tests Akaike Information Criterion (AIC) and Bayesian Information Citerion (BIC). They used IBM SPSS Version 25â„¢ and Microsoft surpass â„¢ 2016 for data preparation and analysis. Results: For the study period, there were 31,368,200 EDVs. Of these, 4,120,200 were uninsured (mean (SD) 147,150 (6376); variance 1,138,281,481)). The ratio of UiEDV to total EDVs was between 7% and 16%; the peak occurred before ACA implementation. NB modelling demonstrated a IRR of 1.67 (95% CI, 1.53-1.83) of UiEDV pre-ACA compared post-ACA with a 40 percent reduction of UiEDV. Goodness of apt demonstrated lower AIC (629 vs 40361) and BIC 626 vs 40360) when compared both models were compared indicating NB a better model fit. Conclusion: Insurance is a gateway to healthcare access; because of ACA, cross of uncompensated EDVs decreased substantially in Illinois during the study period. Additional studies to gawk at regional trends, specific groups, continued coverage is needed to quantify the overall effects of ACA.

    Disclaimer: AAAS and EurekAlert! are not accountable for the accuracy of word releases posted to EurekAlert! by contributing institutions or for the exhaust of any information through the EurekAlert system.


    Unfriendly Skies: Predicting Flight Cancellations Using Weather Data, piece 2 | killexams.com true questions and Pass4sure dumps

    Ricardo Balduino and Tim Bohn

    Early Flight, Creative Commons Introduction

    As they described in piece 1 of this series, their objective is to back foretell the probability of the cancellation of a flight between two of the ten U.S. airports most affected by weather conditions. They exhaust historical flights data and historical weather data to obtain predictions for upcoming flights.

    Over the course of this four-part series, they exhaust different platforms to back us with those predictions. Here in piece 2, they exhaust the IBM SPSS Modeler and APIs from The Weather Company.

    Tools used in this exhaust case solution

    IBM SPSS Modeler is designed to back determine patterns and trends in structured and unstructured data with an intuitive visual interface supported by advanced analytics. It provides a compass of advanced algorithms and analysis techniques, including text analytics, entity analytics, decision management and optimization to deliver insights in near real-time. For this exhaust case, they used SPSS Modeler 18.1 to create a visual representation of the solution, or in SPSS terms, a stream. That’s right — not one line of code was written in the making of this blog.

    We too used The Weather Company APIs to retrieve historical weather data for the ten airports over the year 2016. IBM SPSS Modeler supports calling the weather APIs from within a stream. That is accomplished by adding extensions to SPSS, available in the IBM SPSS Predictive Analytics resources page, a.k.a. Extensions Hub.

    A proposed solution

    In this blog, they propound one workable solution for this problem. It’s not meant to be the only or the best workable solution, or a production-level solution for that matter, but the discussion presented here covers the typical iterative process (described in the sections below) that helps us accumulate insights and refine the predictive model across iterations. They cheer the readers to try and advance up with different solutions, and provide us with your feedback for future blogs.

    Business and data understanding

    The first step of the iterative process includes understanding and gathering the data needed to train and test their model later.

    Flights data — We gathered 2016 flights data from the US Bureau of Transportation Statistics website. The website allows us to export one month at a time, so they ended up with 12 csv (comma separated value) files. They used IBM SPSS Modeler to merge complete the csv files into one set and to select the ten airports in their scope. Some data clean-up and formatting was done to validate dates and hours for each flight, as seen in design 1.

    Figure 1 — gathering and preparing flights data in IBM SPSS Modeler

    Weather data — From the Extensions Hub, they added the TWCHistoricalGridded extension to SPSS Modeler, which made the extension available as a node in the tool. That node took a csv file listing the 10 airports latitude and longitude coordinates as input, and generated the historical hourly data for the entire year of 2016, for each airport location, as seen in design 2.

    Figure 2 — gathering and preparing weather data in IBM SPSS Modeler

    Combined flights and weather data — To each flight in the first data set, they added two modern columns: root and DEST, containing the respective airport codes. Next, flight data and the weather data were merged together. Note: the “stars” or SPSS super nodes in design 3 are placeholders for the diagrams in Figures 1 and 2 above.

    Figure 3 — combining flights and weather data in IBM SPSS Modeler Data preparation, modeling, and evaluation

    We iteratively performed the following steps until the desired model qualities were reached:

    · Prepare data

    · perform modeling

    · Evaluate the model

    · Repeat

    Figure 4 shows the first and second iterations of their process in IBM SPSS Modeler.

    Figure 4 — iterations: prepare data, eschew models, evaluate — and achieve it again First iteration

    To start preparing the data, they used the combined flights and weather data from the previous step and performed some data cleanup (e.g. took reliance of null values). In order to better train the model later on, they filtered out rows where flight cancellations were not related to weather conditions (e.g. cancellations due to technical issues, security issues, etc.)

    Figure 5 — imbalanced data found in their input data set

    This is an nosy exhaust case, and often a difficult one to solve, due to the imbalanced data it presents, as seen in design 5. By “imbalanced” they be primary that there were far more non-cancelled flights in the historical data than cancelled ones. They will debate how they dealt with imbalanced data in the following iteration.

    Next, they defined which features were required as inputs to the model (such as flight date, hour, day of the week, root and destination airport codes, and weather conditions), and which one was the target to be generated by the model (i.e. foretell the cancellation status). They then partitioned the data into training and testing sets, using an 85/15 ratio.

    The partitioned data was fed into an SPSS node called Auto Classifier. This node allowed us to eschew multiple models at once and preview their outputs, such as the district under the ROC curve, as seen in design 6.

    Figure 6 — models output provided by the Auto Classifier node

    That was a useful step in making an initial selection of a model for further refinement during subsequent iterations. They decided to exhaust the Random Trees model since the initial analysis showed it has the best district under the curve as compared to the other models in the list.

    Second iteration

    During the second iteration, they addressed the skewedness of the original data. For that purpose, they chose one of the SPSS nodes called SMOTE (Synthetic Minority Over-sampling Technique). This node provides an advanced over-sampling algorithm that deals with imbalanced datasets, which helped their selected model drudgery more effectively.

    Figure 7 — distribution of cancelled and non-cancelled flights after using SMOTE

    In design 7, they notice a more balanced distribution between cancelled and non-cancelled flights after running the data through SMOTE.

    As mentioned earlier, they picked the Random Trees model for this sample solution. This SPSS node provides a model for tree-based classification and prediction that is built on Classification and Regression Tree methodology. Due to its characteristics, this model is much less recumbent to overfitting, which gives a higher likelihood of repeating the very test results when you exhaust modern data, that is, data that was not piece of the original training and testing data sets. Another advantage of this method — in particular for their exhaust case — is its aptitude to wield imbalanced data.

    Since in this exhaust case they are dealing with classification analysis, they used two common ways to evaluate the performance of the model: confusion matrix and ROC curve. One of the outputs of running the Random Trees model in SPSS is the confusion matrix seen in design 8. The table shows the precision achieved by the model during training.

    Figure 8 — Confusion Matrix for cancelled vs. non-cancelled flights

    In this case, the model’s precision was about 95% for predicting cancelled flights (true positives), and about 94% for predicting non-cancelled flights (true negatives). That means, the model was revise most of the time, but too made wrong predictions about 4–5% of the time (false negatives and fake positives).

    That was the precision given by the model using the training data set. This is too represented by the ROC curve on the left side of design 9. They can see, however, that the district under the curve for the training data set was better than the district under the curve for the testing data set (right side of design 9), which means that during testing, the model did not perform as well as during training (i.e. it presented a higher rate of errors, or higher rate of fake negatives and fake positives).

    Figure 9 — ROC curves for the training and testing data sets

    Nevertheless, they decided that the results were soundless wonderful for the purposes of their discussion in this blog, and they stopped their iterations here. They cheer readers to further refine this model or even to exhaust other models that could unravel this exhaust case.

    Deploying the model

    Finally, they deployed the model as a ease API that developers can muster from their applications. For that, they created a “deployment branch” in the SPSS stream. Then, they used the IBM Watson Machine Learning service available on IBM Bluemix here. They imported the SPSS stream into the Bluemix service, which generated a scoring endpoint (or URL) that application developers can call. Developers can too muster The Weather Company APIs directly from their application code to retrieve the forecast data for the next day, week, and so on, in order to pass the required data to the scoring endpoint and obtain the prediction.

    A typical scoring endpoint provided by the Watson Machine Learning service would gawk enjoy the URL shown below.

    https://ibm-watson-ml.mybluemix.net/pm/v1/score/flights-cancellation?accesskey=<provided by WML service>

    By passing the expected JSON cadaver that includes the required inputs for scoring (such as the future flight data and forecast weather data), the scoring endpoint above returns if a given flight is likely to be cancelled or not. This is seen in design 10, which shows a muster being made to the scoring endpoint — and its response — using an HTTP requester implement available in a web browser.

    Figure 10 — actual request URL, JSON body, and response from scoring endpoint

    Notice in the JSON response above that the deployed model predicted this particular flight from Newark to Chicago would be 88.8% likely to be cancelled, based on forecast weather conditions.

    Conclusion

    IBM SPSS Modeler is a powerful implement that helped us visually create a solution for this exhaust case without writing a single line of code. They were able to ensue an iterative process that helped us understand and prepare the data, then model and evaluate the solution, to finally deploy the model as an API for consumption by application developers.

    Resources

    The IBM SPSS stream and data used as the basis for this blog are available on GitHub. There you can too find instructions on how to download IBM SPSS Modeler, Get a key for The Weather Channel APIs, and much more.


    A simple Play On Self-Service gargantuan Data Prep And Analytics: Wait For Smarter Valuation Entry Point | killexams.com true questions and Pass4sure dumps

    No result found, try modern keyword!We would wait for a more attractive valuation flat to initiate positions ... open source statistical programming language R (which is supported by Alteryx), IBM SPSS Statistics, the SAS programming l...


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