IBM IBMSPSSSTATL1P - IBM SPSS

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

IBM is buying analytics software and solutions company SPSS in an all 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 field 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 take information and turn 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 place viewpoint.

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

In becoming a member of with IBM, we can increase Predictive Analytics as a competitive knowledge for groups and groups international. We see this as a transformative adventure if you want to accelerate the adoption of Predictive Analytics.

Between now and the acquisition close, we are able to continue to assist our 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 full figuring out of expected future behaviors, so groups could make smarter decisions for more desirable company results.

IBM says it will proceed to aid and enhance SPSS technologies whereas allowing clients to take potential of its personal product portfolio. SPSS will become a part 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. financial phrases had been not disclosed.

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

IBM's SPSS predictive analytics tools include 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 have little 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 distinct techniques and algorithms to assist the user find counsel hidden in the information. The device can additionally help in integrating and consolidating every kind 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.

edition 18 offers right here facets:

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 part runs on Unix, Linux and home windows.

IBM SPSS Modeler gives here versions:

SPSS statistics version 24 includes here new elements:

IBM SPSS statistics offers the following three versions (each and every with extra modules):

Pricing for the SPSS Modeler and SPSS records predictive analytics equipment differ 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.

Why the time period unstructured records is a misnomer

How massive data is changing information modeling ideas

massive records systems pose new challenges to records governance

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IBMSPSSSTATL1P - IBM SPSS Statistics Level 1

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

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

The study abstract, "Effect of Affordable Care 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 care 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 State University's Helen Devos Children's Hospital who previously studied in Illinois. "During the same time, we 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 state 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 body 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 slow the reduction in uncompensated emergency department visits. "With increasing uncertainty about the long-term plan for the ACA, enrollment has started to decline," he said. "So, it will be interesting to see whether we'll see a rise 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 Center in Orlando, FL.

Please note: only the abstract is being presented at the meeting. In some cases, the researcher may have more data available to share 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 care pediatricians, pediatric medical subspecialists and pediatric surgical specialists dedicated to the health, safety and well-being of infants, children, adolescents and young adults. For more information, visit http://www. aap. org. Reporters can access the meeting program and other relevant meeting information through the AAP meeting website at http://www. aapexperience. org/

Abstract Title:

Effect of Affordable Care 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 Care Act (ACA) designed to reduce the number of people without health insurance. The ACA extended Medicaid coverage to all adult citizens with annual incomes up to 133% of the federal poverty level. Purpose: Our study objective was to study the impact of ACA on Uninsured Emergency Department Visits (UiEDV). Methods: We downloaded aggregated EDV from the Healthcare Cost and Utilization Project (HCUP) website and extracted state of Illinois EDV and sorted the data by year and quarter. We looked for trends in the quarterly UiEDV data and calculated summary statistics to determine the variance and mean. We used generalized linear modeling (Poisson and a customized negative binomial regression (NB)) to model UiEDV using quarterly UiEDV count as a dependent variable and pre and post ACA implementation periods as independent factors. We used log as the link function, and the software calculated the estimated parameter. From the model, we derived the Incidence Rate Ratio (IRR) and its corresponding 95% Confidence Interval (95%CI); we compared both models using Goodness of fit tests Akaike Information Criterion (AIC) and Bayesian Information Citerion (BIC). We used IBM SPSS Version 25â„¢ and Microsoft Excel 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 fit 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, burden of uncompensated EDVs decreased substantially in Illinois during the study period. Additional studies to look at regional trends, specific groups, continued coverage is needed to quantify the overall effects of ACA.

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

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

Ricardo Balduino and Tim Bohn

As we described in Part 1 of this series, our objective is to help predict the probability of the cancellation of a flight between two of the ten U.S. airports most affected by weather conditions. We use historical flights data and historical weather data to make predictions for upcoming flights.

Over the course of this four-part series, we use different platforms to help us with those predictions. Here in Part 2, we use the IBM SPSS Modeler and APIs from The Weather Company.

IBM SPSS Modeler is designed to help discover patterns and trends in structured and unstructured data with an intuitive visual interface supported by advanced analytics. It provides a range of advanced algorithms and analysis techniques, including text analytics, entity analytics, decision management and optimization to deliver insights in near real-time. For this use case, we 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 also 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.

In this blog, we propose one possible solution for this problem. It’s not meant to be the only or the best possible 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. We encourage the readers to try and come up with different solutions, and provide us with your feedback for future blogs.

The first step of the iterative process includes understanding and gathering the data needed to train and test our 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 we ended up with 12 csv (comma separated value) files. We used IBM SPSS Modeler to merge all the csv files into one set and to select the ten airports in our scope. Some data clean-up and formatting was done to validate dates and hours for each flight, as seen in Figure 1.

Weather data — From the Extensions Hub, we 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 Figure 2.

Combined flights and weather data — To each flight in the first data set, we added two new columns: ORIGIN 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 Figure 3 are placeholders for the diagrams in Figures 1 and 2 above.

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 our process in IBM SPSS Modeler.

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

This is an interesting use case, and often a hard one to solve, due to the imbalanced data it presents, as seen in Figure 5. By “imbalanced” we mean that there were far more non-cancelled flights in the historical data than cancelled ones. We will discuss how we dealt with imbalanced data in the following iteration.

Next, we defined which features were required as inputs to the model (such as flight date, hour, day of the week, origin and destination airport codes, and weather conditions), and which one was the target to be generated by the model (i.e. predict the cancellation status). We 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 run multiple models at once and preview their outputs, such as the area under the ROC curve, as seen in Figure 6.

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

During the second iteration, we addressed the skewedness of the original data. For that purpose, we 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 our selected model work more effectively.

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

As mentioned earlier, we 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 prone to overfitting, which gives a higher likelihood of repeating the same test results when you use new data, that is, data that was not part of the original training and testing data sets. Another advantage of this method — in particular for our use case — is its ability to handle imbalanced data.

Since in this use case we are dealing with classification analysis, we 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 Figure 8. The table shows the precision achieved by the model during training.

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 correct most of the time, but also made wrong predictions about 4–5% of the time (false negatives and false positives).

That was the precision given by the model using the training data set. This is also represented by the ROC curve on the left side of Figure 9. We can see, however, that the area under the curve for the training data set was better than the area under the curve for the testing data set (right side of Figure 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 false negatives and false positives).

Nevertheless, we decided that the results were still good for the purposes of our discussion in this blog, and we stopped our iterations here. We encourage readers to further refine this model or even to use other models that could solve this use case.

Finally, we deployed the model as a REST API that developers can call from their applications. For that, we created a “deployment branch” in the SPSS stream. Then, we used the IBM Watson Machine Learning service available on IBM Bluemix here. We imported the SPSS stream into the Bluemix service, which generated a scoring endpoint (or URL) that application developers can call. Developers can also call 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 make the prediction.

A typical scoring endpoint provided by the Watson Machine Learning service would look like 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 body 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 Figure 10, which shows a call being made to the scoring endpoint — and its response — using an HTTP requester tool available in a web browser.

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.

IBM SPSS Modeler is a powerful tool that helped us visually create a solution for this use case without writing a single line of code. We were able to follow 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.

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

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

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Fortinet [12 Certification Exam(s) ]
Foundry [6 Certification Exam(s) ]
FSMTB [1 Certification Exam(s) ]
Fujitsu [2 Certification Exam(s) ]
GAQM [9 Certification Exam(s) ]
Genesys [4 Certification Exam(s) ]
GIAC [15 Certification Exam(s) ]
Google [4 Certification Exam(s) ]
GuidanceSoftware [2 Certification Exam(s) ]
H3C [1 Certification Exam(s) ]
HDI [9 Certification Exam(s) ]
Healthcare [3 Certification Exam(s) ]
HIPAA [2 Certification Exam(s) ]
Hitachi [30 Certification Exam(s) ]
Hortonworks [4 Certification Exam(s) ]
Hospitality [2 Certification Exam(s) ]
HP [746 Certification Exam(s) ]
HR [4 Certification Exam(s) ]
HRCI [1 Certification Exam(s) ]
Huawei [21 Certification Exam(s) ]
Hyperion [10 Certification Exam(s) ]
IAAP [1 Certification Exam(s) ]
IAHCSMM [1 Certification Exam(s) ]
IBM [1530 Certification Exam(s) ]
IBQH [1 Certification Exam(s) ]
ICAI [1 Certification Exam(s) ]
ICDL [6 Certification Exam(s) ]
IEEE [1 Certification Exam(s) ]
IELTS [1 Certification Exam(s) ]
IFPUG [1 Certification Exam(s) ]
IIA [3 Certification Exam(s) ]
IIBA [2 Certification Exam(s) ]
IISFA [1 Certification Exam(s) ]
Intel [2 Certification Exam(s) ]
IQN [1 Certification Exam(s) ]
IRS [1 Certification Exam(s) ]
ISA [1 Certification Exam(s) ]
ISACA [4 Certification Exam(s) ]
ISC2 [6 Certification Exam(s) ]
ISEB [24 Certification Exam(s) ]
Isilon [4 Certification Exam(s) ]
ISM [6 Certification Exam(s) ]
iSQI [7 Certification Exam(s) ]
ITEC [1 Certification Exam(s) ]
Juniper [63 Certification Exam(s) ]
LEED [1 Certification Exam(s) ]
Legato [5 Certification Exam(s) ]
Liferay [1 Certification Exam(s) ]
Logical-Operations [1 Certification Exam(s) ]
Lotus [66 Certification Exam(s) ]
LPI [24 Certification Exam(s) ]
LSI [3 Certification Exam(s) ]
Magento [3 Certification Exam(s) ]
Maintenance [2 Certification Exam(s) ]
McAfee [8 Certification Exam(s) ]
McData [3 Certification Exam(s) ]
Medical [69 Certification Exam(s) ]
Microsoft [368 Certification Exam(s) ]
Mile2 [2 Certification Exam(s) ]
Military [1 Certification Exam(s) ]
Misc [1 Certification Exam(s) ]
Motorola [7 Certification Exam(s) ]
mySQL [4 Certification Exam(s) ]
NBSTSA [1 Certification Exam(s) ]
NCEES [2 Certification Exam(s) ]
NCIDQ [1 Certification Exam(s) ]
NCLEX [2 Certification Exam(s) ]
Network-General [12 Certification Exam(s) ]
NetworkAppliance [36 Certification Exam(s) ]
NI [1 Certification Exam(s) ]
NIELIT [1 Certification Exam(s) ]
Nokia [6 Certification Exam(s) ]
Nortel [130 Certification Exam(s) ]
Novell [37 Certification Exam(s) ]
OMG [10 Certification Exam(s) ]
Oracle [269 Certification Exam(s) ]
P&C [2 Certification Exam(s) ]
Palo-Alto [4 Certification Exam(s) ]
PARCC [1 Certification Exam(s) ]
PayPal [1 Certification Exam(s) ]
Pegasystems [11 Certification Exam(s) ]
PEOPLECERT [4 Certification Exam(s) ]
PMI [15 Certification Exam(s) ]
Polycom [2 Certification Exam(s) ]
PostgreSQL-CE [1 Certification Exam(s) ]
Prince2 [6 Certification Exam(s) ]
PRMIA [1 Certification Exam(s) ]
PsychCorp [1 Certification Exam(s) ]
PTCB [2 Certification Exam(s) ]
QAI [1 Certification Exam(s) ]
QlikView [1 Certification Exam(s) ]
Quality-Assurance [7 Certification Exam(s) ]
RACC [1 Certification Exam(s) ]
Real-Estate [1 Certification Exam(s) ]
RedHat [8 Certification Exam(s) ]
RES [5 Certification Exam(s) ]
Riverbed [8 Certification Exam(s) ]
RSA [15 Certification Exam(s) ]
Sair [8 Certification Exam(s) ]
Salesforce [5 Certification Exam(s) ]
SANS [1 Certification Exam(s) ]
SAP [98 Certification Exam(s) ]
SASInstitute [15 Certification Exam(s) ]
SAT [1 Certification Exam(s) ]
SCO [10 Certification Exam(s) ]
SCP [6 Certification Exam(s) ]
SDI [3 Certification Exam(s) ]
See-Beyond [1 Certification Exam(s) ]
Siemens [1 Certification Exam(s) ]
Snia [7 Certification Exam(s) ]
SOA [15 Certification Exam(s) ]
Social-Work-Board [4 Certification Exam(s) ]
SpringSource [1 Certification Exam(s) ]
SUN [63 Certification Exam(s) ]
SUSE [1 Certification Exam(s) ]
Sybase [17 Certification Exam(s) ]
Symantec [134 Certification Exam(s) ]
Teacher-Certification [4 Certification Exam(s) ]
The-Open-Group [8 Certification Exam(s) ]
TIA [3 Certification Exam(s) ]
Tibco [18 Certification Exam(s) ]
Trainers [3 Certification Exam(s) ]
Trend [1 Certification Exam(s) ]
TruSecure [1 Certification Exam(s) ]
USMLE [1 Certification Exam(s) ]
VCE [6 Certification Exam(s) ]
Veeam [2 Certification Exam(s) ]
Veritas [33 Certification Exam(s) ]
Vmware [58 Certification Exam(s) ]
Wonderlic [2 Certification Exam(s) ]
Worldatwork [2 Certification Exam(s) ]
XML-Master [3 Certification Exam(s) ]
Zend [6 Certification Exam(s) ]

References :

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