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SPS-100 - IBMSPSSSTATL1P - IBM SPSS Statistics Level 1 - BrainDump Information

Vendor Name : IBM
Exam Code : SPS-100
Exam Name : IBMSPSSSTATL1P - IBM SPSS Statistics Level 1
Questions and Answers : 70 Q & A
Updated On : March 22, 2019
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SPS-100 exam Dumps Source : IBMSPSSSTATL1P - IBM SPSS Statistics Level 1

Test Code : SPS-100
Test Name : IBMSPSSSTATL1P - IBM SPSS Statistics Level 1
Vendor Name : IBM
Q&A : 70 Real Questions

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IBM IBMSPSSSTATL1P - IBM SPSS

IBM Watson Studio: Product Overview and perception | killexams.com Real Questions and Pass4sure dumps

down load the authoritative book: Cloud Computing 2019: using the Cloud for aggressive competencies

See the entire checklist of machine researching SolutionsSee user stories of IBM Watson Studio

final analysis

Watson is an umbrella for all IBM deep gaining knowledge of and artificial intelligence, as well as machine discovering. The company was a pioneer in introducing AI technologies to the company world. What this capability for patrons: Watson Studio is a proper contender for any firm looking to deploy computer getting to know and deep discovering technologies.

The platform provides extensive equipment and technologies for statistics scientists, builders and field remember experts that desire to discover statistics, construct fashions, and train and deploy laptop getting to know fashions at scale. The solution contains equipment to share visualizations and consequences with others. Watson Studio supports cloud, computer and native deployment frameworks.

The latter resides at the back of a firm’s firewall or as a SaaS solution running in an IBM private cloud. IBM Watson Studio is ranked as a “leader” within the Forrester Wave. It was a purchasers’ alternative 2018 recipient at Gartner Peer Insights.

Product Description

Watson Studio relies on a group of IBM equipment and applied sciences to construct powerful desktop gaining knowledge of functions and functions. This includes IBM Cloud pretrained computing device getting to know fashions reminiscent of visible recognition, Watson natural Language Classifier, and others. The atmosphere uses Jupyter Notebooks along with different open supply tools and scripting languages to complement developed-in collaborative assignment features.

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The outcome is an ambiance that helps speedy and strong desktop researching building and quality tuning of models. records scientists and others can choose between a lot of capacities of Anaconda, Spark and GPU environments.

Watson Studio helps superior visible modeling through a drag-and-drop interface supplied by IBM’s SPSS Modeler. moreover, it comprises automated deep getting to know using a drag-and-drop, no-code interface in Neural community Modeler.

Overview and lines user Base

facts scientists, builders and subject matter consultants.

Interface

Graphical drag-and-drop and command line.

Scripting Languages/formats Supported

helps Anaconda and Apache Spark. The latter offers Scala, Python and R interfaces.

formats Supported

Most principal data and file codecs are supported via open source Jupyter Notebooks.

Integration

IBM Watson Studio connects a few IBM items, including SPSS Modeler and statistics Science experience (DSX) along with open supply tools, with the intention to bring a sturdy Predictive Analytics and computing device getting to know (PAML) solution.

The environment incorporates open statistics sets through Jupyter Notebooks, Apache Spark and the Python Pixiedust library. The cloud version points interactivity with workstation servers and R Studio, together with Python, R., and Scala coder for facts scientists.

Reporting and Visualization

Visualization via SPSS Modeler. robust logging and reporting features are constructed into the product.

Pricing

IBM has adopted a pay-as-you-go mannequin. Watson Studio Cloud – common charges $ninety nine monthly with 50 capacity unit hours monthly blanketed. Watson Studio Cloud - enterprise runs $6,000 per thirty days with 5,000 skill unit hours. Watson Studio laptop charges $199 monthly with limitless modeling. Watson Studio local – for enterprise statistics science teams N/A.

IBM Watson Studio Overview and lines at a look:

seller and features

IBM Watson Studio

ML focal point

broad statistics science center of attention with cloud and computer ML systems.

Key aspects and capabilities

robust visible cognizance and natural classification tools. flexible strategy that accommodates open source equipment. Connects to IBM SPSS Modeler.

consumer feedback

incredibly rated for elements and capabilities. Some complaints revolving round using notebooks.

Pricing and licensing

Tiered mannequin from $99 monthly per person to $6,000 monthly per person or greater at enterprise level.


IBM sends Cognos, SPSS to the cloud | killexams.com Real Questions and Pass4sure dumps

Two of IBM’s most frequent evaluation products, the Cognos company Intelligence and the SPSS predictive analytics equipment, are headed for the cloud, the latest in an ongoing push by using IBM to port its significant software portfolio to the cloud.

getting access to any such application from a hosted environment, rather than buying the package outright, offers a number of merits to valued clientele.

“We manage the infrastructure, and this allows you to scale greater without difficulty and get all started with less upfront investment,” talked about Eric Sall, IBM vice president of international analytics advertising.

IBM announced these additions to its cloud features, as well as a number of new choices, at its insight user conference for data analytics, held this week in Las Vegas.

by way of 2016, 25 p.c of recent company evaluation deployments may be completed within the cloud, in line with Gartner.

Analytics could aid businesses in many techniques, in line with IBM. It might deliver further perception within the buying habits of clients, in addition to perception into how well its personal operations are performing. It could help defend programs from attacks and attempts at fraud, as well as assure that company departments are meeting compliance necessities.

the new on-line edition of Cognos, IBM Cognos enterprise Intelligence on Cloud, can currently be verified in a preview mode. IBM plans to present Cognos as a full commercial service early next year. users can run Cognos against records they retain in the IBM cloud, or in opposition t information they store on premises.

A full industrial version of the online IBM SPSS Modeler may be available inside 30 days. This package will encompass all of the SPSS components for records based mostly predictive modeling, corresponding to a modeler server, analytics determination administration utility and a information server.

earlier this 12 months, IBM pledged to offer tons of its application portfolio as cloud capabilities, many through its Bluemix set of platform services.

besides Cognos and SPSS, IBM also unveiled a few new and updated choices at the conference.

One new service, DataWorks, gives a couple of techniques for refining and cleaning information so it is equipped for analysis. The enterprise has launched a cloud-based mostly data warehousing service, known as dashDB. a new Watson-based mostly carrier, known as Watson Explorer, gives a method for users to ask herbal language questions about varied units of interior facts.

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IBM, SAS, and SAP Listed as Visionary Leaders by means of 360Quadrants for Predictive Analytics | killexams.com Real Questions and Pass4sure dumps

PUNE, India, Feb. 27, 2019 /PRNewswire/ -- 360Quadrants powered by means of MarketsandMarkets™, the area's only comparison platform that combines knowledgeable evaluation with crowdsourced insights has released a quadrant on Predictive Analytics software to help corporations make quicker and extra advised choices. the first unencumber of the quadrant has IBM, SAS, and SAP sharing space as Visionary Leaders. 360Quadrants are generated submit evaluation of corporations (product portfolios and company strategy). Quadrants might be up to date every three months, and the place of carriers will mirror how patrons, business specialists, and other vendors expense them on distinctive parameters.

Quadrant highlights

one hundred fifty+ agencies offering predictive analytics software were analyzed of which 31 businesses were shortlisted and classified on a quadrant beneath Visionary Leaders, Innovators, Dynamic Differentiators, and emerging Leaders.

IBM SPSS Modeler, SAS advanced Analytics, SAP company Objects, FICO choice administration Suite, Tableau application, RapidMiner Studio, Oracle superior Analytics, and Angoss potential Studio had been recognized as visionary leaders as they have established product portfolios and a robust market presence and business strategy.

guidance Builders WebFocus, Knime AG, MicroStrategy, NTT Analytics solution, Alteryx Predictive Analytics, Dataiku, GoodData, and TIBCO Spotfire had been identified as innovators as they have got concentrated product portfolios, but a mediocre business strategy.

AgileOne Cortex, Kognito, Exago, Qlik View, 6Sense ABM Orchestration Platform, figure Eight, Opera options sign Hub, Radius Intelligence, Domino facts Lab, Civis Analytics, and Lytics had been identified as rising groups as they've a gap product offering but terrible enterprise strategy. Greenwave Axon Predict, Teradata Analytics, Microsoft Azure computing device researching, and Sisense were identified as dynamic differentiators.

The 360Quadrants platform gives probably the most granular predictive analytics utility evaluation between carriers.

Methodology

The methodology used to rank predictive analytics application businesses concerned using wide secondary analysis to identify key vendors via referring to annual experiences, press releases, investor displays, white papers, and numerous connected directories and databases. 31 key vendors had been shortlisted on the groundwork of their breadth of product choices, firm measurement, and different standards. The scores and weights for shortlisted vendors against each parameter have been finalized post analysis.  After the ratings had been finalized, each supplier was placed in respective quadrants according to their score within the product providing and enterprise strategy parameters.

About 360Quadrants

360Quadrants pretty much compares groups in rising applied sciences on 6 large maturities: product maturity, enterprise maturity, use-case maturity, investment maturity, technology maturity, and enterprise outcomes maturity. every business is reviewed through four stakeholders—patrons, trade experts, other vendors, and MarketsandMarkets analysts—to make it independent. 360 aims to simplify and de-possibility complex buy selections. patrons get to personalize their quadrant towards their specific needs. The mixed insights from peers, analysts, consultants, and vendors cut the bias and helps the purchaser discover the gold standard fit solution. carriers get to position themselves to win greatest new consumers, customise their quadrants, make a decision key parameters, and place themselves strategically in a gap house, to be consumed by way of giants and begin-united states of americaalike. experts get to grow their personal company and boost their notion leadership. The 360 platform targets the building of a social network that hyperlinks trade specialists with agencies worldwide.

360Quadrants has additionally launched quadrants in fields like utility Modernization features, AI in Fintech solutions, and Cognitive Analytics solutions.

About MarketsandMarkets™

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IBMSPSSSTATL1P - IBM SPSS Statistics Level 1

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Experimental crossbreeding reveals strain-specific variation in mortality, growth and personality in the brown trout (Salmo trutta) | killexams.com real questions and Pass4sure dumps

Study fish

Handling and rearing of fish were conducted in accordance with the National Animal Experiment Board’s approval (ESAVI/2458/04.10.03/2011). All animal experimentation reported meets the ABS/ASAB guidelines for ethical treatment of animals and comply with the current Finnish legislation. The parents of this study fish were obtained from the broodstocks maintained by the Finnish Game and Fisheries Research Institute (currently the Natural Resources Institute Finland), and represented populations that show economic or scientific importance in Finland. All females originated from the River Oulujoki watercourse broodstock (3rd or 4th hatchery generation) that had originally been founded using wild brown trout from Rivers Varisjoki and Kongasjoki, both discharging to the Lake Oulujärvi (Fig. 1). For the veterinary reasons, we were able to use females only from this population. The females were mated with males from the same strain (also representing 3rd or 4th hatchery generation fish) as well as with males from three other brown trout strains, thus resulting in one control (purebred) F1 group and three hybrid F1 groups (Table 1). The sires of the three hybrid groups originated from the River Vaarainjoki (located next to River Kongasjoki and upstream from River Varisjoki with one lake between, wild-caught individuals, Fig. 1), the Lake Kitkajärvi (1st hatchery generation, strain above the Jyrävä waterfall) and the Rautalampi watercourse (5th or 6th hatchery generation). The Rautalampi hatchery strain represents a collection of several origins of fish (Äyskoski, Tyyrinvirta, Siikakoski and Simunankoski, Fig. 1) and had the longest history of captive breeding in a hatchery. Brown trout from River Vaarainjoki and River Oulujoki watercourse broodstock are moderately genetically differentiated (FST = 0.109 based on 4876 SNP loci, Prokkola et al. submitted MS 2018). Genetic distance (FST) between River Oulujoki watercourse broodstock and Rautalampi hatchery strain is at the level of 0.073 (M.-L. Koljonen and J. Koskenniemi, unpublished data 2016 based on 16 microsatellite markers). Lake Kitkajärvi strain has not yet been compared to the other included strains. Apart from the resident River Vaarainjoki strain, the other strains were classified migratory. This classification was based on original status of the stocks taken to hatcheries, indirect genetic evidence (large heterogeneity indicates migratory status)43 and experimental evidence between OUV and VAA populations (authors’ unpublished data). We use the following abbreviations to identify the F1 groups: OUV (River Oulujoki ♂ × River Oulujoki ♀), VAA (River Vaarainjoki ♂ × River Oulujoki ♀), KIT (Lake Kitkajärvi ♂ × River Oulujoki ♀) and RAU (Rautalampi watercourse ♂ × River Oulujoki ♀).

Five females from the River Oulujoki watercourse strain and five males from each of the four strains (20 males in total) were used for fertilizations that were carried out in October 12th in 2011, resulting in 100 female-male combinations (i.e. 25 half-sib families per group). The fertilizations took place at the Kainuu Fisheries Research Station (www.kfrs.fi), where all the F1 offspring were raised in the same hatchery conditions. Each fertilization combination was divided into three equal replicates (300 incubation units in total, approx. 100 eggs each). The incubation units were open plastic tubes with polystyrene floats and a mesh bottom (100 mm in length and diameter). The incubators were divided into six flow-through tanks (3 m long, 50 incubators per tank) so that OUV and KIT families were always in the same three tanks and KIT and RAU families together in other three tanks. After the first three days, the egg count for the fertilized eggs per each unit was 89 ± 0.85 (mean ± S.D) eggs per unit. The water used in the rearing tanks came from the adjacent Lake Kivesjärvi, and the variations of temperature and oxygen levels during the study followed those in natural conditions.

After the first three days, the incubation units were moved to circular 3.2 m2 tanks (water volume 800 l), where they floated vertically in. Eggs and alevins were incubated in these tanks in three replicates until May 2012. All eggs hatched by 20th of March 2012. On 21st−23th May in 2012, a total sample of 6300 start-feeding fry was transported to 60 separate rearing tanks (surface area 0.4 m2; water volume during the first two weeks 80 l, then 160 l) for on-growing until the end of the experiment. The feeding of the fry was also started at this time. The fish were fed ad libitum by automatic belt feeders with commercial dry salmonid food (Biomar INICIO plus G; 0.4–1.1 mm). The offspring of males from the OUV and VAA strains were placed in 50 tanks in full-sib families consisting of 105 individuals/family. The families were formed by selecting the same number of individuals from each of the three egg incubation replicates for further rearing, if possible (35 fish/repetition in all but two cases). For logistic constrains, the half-sib families of males from KIT and RAU strains were placed together in 10 tanks so that in each tank there were 105 individuals that shared the same male parent but not the same female parent. These half-sib families were also formed by taking the same number of individuals from each female-male fertilization replicate (7 fish repetition−1 in all cases). As a result of these combinations, 25 tanks were formed for both OUV and VAA full-sib families, and 5 tanks for both KIT and RAU half-sib families. Behavioral experiments were performed for five individuals from each of these 60 rearing tanks (for 300 individuals in total, see below).

Mortality of the offspring

The mortality of the F1 offspring was monitored every few days during the egg incubation and hatching period (from fertilization on 12th October 2011 until 21th May 2012) and then daily during the period of on-growing (from 24th May 2012 until 6th September 2012). Both dead eggs and fry were counted and removed.

Body length measurements

The first body length measurements were performed between 25th May–1st June 2012. After transporting 6300 start-feeding fry into rearing tanks for on-growing and behavioral experiments, 1180 of the fry remaining in the incubation tubes were measured for total body length (29.5 ± 1.2 mm, mean ± S.D). Four individuals from each of the 300 incubation tubes were haphazardly selected for the measurement, except for the six cases where there were less than four “surplus” individuals remaining after the transport (in these cases 0–3 individuals were measured). Since all the incubation tubes having less than four surplus individuals belonged to VAA test group, slightly smaller sample sizes for VAA test group was used (280 measured individuals from VAA group and 300 from each of the other three test groups). The fish sizes were measured in a rotating order, so that four individuals from 25 OUV tubes were measured first, then the same number from KIT tubes, RAU tubes and finally from VAA tubes.

The second body length measurements took place approximately one month later, between 27th June and 20th July 2012, when 300 individuals (48.0 ± 5.7 mm, mean ± S.D), five individuals/tank, were measured as a part of the behavioral assays. Haphazard netting of the study fish according to a randomized order of the rearing tanks (families) was used to select the individuals for behavioral trials and subsequent measurements. However, the order of the rearing tanks was randomized in a way that both OUV and VAA individuals were tested and measured first (27th June to 19th July), whereas individuals from KIT and RAU hybrid populations were tested and measured during the last four days of the experiments (between 17th and 20th July 2012). OUV and VAA groups were prioritized to secure the quantitative genetic parameter estimations in the case of any disease epidemics. The measurement order thus resulted in greater body length and weight values for KIT and RAU crossing groups, and this bias was accounted for in the analyses and interpretation of the results.

Third measurement period took place in 3rd–4th September in 2012. At this time, 1113 individuals remaining in the rearing tanks were measured for their body lengths (77.4 ± 8.2 mm, mean ± S.D). This measurement group consisted of 450 offspring of OUV and VAA males, 108 offspring of KIT males and 103 offspring of RAU males.

Quantification of behavioral traits and personality

The behavioral trials quantifying individuals’ boldness and exploration tendency were performed between 27th June and 20th July 2012. Five haphazardly dipnetted individuals from each of the 60 full-sib or half-sib families were included in the experiment (300 individuals in total: 125 individuals from OUV and VAA groups and 25 individuals from KIT and RAU groups). The study fish were deprived of food for approximately 36 hours before the experiment individually in small acclimation tanks (140 × 120 mm, water depth approx. 50 mm).

In the personality assay, the study fish were placed one at a time in a specially made emergence test tank (see details in39), that consisted of a darker-walled starting compartment, i.e. box, (separated from the rest of the tank by a door that could be lifted from a distance by pulling a line) and a larger, lighter-walled test arena (with uniform light gray floor). On the bottom of the test arena, two drawn lines allowed us to evaluate the time that it took for the study fish to swim further into the arena (to cross the first and the second line). To measure the boldness and exploration tendency of the study fish, the test tank included two rocks for shelter and a mirror covering the end wall of the arena. At the beginning of the trial, the study fish were placed in the starting compartment, where they were allowed to acclimate to the circumstances for three minutes. After the acclimation period, the door of the starting compartment was lifted, allowing the fish to swim into the test arena. We used software assisted timing (custom software by A.V.) to record the time it took for the fish to activate (move for the first time in the starting compartment), leave the starting compartment, swim over the lines in the arena and touch the mirror at the other end. We also recorded if the fish swam back to the starting compartment, touched the mirror for more than one time or exhibited freezing behavior (stayed motionless for more than one second, as indication of fear or stress39. The duration of the experiment was eight minutes from the moment the door of the starting compartment was lifted.

As expected, not all study fish performed all of the behaviors described above during the test period. To prevent any bias in the data, it was important to include these individuals in statistical analysis. Therefore, when a value was missing for any behavioral variable, a maximum value was used (the duration of a trial, 8 min). For example, if an individual never activated during the trial, it was given the maximum value (8 min) for activation and all possible further behaviors (entering the test arena, crossing the first and second line in the arena and touching the mirror at the other end). Maximum values were used because they represented the actual behavior of more passive study individuals better than not giving them any values, in which case the study group would have seemed, on average, more active and/or bold than in reality.

The behavioral trial was performed twice for each tested individual to enable the evaluation of short term repeatability of behavior39. Both trials were always performed on the same day, with at least three hours in between the trial times to recover from possible stress caused by the first trial (recovery time 258.01 ± 38.79 min, mean ± S.D). After the trials the study fish were euthanatized using an overdose of anesthetic (clove oil, 500 mg l−1) and their lengths and weights were measured.

Statistical analysis Mortality

Mortality was analyzed separately for the egg incubation-alevin period and for the period of on-growing (from start-feeding onwards). Since the number of fertilized eggs per family varied slightly in the beginning of the experiment (mean ± S.D. = 89.3 ± 14.8, range 40–143), arcsine square root transformed proportions of dead eggs were used in the analysis. The number of fish in each half-sib family was equalized when the alevins were transported to the rearing tanks (105 individuals per tank). However, due to a block in a faucet, 58 individuals died in one of the rearing tanks. To include this tank in the data, arcsine square-root transformed proportions of mortality were also used in the analysis of this period (deaths caused by the block were not included in the mortality data). The mortalities during both periods were analyzed using a linear mixed effect model using restricted maximum likelihood estimation (REML) in SAS 9.4. software (SAS Inst. Inc., Cary, NC). The significance of different random effects in the models (e.g. incubation tank or male identity, nested within male strain, or interaction between female and male strain) was separately tested by comparing the goodness of fit of the alternative models either containing or missing the effect (likelihood ratio test with one degree of freedom)47. Further, the appropriate error structures were chosen for the models based on the values of Akaike’s Information Criteria (AIC). Insignificant (co)variances were excluded from the final models. For the first (egg-alevin) period, the model was:

$${y}_{ijkl}={\mu }+{\rm{male}}\,{{\rm{strain}}}_{j}+{{\rm{female}}}_{k}+{{\rm{male}}}_{l(j)}+{e}_{ijkl},$$

where yijkl is the arcsin square root transformed mortality within an incubator i, µ is the model intercept (overall population mean), male strainj is the fixed effect of male strain (j = 1–4), femalek is the random effect of female parent (k = 1–5), malel(j) is the random effect of male parent (l = 1–20), nested within male strain, and eijkl is the random error term.

For the second (on-growing) period the model was:

$${y}_{ij}={\mu }+{\rm{male}}\,{{\rm{strain}}}_{j}+{e}_{ij}$$

where yijm is the arcsin square root transformed mortality within a rearing tank i (i = 1–60 tanks). Tukey-Kramer -type post hoc tests were used to identify pairwise differences among the four male strains.

Body length measurements

Body length differences among the male strains were tested separately at the three different measurement periods (period 1: 1180 fry measured between 25th May–1st June 2012, period 2: 300 fingerlings measured between 27th June–20th July 2012 and period 3: 1113 individuals measured between 3th–4th September 2012). For the first period, the linear mixed effect model was:

$${y}_{ijklm}={\mu }+{\rm{male}}\,{{\rm{strain}}}_{j}+{{\rm{day}}}_{m}+{{\rm{female}}}_{k}+{{\rm{male}}}_{l(j)}+{e}_{ijklm},$$

where yijklm is the body length of an individual i. Residual covariances among individual fish were estimated for each incubation tank separately. Further, because the first measurement period lasted for 9 days the day of measurement (calculated since the beginning of the measurement period in question) was included in the model as a fixed covariate.

For the second and third periods, the linear mixed model was of form:

$${y}_{ijklm}={\mu }+{\rm{male}}\,{{\rm{strain}}}_{j}+{{\rm{day}}}_{m}+{{\rm{tank}}}_{n(j)}+{e}_{ijklm},$$

where tankn(j) is the random rearing tank effect (n = 1–60), nested within male strain. It is noteworthy here that KIT and RAU fish were kept in paternal half-sib tanks, and consequently in these two groups the tank effect on fish growth may be partially confounded with maternal identity effect. The date of measurement (m = 1–23 days) was included as a fixed covariate for the model of the second length data only. Separate residual variances were estimated for each male strain. Tukey-Kramer pairwise comparisons were used to find which male strains differed from one another.

Behavioral data

The assumption on normal distribution of residuals was tested using one-sample Kolmogorov-Smirnov test. Since the normality of some variables was improved by logarithm transformation, Ln-transformation was used for all variables (Ln(X + 1) was used for number of times mirror touched, number of times frozen and freezing time). The repeatability of individual behavioral variables was analyzed using the Interclass Correlation Coefficient (ICC)48 prior to inclusion in PCA, since analyzing the heritability of a non-repeatable behavior would not be reasonable49.

Principal component analysis (PCA; IBM SPSS Statistics) with varimax rotation was used to combine multiple behavioral variables into uncorrelated principal components (PC), as this approach has been adopted in recent personality studies in fish39, which allows us to compare the results with earlier studies. The variables included in PCA were (1) entering the arena, (2) crossing the first and (3) second line in the arena, (4) touching the mirror (for the first time during trial), (5) number of times the mirror was touched during the trial, (6) time spent in the starting compartment, (7) number of times the individual showed freezing behavior and (8) total time spend freezing during the trial. Behavioral data from all groups was included in the same principal component analysis.

The genetic parameters for the two obtained PCs were analyzed using REML estimation in ASReml 3.0 software50. Because the identity of both male and female parents were only known for OUV (control) and VAA crossing groups, the data from only these two groups were used for the genetic models. Due to a relatively low number of families (and low number of tested offspring per family) within the groups (125 fish per strain), the genetic models were run for a combined data including both groups together. Estimation of common genetic variances for the two crossing groups is justified as these groups are not genetically independent, separate populations but share the same mothers. The variance components for each PC were estimated using a repeated measures animal model, which can be written in matrix notation as:

$$y={\bf{X}}{\bf{b}}+{{\bf{Z}}}_{a}{\bf{a}}+{{\bf{Z}}}_{p}{\bf{p}}+{\bf{e}}$$

(1)

where y is the vector of individual PC scores, b is the vector of fixed effects, a is the vector of random additive genetic effects, p is the vector of random permanent environment effects and e is the vector of random residual effects. The X is the design matrix associated with b, and Za and Zb are incidence matrices assigning observations to the levels of additive genetic effects and permanent environment effects (i.e., non-additive contributions to fixed among-individual differences), respectively. Random variables a, p and e were assumed to be normally distributed. Specifically, \({\bf{a}} \sim N(0,\,{\bf{A}}{\sigma }_{a}^{2})\), where \({\sigma }_{a}^{2}\) is the additive genetic variance and \({\bf{A}}\) is the additive genetic relationship matrix derived from the parental generation; \({\bf{p}} \sim N(0,\,{\bf{I}}{\sigma }_{pe}^{2})\), where \({\sigma }_{pe}^{2}\) is the common environment variance; \(e \sim N(0,\,{\bf{I}}{\sigma }_{e}^{2})\), where \({\sigma }_{e}^{2}\) is the residual variance and \({\bf{I}}\) is the identity matrix.

Further, the significance of an additional variance due to random rearing tank of individuals was also tested using the likelihood ratio test47.

Conditional Wald statistics was used to evaluate the significance of the fixed effects. Only the variables with significant contribution to the variation of behavioral PCs were included in the final models (P < 0.05). For both behavioral PCs, the confounding effects of water temperature and testing time (in minutes from 00:00) were fitted in the model as fixed covariates. For PC1 (exploratory tendency), the fixed effects also included the number of testing day (0–22) whereas for PC2 (freezing) fish body length was included.

The repeatability (r) of both behavioral PCs was calculated as:

$$r=\frac{{\sigma }_{a}^{2}+{\sigma }_{pe}^{2}}{{\sigma }_{a}^{2}+{\sigma }_{pe}^{2}+{\sigma }_{e}^{2}}$$

(2)

Correspondingly, heritability (h2) and permanent environment effect ratio (p2) were calculated for each PC as:

$${h}^{2}=\frac{{\sigma }_{a}^{2}}{{\sigma }_{a}^{2}+{\sigma }_{pe}^{2}+{\sigma }_{e}^{2}}\,{\rm{and}}\,{p}^{2}=\frac{{\sigma }_{pe}^{2}}{{\sigma }_{a}^{2}+{\sigma }_{pe}^{2}+{\sigma }_{e}^{2}},\,{\rm{respectively}}{\rm{.}}$$

(3)

Approximate standard errors were calculated for estimated variance components and variance ratios using ASReml.

We tested for differences in group means in individual additive genetic solutions (i.e., best linear unbiased predictions (BLUPs) of breeding values obtained from ASReml) for both PCs using a linear mixed model in SAS (group as a fixed effect). Similarly, the difference of means in individual permanent environment effect solutions was tested between OUV and VAA groups.

To analyze whether there were differences among the four F1 groups in the two behavioral PCs, linear mixed effect (LME) models were fitted to the data in SPSS 23.0.02 (IBM Corp, USA). Environmental variables that might have had an effect on the behaviors were included in the model. The variables included were water temperature and oxygen level (measured daily), repetition (1st or 2nd trial), size of the fish (measured as body length at the day of the behavioral trial), time of the trial (as minutes from 00:00 am), and recovery time between the trials (as minutes). Date was controlled by including strongly correlated water temperature as a covariate (Pearson’s r = 0.88, p = 0.01) and testing the day effect separately by adding it to the final model. Neither tank effect nor maternal effects could be independently included in the model. This was because each tank contained offspring from just one male parent (and from just one female parent in the case of OUV and VAA offspring). Since the offspring of both KIT and RAU sires were combined into half-sib families, the identity of the female parent was not known for these groups. Thus, the rearing tank identity, nested within male strain was included in the model as a random effect to control for the dependency arising from the common rearing environment. Bonferroni -type post hoc tests were used for pairwise comparisons of the four F1 groups. Model residuals were inspected for normality and found to satisfy the model assumptions.


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

Ricardo Balduino and Tim Bohn

Early Flight, Creative Commons Introduction

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.

Tools used in this use case solution

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.

A proposed solution

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.

Business and data understanding

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.

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

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.

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

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.

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 our process in IBM SPSS Modeler.

Figure 4 — iterations: prepare data, run models, evaluate — and do it again First iteration

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.)

Figure 5 — imbalanced data found in our input data set

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.

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. 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.

Second iteration

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.

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

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.

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 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).

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

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.

Deploying the model

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.

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 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.

Resources

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.


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