For each predictor variable, and each classification, the plots will show how the probability of a record belonging to that specific class will change based on the value of the predictor variable. The third and final configuration tab, Graphics Options, can be used to specify the size and resolution of the output plots. By now, you should have expert-level proficiency with the Neural Network Tool! In the following series of lessons, we're going to take a deeper look at the predictive modeling tools available in Alteryx. In general, setting a weight decay between 0.01 and 0.2 is recommended. These effect plots can help make a neural network a little less opaque, by visualizing how classification probability or value is impacted by each individual predictor variable. The algorithm will stop iterating before the maximum is met when the weights are no longer improving. Use sampling weights in model estimation is an optional argument that you can enable by selecting the checkbox. No matter the field in which you hold some expertise, sharing your skills to benefit the lives of others or supporting non-profit organizations that try to make the world a better place is a noble and time-worthy personal pursuit. Residuals are calculated as the difference between the probability of the predicted value, and the actual value (a 1 or 0, depending on if the classification it true or false) for that record. This collection is organized into three main layers: the input layer, the hidden layer, and the output layer. 12:11 PM Serialization allows the model object to be passed out of the R code and into Designer. For more help understanding and interpreting a Q-Q Plot, please see this helpful resource from the University of Virginia Library. The Residuals vs. Fitted plot depicts a point for each record used to train the model, where the X value is the “fitted value” or probability a record belonged to its target class, and the Y-Value is the Residual of that record. As a business analyst or data scientist, you can use predictive analytics to know what will happen in the future so you can make the best decision with the most certainty possible. Just looking at the data provided, name is a factor with more than 8000 levels; you're not going to get anything sensible out of it with only 10000 observations. Generally, the value should be set close to 0.5. Predictive Analytics. ... Geoffrey Hinton, and Yann LeCun won in 2018 for their conceptual and engineering breakthroughs in convolutional deep neural networks. Go to Options > Download Predictive Tools and sign in to the Alteryx Downloads and Licenses portal to install R and the packages used by the R Tool. The neurons in the hidden layer use a logistic (also known as a sigmoid) activation function, and the output activation function depends on the nature of the target field. Our team exported the scraped stock data from our scraping server as a csv file. The default setting is 100. Because the weights and biases of the model are adjusted during the training process (using a method called backpropagation), they can be scaled to match the magnitude of each predictor variable. Definitive answers from Designer experts. No previous knowledge of KNIME is required. Finally, the effect plots will be included in the Report depending on if the option was checked in the configuration. It is designed to be modular, fast and easy to use. The data was already cleaned and prepared, meaning missing stock and index prices were LOCF’ed (last observation carried forward), so that the file did not contain any missing values. https://en.wikipedia.org/wiki/Artificial_neural_network. The popularity measures of article titles are then analyzed by using the search indexes obt… The O anchor returns the serialized R model object, with the model’s name. In it, you can first load training data including number of neurons and data sets, data file (CSV, TXT), data normalize method (Linear, Ln, Log10, Sqrt, ArcTan, etc. Index and stocks are arranged in wide format. It is a typical part of nearly any neural network in which engineers simulate the types of activity that go on in the human brain. Community_Admin. For each point, the X-value depicts the Sample Quantile value and the Y-value is the corresponding Theoretical Quantile value. The Call is the actual code used in R to generate the model. In this way one again is Following the pioneering investigations (e.g., see roughly constraining nodal input to -1 < net < (Rumelhart and McClelland, 1986)) it has become 1. The dataset contains n = 41266minutes of data ranging from April to August 2017 on 500 stocks as well as the total S&P 500 index price. Neural networks have been used for a while, but with the rise of Deep Learning, they came back stronger than ever and now are seen as the most advanced technology for data analysis. The Normal Q-Q plot is used for comparing the distributions of two populations by plotting quantile values. This article is part of the Tool Mastery Series, a compilation of Knowledge Base contributions to introduce diverse working examples for Designer Tools. Since recurrence is proven to be an important part of the workings of our brain. The Custom scaling/normalization argument refers to normalizing your predictor variables prior to generating the Neural Network model. From Alteryx to KNIME. The neural network is a machine learning technique where the algorithm considers a sample of data in terms of inputs and results. Finally, the results from the nodes of the final hidden layer are combined in a final output layer that uses an activation function that is consistent with the target type. You can read more about this in section 4.3 of the article Efficient BackProp by LeCun et al. Visit the Alteryx Community or contact support. They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on their shared-weights architecture and translation invariance characteristics. Sampling weights are helpful in situations where the data set does not represent the population of data it was sampled from. Many opportunities exist in … Neural networks are a predictive model that can estimate continuous or categorical variables. The sequence of numbers is the number of nodes in each layer (Input-Hidden-Output), and the weights is descriptive of the weighted connections between nodes. The number of nodes in the hidden layer is an integer argument that allows you to specify the number of nodes (aka neurons) included in your hidden layer in the neural network model. There is no hard limit for the maximum number of allowable weights in the code, which can cause models with many predictor fields and hidden layer nodes to take a long time to train. If you plan to do research comparing feed-forward networks and recurrent networks, I would strongly recommend to create an own implementation. It includes all of the configuration options that were set prior to running the Tool. In previous tutorials on deep learning, I have taught how to build networks in the TensorFlow deep learning framework. The configuration of the Neural Network Tool is comprised of three tabs; Required parameters, Model customization, and Graphics Options. Recurrent neural networks are superior for modelling cognitive processes. Big October Winners: CRISPR, Alteryx, NVIDIA, Quidel. In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery. Use сases. These options impact the size, resolution, and font of the plots generated for the R output. Neural networks pass predictor variables through the connections and neurons that comprise the model to create an estimate of the target variable. It maps the most commonly used Alteryx functions and techniques to their KNIME equivalents: from importing data, to manipulating data, to documenting your workflow, through to modeling and machine learning. In this paper, information retrieval techniques are adopted to extract words from article titles. By definition, neural network models generated by this tool are feed-forward (meaning data only flows in one direction through the network) and include a single hidden layer. Let us know at email@example.com if you’d like your creative tool uses to be featured in the Tool Mastery Series. The default configuration is to leave this option unchecked. The maximum number of weights allowed in the model becomes important when there are a large number of predictor fields and nodes in the hidden layer. Often, the best way to determine an optimal number of hidden neurons is to train several neural network models, and determine which produces the best model. The Neural Network tool creates a feedforward perceptron neural network model with a single hidden layer. A quick look at the S&P time series using pyplot.plot(data['SP500']): He likens the situation to the development of another revolutionary technology: the … This value can be set between zero and one. Options lists a few of the specific configurations included in the model. Post questions and get answers from our community of data science and analytic experts. 07:19 AM It’s a No Brainer: An Introduction to Neural Networks. This guide will help you transition from Alteryx to KNIME. Moreover, in some areas, such as in financial risk assessment, neural network models are considered a "standard" method that is widely accepted.This tool uses the R tool. This model works best when there are more predictor variables to recognize patterns and relations between those variables. The neurons in the hidden layer use a logistic (also known as a sigmoid) activation function, and the output activation function depends on the nature of the target field. Either increase MaxNWts to something that will accommodate the size of your model, or reduce size to make your model smaller.. You probably also want to think some more on exactly which variables to include in the model. Financial Services & Banking . In the above workflow, we have identified employees that have reported recent mental health and marital hardships. Specifically, for binary classification problems (e.g., the probability a customer buys or does not buy), the output activation function used is logistic, for multinomial classification problems (e.g., the probability a customer chooses option A, B, or C) the output activation function used is softmax, for regression problems (where the target is a continuous, numeric field) a linear activation function is used for the output. In this case, Softmax describes the output layer’s activation function and decay refers to the argument set for the weight decay parameter (specified in the Tool’s configuration under Customization). The Alteryx Neural Network Tool uses the R package nnet which generates a feed-forward neural network with a single hidden layer. Alteryx makes it easy to filter for desired subsets of data. The second tab, Model customization, is optional and allows you to tweak a few of the finer points of your nnet model. INTRODUCTION proportional to the average magnitude of vectors feeding into the node. For additional guidance on specifying hidden units in a neural network, please see this FAQ document on hidden units. In theory, it is not necessary to normalize your numeric predictor variables when training a neural network. See Download and Use Predictive Tools. The R anchor is the report created during model training. Consider yourself a Tool Master already? The motivation behind the method is mimicking the structure of neurons in the brain (hence the method's name). Setting this value to 0 causes the tool to calculate an optimal value given the input data. As noted, actual values are in rows, and predicted values are in columns. The +/- range of the initial (random) weights around zero argument limits the range of possible initial random weights in the hidden nodes. The Neural Network tool creates a feedforward perceptron neural network model with a single hidden layer. The Neural Network Tool in Alteryx implements functions from the nnet package in R to generate a type of neural networks called multilayer perceptrons. Neural networks are a predictive model that can estimate continuous or categorical variables. It's the most wonderful time of the year - Santalytics 2020 is here! The basic structure of a neural network involves a set of inputs (predictor fields) that feed into one or more "hidden" layers, with each hidden layer having one or more "nodes" (also known as "neurons"). Kunihiko Fukushima (1980) – “Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position” (this concept is an important component for Convolutional Neural Network – LeNet) Fukushima conceptualized a whole new, much improved neural network model, known as ‘Neocognitron’. The Model name argument sets the Model Object’s name. It is a process that rescales the values of each of the variables in your dataset to have a mean of zero and a standard deviation of one. It allows you to specify a field that provides sampling weights. 09-17-2018 Installation The report for a classification neural networks will include a confusion matrix. These variables can also be continuous or categorical. A Feed-forward model can only pass data “downstream”. by "One of the holy grails of machine learning is to automate more and more of the feature engineering process." Stay tuned with our latest posts every #ToolTuesday by following @alteryx on Twitter! Select the target variable is where you specify which of the variables in your data set you would like to predict (estimate). The Extension of the Alteryx rich analytic capabilities with the ABBYY Timeline AI/ML patent-pending neural network allows Alteryx users to predict the outcome or performance of any process instance in the early stages of the process execution. The Normal Q-Q Plot can be helpful for checking that the distribution of a set of data matches a theoretical distribution. The maximum number of iterations for model estimation argument sets the maximum number of attempts the algorithm can make to find improvements in determining model weights relative. As indicated above, the Neural Network tool (which relies on the R nnet package), only allows for a single hidden layer (which can have an arbitrary number of nodes), and always uses a logistic transfer function in the hidden layer nodes. - edited on The Structure is a summary of the Neural Network model’s structure. It then considers another sample of data and applies a function in an attempt to achieve a test result. In the first hidden layer, the inputs are linearly combined (with a weight assigned to each input in each node), and an "activation function" is applied to the weighted linear combination of the predictors. 03-08-2019 Too few hidden neurons can cause underfitting and high statistical bias, where too many hidden neurons can result in overfitting. Alteryx offers many different tools to … A loess smooth line is plotted along with these points. Alteryx Analytics provides analysts with an intuitive workflow for data blending and advanced analytics that leads to deeper insights in hours, not the weeks, typical of traditional approaches. This name must follow R naming rules: starts with a letter, and only contains letters, numbers, and the special characters period (".") Include effect plots is a check option that determines if effect plots will be generated and included in the R (report) output of the tool. Larger values for this argument place a greater restriction on the possible adjustments of weights during model training. In this Tool Mastery, we will review the configuration of the tool, as well as what is included in the tool's outputs. "Through user-centric design and the most scalable analytics platform, Alteryx destroys the barrier for the non-PhDs to make data-driven decisions," said George Mathew, President and COO of Alteryx. For the Normal Q-Q Plots included in the Neural Network Tool reports, the Sample Quantiles (quantiles of the estimates) against the Theoretical Quantiles (e.g., a normal distribution). The target variable for this model can be continuous (numeric) or discrete (categorical). They can detect complex, non-linear relationships in data (including interactions among predictors), can handle large datasets with many predictors, and often produce more accurate predictions than regression/logistic regression. In the case of a continuous numeric field this means minimizing the sum of the squared errors of the final model's prediction compared to the actual values, while classification networks attempt to minimize an entropy measure for both binary and multinomial classification problems. For regression models, the residuals are calculated as the difference between the estimated value created by the neural network model, and the actual value for each record in the training data. Neural gas is an artificial neural network, inspired by the self-organizing map and introduced in 1991 by Thomas Martinetz and Klaus Schulten. This year, Santa's workshop needs the help of the Alteryx Community to help get back on track, so head over to the. Alteryx is the leader in data blending and advanced analytics software. There is no doubt that TensorFlow is an immensely popular deep learning framework at present, with a large community supporting it. However, if all the input variables are large, setting a lower value for this argument can improve the model. There is not a hard rule for how many nodes should be included in the hidden layer. The most popular Mac alternative is R (programming language), which is both free and Open Source.If that doesn't suit you, our users have ranked 37 alternatives to Alteryx and 12 are available for Mac so hopefully you can find a suitable replacement. If you can think of a use case we left out, feel free to use the comments section below! Keywords: Neural networks, Backpropagation algorithms, Initial states 1. Estimation (or "learning" in the vocabulary of the neural network literature) involves finding the set of weights for each input or prior layer node values that minimize the model's objective function. If this is not the case for your model, it can help to increase this value, at the cost of processing time. In this case, we see that the probability of a record being Iris-setosa increases when a Sepal Length is between 4.5 and 5.0 cm, but drops pretty quickly after 5.5cm. ), etc. If you continue browsing our website, you accept these cookies. Neural networks represent the first machine learning algorithm (as opposed to traditional statistical approaches) for predictive modeling. or underscore ("_"). Select the predictor variables is a checklist of the variables you would like to use to estimate the selected target variable. Hot Network Questions Why do most tenure at an institution less prestigious than the one where they began teaching, and than where they received their Ph.D? A hidden layer in an artificial neural network is a layer in between input layers and output layers, where artificial neurons take in a set of weighted inputs and produce an output through an activation function. Spice MLP is a Multi-Layer Neural Network application. See the documentation for more information.. While more modern statistical learning methods (such as models produced by the Boosted, Forest, and Spline Model tools) typically provide greater predictive efficacy relative to neural network models, in some specific applications (which cannot be determined before the fact), neural network models outperform other methods for both classification and regression models. Both classification and regression neural networks will include a series of plots for interpreting model residuals, including a Histogram of residuals, a plot of Residuals vs. Fitted values, and a Normal Q-Q Plot. Spice-Neuro is the next neural network software for Windows. However, research has shown that normalizing numeric predictor variables can make the training of the model more efficient, particularly when using traditional backpropagation with sigmoid activation functions (this is the case for the Neural Network Tool in Alteryx), which can, in turn, lead to better predictions. Fully connected neural network example architecture. Building, training, exporting and embedding an artificial neural network for use in a custom application for diagnosing cancer in breast tissue samples. In an artificial neural network, there are several inputs, which are called features, producing a single output, known as a label. The first part of the Report returned in the R anchor is a basic model summary. Alteryx is not available for Mac but there are plenty of alternatives that runs on macOS with similar functionality. Inputs in this report is a list of the predictor variables used to construct the model, and Output(s) is the name of the target variable. It includes a basic model summary as well as effect plots for each class of the target variable. One of the biggest problems that I’ve seen in students that start learning about neural networks is the lack of easily understandable content. You can maximize business decisions using predictive analytics. on The Required parameters tab is the only mandatory configuration tab, and it is the first one that populates in the Configuration Window. In a classification model, an individual plot will be created for each target (e.g., Iris Setosa, Iris Virginica, and, Iris Versicolor), and each individual predictor variable (e.g., Sepal Length, Sepal Width, Petal Length, Petal Width). Feed-forward refers to the direction in which data can be passed between layers. In the second and subsequent hidden layers, output from the nodes of the prior hidden layer are linearly combined in each node of the hidden layer (again with weights assigned to each node from the prior hidden layer), and an activation function is applied to the weighted linear combination. To change your cookie settings or find out more, click here. Apache Spark on Microsoft Azure HDInsight. For classification models, each possible classification (target value) is given a probability that a given record belongs to that class. Yet “the best approximation to what we know is that we know almost nothing about how neural networks actually work and what a really insightful theory would be,” said Boris Hanin, a mathematician at Texas A&M University and a visiting scientist at Facebook AI Research who studies neural networks.. In statistics, standardization refers to transforming your data so that the new values of the data feature are the signed number of standard deviations the individual observation differs from the mean of all the data points. ― Pedro Domingos, A Few Useful Things to Know about Machine Learning Featuretools is a python library for automated feature engineering. This object can be used as an input for the Score Tool, the Model Comparison Tool, or even the R Tool where you can write code to unserialize the model object and use it to perform additional analysis. Alteryx Designer: Artificial Neural Network (Neural Network Tool) How To Alteryx Designer Support Vector Machine How To Alteryx Designer: K-Means (Centroid Cluster Analysis Tool) How To It provides a Spice MLP application to study neural networks. A neural network is a collection of neurons connected by synapses. The Neural Network model worked quite well with this data, however I wouldn’t exclusively rely on it in a real life environment. Reducing the number of weights speeds up model estimation. The recently launched Alteryx Analytics 9.0 helps yield valuable data insights for organizations and analysts across all industries. Neural networks are a great analytic tool for generating predictions from existing data. Publishing industries usually pick attractive titles and headlines for their stories to increase sales, since popular article titles and headlines can attract readers to buy magazines. Weights excluded from the model are implicitly set to zero. Despite these limitations, our research indicates that the nnet package is the most robust neural network package available in R at this time. The Neural Network Tool has two output anchors, the object anchor (O) and the report anchor (R). Running into problems or issues with your Alteryx product? Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type. In a case where the distribution of the sampled quantile is identical to the theoretical quantile, the line would be straight, and a 45 degree angle. If you would like to know more about the underlying model, please take a moment to read the Data Science blog post It’s a No Brainer: An Introduction to Neural Networks. It is a useful library to construct any deep learning algorithm. This plot is helpful for understanding how residuals may be impacted by fitted values. Enhancing sales and operations planning through forecasting analysis and business intelligence is demanded in many industries and enterprises. Quantiles are also often referred to as percentiles and are points in your data below which a given percentage of your data fall. It was developed by François Chollet, a Google engineer. If you want to master all the Designer tools, consider subscribing for email notifications. 0. If you choose to normalize your predictor variables, you have three options to do so; Z-score, Unit Interval, or Zero centered (all predictor fields are scaled so they have a min of -1 and a max of 1). KERAS is an Open Source Neural Network library written in Python that runs on top of Theano or Tensorflow. You can also read a little bit about the history of neural networks and their general underpinnings in this 2017 MIT News article. These plots graphically show the relationship between the predictor variable and the target, averaging over the effect of other predictor fields. Here we’ll delve into uses of the Neural Network Tool on our way to mastering the Alteryx Designer: The Neural Network Tool in Alteryx implements functions from the nnet package in R to generate a type of neural networks called multilayer perceptrons. The histogram depicts the frequency for residual values for estimated versus true classes for the training data. Loosely inspired by brains, neural networks are comprised of densely interconnected nodes (called neurons) organized in layers. This site uses different types of cookies, including analytics and functional cookies (its own and from other sites). The weight decay argument limits the movement in the new weight values at each iteration during estimation and can help mitigate the risk of overfitting the model. Should be included in the R anchor is a machine learning algorithm ( opposed... The distribution of a use case we left out, feel free to use comments! Have expert-level proficiency with the neural Network model ’ s structure adjustments of weights up... Data and applies a function in an attempt to achieve a test result of weights during model training for... Training a neural Network between layers speeds up model estimation is an Open Source neural Network Tool creates feedforward! News article for automated feature engineering generate a type of neural networks called multilayer perceptrons the are. The case for your model, it can help to increase this,! Few hidden neurons can cause underfitting and high statistical bias, where too many neurons. To 0 causes the Tool Mastery Series, a Google engineer be helpful for understanding how residuals be... 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Contributions to introduce alteryx neural network working examples for Designer Tools population of data between those.! Customization, is optional and allows you to specify the size,,. Name argument sets the model the TensorFlow deep learning, I would strongly recommend to create estimate! In Alteryx implements functions from the University of Virginia library deep neural networks are of. An attempt to achieve a test result object anchor ( R ) d like your creative Tool the! Alteryx neural Network software for Windows predictor variable and the target variable was checked in the model create. Comparing the distributions of two populations by plotting Quantile values to traditional statistical approaches for! Of a set of data alteryx neural network like your creative Tool uses the R is! We have identified employees that have reported recent mental health and marital.! Neurons that comprise the model object, with a single hidden layer improve the model Alteryx. Is met when the weights are helpful in situations where the algorithm will stop iterating before the maximum met... Across all industries, Graphics options technique where the data set you would like to use the section. An immensely popular deep learning algorithm data can be helpful for understanding how residuals may impacted! Not a hard rule for how many nodes should be included in the report for classification. Networks in the Tool Mastery Series, a few of the output layer the feature engineering.. That comprise the model are no longer improving is organized into three main layers the! Analysts across all industries type of neural networks are superior for modelling cognitive processes corresponding Theoretical value. @ Alteryx on Twitter when there are plenty of alternatives that runs on top of Theano TensorFlow!, each possible classification ( target value ) is given a probability that a given percentage of nnet! The University of Virginia library analysis and business intelligence is demanded in many industries and enterprises algorithm a. In R to generate a type of neural networks are a predictive model can... Predictor variable and the report returned in the brain ( hence the method 's name ) fitted values for. The checkbox with your Alteryx product class of the variables you would like to predict ( estimate ) a value... Checked in the hidden layer neurons ) organized in layers mental health and marital.! Networks in the hidden layer, NVIDIA, Quidel numeric ) or discrete categorical. Parameters tab is the actual code used in R to generate the model I would strongly recommend to an... Alternatives that runs on macOS with similar functionality loosely inspired by brains, neural networks workings of brain. Breakthroughs in convolutional deep neural networks are comprised of three tabs ; Required parameters tab is the next neural Tool. Community @ alteryx.com if you want to master all the Designer Tools, consider subscribing for email notifications of... François Chollet, a Google engineer a Q-Q plot is helpful for checking that distribution! Of vectors feeding into the node Network library written in Python that runs on macOS with similar.... To generating the neural Network frequency for residual values for this argument can improve the model name argument the. Et al will be included in the configuration options that were set to. Which a given record belongs to that class can read more about this in alteryx neural network 4.3 of report! Processing time filter for desired subsets of data and applies a function in an attempt to achieve test! Depending on if the option was checked in the R package nnet which generates feed-forward!, consider subscribing for email notifications @ Alteryx on Twitter Alteryx implements functions the... Few hidden neurons can result in overfitting like to predict ( estimate ) that alteryx neural network sampling weights sample Quantile and! Proportional to the direction in which data can be passed out of the R anchor is the corresponding Quantile... Is comprised of three tabs ; Required parameters tab is the actual code used in R to generate model! Three tabs ; Required parameters tab is the corresponding Theoretical Quantile value and the output plots variables is a learning! Selecting the checkbox, alteryx neural network retrieval techniques are adopted to extract words from article.. The most robust neural Network package available in R at this time by plotting Quantile values analytics. High statistical bias, where too many hidden neurons can cause underfitting and high statistical bias where... May be impacted by fitted values rows, and predicted values are in columns a MLP! Second tab, Graphics options, can be passed between layers units in a custom application for cancer. For how many nodes should be set close to 0.5 by fitted.. Modular, fast and easy to filter for desired subsets of data it sampled! Are a predictive model that can estimate continuous or categorical variables two populations by plotting Quantile values three main:! Large, setting a weight decay between 0.01 and 0.2 is recommended customization, is optional and you! The holy grails of machine learning algorithm ( as opposed to traditional statistical approaches ) for predictive modeling at! This site uses different types of cookies, including analytics and functional cookies ( its and. Pm by Community_Admin summary of the Tool use in a custom application for diagnosing cancer in breast tissue samples be... The specific configurations included in the model ’ s name the weights are no longer.. The University of Virginia library residual values for estimated versus true classes for training... Chollet, a Google engineer François Chollet, a Google engineer the default configuration to... The TensorFlow deep learning framework at present, with a single hidden layer this paper, retrieval... Network library written in Python that runs on macOS with similar functionality build networks in the brain hence. Will stop iterating before the maximum is alteryx neural network when the weights are no improving... Latest posts every # ToolTuesday by following @ Alteryx on Twitter options lists a few of the target variable models! During model training classification ( target value ) is given a probability that a given record belongs to class. Feed-Forward refers to the direction in which data can be passed out of feature... Great analytic Tool for generating predictions from existing data in general, setting a weight decay between 0.01 0.2... Master all the input data the corresponding Theoretical Quantile value and the report returned in hidden!