A00-240 SAS Statistical Business Analysis SAS9: Regression and Model dumps with braindumps made up good pass marks

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Exam Code: A00-240 Practice exam 2022 by Killexams.com team
A00-240 SAS Statistical Business Analysis SAS9: Regression and Model

This exam is administered by SAS and Pearson VUE.
60 scored multiple-choice and short-answer questions.
(Must achieve score of 68 percent correct to pass)
In addition to the 60 scored items, there may be up to five unscored items.
Two hours to complete exam.
Use exam ID A00-240; required when registering with Pearson VUE.

ANOVA - 10%
Verify the assumptions of ANOVA
Analyze differences between population means using the GLM and TTEST procedures
Perform ANOVA post hoc test to evaluate treatment effect
Detect and analyze interactions between factors

Linear Regression - 20%
Fit a multiple linear regression model using the REG and GLM procedures
Analyze the output of the REG, PLM, and GLM procedures for multiple linear regression models
Use the REG or GLMSELECT procedure to perform model selection
Assess the validity of a given regression model through the use of diagnostic and residual analysis

Logistic Regression - 25%
Perform logistic regression with the LOGISTIC procedure
Optimize model performance through input selection
Interpret the output of the LOGISTIC procedure
Score new data sets using the LOGISTIC and PLM procedures

Prepare Inputs for Predictive Model Performance - 20%
Identify the potential challenges when preparing input data for a model
Use the DATA step to manipulate data with loops, arrays, conditional statements and functions
Improve the predictive power of categorical inputs
Screen variables for irrelevance and non-linear association using the CORR procedure
Screen variables for non-linearity using empirical logit plots

Measure Model Performance - 25%
Apply the principles of honest assessment to model performance measurement
Assess classifier performance using the confusion matrix
Model selection and validation using training and validation data
Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection
Establish effective decision cut-off values for scoring

Verify the assumptions of ANOVA
 Explain the central limit theorem and when it must be applied
 Examine the distribution of continuous variables (histogram, box -whisker, Q-Q plots)
 Describe the effect of skewness on the normal distribution
 Define H0, H1, Type I/II error, statistical power, p-value
 Describe the effect of sample size on p-value and power
 Interpret the results of hypothesis testing
 Interpret histograms and normal probability charts
 Draw conclusions about your data from histogram, box-whisker, and Q-Q plots
 Identify the kinds of problems may be present in the data: (biased sample, outliers, extreme values)
 For a given experiment, verify that the observations are independent
 For a given experiment, verify the errors are normally distributed
 Use the UNIVARIATE procedure to examine residuals
 For a given experiment, verify all groups have equal response variance
 Use the HOVTEST option of MEANS statement in PROC GLM to asses response variance

Analyze differences between population means using the GLM and TTEST procedures
 Use the GLM Procedure to perform ANOVA
o CLASS statement
o MODEL statement
o MEANS statement
o OUTPUT statement
 Evaluate the null hypothesis using the output of the GLM procedure
 Interpret the statistical output of the GLM procedure (variance derived from MSE, Fvalue, p-value R**2, Levene's test)
 Interpret the graphical output of the GLM procedure
 Use the TTEST Procedure to compare means Perform ANOVA post hoc test to evaluate treatment effect

Use the LSMEANS statement in the GLM or PLM procedure to perform pairwise comparisons
 Use PDIFF option of LSMEANS statement
 Use ADJUST option of the LSMEANS statement (TUKEY and DUNNETT)
 Interpret diffograms to evaluate pairwise comparisons
 Interpret control plots to evaluate pairwise comparisons
 Compare/Contrast use of pairwise T-Tests, Tukey and Dunnett comparison methods Detect and analyze interactions between factors
 Use the GLM procedure to produce reports that will help determine the significance of the interaction between factors. MODEL statement
 LSMEANS with SLICE=option (Also using PROC PLM)
 ODS SELECT
 Interpret the output of the GLM procedure to identify interaction between factors:
 p-value
 F Value
 R Squared
 TYPE I SS
 TYPE III SS

Linear Regression - 20%

Fit a multiple linear regression model using the REG and GLM procedures
 Use the REG procedure to fit a multiple linear regression model
 Use the GLM procedure to fit a multiple linear regression model

Analyze the output of the REG, PLM, and GLM procedures for multiple linear regression models
 Interpret REG or GLM procedure output for a multiple linear regression model:
 convert models to algebraic expressions
 Convert models to algebraic expressions
 Identify missing degrees of freedom
 Identify variance due to model/error, and total variance
 Calculate a missing F value
 Identify variable with largest impact to model
 For output from two models, identify which model is better
 Identify how much of the variation in the dependent variable is explained by the model
 Conclusions that can be drawn from REG, GLM, or PLM output: (about H0, model quality, graphics)
Use the REG or GLMSELECT procedure to perform model selection

Use the SELECTION option of the model statement in the GLMSELECT procedure
 Compare the differentmodel selection methods (STEPWISE, FORWARD, BACKWARD)
 Enable ODS graphics to display graphs from the REG or GLMSELECT procedure
 Identify best models by examining the graphical output (fit criterion from the REG or GLMSELECT procedure)
 Assign names to models in the REG procedure (multiple model statements)
Assess the validity of a given regression model through the use of diagnostic and residual analysis
 Explain the assumptions for linear regression
 From a set of residuals plots, asses which assumption about the error terms has been violated
 Use REG procedure MODEL statement options to identify influential observations (Student Residuals, Cook's D, DFFITS, DFBETAS)
 Explain options for handling influential observations
 Identify collinearity problems by examining REG procedure output
 Use MODEL statement options to diagnose collinearity problems (VIF, COLLIN, COLLINOINT)

Logistic Regression - 25%
Perform logistic regression with the LOGISTIC procedure
 Identify experiments that require analysis via logistic regression
 Identify logistic regression assumptions
 logistic regression concepts (log odds, logit transformation, sigmoidal relationship between p and X)
 Use the LOGISTIC procedure to fit a binary logistic regression model (MODEL and CLASS statements)

Optimize model performance through input selection
 Use the LOGISTIC procedure to fit a multiple logistic regression model
 LOGISTIC procedure SELECTION=SCORE option
 Perform Model Selection (STEPWISE, FORWARD, BACKWARD) within the LOGISTIC procedure

Interpret the output of the LOGISTIC procedure
 Interpret the output from the LOGISTIC procedure for binary logistic regression models: Model Convergence section
 Testing Global Null Hypothesis table
 Type 3 Analysis of Effects table
 Analysis of Maximum Likelihood Estimates table

Association of Predicted Probabilities and Observed Responses
Score new data sets using the LOGISTIC and PLM procedures
 Use the SCORE statement in the PLM procedure to score new cases
 Use the CODE statement in PROC LOGISTIC to score new data
 Describe when you would use the SCORE statement vs the CODE statement in PROC LOGISTIC
 Use the INMODEL/OUTMODEL options in PROC LOGISTIC
 Explain how to score new data when you have developed a model from a biased sample
Prepare Inputs for Predictive Model

Performance - 20%
Identify the potential challenges when preparing input data for a model
 Identify problems that missing values can cause in creating predictive models and scoring new data sets
 Identify limitations of Complete Case Analysis
 Explain problems caused by categorical variables with numerous levels
 Discuss the problem of redundant variables
 Discuss the problem of irrelevant and redundant variables
 Discuss the non-linearities and the problems they create in predictive models
 Discuss outliers and the problems they create in predictive models
 Describe quasi-complete separation
 Discuss the effect of interactions
 Determine when it is necessary to oversample data

Use the DATA step to manipulate data with loops, arrays, conditional statements and functions
 Use ARRAYs to create missing indicators
 Use ARRAYS, LOOP, IF, and explicit OUTPUT statements

Improve the predictive power of categorical inputs
 Reduce the number of levels of a categorical variable
 Explain thresholding
 Explain Greenacre's method
 Cluster the levels of a categorical variable via Greenacre's method using the CLUSTER procedure
o METHOD=WARD option
o FREQ, VAR, ID statement

Use of ODS output to create an output data set
 Convert categorical variables to continuous using smooth weight of evidence

Screen variables for irrelevance and non-linear association using the CORR procedure
 Explain how Hoeffding's D and Spearman statistics can be used to find irrelevant variables and non-linear associations
 Produce Spearman and Hoeffding's D statistic using the CORR procedure (VAR, WITH statement)
 Interpret a scatter plot of Hoeffding's D and Spearman statistic to identify irrelevant variables and non-linear associations Screen variables for non-linearity using empirical logit plots
 Use the RANK procedure to bin continuous input variables (GROUPS=, OUT= option; VAR, RANK statements)
 Interpret RANK procedure output
 Use the MEANS procedure to calculate the sum and means for the target cases and total events (NWAY option; CLASS, VAR, OUTPUT statements)
 Create empirical logit plots with the SGPLOT procedure
 Interpret empirical logit plots

Measure Model Performance - 25%
Apply the principles of honest assessment to model performance measurement
 Explain techniques to honestly assess classifier performance
 Explain overfitting
 Explain differences between validation and test data
 Identify the impact of performing data preparation before data is split Assess classifier performance using the confusion matrix
 Explain the confusion matrix
 Define: Accuracy, Error Rate, Sensitivity, Specificity, PV+, PV-
 Explain the effect of oversampling on the confusion matrix
 Adjust the confusion matrix for oversampling

Model selection and validation using training and validation data
 Divide data into training and validation data sets using the SURVEYSELECT procedure
 Discuss the subset selection methods available in PROC LOGISTIC
 Discuss methods to determine interactions (forward selection, with bar and @ notation)

Create interaction plot with the results from PROC LOGISTIC
 Select the model with fit statistics (BIC, AIC, KS, Brier score)
Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection
 Explain and interpret charts (ROC, Lift, Gains)
 Create a ROC curve (OUTROC option of the SCORE statement in the LOGISTIC procedure)
 Use the ROC and ROCCONTRAST statements to create an overlay plot of ROC curves for two or more models
 Explain the concept of depth as it relates to the gains chart

Establish effective decision cut-off values for scoring
 Illustrate a decision rule that maximizes the expected profit
 Explain the profit matrix and how to use it to estimate the profit per scored customer
 Calculate decision cutoffs using Bayes rule, given a profit matrix
 Determine optimum cutoff values from profit plots
 Given a profit matrix, and model results, determine the model with the highest average profit

SAS Statistical Business Analysis SAS9: Regression and Model
SASInstitute Statistical learn
Killexams : SASInstitute Statistical learn - BingNews https://killexams.com/pass4sure/exam-detail/A00-240 Search results Killexams : SASInstitute Statistical learn - BingNews https://killexams.com/pass4sure/exam-detail/A00-240 https://killexams.com/exam_list/SASInstitute Killexams : SAS puts ML and analytics suite on Azure Marketplace © Provided by The Register

50-year-old software project provides a button the boss can click

Analytics stalwart SAS is making its cloud-based Viya platform available in the Microsoft Azure Marketplace in the hopes users will be tempted by a clickable, pay-as-you-go option for its ML, data management, and analytics tools.…

The Statistical Analysis System (SAS) software was first developed at North Carolina State University for analysis of agricultural data. The company that grew out of the project, the SAS Institute, was founded in 1976, and went on to benefit from growing interest in analytics across a broad set of industries during the client-server era.

Back in 2016, Forrester noted that SAS had been slow to embrace features such as public cloud deployment; open, API-enabled architectures; and support for other programming languages.

With more marketplace news to follow, SAS will be expecting to put the laggard tag behind it.

In May, SAS announced support for Python in its proprietary analytics studio. It also announced an on-premises version of its "cloud-native" AI, analytics, and data management platform Viya. It expects to be able to deploy in Kubernetes on-premises in Q3 of this year. The fully containerized service was released on Microsoft Azure, AWS, Google Cloud, and Red Hat OpenShift last year.

Bryan Harris, SAS CTO, told The Register Viya was already available in its container registry to obtain and deploy on AWS, Azure or Google Cloud. But the move to offer the software via Azure's marketplace would speed up deployments, he said.

"What's different about this is this it is out of the public marketplace. Through the cloud console subscription within Microsoft Azure, users can, with the click of a button, be up and running within an hour," he said.

The offer promises the "full analytics lifecycle" including data management, exploration, data visualization, and data reporting. It also offers the SAS Model Studio, which allows users to build machine learning model pipelines and model tournaments, and Model Manager, which registers those models and deploys them into the organization to score their performance, Harris said.

But keen observers of the cloud analytics and machine learning market might have noticed Microsoft sells its own Machine Learning Studio.

SAS promises users can pick and choose the elements they want from Microsoft and the tools they want from SAS. For example, role-based access to data can be set with the Microsoft environment and carried over to SAS. The same applies to data management, visualization, and model tournaments, Harris said.

"When you log into Viya you can see those assets in the existing tenant inside Azure, as if it's seamless to them. So that was one of the big efforts for the engineering team over the last few years [of the Microsoft] partnership," he said.

SAS also integrates with PowerPoint and Excel, with Word integration soon to follow, Harris added.

"We can take outcomes from SAS Viya and then drive them straight into the office products so that customers can communicate like they do naturally with their office products around the business."

SAS started with the Azure Marketplace because of its Microsoft partnership but aims to announce similar moves for AWS and Google cloud, Harris said. ®

Tue, 27 Sep 2022 07:04:45 -0500 en-US text/html https://www.msn.com/en-us/money/technologyinvesting/sas-puts-ml-and-analytics-suite-on-azure-marketplace/ar-AA12jI1M
Killexams : SAS launches first cloud app with pay-as-you-go pricing

SAS Institute Inc. is making one of its top software applications available on a pay-as-you-go basis for the first time with today’s announcement that its Viya analytics platform is now available on the Microsoft Azure Marketplace.

The announcement represents an evolution in thinking at the 46-year-old maker of statistics and analytics software. Although the company has listed applications on various public cloud platforms for some time, this is the first that users can provision immediately without contacting the company to negotiate a license fee or make a monthly payment. “This is an opportunity to make analytics to everyone, everywhere in a flexible way with self-service,” said Alice McClure, director of artificial intelligence and analytics at SAS.

Like many traditional enterprise software vendors, SAS has wrestled with the transition to making its products available on demand without charging up-front and often expensive license fees. Enterprise software has been “built for a high-touch sales process,” said SAS Chief Information Officer Jay Upchurch. “The cloud marketplace for many of those companies is intimidating. This gets enterprise capability delivered to the individual. That’s very different from what we have traditionally done.”

SAS is a private company that self-reported revenue of $3.2 billion in 2021 as it prepares for an anticipated 2024 public offering. It claims 88 of the 2021 Fortune 100 or their affiliates are customers.

Cloud mission

The move is intended to telegraph that “we are on a cloud mission,” Upchurch said. Viya has been substantially rewritten using cloud native constructs like software containers and microservices. “We will continue to deliver more offers into this space in both the traditional enterprise and pay-as-you-go model,” he said. While Microsoft Corp. is the company’s principal cloud partner, SAS also expects to offer its products on other infrastructure-as-a-service platforms, McClure said.

Unlike many of SAS’ products, which are aimed at data scientists and use cases heavy on statistics, Viya is also targeted at business analysts and end users. Its capabilities include data analytics, visual statistics, data mining, machine learning model development, integrated workflows, information governance and econometrics. It supports both programming and low- or no-code options in a single visual interface and can be used with SAS’ proprietary development language as well as Python and R.

“SAS users who may not be an Azure subscriber currently are looking for flexible ways to get started,” McClure said. “This is all about getting Viya in the hands of users.”

The software is priced at 55 cents per hour based on utilized virtual CPU clusters. SAS opted for a lower-cost community rather than dedicated support option. “Our SAS community is massive,” Upchurch said. “It is a wonderful place to get great information. That said, we’ll continue to support our customers as we go.”

Photo: SAS Institute

Show your support for our mission by joining our Cube Club and Cube Event Community of experts. Join the community that includes Amazon Web Services and Amazon.com CEO Andy Jassy, Dell Technologies founder and CEO Michael Dell, Intel CEO Pat Gelsinger and many more luminaries and experts.

Tue, 27 Sep 2022 03:30:00 -0500 en-US text/html https://siliconangle.com/2022/09/27/sas-launches-first-cloud-app-pay-go-pricing/ Killexams : Top Predictive Analytics Solutions

Understanding complex events in today’s business world is critical. The explosion of data analytics—and the desire for insights and knowledge—represents both an opportunity and a challenge.

At the center of this effort is predictive analytics. The ability to transform raw data into insights and make informed business decisions is crucial. Today, predictive analytics plays a role in almost every corner of the enterprise, from finance and marketing to operations and cybersecurity. It involves pulling data from legacy databases, data lakes, clouds, social media sites, point of sale terminals and IoT on the edge.

It’s critical to select a predictive analytics platform that generates actionable information. For example, financial institutions use predictive analytics to understand loan and credit card applications, and even grant a line of credit on the spot. Operations departments use predictive analytics to understand maintenance and repairs for equipment and vehicles, and marketing and sales use it to gauge interest in a new product.

Top predictive analytics platforms deliver powerful tools for ingesting and exporting data, processing it and delivering reports and visualizations that guide enterprise decision-making. They also support more advanced capabilities, such as machine learning (ML), deep learning (DL), artificial intelligence (AI) and even digital twins. Many solutions also provide robust tools for sharing visualizations and reports. 

Types of Predictive Models

Predictive models are designed to deliver insights that guide enterprise decision-making. Solutions incorporate techniques such as data mining, statistical analysis, machine learning and AI. They are typically used for optimizing marketing, sales and operations; improving profits and reducing costs; and reducing risks related to things like security and climate change.

Also see: Best Data Analytics Tools 

Four major types of predictive models exist:

Regression models

A regression model estimates the relationship between different variables to deliver information and insight about future scenarios and impacts. It is sometimes referred to as “what if” analysis.

For example, a food manufacturer might study how different ingredients impact quality and sales. A clothing manufacturer might analyze how different colors increase or decrease the likelihood of a purchase. Models can incorporate correlations (relationships) and causality (reasons).

Classification models

These predictive models place data and information in categories based on past histories and historical knowledge. The data is labeled, and an algorithm learns the correlations. The model can then be updated with new data, as it arrives. These models are commonly used for fraud detection and to identify cybersecurity attacks.

Clustering models

A cluster model assembles data into groups, based on common attributes and characteristics. It often spots hidden patterns in systems. In a factory, this might mean spotting misplaced supplies and equipment and then using the data to predict where it will be during a typical workday. In retail, a store might send out marketing materials to a specific group, based on a combination of factors such as income, occupation and distance.

Time-Series models

As the name implies, a time-series model looks at data during a given period, such as a day, month or year. Using predictive analytics, it’s possible to estimate what the trend will be in an upcoming period. It can be combined with other methods to understand underlying factors. For instance, a healthcare system might predict when the flu will peak—based on past and current time-series models.

In more advanced scenarios, predictive analytics also uses deep learning techniques that mimic the human brain through artificial neural networks. These methods may incorporate video, audio, text and other forms of unstructured data. For instance, voice recognition or facial recognition might analyze the tone or expression a person displays, and a system can then respond accordingly.

Also see: Top Business Intelligence Software 

How to Select a Predictive Analytics Platform

All major predictive analytics platforms are capable of producing valuable insights. It’s important to conduct a thorough internal review and understand what platform or platforms are the best fit for an enterprise. All predictive analytics solutions generate reports, charts, graphics and dashboards. The data must also be embedded into automation processes that are driven by other enterprise applications, such as an ERP or CRM system.

Step 1: Understand your needs and requirements

An internal evaluation should include the types of predictive analytics you need and what you want to do with the data. It should also include what type of user—business analyst or data scientist—will use the platform. This typically requires a detailed discussion with different business and technical groups to determine what types of analytics, models, insights and automations are needed and how they will be used.

Step 2: Survey the vendor landscape

The capabilities of today’s predictive analytics platforms is impressive—and companies add features and capabilities all the time. It’s critical to review your requirements and find an excellent match. This includes more than simply extracting value from your data. It’s important to review a vendor’s roadmap, its commitment to updates and security, and what quality assurance standards it has in place. Other factors include mobile support and scalability, Internet of Things (IoT) functionality, APIs to connect data with partners and others in a supply chain, and training requirements to get everyone up to speed.

Step 3: Choose a platform

Critical factors for vendor selection include support for required data formats, strong data import and export capabilities, cleansing features, templates, workflows and embedded analytics capabilities. The latter is critical because predictive analytics data is typically used across applications, websites and companies.

A credit card check, for example, must pull data from a credit bureau but also internal and other partner systems. The APIs that run the task are critical. But there are other things to focus on, including the user interface (UI) and usability (UX). This extends to visual dashboards that staff uses to view data and understand events. It’s also vital to look at licensing costs and the level of support a vendor delivers. This might include online resources and communities as well as direct support.

Top Predictive Analytics Platforms

Here are 10 of the top predictive analytics solutions:

Google Looker

Key Insight: The drag-and-drop platform is adept at generating rich visualizations and excellent dashboards. It ranks high on flexibility, with support for almost any type of desired chart or graphics. It can generate valuable data for predictive analytics by connecting to numerous other data sources, including Microsoft SQL and Excel, Snowflake, SAP HANA, Salesforce and Amazon Redshift. It also includes powerful tools for selecting parameters, filtering data, building data-driven workflows and obtaining results. While the platform is part of Google Cloud and it is optimized for use within this environment, it supports custom applications.

Pros

  • The highly flexible platform runs on Windows, Macs and Linux. It offers strong mobile device support.
  • Generates strong data-driven workflows and supports custom applications.
  • Users deliver the platform High Marks for its interface and ease of use.

Cons

  • Lacks some customization features found in other analytics solutions.

IBM SPSS Modeler

Key Insight: The predictive analytics platform is designed to put statistical data to work across a wide array of industries and use cases. It includes powerful data ingestion capabilities, ad hoc reporting, predictive analytics, hypothesis analysis, statistical and geospatial analysis and 30 base machine learning components. IBM SPSS Modeler includes a rich set of tools and features, accessible through sophisticated dashboards.

Pros 

  • Offers excellent open-source extensibility through R and Python, along with support for numerous data sources, including flat files, spreadsheets, major relational databases, IBM Planning Analytics and Hadoop.
  • Visual drag-and-drop interface speeds tasks for data analysts and data scientists.
  • Works on all major operating systems, including Windows, Linux, Unix and Mac OS X.

Cons

  • Not as user friendly as other platform analytics platforms. Typically requires training to use it effectively.

Qlik Sense

Key Insight: Qlik Sense is a cloud-native platform designed specifically for business intelligence and predictive analytics. It delivers a robust set of features and capabilities, available through dashboards and visualizations. Qlik Sense includes AI and machine learning components; embedded analytics that can be used for websites, business applications and commercial software; and strong support for mobile devices. The solution supports hundreds of data types, and a broad array of analytics use cases.

Pros

  • Offers powerful drag-and-drop interface to enable fast modeling.
  • A “Smart Search” feature aids in uncovering and connecting complex data relationships.
  • Provides rich visualizations along with highly interactive and powerful dashboard features.

Cons

  • Can be pricy, particularly with additional add-ons.

Salesforce

Key Insight:  The widely used CRM and sales automation platform includes powerful analytics and business intelligence tools, including features driven by the company’s AI-focused Einstein Analytics. It delivers insights and suggestions through specialized AI agents. A centralized Salesforce dashboard offers charts, graphs and other insights, along with robust reporting capabilities. There’s also deep integration with the Tableau analytics platform, which is owned by Salesforce.

Pros

  • Powerful features and highly customizable views of data.
  • Integrates with more than 1,000 platforms. Strong data ingestion capabilities.
  • A generally intuitive UI and strong UX make the platform powerful yet relatively easy to use.

Cons

  • No trial or free version available.

SAP Analytics Cloud

Key Insight: Formerly known as BusinessObjects for cloud, SAP Analytics Cloud can pull data from a broad array of sources and platforms. It is adept at data discovery, compilation, ad-hoc reporting and predictive analytics. It includes machine learning and AI functions that can guide data analysis and aid in modeling and planning. A dashboard offers flexible options for displaying analytics data in numerous ways.

Pros

  • Accommodates large data sets from a diverse range of sources.
  • Offers highly customizable, interactive charts and other graphics.
  • Includes powerful ML and AI components.

Cons

  • Not as visually rich as other platforms.

SAS Visual Analytics

Key Insight: The low-code cloud solution is designed to serve as a “single application for reporting, data exploration and analytics.” It imports data from numerous sources; supports rich dashboards and visualizations, with strong drill-down features; includes augmented analytics and ML; and includes robust collaboration and data sharing features. The platform also includes natural language chatbots that aid business users and other non-data scientists in content creation and management.

Pros

  • Delivers fast performance, even with large data volumes.
  • Supports a wide array of predictive analytics use cases.
  • Includes smart algorithms that accommodate predictive analytics without the need for programming skills.

Cons

  • Expensive, particularly for smaller organizations.

Tableau Desktop

Key Insight: The enormous popularity of Tableau is based on the platform’s powerful features and its ability to generate a wide range of appealing and useful charts, graphs, maps and other visualizations through highly interactive real-time dashboards. The platform offers support for numerous predictive analytics frameworks, including regression models, classification models, clustering models, time-series models. Non data scientists typically find its user interface accommodating and easy-to-learn.

Pros

  • Excellent UI and UX. Most users find it easy to generate useful analytics models and visualizations.
  • Supports flexible, scalable and highly customizable predictive analytics use cases.
  • Tight integration with Salesforce CRM. Generates excellent visualizations.

Cons

  • Sometimes pulls considerable compute resources, including CPUs and RAM. It can also perform slowly on mobile devices.

Teradata Vantage

Key Insight: Teradata Vantage delivers powerful predictive analytics capabilities, including the ability to use data from both on-premises legacy hardware sources and multicloud public frameworks, including AWS, Azure and Google Cloud. The solution also works across virtually any data source, including data lakes and data warehouses. It supports sophisticated AI and ML functionality and includes no-code and low-code drag and drop components for building models and visuals. The company is especially known for its fraud prevention analytics tools, though it offers numerous other predictive tools and capabilities.

Pros

  • Highly flexible. Powerful SQL engine supports broad, deep and complex queries.
  • Accommodates huge workloads, almost any type of query, and delivers fast and efficient data processing.
  • Generates dynamic visualizations and an easy-to-use yet powerful interface.

Cons

  • Some users complain about a lack of documentation and training materials.

TIBCO Spotfire

Key Insight: TIBCO Spotfire offers a powerful platform for performing predictive analytics. It connects to numerous data sources and includes real-time feeds that can be highly filtered and customized. The solution is designed for both business users and data scientists. It includes rich visualizations and supports customizations through R and Python.

Pros:

  • TIBCO offers a strong API management framework.
  • Delivers rich visualizations and immersive interactive visual analytics.
  • Strong support for real-time decision making across multiple industries and use cases.

Cons:

  • Customizations can be time consuming and difficult.

ThoughtSpot

Key Insight: The analytics cloud delivers highly flexible self-service predictive analytics. The query engine is designed to search on virtually any data format and understand complex table structures over billions of rows. It offers powerful search and filtering capabilities that extend to natural language queries. ThoughtSpot also provides a powerful processing engine that generates a range of visualizations, including social media intelligence. The solution includes a machine learning component that ranks the relevancy of results.

 Pros

  • The platform gets High Marks from users for its user interface and ease of use.
  • ThoughtSpot delivers a high level of flexibility, including an ability to search Snowflake, BigQuery and other cloud data warehouses in real time.
  • Natural language search reduces the need for complex SQL input.

Cons

  • Users complain that data source connectivity isn’t as robust as other analytics platforms.

Also see: Top Cloud Companies

Predictive Analytics Vendor Comparison Chart

Company Key Product Pros Cons
Google Looker Excellent drag-and-drop interface with appealing visualizations and a high level of flexibility Support is almost entirely online
IBM SPSS Modeler Excellent open-source extensibility; strong drag-and-drop functionality Not as user friendly as other analytics solutions
Qlik Qlik Sense Powerful and versatile platform with strong modeling features Some complaints about customer support
Salesforce Salesforce Highly customizable; strong integration with other platforms and data sources Expensive
SAP Analytics Cloud Supports extremely large datasets and has powerful capabilities Visualizations are not always as appealing as other platforms
SAS Visual Analytics High performance platform that supports numerous data types and visualizations May require customization
Tableau Tableau Desktop Outstanding UI and UX, with deep Salesforce/CRM integration Can drain CPUs and RAM
TIBCO Spotfire Powerful predictive analytics platform with excellent visual output Steep learning curve
Teradata Advantage Highly flexible with a powerful SQL engine; generates rich visualizations Some users complain about an aging UI.
ThoughtSpot ThoughtSpot Flexible, powerful capabilities with a strong UI and UX Visualizations sometimes lag behind competitors
Sun, 25 Sep 2022 06:33:00 -0500 en-US text/html https://www.eweek.com/cloud/predictive-analytics-solutions/
Killexams : The Learning Network No result found, try new keyword!“Make a plan first.” “If the weather permits, wear velvet.” And more. By The Learning Network A NASA spacecraft successfully smashed into a small asteroid and changed its orbit: Should we ... Thu, 13 Oct 2022 19:40:00 -0500 en text/html https://www.nytimes.com/section/learning Killexams : 3 Day In-Depth Practical Statistical Analysis for the Energy & Power Markets Course (Houston, United States - December 7-9, 2022)

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Dublin, Oct. 10, 2022 (GLOBE NEWSWIRE) -- The "In-Depth: Practical Statistical Analysis for the Energy & Power Markets" training has been added to ResearchAndMarkets.com's offering.

This course adds a third day to the popular Energy Statistical Analysis seminar to allow the time needed for a more in-depth discussion and explanation of many important topics. Additionally, this three-day course is designed as a hand-on workshop. Not only will you learn about practical energy statistical techniques and tools, but you will practice building statistical models in a workshop format.

Learn why companies continue to be exposed to significant energy and electricity related price risk, and how risk and value are properly quantified. Energy and electricity companies worldwide depend on accurate information about the risks and opportunities facing day-to-day decisions. Statistical analysis is frequently misapplied and many companies find that "a little bit of knowledge is a dangerous thing."

This comprehensive three-day program is designed to provide a solid understanding of key statistical and analytic tools used in the energy and electric power markets. Through a combination of lecture and hands-on exercises that you will complete using your own laptop, participants will learn and practice key energy applications of statistical modeling. Be armed with the tools and methods needed to properly analyze and measure data to reduce risk and increase earnings for your organization.

Who Should Attend:

Among those who will benefit from this seminar include energy and electric power executives; attorneys; government regulators; traders & trading support staff; marketing, sales, purchasing & risk management personnel; accountants & auditors; plant operators; engineers; and corporate planners. Types of companies that typically attend this program include energy producers and marketers; utilities; banks & financial houses; industrial companies; accounting, consulting & law firms; municipal utilities; government regulators, and electric generators.

What You Will Learn

Key subjects Covered:

DAY ONE:

The Basics of Deterministic vs. Probabilistic Thinking for Energy Applications

Fundamental Modeling Tools and Simulation

Application: Calculating Value at Risk (VaR)

Application: Hedging Energy Exposure

Application: Component Risk Analysis

Correlation and Regression Analysis for Maintaining the Competitive Edge

DAY TWO:

The Energy Forecasting Toolbox

DAY Three:

Introduction to Real Options Analysis

Speaker

Kenneth Skinner
VP and Chief Operating Officer
Integral Analytics

Kenneth Skinner, Ph.D. is Vice President of Risk & Evaluation Products for Integral Analytics, an analytical software and management consulting firm focused on operational, planning, and market research solutions.

Dr. Skinner has over 20 years' experience in evaluation and risk measurement, having worked as an energy consultant with PHB Hagler Bailly and Financial Times (FT) Energy, and as the Derivative Structuring Manager for the retail energy supplier Sempra Energy Solutions. He has his Ph.D. from Colorado School of Mines, in Mineral Economics, with an emphasis in Operations Research, an MBA from Regis University and his BS in Engineering from Letourneau University.

Dr. Skinner is a nationally recognized expert in economic evaluation and modeling of energy assets including energy storage, distribution and generation, efficiency and demand response, renewable energy alternatives, financial derivatives and structured contracts using net present value, econometric and statistical methods, optimization principles, and real option valuation techniques.

Dr. Skinner is currently the technology columnist for Wiley Natural Gas and Electricity Journal and is a noted speaker on energy related subjects for organizations such as AESP, IAEE, ACEEE, PLMA, IEPEC, INFORMS, Infocast, EUCI, SNL Energy and PGS Energy Training.

For more information about this training visit https://www.researchandmarkets.com/r/51w2m1

CONTACT: CONTACT: ResearchAndMarkets.com Laura Wood,Senior Press Manager press@researchandmarkets.com For E.S.T Office Hours Call 1-917-300-0470 For U.S./ CAN Toll Free Call 1-800-526-8630 For GMT Office Hours Call +353-1-416-8900
Sun, 09 Oct 2022 20:13:00 -0500 en-US text/html https://www.yahoo.com/now/3-day-depth-practical-statistical-081300245.html Killexams : E-Learning Market Revenue Statistics And Growth Insights To 2028

(MENAFN- Ameliorate Digital Consultancy)

Global e-learning market is projected to register remuneration of nearly $1 trillion by 2028. E-learning is coming to be considered a pillar for the future of education and training, rapidly replacing traditional methods due to advantages like flexibility, an abundance of resources, and accessibility. As compared to face-to-face training, e-courses are becoming more effective teaching methods ensuring high retention rates for learners.

According to the Research Institute of America, e-learning can increase retention rates by 25% to 60%, while the retention rates of in-person training are only 8% to 10%. Furthermore, student enrollments for online education have surged in wake of the COVID-19 pandemic, adding impetus to e-learning market development over accurate years.

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have become highly imperative. According to data from Business Standard, EdTech has garnered investments of around $1.1 billion in 2020, representing the highest-ever annual tally. Situations like these are encouraging educational institutions as well as enterprises to rely on electronic learning globally.

Increasing reliance on e-learning for healthcare and educational purposes

The availability of a skilled workforce has become one of the biggest challenges in the healthcare sector, which has been further exacerbated due to the COVID-19 outbreak. In this scenario, E-learning and digital media are expected to emerge as key solutions for healthcare organizations to meet the educational and training needs of the workforce.

According to Education Data 2021, 98% of educational institutions shifted a majority of their classes to online courses from April 2020. In addition, 43% of these institutions have invested in new digital learning tools and resources as of April 2020, thus strengthening the scope of the e-learning market to limit the barriers for institutions in terms of teaching and upskilling.

Rapid expansion of broadband networks in North America

Given the transformation of learning throughout the COVID-19 outbreak, the need for reliable Internet services has become more apparent across multiple regions including North America. In February 2022, the Canadian Minister of Rural Economic Development, Gudie Hutchings, announced $555,777 in funding to bring reliable, high-speed Internet to over 136 households across rural areas near Ontario, in turn asserting a positive influence on the electronic learning business. This announcement was built on the government's step toward allowing 98% of Canadians to gain access to high-speed Internet services by 2026.

Moreover, the U.S. government has also been passing federal legislation to ensure the widespread expansion of broadband networks. During the pandemic, the Consolidated Appropriations Act and CARES Act, for example, allocated $7.5 billion and $125 million, respectively, for broadband expansion. In addition, the American Rescue Plan Act also allocated $20 billion for multiple programs exclusively dedicated to broadband.

With the escalating penetration of high-speed Internet and broadband services, students and employees can seamlessly gain access to e-courses and training remotely, which could generate lucrative growth opportunities for the North America e-learning market over the years ahead.

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Partial Chapter of the Table of Content

Chapter 5 E-learning Market, By Technology

5.1 E-learning market share, by technology, 2021 & 2028

5.2 Online e-learning

5.2.1 Market estimates and forecast, 2018 – 2028

5.3 Learning Management System (LMS)

5.3.1 Market estimates and forecast, 2018 – 2028

5.4 Mobile e-learning

5.4.1 Market estimates and forecast, 2018 – 2028

5.5 Rapid e-learning

5.5.1 Market estimates and forecast, 2018 – 2028

5.6 Virtual classroom

5.6.1 Market estimates and forecast, 2018 – 2028

5.7 Others

5.7.1 Market estimates and forecast, 2018 – 2028

Chapter 6 E-learning Market, By Provider

6.1 E-learning market share, by provider, 2021 & 2028

6.2 Service

6.2.1 Market estimates and forecast, 2018 – 2028

6.3 Content

6.3.1 Market estimates and forecast, 2018 – 2028

Browse complete Table of Contents (ToC) of this research report @ 

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Wed, 21 Sep 2022 16:29:00 -0500 Date text/html https://menafn.com/1104904938/E-Learning-Market-Revenue-Statistics-And-Growth-Insights-To-2028
Killexams : Will Pandemic Learning Loss Cost $700 Billion to Fix?

A new study released on Tuesday by Kenneth Shores and Matthew Steinberg tackles the question of whether federal pandemic relief for public schools was provided in the right way and in the right amount. The study does some informed but essentially back-of-the-envelope math to estimate how much it would take to fix learning loss, and although the authors deliver a range, the number circulating in the headlines is $700 billion. Not only is that estimate far more than the $190 billion in Elementary and Secondary School Emergency Relief (ESSER) federal COVID relief funding that went to schools: It’s also more than the Census’s figure for total current expenditures for all public schools in the US in FY 2020. However, this rough figure has a host of estimation problems, and a perspective problem to boot.

The totals Shores and Steinberg provide range between $325 billion and $1.4 trillion. That enormous range—roughly 150 percent of total current public school spending—should cast some doubt on the middle $700B figure the media latched on to, and some estimation problems bear out such doubts. Shores and Steinberg’s total costs estimates are the product of three measures: 1) The duration of students’ remote instruction, 2) remote students’ learning loss and 3) the cost to recover that lost learning. All three of these measures are hard to pin down (disclosure: they use my AEI Return To Learn (R2L) data for some of their estimates) and thus result in wildly different cost estimates.

For calculating the duration of students’ remote instruction, the authors base their high-end estimates on anonymized cellphone tracking data. My technical concerns about these data’s precision aren’t hard to see. If you gauge how many students are in school based on the relative number of cellphones that show up in the building, you might get decent estimates for high schools, but not for elementary grades. High schoolers were both more likely to have cell phones and stay remote longer, so the cell data probably overestimates remote instruction.

The lower end estimates of remote instruction use my R2L data, but even that data tracks districts instructional offerings, not how many students showed up in-person when they could choose to stay remote. For these reasons, all estimates of the duration of remote instruction are uncertain.

Estimates of learning loss (from America) are reasonable to a point. Estimates from studies by Goldhaber et al and Kuhfeld et al are quite similar (and the Goldhaber paper also uses R2L data, which helps with apples to apples comparisons), and both lean towards the lower end. Nevertheless, even these estimates don’t mesh with the rapid progress already seen in some states. In Tennessee, Texas, and Mississippi, spring 2022 test scores in studying almost bounced back to pre-pandemic levels, and test scores in math saw significant progress as well. Most states are making slower but still substantial progress, which suggests that the durability of learning loss may be less than measurements from fall 2021 suggest.

Primarily, the totals the study provides hinge on how much it costs to make up a given unit of lost learning. The authors do their best to find an informed estimate of that unit cost, but that estimate is, to put it bluntly, a guess and one that doesn’t wash with the progress we have already seen in states. As mentioned above, academic recovery is already progressing to some degree without anything like the spending Shores and Steinberg suggest. That’s important, because it suggests that their unit cost is excessive and that so are their estimated totals.

Overall, these estimates have problems, but they are not the only problems here. This study, and news coverage of it, willfully ignores why remote instruction, and the learning loss it drove, was so uneven. The R2LTracker shows 2020-21’s closures differed tremendously between districts. Those closures were local policy choices and these policy choices had consequences—terrible consequences for students. The authors’ suggestion that “adapting ESSER allocations to remote learning would have been a feasible adjustment the federal government could have made in real time” would have meant that more federal relief would flow to districts’ whose excessive COVID caution overextended school closures and resulted in greater learning loss. Such a policy would have both treated that excessive caution as acceptable and shortchanged districts who reopened sooner.

A thorough discussion of what else needs to be done to make up for learning loss is worth having. Recovering from the pandemic will be schools’ primary challenge for years to come, and no doubt, it will be difficult to do with the limited data on hand. However, that discussion needs a clear-eyed look at what caused pandemic learning loss if it is to honestly deal with what should be done and spent, and by whom.

Thu, 13 Oct 2022 02:57:00 -0500 en-US text/html https://www.aei.org/education/will-pandemic-learning-loss-cost-700-billion-to-fix/
Killexams : Federal Statistical Office of Germany

Microcensus 2022

A total of roughly 810,000 people in about 370,000 randomly selected households will be surveyed between January and December 2022. This is approximately 1% of Germany's population. The data collected, which provide information about school education and studies, training and advanced training, occupation and search for work, income and living conditions, and childcare, are an important basis for planning and decision-making. More information is available in our relevant explanatory video and the press release.

Explanatory video about the microcensus

Thu, 13 Oct 2022 11:59:00 -0500 en text/html https://www.destatis.de/EN/Home/_node.html
Killexams : Statistical oversight could explain inconsistencies in nutritional research

People often wonder why one nutritional study tells them that eating too many eggs, for instance, will lead to heart disease and another tells them the opposite. The answer to this and other conflicting food studies may lie in the use of statistics, according to a report published today in the American Journal of Clinical Nutrition.

Research led by scientists at the University of Leeds and The Alan Turing Institute—The National Institute for and —reveals that the standard and most common statistical approach to studying the relationship between and can deliver misleading and meaningless results.

Lead author Georgia Tomova, a Ph.D. researcher in the University of Leeds' Institute for Data Analytics and The Alan Turing Institute, said, "These findings are relevant to everything we think we know about the effect of food on health.

"It is well known that different nutritional studies tend to find different results. One week a food is apparently harmful and the next week it is apparently good for you."

The researchers found that the widespread practice of statistically controlling, or allowing for, someone's total energy intake can lead to dramatic changes in the interpretation of the results.

Controlling for other foods eaten can then further skew the results, so that a harmful food appears beneficial or vice versa.

Ms Tomova added: "Because of the big differences between individual studies, we tend to rely on review articles to provide an average estimate of whether, and to what extent, a particular food causes a particular health condition.

"Unfortunately, because most studies have different approaches to controlling for the rest of the diet, it is likely that each study is estimating a very different quantity, making the 'average' rather meaningless."

The identified the problem by using new 'causal inference' methods, which were popularized by Judea Pearl, the author of "The Book of Why."

Senior author Dr. Peter Tennant, Associate Professor of Health Data Science in Leeds' School of Medicine explained: "When you cannot run an experiment, it is very difficult to determine whether, and to what extent, something causes something else.

"That is why people say, 'correlation does not equal causation." These new 'causal inference' methods promise to help us to identify causal effects from correlations, but in doing so they have also highlighted quite a few areas which we did not fully understand."

The authors hope that this new research will help nutritional scientists to better understand the issues with inappropriately controlling for total energy intake and overall diet and gain a clearer understanding of the effects of the diet on health.

Dr. Tennant added: "Different studies can provide different estimates for a range of reasons but we think that this one statistical issue may explain a lot of the disagreement. Fortunately, this can be easily avoided in the future."



More information: Georgia D Tomova et al, Theory and performance of substitution models for estimating relative causal effects in nutritional epidemiology, American Journal of Clinical Nutrition (2022). DOI: 10.1093/ajcn/nqac188

Citation: Statistical oversight could explain inconsistencies in nutritional research (2022, October 13) retrieved 17 October 2022 from https://medicalxpress.com/news/2022-10-statistical-oversight-inconsistencies-nutritional.html

This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.

Wed, 12 Oct 2022 20:29:00 -0500 en text/html https://medicalxpress.com/news/2022-10-statistical-oversight-inconsistencies-nutritional.html
Killexams : Beyond the statistical soundbites: why data matter

While collecting official statistics will always be a purview of state institutions, they must be governed by independent governing bodies that can ensure scientific integrity and broad oversight

While collecting official statistics will always be a purview of state institutions, they must be governed by independent governing bodies that can ensure scientific integrity and broad oversight

We seem fascinated with statistical soundbites, vacillating between the bizarre and the expedient. In October 2021, major newspapers featured stories that claimed that over 100 million Indians owned cryptocurrencies. This put Indians at the top of the global cryptocurrency such as Bitcoin, Dogecoin, etc. Strangely these stories exhibited no scepticism. 

When  Coin Crunch, a cryptocurrency news magazine, dug deeper into the source of this data, they discovered how hollow these claims were.  They traced the source to data from market research, where 2,000 to 12,000 in each country were asked to complete online surveys. Generalising from Internet survey respondents to the Indian population takes quite a stretch of the imagination.

However, these outlandish statistics are not the only ones that get uncritical attention. On the release of the factsheets from the fifth round of the National Family Health Survey (NFHS-5), conducted in 2019-20, some headlines focused on increasing Severe Acute Malnutrition (SAM) in India. Between 2015-16 and 2019-21, children who are too thin for their height, identified as suffering from severe wasting, called SAM, increased from 7.5% of the population to 7.7%, although stunting (low height for age) decreased from 38.4% to 35.5%. This slight increase in SAM would be a cause of concern since these children are most at risk for nutritional failure

Differences in methodology

However, both in the press and in the presentation of the data by researchers, differences in methodology between NFHS-4 and NFHS-5 received little attention. A paper published in the journal  Plos One by Robert Johnston and others compared NFHS-3 and NFHS-4 as well as several other nutrition surveys and found that due to diarrhoea and other diseases, children are thinner in interviews conducted during the monsoon season. Studies in other countries have made similar observations. My calculations suggest that while only 12% of the NFHS-4 surveys were conducted between July and October, 40% of the NFHS-5 surveys were conducted over these months due to pandemic-related fieldwork restructuring. When we compare SAM for children surveyed during monsoon, and outside of monsoon months, we find that for each period, the prevalence of SAM was slightly lower for NFHS-5 than for NFHS-4 (7.3% in NFHS-4 vs 7% in NFHS-5 outside the monsoon period; and 8.9% vs 8.6% during monsoon interviews). This change is only minor in magnitude, but the difference between a slight increase in malnutrition and a slight decrease changes the tone of the discourse. 

These challenges are not unique to India. For over a decade, the popular narrative about maternal mortality in the U.S. suggested that while globally maternal mortality was declining, in the U.S. between 2000 and 2014, it increased by 26%. It was not until Marion MacDorman and her colleagues at the National Center for Health Statistics (NCHS) carefully analysed how maternal mortality statistics were collected that a different conclusion emerged. The NCHS studies found a decrease rather than an increase in the maternal mortality rate over time. The apparent increase was entirely due to how the maternal mortality data were collected. 

These examples highlight the challenges researchers, journalists, the informed public and policymakers face. We live in a world where data collectors and researchers are expected to provide data in a rapid cycle with little time to interpret the results and explore anomalies. Media rely on the data presented to them to file stories with statistical soundbites, often even uncritically accepting information provided by market research firms commissioned by industry bodies with a vested interest. In some cases, a political predisposition allows some data to be accepted uncritically while others are scrutinised extensively. 

What is a way out of this conundrum? How can we build sensible public discourse and rational, evidence-informed policy design? It will require a substantial redesign of our data and evidence infrastructure with a troika of improved data collection, interpretation and reporting infrastructure. 

Independent oversight

While collecting official statistics will always be a purview of state institutions, they must be governed by independent governing bodies that can ensure scientific integrity and broad oversight. The term of the National Statistical Commission (NSC) expired a few months ago. Reappointing the NSC is urgently needed. Moreover, it is also essential that publicly funded but independent data collection also find space in our statistical infrastructure. Consistent experimentation is required in a rapidly changing society and growing technical infrastructure for data collection. In most countries, publicly funded experiments in data collection are carried out by universities or research institutions. 

Data collectors must develop the capacity to interpret their data carefully and responsibly. Users often do not know the sampling strategy or minute operational details of data collection. Hence, data collectors must help interpret the data they collect and provide good documentation to users. Today, the National Statistical Office has no data analytical wing. National Family Health Survey reports are simple tabulations without any information about standard errors or attempts at interpretation. These institutions must be strengthened and fully funded to provide data quality analysis and explore their results’ implications. 

Researchers and journalists must develop the self-discipline to use and report only reliable data and be cautious of for-profit institutions with a vested interest in providing statistics and reports. The cryptocurrency example above is sobering but not the only example.  Lancet, one of the most reputable journals, was forced to withdraw papers based on Hydroxychloroquine trials because the for-profit company that supplied the data was unwilling to share it for verification. Academic publishing and deadline-driven journalistic pressures must be balanced with the responsibility of not misleading the public discourse with inadequately documented information that is either unavailable for verification or is so expensive that it is effectively out of reach of most researchers. 

Most importantly, we must develop professional ethics that demand sincere efforts at collecting, interpreting and reporting evidence and institutional infrastructure and public funding that makes this arduous task feasible. As any self-aware data collector and researcher will acknowledge, errors will occur even with the best efforts. Data collectors are not perfect and statistical techniques continue to evolve. However, unless we put thoughtful processes in place for evidence required to support sound policy design, we have no hopes of minimising misdirection. 

(S onalde Desai is Professor and Director of NCAER National Data Innovation Centre and Distinguished University Professor at the University of Maryland. Views are personal.)

Tue, 20 Sep 2022 23:53:00 -0500 en text/html https://www.thehindu.com/opinion/op-ed/beyond-the-statistical-soundbites-why-data-matter/article65917243.ece
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