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A00-240 test Format | A00-240 Course Contents | A00-240 Course Outline | A00-240 test Syllabus | A00-240 test Objectives


This test 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 test 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 trial 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



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SASInstitute Statistical Latest Topics

Analytics-As-A-provider Market present And Upcoming tendencies With COVID 19 impact look at | SAS Institute, Google Inc | A00-240 PDF obtain and Practice Test

The MarketWatch information department become no longer involved within the introduction of this content material.

Feb 02, 2021 (WiredRelease by way of Comtex) -- A consciously conceived and designed enterprise intelligence report titled international Analytics-As-A-provider market 2021 by producers, category, and utility, Forecast to 2030 by way of MarketResearch.biz discloses a succinct analysis of the regional spectrum, market dimension, and profits forecast concerning the market. This document sheds mild on the a must-have traits together with other movements happening in the global Analytics-As-A-carrier market which is marking on the growth and opening doorways for outlook boom in the coming years.

this is the newest report, covering the latest COVID-19/Corona Virus pandemic influence on the market which has affected each factor of existence globally. This has brought along a few adjustments in market conditions and the enterprise areas. The hastily altering market situation and preliminary and future evaluation of the have an effect on are coated within the Analytics-As-A-provider market file. 

For All-Inclusive assistance: obtain a FREE pattern replica of Analytics-As-A-provider Market document study 2021-2030 at https://marketresearch.biz/file/analytics-as-a-carrier-market/request-pattern

(Our FREE pattern copy of the document gives a short introduction to the analysis report outlook, list of tables and figures, have an effect on analysis of COVID-19, TOC, an outlook to key avid gamers of the market and comprising key areas.)

aggressive evaluation:

The primary businesses are highly focused on innovation in Analytics-As-A-service production expertise to enhance ledge existence and effectivity. The most useful long-time period building path for Analytics-As-A-carrier market will also be caught by way of guaranteeing fiscal pliancy to invest in the most beneficial techniques and existing process improvement.

Key manufacturers are blanketed in accordance with the business profile, sales information, and product specifications, and so on: SAS Institute, Google Inc, EMC enterprise, IBM enterprise, Oracle agency, GoodData corporation, Microsoft supplier, Amazon net capabilities (AWS) Inc, Hewlett-Packard enterprise (HPE), desktop Science organisation (CSC)

each brand or Analytics-As-A-carrier market participant’s growth fee, gross income margin, and revenue figures is equipped in a tabular, essential format for few years and an individual area on Analytics-As-A-provider market contemporary development such as collaboration, mergers, acquisition, and any new service or new product launching available in the market is obtainable.

Analytics-As-A-carrier Market Segmentation Outlook by way of element, organization measurement, deployment mode, industry vertical, and region:

Segmentation via component:

SolutionServicesSegmentation by company size:

giant EnterprisesSmall and Medium-Sized EnterprisesSegmentation by way of Deployment Mode:

Public cloudPrivate cloudHybrid cloudSegmentation via industry Vertical:

BFSIHealthcareManufacturingEnergy and UtilityTravel and HospitalityRetail and e-CommerceTelecommunication and ITTransportation and LogisticsOthers (government, Media and leisure, trip and Hospitality, meals & Beverage, Transportation and logistics, business, and so forth.)

down load Now And Browse finished counsel On The COVID 19 affect analysis On Analytics-As-A-carrier Market: https://marketresearch.biz/file/analytics-as-a-carrier-market/covid-19-influence

Regional analysis:On the idea of geography, the Analytics-As-A-carrier Market document covers records for a couple of geographies inclusive of, North the us (U.S., Mexico, Canada) South the usa (Argentina, Brazil) The center East & Africa (South Africa, Saudi Arabia) Asia-Pacific (China, Japan, India, Southeast Asia) Europe (U.okay., Spain, Italy, Germany, France, Russia)

additionally, here years regarded for this look at to forecast the world Analytics-As-A-carrier market size are as follows:

– specific year: 2020

– Estimated 12 months: 2021

– Forecast year: 2021–2030

Some basic file Highlights:

– Thorough outline of the determine market

– changing market dynamics withinside the Analytics-As-A-provider enterprise

– wide Analytics-As-A-carrier market segmentation contains kinds, applications, geographical and expertise past, current-day and projected Analytics-As-A-service market size withinside the premise of extent and cost

– contemporary Analytics-As-A-carrier commercial enterprise developments and trends

– The sturdy footing withinside the competitive panorama comprises business business profiles

– thoughts of Analytics-As-A-carrier key gamers and merchandise offered

– potential and area of interest segments, geographical areas showing promising Analytics-As-A-service boost

– A neutral angle on Analytics-As-A-provider market common performance

– have to-have statistics for Analytics-As-A-provider market gamers to keep and progress their business footprint.

-extra, simple tremendous-scale and secondary research facts of Analytics-As-A-service were amassed to constitution the Analytics-As-A-carrier document and it presents the key statistic forecasts, in terms of profits(Mn).

Supplementary courses enclosed on this planet Analytics-As-A-service market file are as under:

Analytics-As-A-provider advertising quite a few options and guidelines, suppliers, and distributors, analyzes points persuading market increase, creation trends, and tracking innovations. The record performs SWOT analysis and PESTEL evaluation to display the steadiness, flaws, alternatives, and risks in Analytics-As-A-carrier business. additionally, it compares the previous years’ information to understand the limitations confronted by way of new players within the Analytics-As-A-carrier market globally.

Inquire/communicate To knowledgeable for further exact counsel About Analytics-As-A-service document: https://marketresearch.biz/record/analytics-as-a-service-market/#inquiry

The global Analytics-As-A-provider market record gives responses for many vital queries comparable to the boom of the Analytics-As-A-service market equivalent to:

– What should be the worldwide and place-intelligent Analytics-As-A-carrier market extent and the futuristic probabilities analogous to the construction of Analytics-As-A-carrier market during the forecast 2030?

– who are the main manufacturers/gamers/distributors of international Analytics-As-A-carrier market, together with the rationalization of product, company profiles, Analytics-As-A-provider market define?

– What are the market circumstance and existing trends in Analytics-As-A-provider market by segmentation?

– What are the different driving factors associated with Analytics-As-A-provider market increase, most important challenges, and Analytics-As-A-carrier opportunities?

– What are the Analytics-As-A-provider market dynamics, the scope of production, a analyze on the finished pricing of the proper manufacturer?

– What are the key Analytics-As-A-carrier driving forces, for each section by way of product class, utility, and nation-states?

To purchase This premium record click here: https://marketresearch.biz/buy-record/?report_id=12404

Our analysis Methodology is according to the following leading features:

1. facts Collections and Interpretation

2. analysis

3. information Validation

four. remaining Projections and Conclusion

table Of content material: Some Chapterwise aspects

Chapter 01 – government abstract and Highlights

1.1 Coronavirus Crown?

1.2 Analytics-As-A-provider is starting to be

1.three New Vectors

Chapter 02 – Market Introduction

2.1 study desires and objectives

2.2 factors for Doing Analytics-As-A-provider look at

2.three Scope of file

2.4 tips Sources

2.5 Methodology

2.6 Geographic Breakdown

Chapter 03 – international Analytics-As-A-provider Market : expertise background constitution Overview

3.1 pleasing exact business evaluation and Market Share

three.2 Market structure

three.three styles of Analytics-As-A-carrier

Chapter 04 – Analytics-As-A-provider Market analysis and Forecast 2021-2030

Chapter 05 – Breakdown with the help of using area, end-consumer

Chapter 06 – aggressive assessment

Chapter 07 – Assumptions and Acronyms

Chapter 08 – analysis Methodology

Browse Full summary of Analytics-As-A-provider Market Enabled with Respective Tables and Figures at: https://marketresearch.biz/report/analytics-as-a-carrier-market/#toc

The examine abstract the regulatory framework embody and protecting a considerable number of particulars of the market globally. in addition, Analytics-As-A-service business building competition scenario, the company point of view, and analysis selections are explained. the important thing assessment included from 2021 to 2030 creates the facts effective elements for business avid gamers, managers, experts, commerce advisers, boosting, profits, together with the others who are trying to locate important Analytics-As-A-provider trade guidance with demonstrably together with tables, charts, and graphs.

Customization of this report: if you would like the rest more than these then let us know and they are able to prepare the record based on your requirement.

examine more, increase prospects intelligence reviews:-

1. weather Forecasting services Market production, CAGR, producers increase Outlook and Rising traits | global climate provider and AccuWeather Inc

2. web Protocol tv (IPTV) Market manufacturers increase Outlook, production, CAGR and Rising developments | AT&T and AT&T

3. car Sunroofs Market Product evaluation, Market Demand, boom, probability and profits, Forecast till 2030 | ACS France SAS and Webasto Roof methods

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  MASWALI, MAONI NA MAJIBU WAKATI WA MIKUTANO KATI YA BODI YA TAIFA YA URATIBU WA NGOs NA WADAU WA SEK-
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Mwongozo wa Uratibu wa Mashirika Yasiyo ya Kiserikali Tanzania Bara
Orodha ya Mashirika yasiyo ya Kiserikali ambayo hayakuwasilisha Taarifa ya Mwaka
Kuanza Kutumika kwa Mfumo wa Usajili wa Mashirika Yasiyo ya Kiserikali (NGOs)
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Taarifa kwa Mashirika yasiyo ya Kiserikali (NGOs), Kuwasilisha Taarifa za mwaka
NGO Regulation GN No 685
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Draft for Constitutions Registered Under the NGO Act No. 24/2002 :
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Orodha ya Mashirika yanayoondolewa kwenye daftari la Msajili wa NGOs  
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The Written Laws Miscelleneous No.3 Act of 2019  
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Taarifa kwa Umma, Kuwasilisha Taarifa za Mwaka za Mashirika Yasiyo ya Kiserikali  
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