Predictive Modeling Using Logistic Regression training in delhi ncr:iisastr

April 24th, 2012

More detail visit www.iisastr.com
contact Ph: 9312506496
email: info@iisastr.com
IISASTR :P redictive Modeling Using Logistic Regression
syllabus
Predictive Modeling

business applications
analytical challenges

Fitting the Model

parameter estimation
adjustments for oversampling

Preparing the Input Variables

missing values
categorical inputs
variable clustering
variable screening
subset selection

Classifier Performance

ROC curves and Lift charts
optimal cutoffs
K-S statistic
c statistic
profit
evaluating a series of models

log on to www.iisastr.com
contact Ph: 9312506496
email: info@iisastr.com

Tags: , , , ,
Posted in SAS Base Statistical Business Analyst using SAS 9 credential | No Comments »

IBM SPSS Crtification training in delhi Testimony

April 12th, 2012

IISASTR is a very good institute for the training in statistical software SPSS and minitab .
The training provided for both the software along with IBM Certified Specialist – SPSS Statistics Level 1was very informative,up to date and accurate.
The best part of the institute is the faculty ,whose understanding of the concepts is very strong and he is successful in effectively transmitting these concepts to the students.
I would highly recommend IISASTR to any one who would like to train themselves in usages of SPSS and minitab.

Nida Siddiqui
Msc in mathematics (Lucknow University)
working as a lecturer in OMAN

More detail about training visit www.iisastr.com
ph-9312506496

Tags: ,
Posted in we | No Comments »

Minitab Training in Delhi NCR

February 27th, 2012

Minitab Syllabus
Frequency Distribution
Introduction to Graphing
Advanced Graphing
Measures of Central Tendency
Measures of Variation
Boxplot-and-Whisker Plot
Discrete Distributions
Binomial Distributions
Central Limit Theorem
Confidence Intervals
Hypothesis Testing;
Large Sample
Hypothesis Testing;
Small Sample
Hypothesis Testing;
Independent Samples
Hypothesis Testing;
Dependent Samples
t-Tests
F-Tests
Analysis of Variance

Regression & Correlation
ANOVA
Sign Test

Tags: , , , , , , ,
Posted in Minitab Training in India | No Comments »

Q3.sas assignment on proc freq

February 4th, 2012

Q3.sas assignment
Consider the following data set watching tv (wt variable) in minutes of 18 participant.

obs wt
01 540
02 510
03 500
04 490
05 520
06 520
07 510
08 530
09 500
10 490
11 510
12 500
13 500
14 480
15 490
16 480
17 470
18 460

What is the lowest observed value for the WT variable?
What is the highest observed value for the WT variable?
How many people had an watching TV in minutes of 490?
What percentage of people had an watching TV in minutes of 500?
How many people had an watching TV in minutes of 480 or lower?
What percentage of people had an watching TV in minutes of 520 or lower?
What is the total number of usable observations for the wt variable in this data set?

HINT:use proc freq

For Other data set and assignment ,conatct at class room.

More detail visit www.iisastr.com
contact Ph: 9312506496
email: info@iisastr.com

Tags: , ,
Posted in SAS Assignment at IISASTR | No Comments »

Time series data analysis using R training in delhi

January 3rd, 2012

More detail visit www.iisastr.com
contact Ph: 9312506496
email: info@iisastr.com
Time series data analysis using R training in delhi
Syllabus:
MODELS FOR STATIONARY TIME SERIES
General Linear Processes
Moving Average Processes
Autoregressive Processes
The Mixed Autoregressive Moving Average Model

MODELS FOR NONSTATIONARY TIME SERIES
Stationarity Through Differencing
ARIMA Models
Constant Terms in ARIMA Models

MODEL SPECIFICATION
Properties of the Sample Autocorrelation Function
The Partial and Extended Autocorrelation Functions
Specification of Some Simulated Time Series
Nonstationarity

PARAMETER ESTIMATION
The Method of Moments
Least Squares Estimation
Maximum Likelihood and Unconditional Least Squares
Properties of the Estimates
Illustrations of Parameter Estimation
Bootstrapping ARIMA Models

MODEL DIAGNOSTICS
Residual Analysis
Overfitting and Parameter Redundancy

Day-4

FORECASTING
Minimum Mean Square Error Forecasting
Deterministic Trends
ARIMA Forecasting
Prediction Limits
Forecasting Illustrations
Updating ARIMA Forecasts
Forecast Weights and Exponentially Weighted
Moving Averages
Forecasting Transformed Series

SEASONAL MODELS
Seasonal ARIMA Models
Multiplicative Seasonal ARMA Models
Nonstationary Seasonal ARIMA Models
Model Specification, Fitting, and Checking
Forecasting Seasonal Models

TIME SERIES REGRESSION MODELS
Intervention Analysis
Outliers
Spurious Correlation
Prewhitening and Stochastic Regression

TIME SERIES MODELS OF
HETEROSCEDASTICITY
Some Common Features of Financial Time Series
The ARCH(1) Model
GARCH Models
Maximum Likelihood Estimation
Model Diagnostics
Conditions for the Nonnegativity of the
Conditional Variances
Some Extensions of the GARCH Model

log on to www.iisastr.com
contact Ph: 9312506496
email: info@iisastr.com

Tags: , , , , , , , ,
Posted in Data Analytics using R, Time series data analysis using R | No Comments »

Time Series data analysis using SAS training in Delhi NCR

January 3rd, 2012

More detail visit www.iisastr.com
contact Ph: 9312506496
email: info@iisastr.com
Time Series data analysis using SAS training in Delhi
Syllabus:
Session 1 Overview of Time Series
1.1 Introduction
1.2 Analysis Methods and SAS/ETS Software
1.2.1 Options
1.2.2 How SAS/ETS Software Procedures Interrelate
1.3 Simple Models: Regression
1.3.1 Linear Regression
1.3.2 Highly Regular Seasonality
1.3.3 Regression with Transformed Data
Session 2 Simple Models: Autoregression
2.1 Introduction
2.1.1 Terminology and Notation
2.1.2 Statistical Background
2.2 Forecasting
2.2.1 Forecasting with PROC ARIMA
2.2.2 Backshift Notation B for Time Series
2.2.3 Yule-Walker Equations for Covariances
2.3 Fitting an AR Model in PROC REG
Session 3 The General ARIMA Model
3.1 Introduction
3.1.1 Statistical Background
3.1.2 Terminology and Notation
3.2 Prediction
3.2.1 One-Step-Ahead Predictions
3.2.2 Future Predictions
3.3 Model Identification
3.3.1 Stationarity and Invertibility
3.3.2 Time Series Identification
3.3.3 Chi-Square Check of Residuals
3.3.4 Summary of Model Identification
3.4 Examples and Instructions
3.4.1 IDENTIFY Statement for Series
3.4.2 Example: Iron and Steel Export Analysis
3.4.3 Estimation Methods Used in PROC ARIMA
3.4.4 ESTIMATE Statement for Series
3.4.5 Nonstationary Series
3.4.6 Effect of Differencing on Forecasts
3.4.7 Models for Nonstationary Data
3.4.8 Differencing to Remove a Linear Trend
3.4.9 Other Identification Techniques

Session 4 The ARIMA Model

4.1.1 Introduction to Seasonal Modeling
4.1.2 Model Identification
4.2 Models with Explanatory Variables
4.2.1 Case 1: Regression with Time Series Errors
4.2.2 Case 1A: Intervention
4.2.3 Case 2: Simple Transfer Fun
4.2.4 Case 3: General Transfer Function
4.2.5 Case 3A: Leading Indicators
4.2.6 Case 3B: Intervention
4.3 Methodology and Example
4.3.1 Case 1: Regression with Time Series Errors
4.3.2 Case 2: Simple Transfer Function
4.3.3 Case 3: General Transfer Functions
4.3.4 Case 3B: Intervention
Session 5 The ARIMA Model: Special Applications
5.1 Regression with Time Series Errors and Unequal Variances
5.1.1 Autoregressive Errors
5.1.2 Example: Energy Demand at a University
5.1.3 Unequal Variances
5.1.4 ARCH, GARCH, and IGARCH for Unequal Variances
5.2 Cointegration
5.2.1 Introduction
5.2.2 Cointegration and Eigenvalues
5.2.3 Impulse Response Function
5.2.4 Roots in Higher-Order Models
5.2.5 Cointegration and Unit Roots
5.2.6 An Illustrative Example
5.2.7 Estimating the Cointegrating Vector
5.2.8 Intercepts and More Lags
5.2.9 PROC VARMAX
5.2.10 Interpreting the Estimates
5.2.11 Diagnostics and Forecasts
Session 6 State Space Modeling
6.1 Introduction
6.1.1 Some Simple Univariate Examples
6.1.2 A Simple Multivariate Example
6.1.3 Equivalence of State Space and Vector ARMA Models
6.2 More Examples
6.2.1 Some Univariate Examples
6.2.2 ARMA(1,1) of Dimension 2
6.3 PROC STATESPACE
6.3.1 State Vectors Determined from Covariances
6.3.2 Canonical Correlations
6.3.3 Simulated Example
and Pure Delay

log on to www.iisastr.com
contact Ph: 9312506496
email: info@iisastr.com

Tags: , , , , , , ,
Posted in SAS Analytics, Time series data analysis using SAS | No Comments »

Data Analysis and Business Modeling Using Excel training in Delhi

January 3rd, 2012

More detail visit www.iisastr.com
contact Ph: 9312506496
email: info@iisastr.com
Data Analysis and Business Modeling Using Excel 2010
Syllabus :
1 What’s New in to Excel 2010
? Customizable Ribbon
? Sparklines
? Slicers
? PowerPivot
? New Excel Solver
? File Tab
? New Excel Functions
? New Equation Editor
? Improved Data Bars
? Paste Special Live Preview
2 Range Names
How Can I Create Named Ranges?
3 Lookup Functions
4 The INDEX Function
5 The MATCH Function
6 Text Functions
7 Dates and Date Functions
8 Evaluating Investments by Using Net Present Value Criteria
9 Internal Rate of Return
10 More Excel Financial Functions
11 Circular References
12 IF Statements
13 Time and Time Functions
14 The Paste Special Command

15 Three-Dimensional Formulas
16 The Auditing Tool
17 Sensitivity Analysis with Data Tables
18 The Goal Seek Command
19 Using the Scenario Manager for Sensitivity Analysis
20 The COUNTIF, COUNTIFS, COUNT, COUNTA, and COUNTBLANK Functions
21 The SUMIF, AVERAGEIF, SUMIFS, and AVERAGEIFS Functions
22 The OFFSET Function
23 The INDIRECT Function
24 Conditional Formatting
25 Sorting in Excel
26 Tables
27 Spin Buttons, Scroll Bars, Option Buttons, Check Boxes, Combo Boxes, and Group List Boxes
28 An Introduction to Optimization with Excel Solver
29 Using Solver to Determine the Optimal Product Mix
30 Using Solver to Schedule Your Workforce
31 Using Solver to Solve Transportation or Distribution Problems
32 Using Solver for Capital Budgeting
33 Using Solver for Financial Planning
34 Using Solver to Rate Sports Teams
35 Warehouse Location and the GRG Multistart and Evolutionary Solver Engines
Understanding the GRG Multistart and Evolutionary Solver Engines
36 Penalties and the Evolutionary Solver
37 The Traveling Salesperson Problem
38 Importing Data from a Text File or Document
39 Importing Data from the Internet
40 Validating Data
41 Summarizing Data by Using Histograms
42 Summarizing Data by Using Descriptive Statistics
43 Using PivotTables and Slicers to Describe Data
44 Sparklines
45 Summarizing Data with Database Statistical Functions
46 Filtering Data and Removing Duplicates
47 Consolidating Data
48 Creating Subtotals
49 Estimating Straight Line Relationships
50 Modeling Exponential Growth
51 The Power Curve
52 Using Correlations to Summarize Relationships
53 Introduction to Multiple Regression
54 Incorporating Qualitative Factors into Multiple Regression
55 Modeling Nonlinearities and Interactions
56 Analysis of Variance: One-Way ANOVA
57 Randomized Blocks and Two-Way ANOVA
58 Using Moving Averages to Understand Time Series
59 Winters’s Method
? Time Series Characteristics
? Parameter Definitions
? Initializing Winters’s Method
? Estimating the Smoothing Constants
60 Ratio-to-Moving-Average Forecast Method
61 Forecasting in the Presence of Special Events
62 An Introduction to Random Variables
63 The Binomial, Hypergeometric, and Negative Binomial Random Variables
64 The Poisson and Exponential Random Variable
65 The Normal Random Variable
66 Weibull and Beta Distributions: Modeling Machine Life and Duration of a Project
67 Making Probability Statements from Forecasts
68 Using the Lognormal Random Variable to Model Stock Prices
69 Introduction to Monte Carlo Simulation
70 Calculating an Optimal Bid
71 Simulating Stock Prices and Asset Allocation Modeling
72 Fun and Games: Simulating Gambling and Sporting Event Probabilities
73 Using Resampling to Analyze Data
74 Pricing Stock Options
75 Determining Customer Value
76 The Economic Order Quantity Inventory Model
77 Inventory Modeling with Uncertain Demand
78 Queuing Theory: The Mathematics of Waiting in Line
79 Estimating a Demand Curve
80 Pricing Products by Using Tie-Ins
81 Pricing Products by Using Subjectively Determined Demand
82 Nonlinear Pricing
83 Array Formulas and Functions
84 PowerPivot

log on to www.iisastr.com
contact Ph: 9312506496
email: info@iisastr.com

Tags: , , , , , , , ,
Posted in Excel Analytics training in delhi | No Comments »

Tableau Fundamentals training in delhi

December 15th, 2011

More detail visit www.iisastr.com
contact Ph: 9312506496
email: info@iisastr.com

Audience: This course is designed for the beginner to intermediate-level Tableau user. It is for
anyone who works with data – regardless of technical or analytical background. This course is
designed to help you understand the important concepts and techniques used in Tableau to
move from simple to complex visualizations and learn how to combine them in interactive
dashboards.

Prerequisites: None

Learning Objectives: At the end of this class, the student will be able to:

* Understand the many options for connecting to data.
* Understand the Tableau interface / paradigm – components, shelves, data elements, and
terminology. The student will be able to use this knowledge to effectively create the most
powerful visualizations.
* Create basic calculations including string manipulation, basic arithmetic calculations, custom
aggregations and ratios, date math, logic statements and quick table calculations.
* Able to represent your data using the following visualization types:

o Cross Map

o Scatter Plots

o Geographic Map

o Pie Charts and Bar Charts

o Page Trails

o Small Multiples

o Heat Map

o Dual Axis and Combo Charts with different mark types

o Density Chart

o Options for drill down and drill across

* Use Trend Lines, Reference Lines and statistical techniques to describe your data.
* Understanding how to use group, bin, hierarchy, sort, set and filter options effectively.
* Work with the many formatting options to fine tune the presentation of your visualizations.
* Understand how and when to Use Measure Name and Measure Value.
* Understand how to deal with data changes in your data source such as field addition, deletion or
name change.
* Understand all of your options for sharing your visualizations with others.
* Combine your visualizations into Interactive Dashboards and publish them to the web.

Course Includes: This course will include extensive hands-on activities to re-enforce the skills and knowledge attained.

log on to www.iisastr.com
contact Ph: 9312506496
email: info@iisastr.com

Tags: , , , , , , , , , , , , , ,
Posted in Tableau training in Delhi NCR | No Comments »

QlikView Designer I Version 10

November 21st, 2011

QlikView Designer I Version 10
Duration: 12 hours
Recommended Prerequisites: No special prerequisites
Who Should Attend: Business Analysts, Designers, Project Managers, Developers & End-Users

More detail visit www.iisastr.com
contact Ph: 9312506496
email: info@iisastr.com
Course Objectives

By the end of this course, you will be able to:

* Incorporate best practices for application design and layout in the development of QlikView applications.
* Build a QlikView application using sheets and some of the common QlikView objects such as list boxes and table boxes.
* Search and analyze data in existing QlikView applications.

Course Outline

* Introduction to QlikView
* Layout and design fundamentals and best practices
* What to consider when designing a QlikView application
* Definitions and uses of: sheets, sheet objects, list boxes and table boxes
* How to create charts in QlikView
* Chart properties, how to edit the format and layout of charts
* How to load a simple list of data into QlikView

Course Objectives

By the end of this course, you will be able to:

* Incorporate best practices for application design and layout in the development of QlikView applications.
* Build a QlikView application using sheets and some of the common QlikView objects such as list boxes and table boxes.
* Search and analyze data in existing QlikView applications.

log on to www.iisastr.com
contact Ph: 9312506496
email: info@iisastr.com

Tags: , , , , , , , , , , , ,
Posted in QlikView training in India | No Comments »

SAS India,pune looking for SAS Programmer

November 20th, 2011

One of our client Fortune 100 Best Companies to work for(SAS India,pune) looking for SAS Programmer(4 nos):Permanent Position.Experience: Minimum 2-5 years of experience in BASE SAS,SAS/SQL,SAS/Macros. Banking domain preferred
Pl send your resume to hr@iisastr.com

Detail JD:
SAS Programmer(1): Contract position of 4-6 months.

Experience: Minimum 2+ years of experience in BASE SAS,SAS/SQL,SAS/Macros.

Job Location: Pune. Looking to have this person on board by December’11 first week. Urgent requirement.

2) SAS Programmer(4 nos):Permanent Position.

Experience: Minimum 3-5 years of experience in BASE SAS,SAS/SQL,SAS/Macros.

Job Location: Pune

Pl send your resume to hr@iisastr.com

Banking domain preferred for both positions.

Pl send your resume to hr@iisastr.com

Tags: , , , , , , , ,
Posted in Openings at SAS India Pune | No Comments »

« Older Entries