# A & P Modelling Course

Predictive modelling leverages statistics to predict outcomes.

## Analytics and Predictive Modelling Online Training

SAS Rapid Predictive Modeler automatically guides users through a behind-the-scenes workflow of data preparation and data mining tasks, enabling them to generate their own models, derive on-demand insights and act on them to solve business problems quickly and effectively.

## What you will learn

It also allows statisticians or modelers to generate quick, baseline models when they are short on time and resources. For the purposes of this paper, a customer level dataset is one in which each customer is represented on its own rowin a table so that customers can be the unit of analysis fora predictive model.

• Introduction to Statistics
• Analysing Categorical Data
• Predictive Modelling

#### Analytics and Predictive Modelling Online Training Course Content

• Introduction to Statistics
• Introduction
• Variables
• Data Types
• Scaling Techniques
• Frequency Distributions
• Descriptive Statistics
• Introduction
• Measure of Central Tendency
• Measure of Dispersion
• Skew ness and Kurtosis
• Frequency Distributions
• Bar Graphs
• Plotting Data
• Histograms, QQ Plots, and Probability Plots
• Probability & Sampling
• Introduction
• Random Variable
• Expectations
• Continuous Probability Distributions [Uniform, Normal, Exponential]
• Discrete Probability Distributions[Binomial, Poisson, Negative Binomial, Hyper Geometric]
• Estimations
• Sampling Theory[Probability and Non Probability]
• Inferential Statistics
• Introduction
• Inferential Statistics – Hypothesis Testing [Parametric & Non-Parametric]
• T-test: Testing Single Means
• T-test: Testing Differences between Two Means
• Random Assignment of Subjects
• Two Independent Samples: Distribution Free Tests
• One-tailed versus Two-tailed Tests
• Paired T-tests (Related Samples)
• F – Test for Variances
• Analysis of Variance
• Introduction
• One-way Analysis of Variance
• Two-way Analysis of Variance
• Interpreting Significant Interactions
• Unbalanced Designs: PROC GLM
• Analysis of Covariance [ANCOVA]
• Analysing Categorical Data
• Introduction
• Questionnaire Design and Analysis
• Two-way Frequency Tables
• A Short-cut Way to Request Multiple Tables
• Computing Chi-square from Frequency Counts
• McNamara’s Test for Paired Data
• Computing the Kappa Statistics (Coefficient of Agreement)
• Odds Ratios
• Relative Risk
• Chi-square Test for Trend
• Mantel-Haenszel Chi-square for Stratified Tables and Meta-Analysis
• Correlation and Simple Regression
• Correlation
• Significance of a Correlation Coefficient
• Partial Correlations
• Linear Regression
• Partitioning the Total Sum of Squares
• Plotting the Points on the Regression Line
• Plotting Residuals and Confidence Limits
• Transforming Data
• Predictive Modelling
• Introduction
• Preparation of Data
• Multiple Regression [Model diagnostics]
• Logistic Regression [Model diagnostics]
• Factor Analysis - PCA
• Discriminant Analysis
• Pattern Discovery [Cluster Analysis, Market Basket Analysis]
• Forecasting [MA, AR, ARMA, ARIMA]
• Decision Tree Analysis [CHAID, CART]
• Neural Networks
• Modell Assessment
• Model Implementation
• This Course is taught through one of the Real Time Projects
• Capital Market Domain,
• Market Research Domain,
• Finance Domain[Credit Card/ Insurance and Banking Domains]