Understand how to use predictive analytics tools to solve real time business problems using Minitab
What you will learn
Learn Data Analysis and Manipulation, Visualization, Statistics, Hypothesis Testing
Use predictive analytics techniques to interpret model outputs
Understand how to use predictive analytics tools to solve real time business problems
Learn about predictive models like regression, clustering and others
Description
This course contains the following basic steps involved in predictive modeling
- Defining the objective – This section deals with the ways to define the objective of predictive models with relation to the goals of the business.
- Gathering the data -Collecting data from various sources is another important step in building predictive model. Examples are provided for collection of different types of data from various sources.
- Preparing the data for modelling – This section deals with the segregation of data and how determines how they can be used in predictive modeling.
- Selecting and transforming the variables – This topic deals with the steps for transformation of independent variables to best fit the dependent variable.
- Processing and evaluating the model – In this chapter you will go through several methods of processing and evaluating the model
- Validating the model – The predictive models should perform well on the data. This chapter deals with three powerful methods for ensuring the model fit.
- Implementing and maintaining the model – Effective implementation of Predictive model is another important step. This chapter discusses various auditing procedures and model maintenance practices
Predictive analytics is an emerging strategy across many business sectors and they are used to improve the performance of the companies. Predictive modeling is a part of predictive analytics which is used to create a statistical model to predict the future behaviour. The predictive modeling can be used on any type of event regardless of its occurrence. The predictive model to be used for a particular situation is often selected on the basis of the detection theory. This course includes an overview of predictive analytics and predictive modeling. This course also includes examples of predictive modeling.
English
language
Content
Minitab and its applications to Predictive Modelling
Introduction of Predictive Modeling
Non Linear Regression
Anova and Control Charts
Understanding, Interpretation and implementation using Minitab
Continue on Interpretation and implementation using Minitab
Observation
Results for NAV Prices
NAV Prices – Observations
Descriptive Statistics
Customer Complaints-Observations
Resting Heart Rate Observations
Results for Loan Applicant MTW
More Details on Results for Loan Applicant MTW
Features of T- Test
Loan Applicant
Paired T – Test
ANOVA Using Minitab
Understanding and Implementation of ANOVA
Pairwise Comparisons
Features of Chi – Test
Preference and Pulse Rate
Diffe. btw Growth Plan ad Dividend Plan in MF
Checking NAV Price and Repurchase Price
Correlation Techniques
Basic Correlation Techniques
More on Basic Correlation Techniques
CT Implementation Using Minitab
Continue on Implemetation using Minitab
Interpretation of Correlation Values
Results for Return
Correlation Values – Observations
Correlation Values – Interpretations
Heart Beat – Objective
Heart Beat – Interpretation
Demographics and Living Standards
Demographics and Living Standards – Observation
Graphical Implementation
Add Regression Fit
Scatterplot with Regression
Scatterplot of Rhdeq vs Rhcap
Regression Modeling
Introduction to Regression Modeling
Identify Independent Variable
Regression Equation
Tabulating the Values
Interpretation and Implementation on Data Sets
Continue on Interpretation on Database
Significant Variable
Calculating Corresponding Values
Identify Dependent Variable
Generate Descriptive Statistics
Scatterplot of Energy Consumption
Identity Equation
P – Value and T – Value
Changes in Tem. and Expansion
Objective of Stock Prices
Interpretations of Example 5
Reliance Return Change
Generate Predicted Values
Scatterplot Return RIL
Basic Multiple Regression
Basic Multiple Regression Continues
Basic Multiple Regression – Interpretation
Generate Basic Statistics
Working on Scatterplot
Dependent Variable Objective
Concept of Multicollinearity
Identify Dependent Variable Y
Outputs and Observation
Interpretations – Example 3
Calculate with and without Flux
Scatterplot of Heart FLux Vs Insolation
Interpretation of Datasets
Implementation of Datasets
Example 4 Observations
Display Descriptive Statistics
Predicted Values Example 4
Scatterplot of Example 4
Calculating IV – Multiple Regression
Calculating Independent Multiple Regression
Understanding Basic Logistic Scatter Plot
Basic Logistic Scatter Plot Continues
Generation of Regression Equation
Tabulated Values
Interpretation and Implementation on Dataset
Interpretation and Implementation on dataset Continues
Output and Observation – Tabulated Values
Business Metrics Example
Example Two and Three Interpretations
Regression Equation Group
Interpretation and Implementation of Scatter Plot
More on Implementation of Scatter Plot
Plastic Case Strength
Separate Equations
Generation of Predicted Values
Scatter Plot Strength Vs Temp
Data of Cereal Purchase
Children Viewed and RE
Predicted Values for Individual Customers
Income Independent Variable
Example of Credit Card Issuing
Example Five – Tabulated Values
Generating Outputs
Example Five Interpretations
Situations Income
Scatterplot
Scatter Plot Scale
Predictive Modeling using MS Excel
Using Data Analysis Toolpak
Implementation of Descriptive Statistics
Descriptive statistics – Input Range
Implementation of ANOVA
Implementation of T – Test
Implementation Using Correlation
Implementation Using Regression