The document covers various predictive modeling techniques using R tools, focusing on regression analysis, including linear and logistic regression, cluster analysis, and time series analysis. It explains the purpose of regression analysis as a statistical tool for predicting and modeling relationships between variables, detailing how simple linear regression is established and its underlying assumptions. Additionally, it provides insights on model building processes and practical applications in business analytics.

1. BUSINESS ANALYTICS FOUNDATION WITH R
TOOLS
Lesson 4 - Predictive Modeling Techniques
Part 1
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2. ● Understand regression analysis and types of regression models
● Know and build a simple linear regression model
● Understand and develop a logistic regression model
● Learn cluster analysis, types and methods to form clusters
● Know time series and its components
● Decompose seasonal and non seasonal time series
● Understand different exponential smoothing methods
● Know the advantages and disadvantages of exponential smoothing
● Understand the concepts of white noise and correlogram
● Apply different time series analysis like Box Jenkins, AR, MA, ARMA etc.
● Understand all the analysis techniques with case studies
OBJECTIVE SLIDE
After completing
this course, you will
be able to:
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3. REGRESSION ANALYSIS
● Regression analysis is a statistical tool for determining causal effect of one or more variables upon
another (or more) variables.
● The variables that associated are thought to be systematically connected by a relationship.
● The variables that are assumed to be the cause are called predictor and the variables that are
assumed to be effect are called the response or target variables.
● The identified relation between these variables is called the regression equation. We say it as the
target is regressed by the predictor.
● Typically, a regression analysis is used for
● Prediction (i.e. forecasting) of the target variable.
● Modeling the relationship between the variables.
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4. TYPES OF REGRESSION MODELS.
Regression
Models
Univariate
Linear
Simple Multiple
NonLinear
Multivariate
Linear NonLinear
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5. • It’s a common technique to determine how one variable of interest is affected by another.
• It is used for three main purposes:
• For describing the linear dependence of one variable on the other.
• For prediction of values of other variable from the one which has more data.
• Correction of linear dependence of one variable on the other.
• A line is fitted through the group of plotted data.
• The distance of the plotted points from the line gives the residual value.
• The residual value is a discrepancy between the actual and the predicted value.
• The procedure to find the best fit is called the least-squares method.
SIMPLE LINEAR REGRESSION
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6. • The equation that represents how an independent variable is related to a dependent variable and
an error term is a regressionmodel.
y = β0 + β1x + ε
Where, β0 and β1 are called parameters of the model,
ε is a random variable called error term.
• Getting the estimates of β0 and β1, i.e. E(Y|X) means finding the best straight line that can be drawn
through the scatter plot Y vs X. This is done by Least Square(LS) estimates.
LINEAR REGRESSION MODEL
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7. SIMPLE LINEAR REGRESSION – GRAPHICAL UNDERSTANDING
y intercept
An observed value of x when x
equals x0
Mean value of y
when x equals x0
X
Y
x0 = A specific value of x, the
independent variable.
β0
Error term
Straight line defined by the equation
y = β0 + β1x
β1
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8. PROCESS TO BUILD A REGRESSION MODEL
Evaluate the model
Identify the target variables
Identify the predictors
Data collection
Decide the relationship
Fit the model
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9. • The predictor variable x is non – random.
• The error term ε is random.
• Error term follows normal distribution.
• Standard deviation of error is independent of x.
• The data being used to estimate the parameters should be independent of each other.
• If any of the above assumptions are violated, modelling procedure must be modified.
LINEAR REGRESSION MODEL ASSUMPTIONS
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10. Thank You
Beamsync is providing business analytics training in Bangalore along with R
tool. If you are looking your career into analytics schedule you’re training here:
http://beamsync.com/business-analytics-training-bangalore/
Copyright 2016,Beamsync, All rights reserved.