Business Analytics Foundation with R Tools Part 1 presented by Beamsync. If you are looking for analytics training in Bangalore visit: http://beamsync.com/business-analytics-training-bangalore/

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.