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Business Analytics Foundation with R Tools Part 1

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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/

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BUSINESS ANALYTICS FOUNDATION WITH R TOOLS Lesson 4 - Predictive Modeling Techniques Part 1 Copyright 2016,Beamsync, All rights reserved.
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: Copyright 2016,Beamsync, All rights reserved.
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. Copyright 2016,Beamsync, All rights reserved.
TYPES OF REGRESSION MODELS. Regression Models Univariate Linear Simple Multiple NonLinear Multivariate Linear NonLinear Copyright 2016,Beamsync, All rights reserved.
Its 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 Copyright 2016,Beamsync, All rights reserved.
The equation that represents how an independent variable is related to a dependent variable and an error term is a regressionmodel. y = 硫0 + 硫1x + 竜 Where, 硫0 and 硫1 are called parameters of the model, 竜 is a random variable called error term. Getting the estimates of 硫0 and 硫1, 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 Copyright 2016,Beamsync, All rights reserved.
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. 硫0 Error term Straight line defined by the equation y = 硫0 + 硫1x 硫1 Copyright 2016,Beamsync, All rights reserved.
PROCESS TO BUILD A REGRESSION MODEL Evaluate the model Identify the target variables Identify the predictors Data collection Decide the relationship Fit the model Copyright 2016,Beamsync, All rights reserved.
The predictor variable x is non random. The error term 竜 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 Copyright 2016,Beamsync, All rights reserved.
Thank You Beamsync is providing business analytics training in Bangalore along with R tool. If you are looking your career into analytics schedule youre training here: http://beamsync.com/business-analytics-training-bangalore/ Copyright 2016,Beamsync, All rights reserved.

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Business Analytics Foundation with R Tools Part 1

  • 1. BUSINESS ANALYTICS FOUNDATION WITH R TOOLS Lesson 4 - Predictive Modeling Techniques Part 1 Copyright 2016,Beamsync, All rights reserved.
  • 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: Copyright 2016,Beamsync, All rights reserved.
  • 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. Copyright 2016,Beamsync, All rights reserved.
  • 4. TYPES OF REGRESSION MODELS. Regression Models Univariate Linear Simple Multiple NonLinear Multivariate Linear NonLinear Copyright 2016,Beamsync, All rights reserved.
  • 5. Its 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 Copyright 2016,Beamsync, All rights reserved.
  • 6. The equation that represents how an independent variable is related to a dependent variable and an error term is a regressionmodel. y = 硫0 + 硫1x + 竜 Where, 硫0 and 硫1 are called parameters of the model, 竜 is a random variable called error term. Getting the estimates of 硫0 and 硫1, 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 Copyright 2016,Beamsync, All rights reserved.
  • 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. 硫0 Error term Straight line defined by the equation y = 硫0 + 硫1x 硫1 Copyright 2016,Beamsync, All rights reserved.
  • 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 Copyright 2016,Beamsync, All rights reserved.
  • 9. The predictor variable x is non random. The error term 竜 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 Copyright 2016,Beamsync, All rights reserved.
  • 10. Thank You Beamsync is providing business analytics training in Bangalore along with R tool. If you are looking your career into analytics schedule youre training here: http://beamsync.com/business-analytics-training-bangalore/ Copyright 2016,Beamsync, All rights reserved.