Business Analytics Foundation with R tools - Part 2
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This document covers predictive modeling techniques, specifically focusing on linear and logistic regression. It explains the coefficient of determination (R²) as a measure of model fit, provides an example of linear regression, and discusses the purpose and mechanics of logistic regression. Additionally, it introduces cluster analysis as a method to group data based on similarities and differences.
Business Analytics Foundation with R tools - Part 2
- 1. BUSINESS ANALYTICS FOUNDATION WITH R TOOLS Lesson 4 - Predictive Modeling Techniques Part 2 Copyright 2016,Beamsync, All rights reserved.
- 2. • A measure of goodness of fit - How well your model does fit the data? COEFFICIENT OF DETERMINATION R2 : R2 = 0 , no linear relationship R2 = -1 , negative linear relationship R2 = +1 , positive linear relationship Copyright 2016,Beamsync, All rights reserved.
- 3. • Based on R2 value , we can explain how well the model explains the data and the percentage of differences that are explained by this model. • The differences between observations that are not explained by the model is the error term or residual . • Suppose we have a case in which R2 value is 0.74. This means that 74% of variance in the values of the dependent variable is explained by the model and the remaining 26 % which is not explained is its residual or error term. HOW GOOD IS THE MODEL ? Copyright 2016,Beamsync, All rights reserved.
- 4. HOW TO FIND LINEAR REGRESSION EQUATION SUBJECT AGE (X) GLUCOSE LEVEL (Y) XY X2 Y2 1 43 99 4257 1849 9801 2 21 65 1365 441 4225 3 25 79 1975 625 6241 4 42 75 3150 1764 5625 5 57 87 4959 3249 7569 6 59 81 4779 3481 6561 Σ 247 486 20485 11409 40022 Y = a + bX => 65.14 + 0.38x Copyright 2016,Beamsync, All rights reserved.
- 5. • It’s a statistical method that is used in analyzing datasets where one or more independent variables would determine the outcome. • In this type of regression the dependent variables are binary, data been coded as 1 for TRUE and 0 for FALSE (dichotomouscharacteristics). • The goal of logistic regression is to find the best fitting model to describe the relationship between the dichotomous characteristic and a set of independent variables. • Logistic regression generates the coefficients of a formula to predict a logit transformation of the probability of presence of the characteristic of interest: logit (p) = β0 + β1 x1 + β2 x2 +β3 x3 + βn xn where, p is the probability of presence of the characteristic of interest. • The logit transformation is defined as the logged odds: odds = (p / 1-p) and logit(p) = ln(p / 1-p) LOGISTIC REGRESSION Copyright 2016,Beamsync, All rights reserved.
- 6. METHOD TO DEVELOP A LOGISTIC MODEL Observation-performance windows Data preparation, data treatment, data hygiene Derived variables identification Fine and coarse classing Logistic modeling and diagnostics Data Logistic Regression Model Copyright 2016,Beamsync, All rights reserved.
- 7. • Linear regression is mainly used to establish a relationship between dependent and independent variable. It helps in estimating the impact of independent variable over a dependent variable. • Example – using a linear regression, the relationship between temperature (T) and ice cream sales (I) is found to be I = 2T + 4000 • This equation says that for every 1 degree raise in temperature , there is a demand of 4002 ice creams. • Logistic regression helps in finding out the probability of an event and this event is captured in binary format i.e. 0 or1. • Example – In order to know whether customers will buy a product or not, run a Logistic Regression on the data. The dependent variable would be a binary variable . • In terms of graphical representation, Linear Regression gives a linear line as an output, once the values are plotted on the graph. Whereas, the logistic regression gives an S-shaped line LINEAR REGRESSION VS LOGISTIC REGRESSION Copyright 2016,Beamsync, All rights reserved.
- 8. CLUSTER ANALYSIS Intra-cluster distance is minimized • It groups the data objects based on the information that is found in the data that describes the objects in other groups. • The goal of this procedure is that the objects in a group are similar to one another and are different from the objects in other groups. • The greater the similarity within a group and greater the difference between the groups, more distinct is the clustering. • Cluster Analysis provides a way for users to discover potential relationships and construct systematic structures in large numbers of variables and observations Inter-cluster distance is maximized Copyright 2016,Beamsync, All rights reserved.
- 9. Thank You Beamsync is providing business analytics training in Bangalore along with certification. 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.