Saturday, November 12, 2022
AI and Software Testing - ppt download
AI and Software Testing - ppt download: ABSTRACT This presentation gives an Introduction to Artificial Intelligence; and how AI touches our lives on day to day basis. It talks about the usage of AI in QA. It explains that how to deploy AI Systems for QA, and what benefits can be expected out of this. This will help Test Managers to decide if using AI techniques for software testing; will be helpful to their organization
Wednesday, January 25, 2012
Introduction to RFM Analysis(Part - I)
RFM is a method used for analyzing customer behavior and defining value based market segments. It is commonly used in database marketing and direct marketing.
– RFM stands for : Recency, frequency, monetary
– RFM is a most commonly used method for analyzing customer behavior and defining market segments.
– Has been used for over 50 years by direct marketers to target a subset of their customers, save mailing costs, and improve profits.
– Based on three simple customer attributes:
• Recency of Purchase: When was the last customer interaction?
• Frequency of purchase: How frequent was the customer in its interactions with the business?
• Monetary value of Purchase: How much the customer is spending?
– Each one of these variables is having relationship with response to an offer
– Customers who have recently made a purchase are likely to purchase again.
– Customers who make frequent purchases are likely to purchase again.
– Customers who have spent a lot of money in the past are likely to spend more money now
– The three attributes: Recency, Frequency, Monetary are easy to comprehend and quite powerful in their predictive ability
RFM Methodology
Saturday, July 9, 2011
Friday, July 8, 2011
Thursday, June 16, 2011
What is the difference between linear regression and logistic regression?
Linear regression analyzes the relationship between two/more variables. For each subject (or experimental unit), one knows independent variables(X’s) and dependent variable(Y) and one wants to find the best straight line through the data points. In some situations, the slope and/or intercept have a scientific meaning(for example, in a log – linear model like lnY=β0+ β 1(lnX1)+ β2(lnx2) + ……….+ βn(lnxn) + Error, β 1 gives the elasticity of Y with respect to X, i.e., the percentage change in Y with respect to percentage change in X. This equation is also known as the constant elasticity form as in this equation, the elasticity of y with respect to changes in x as δ lny/δ lnxn = βn, which does not vary with xn . This log-linear form is often used in models of demands and production.). In other cases, one generally uses the linear regression line as a standard curve to find the values of Y from X’s.
Generally, a linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0).
Logistic regression is quite different than linear regression, the predictor is continuous, but the response is categorical or dichotomous (only 2 options). The logistic regression provides the probability of an event occurring. For every one unit increase in the predictor/s, changes the probability of the occurrence of an event.
For example, one might be interested to look at the relationship between body size and weight of different mammals, so one would use linear regression (please note that the relationship might not actually be linear). However, one might want to look at the relationship between body size of different birds and their chance of surviving a winter storm, so one predicts the body size of birds that do and don't survive the winter storm, and generate a logistic regression that gives the probability that a bird will survive if it has a certain body size. One can also have multiple logistics regression, so one can see what the probability is that bird will survive at a given body size and given wing span.
Logistic regression belongs to the class of 'discrete analysis' techniques. Predicting outcomes that are yes/no or one-of (a,b or c) is very different from predicting outcomes that are simple measures, such as revenue.
For modeling percentage data, Logistic regression is a better option. This is true because percentage is a simple and convenient way to represent binomial data, and logistic regression (not linear regression) should be used for binomial data.
When linear regression is used for binary data, there are three problems:
ü The variance of the error term is not constant,ü The error term is not normally distributed,
The first problem can be handled by using weighted least-square regression. When the sample size is very large, the method of least squares provides estimators that are asymptotically normal under fairly general regulations, even when the distribution of the error term is far from normal. But the third problem is insurmountable.
Logistic regression may seem much more complicated than its linear counterpart. Though most of the statistical software packages can do logistic regression with no more effort than linear regression, it is not as easy and straightforward to interpret the coefficients and test for goodness of fit of logistic models.
References:
Friday, June 10, 2011
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