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CHAID ANALYSIS

The CHAID Analysis (Chi Square Automatic Interaction Detection) is a form of analysis that determines how variables best combine to explain the outcome in a given dependent variable. The model can be used in cases of market penetration, predicting and interpreting responses or a multitude of other research problems.more...
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Cluster Analysis

Cluster Analysis is a statistical technique which is widely used in Market Research industry to develop business insights by analyzing huge volume of data and variables. Cluster Analysis is a technique used for combining observations into groups / clusters having similar properties.more...
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Factor Analysis

Factor Analysis is an excellent technique which is widely used for reduction in variables, detecting relationships between variables, data summarization, identifying factors that are uncorrelated to each other and exploratory analysis to determine how many factors are there. Generally prior to apply any statistical technique like cluster, CHAID, segmentation etc you need to reduce the size of unmanageable data.more...
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Forecasting and Time Series Analysis

Every business requires predicting the future on the basis of historical data. The prediction needs to be close to the actual behavior so that you can be benefited out of your prediction. You need to optimize the inventory using past sales record and several other business examples are there.more...
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Logistic Regression

Here we discuss an approach where some or all of the variables are qualitative. The type of regression models which enables to deal with such scenario is called Logistic regression.

The Logistic Regression is classified into two types: Binary Logistic Regression and Multinomial Logistic regression. Simple Logistic Regression involves the case where the Output (Y) which is a categorical variable is classified into only 2 levels .Examples of this include for e.g (Male(1),Female(0)), (High BP(1), Low BP(0)). more...
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Multiple Linear Regressions

Here we have more than 2 independent variables.

How to interpret the results

1. The p-values of all variables would be of interest. All the variables must have p<0.05 for them to be significant.

2. Which independent variable has more influence on Y? This will be given by the (absolute) value of the Standardized Coefficients Beta, the bigger the more influence

3. In multiple regressions, the R measures the correlation between the observed value of the dependent variable and the predicted value based on the regression model. The sample estimate of R Square tends to be an overestimate of the population parameter; the Adjusted R Square is designed to compensate for the optimistic bias of R Square..more...
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Simple Linear Regression

A simple linear regression model is of the form

Y = A +Bx1+Cx2

Y is the dependent variable. X1 and x2 are the independent variable.

In SPSS, to perform a linear regression, go to Analyze, Regression, and Linear to get the SPSS window 1.more...
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Watch out for more very soon!

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