Category: Learning modules »

Building univariate ARIMA model for time series analysis in STATA

Autoregressive Integrated Moving Average (ARIMA) is popularly known as Box-Jenkins method. The emphasis of this method is on analyzing the probabilistic or stochastic properties of a single time series. Unlike regression models where Y is explained by X1 X2….XN regressor (like the introductory case where GDP is explained by GFC and PFC), ARIMA allows Y (GDP) to be explained by its own past or lagged values. ARIMA is performed on a single time series. Read more »

Getting acquainted with neural network analysis

Neural network, popularly known as Artificial Neural Network (ANN) is an information processing system with a large number of nodes and connections as part of a structure which helps in processing complex information. It is influenced by biological human nervous system which consists of a huge number of neurons connected to each other and work together to find solutions for different specified problems. Similarly, ANN sends different responses from different neurons or nodes to the output layer and this outer layer behaves and takes actions accordingly. Read more »

Performing Canonical Correlation Analysis (CCA)

Until recently, Karl Pearson Correlation analysis was one of the most popular methods to measure linear association between two or more than two variables in a data set. For example, establishing the Karl Pearson Correlation between X variable and Y variable, where both variables belong to a single data set. Canonical Correlation Analysis (CCA), on the other hand, helps measure the correlation among variables which are in different datasets. Read more »

Solving complicated problems with decision tree

A decision tree is a graphical representation of possible solutions to a problem based on given conditions. It is called a tree because diagrammatically it starts with a single box (target variable) and ends up in numerous branches and roots (numerous solutions). It is a type of supervised learning algorithm that have  target variables and in order to select solutions it creates classifications. Based on classifications, however, it is applied on both categorical and continuous variables. Read more »

Meta-analysis for one group

The previous article, explained how to enter data for performing meta-analysis of the studies reporting comparison data. Therefore the present article will explain the data entry methodology, for performing meta-analysis on the outcomes of studies having a single group. For this purpose, the single group of patients who were administered drug treatment will be taken into consideration. Read more »

How to perform cross validation on a data set?

One of the important aspects of data mining is checking the fitness of models for prediction making. However, while checking validity of the models, it becomes difficult to make conclusion since no benchmark result is available for model. Thus to assess the model, a common practice in data science is to iterate over various models and select the most appropriate model. In other words it is important to test the same model with different values of parameters. Read more »

Summary results and analysis for odds ratio

Results can be obtained by clicking on “Run Analysis”. The image below shows the result for meta-analysis of the odds ratio data, for both random and fixed effects model. As seen the relative weighting of studies in the two models are quite different. The random effects model takes into account both the interstudy variance and sample size, resulting in well-distributed weight. Also, as the studies do not follow identical research design, hence random effect model selection ensues. Read more »

Malmquist index summaries interpretations from Malmquist DEA

In the previous article, we had discussed and interpreted results and terms from the results of distance summaries in Malmquist data envelopment analysis (DEA). This article, discusses and interprets the rest of the results from Malmquist DEA. Furthermore, the analysis of Malmquist index summaries for both output and input frontiers are interpreted. Read more »

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