In this article, the basic interface of the GraphPad Prism is demonstrated. The demonstrations will indicate how to get started with the Prism software. In the previous article, a brief introduction to the software was discussed. Read more »
A previous article demonstrated how to predict values for a variable that follows an autoregressive process. It showed that the first step is to identify an appropriate order of the autoregressive process. Then perform ARIMA modelling of the variable before generating the forecast. This article explains how to perform point forecasting in STATA, where one can generate forecast values even without performing ARIMA. Therefore, it is useful in any time series data. Read more »
In the previous article, introductory aspects and the interface of Hamlet II were presented. Text analysis, or text frequency analysis, is an important and common text-based analysis using Wordlist. In this analysis, the transcript or the text file is assessed for occurrence or repetition or frequency of words. This is known as the wordlist analysis in Hamlet II. Wordlist creates a list of all the most repetitive words in a text file thereby showing the importance of the reoccurring words.
Text analysis is the technique of assessing texts from a draft or mainly a transcript. Text analysis also helps to create an overview of the most common words and show relevance to a research study. Text frequency analysis is also known as wordlist. Read more »
The previous article (Pooled panel data regression in STATA) showed how to conduct pooled regression analysis with dummies of 30 American companies. The results revealed that the joint hypothesis of dummies reject the null hypothesis that these companies do not have any alternative or joint effects. Therefore pooled regression is not a favourable technique for the panel data sets. It is also due to the fact that inclusion of too many dummies can lead to consequent loss of degrees of freedom. Therefore pooled regression is not the right technique to analyze panel data series. Therefore the present article intends to introduce to the concept of random effect model in STATA. Read more »
The preceding articles showed how to conduct time series analysis in STATA on a range of univariate and multivariate models including ARIMA, VAR (Lag selection and stationarity in VAR with three variables in STATA) and VECM (VECM in STATA for two cointegrating equations). Time series data requires some diagnostic tests in order to check the properties of the independent variables. This is called ‘normality’. This article explains how to perform normality test in STATA. Read more »
Before applying panel data regression, the first step is to disregard the effects of space and time and perform pooled regression instead. In this, a usual OLS regression helps to see the effect of independent variables on the dependent variables disregarding the fact that data is both cross-sectional and time series. The underlying assumption in pooled regression is that space and time dimensions do not create any distinction within the observations and there are no set of fixed effects in the data. This article explains how to perform pooled panel data regression in STATA. Read more »