The purpose of this article is to explain the process of determining and creating stationarity in time series analysis. Creating a visual plot of data is the first step in time series analysis. Graphical representation of data helps understand it better.
Path analysis is a graphical representation of multiple regression models. In this analysis, the graphs represent the relationship between dependent and independent variables with the help of square and arrows.
The present article takes up the datasheets for the unmatched post and pre or post design and illustrates the results with statistics. The present discussion will focus on the interpretation of the results.
This is the continued article of the interpretations for variable returns to scale (VRS) from the last article. However, this article is about the summary of peers and the rest of the analysis conducted for VRS-DEA (Data envelopment analysis).
Acquaintance with different methods of entering data in the software makes the task simpler. The manual data entry included entering the raw data for the dichotomous category for the study investigating one group.
The previous article illustrated the manual data entry procedure to facilitate data analysis for performing meta-analysis in comprehensive meta analysis (CMA) application.
The meta-analysis results involve basic summary statistics, forest plots and model statistics. Besides these, also funnel plot can be obtained to study publication bias amongst the studies in the analysis.
This article helps in importing and loading data from other programs such as Ms-Excel. It will take up the case example of Cholecystitis and gallbladder carcinoma illustrated in manual data entry procedure.