STATA, like SPSS is a smart data analysis tool used for data management and analysis. It is a fast and easy to use, across all operating systems such as Windows, Unix and Mac. Our module is based on Windows version of STATA 11.0. The software is available for both 32-bit and 64-bit windows operating systems. The standard version of the software can handle upto 2047 variables whereas the expert edition can handle the data upto 32,766 variables. In this first article I am focussing on the basic interface of the software.
Basic STATA interface. Basic interface of STATA. Command: The window labeled command is where you type in your commands. Results: Once the command is entered the results are shown in larger window as tagged above. Review: History of commands entered are added in the command window of review section. Variables: The list of variables in the data set is added in the variable window.
Properties of variables are mentioned in the same window in case of version 11.0, however after v12.0, the properties tab has been shifted to right hand side corner (see figure below). Shruti is B-Tech & M-Tech in Biotechnology. Some of her strengths include, Good interpersonal skills, eye for detail, well devised analytical and decision making skills and a positive attitude towards life. Her aim in life is to obtain a responsible and challenging position where her education and work experience will have valuable application. She is a true Piscean. She loves doing things to perfection with passion.
Version 11.2 of STATA. This page was last edited on 25 December 2017, at 07:07. All structured data from the main, property and lexeme namespaces is available under.
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Related articles. STATA comes with a set of sample data files. This helps the learner in understanding how different set of tests can be applied to single data. Data entered in STATA can be classified either as numeric or string type. Associated with each type of data is its storage type i.e.
The numbers are stored as byte, int, long, float, or double. STATA takes “float” as the default storage type for its variables. Correlation analysis is conducted to examine the relationship between dependent and independent variables. There are two types of correlation analysis in STATA. The previous article based on the Dickey Fuller test established that GDP time series data is non-stationary. This prevented time series analysis from proceeding further. Therefore, in this article possible solution to non-stationarity is explained.
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.
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