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**Stata** is a statistical software with great potential that can be employed to organize, analyse, and create visual representation of data. The word Stata is a combination of the words ‘statistics’ and ‘data.’ It is an intuitive software that has a Graphical User Interface as well as a command line, making the interaction more convenient for the user. Stata is used largely by researchers in different fields including economics, political science, biomedicine, and so on. Some of the features which makes it popular among data scientists are its ability to store data in memory making it super-fast in operation, and its command-driven nature. Along with the command box, there is a menu and a dialogue box which give the users access to the built-in commands.

Beginners need to know a few facts before they can start using the **Stata**: firstly, they can go through the **Stata manuals** which are designed to be directional to make Stata easy for users. They impart knowledge about the commands, procedures, and possibilities that Stata offers, along with detailed examples of commands for beginners to learn from. Users have access to several statistical techniques when they use Stata, and the manuals guide the ways in which they can be used or interpreted. The **Reference Manuals** and **User Guide** are useful for these purposes.

Secondly, Stata has on-line help facilities which is a store of all their manuals, and when faced with any confusion, users can simply type “help

Thirdly, the four principle boxes one needs to be familiar with are the ‘results,’ ‘command,’ ‘variables,’ and ‘review’ boxes. The result box is meant to display the user’s input and corresponding output, and the procedure used for arriving at the output. In case of lengthy outputs which the user does not desire to be displayed, they can simply type in the word ‘quietly’ in front of their command. The command box is the one where the commands are typed in by users. The intuitive aspect of Stata allows it to understand commands and variables in abbreviated forms if the same abbreviation is not used for more than one factor. The Variables box reduce work for users by listing the variables which are present in a given data-set (which are stored in the memory); a user can click on a variable to include it in their data. The review box lists all the commands that have been used by the user and clicking on any of these commands would add them to the present command box.

Fourthly, beginners are advocated to use log-files which maintain a record of a user’s past sessions on Stata. This would help the user to track what they did immediately before but also something they did perhaps a month ago. The user decides the time from which they desire to begin or ‘save’ the log-files and when they want to end or ‘close’ the files.
Lastly, Do-files are largely useful for beginners trying to work on scientific projects because it saves time by easily repeating commands. Instead of having to type the command line by file, the do-files help to apply a command to the entire batch by entering them in a text file first.

Stata is used in research and students often need to use Stata in their coursework for organizing, interpreting, or visualizing data. Most universities have their license for Stata which can be collectively used by students from the university library or lab. However, if the University does not provide access to Stata, students can still use the software on their own. Stata offers a free use period of 7 days to students which benefits anyone who needs to use the software for a single project. However, if a student needs to use the software for more than 7 days, they can purchase a student’s license at discounted price from Stata’s official website.

Both **SPSS** and Stata are used in **statistical analysis** but there are a number of differences between them. Our experts provide excellent solution for Stata as well as SPSS. Given below are seven key differences between SPSS vs Stata:

- Stata is a general-purpose software which can analyse normal data, but it not suited for dealing with extraordinarily complex data; SPSS, on the other hand, is designed to structure immensely complex data and to analyse the same.
- Stata is suited for normal analysis of limited data, but SPSS is built to work with multiple variants, processing and analysing huge quantity of data which are drawn from different sources. SPSS can work with bulk data.
- SPSS is better versed with various statistical models compared to Stata which has moderate functionality. Clearly, SPSS is more advanced than Stata. However, Stata is considerably useful in research. SPSS is ideal for Big Data; for instance, excel spread sheet data can be better managed by SPSS, but Stata would be ideal for research that needs to use state-of-art statistical analysis.
- In SPSS, the output is directly generated into reports but with Stata there is more manual control and better scope for manipulation. The command line and the documentation features leave it in the hands of the users to choose what they want to do with their output.
- SPSS mostly finds use in the fields of Medical and Social Sciences while Stata is used extensively in econometrics.
- SPSS is useful in creating latest forms of charts which can be further customized with Microsoft Office Tools. The SPSS chart builder can generate charts which are ready for publication. Stata has a limited number of models for combining data for visual representation which can be binary, censored, categorical, and can be combined in different manners.
- Various format syntaxes like edit, write, and others available with SPSS enables creation of duplicate lines, deletion, and addition of lines, upward and downward shifting of the lines, thereby providing agility to the user. Stata comes with its own set of attractive functionalities that include spatial autoregression models with observational spatial units that can be used in geographical research.

We can help you with your Statistics, SPSS & Stata homework as well as **R programming** homework. Some key differences between Stata & R are as listed below:

- R is open source and therefore, using R incurs no cost. Stata, however, requires the users to purchase a license. Stata is better than other similar software because it does not require yearly renewals and additional costs need to be paid only when one is trying to upgrade to a newer version.
- Stata is much easier to use compared to R and the way Stata presents the results is popularly preferred over R. However, with change in use of statistics, the softwares in use are required to catch up and R has been more successful in this area compared to Stata. R, being free itself, works with other free softwares on the internet such as knitr and latex. R is ahead of Stata in terms of data mining, automated report writing, computer intensive analysis methods, graphics and so on.
- The quantity of data that can be handled by Stata and R also plays a major role in determining one’s advantage over the other. Stata imposes an arbitrary limitation on the amount of data, which makes it problematic for researchers in many fields to use the software. R, however, is equipped to work with Big Data just like SPSS, and the amount of data the user can feed is only limited by the capacity of their computers.
- Stata is owned and controlled by the parent company StataCorp and growth of the software is in the hands of the company alone. R, being an open source, is basically owned by the users and can therefore be modified and updated at a faster pace. With academic statisticians and statistical computing enthusiasts having adopted R, it has thousands of brains contributing to its growth simultaneously, which is difficult for Stata to keep up with.

**Linear regression analysis**, also called **bivariate regression analysis**, is used to generate the value of a dependant variable based on the value of an independent variable. Stata fits linear regression models under the head least-squares and once that is done, Stata can calculate predictions, standardized residuals, errors in forecasts or predictions or residuals, specification tests, variance-inflation factors, and so on. Factor variables are used in the process. Linear Regression analysis using Stata is based on seven chief assumptions, which are as follows:

- The dependant variable should be at a continuous level for it to be measured. Examples of such variables would be age, height, temperature (weather), sales numbers etc.
- The independent variable should also be measured at a continuous level like the dependant variable, or it could have a categorical level such as ethnicity, gender, physical activities, and so on. Continuous level is ideal for linear regression, and even though Stata can work with categorical independent variable in linear regression, one-way ANOVA or independent t-test would be better alternatives.
- A linear relationship must exist between the dependant and independent variables. This third assumption and the following ones can be checked with the helped of Stata itself. A scatterplot can be created using Stata to visually verify if the variables share a linear relationship.
- There should be no notable outlier in the plot since the outliers can have an impact on the regression equation used to predict the relation between the variables. Outliers reduce the accuracy of statistical results, and in Stata they can be removed using case wise diagnostics.
- Independence of observance must be ensured, which can be performed using Stata’s Durbin-Watson Statistics.
- Data should reflect homoscedasticity.
- Residuals of the plot should be normally distributed. Histogram or normal P-P Plot on Stata can be used to verify this assumption.

When the assumptions are satisfied, two variables should be created, and scores related to each must be entered into the Data Editor spreadsheet in two different columns. Analysis can be performed with the use of code or Stata’s GUI. Results can then be interpreted and reported using Stata. Multiple regression can be performed in the same manner using Stata, however, there the variables would have to meet an additional assumption of multicollinearity, which is a case where two or more independent variables exhibit high level of correlation between themselves. In Stata, this can be verified with the help of correlation coefficients and Tolerance or VIF values.

At the time of writing this article, **Stata 16** is the latest version of the software which was launched in June 2019. This updated version introduced several new features and tools, some significant ones are listed below:

- Improved
**Stata Bayesian analysis**as Stata incorporated new commands such as Multiple Chains, MCMC Replicates, Bayesian Predictions, Posterior predictive p-values, Gelman-Rubin Convergence Diagnostics, Posterior summaries of simulated values and so on. - Frames have been modified and multiple datasets can now be simultaneously loaded into the frames, related frames can be linked, and reports can be recorded in separate frames. The new version accommodates multitasking and runs faster than the previous ones.
- Stata 16 allows for data to be imported from SPSS and SAS, and the additional support radically improves the functionality of Stata the same way R benefits from cooperating with other open source softwares.
- New Meta-Analysis suite of Stata 16 would allow users to summarize results from multiple studies. Users would be able to estimate the size of overall effect, perform meta-regression, analyse subgroups, evaluate publication bias, display results in forest plots, explore effects of small-study, perform cumulative meta-analysis, conduct fixed effects, random effects, and common effect meta-analysis and so on.
- In addition to SPSS and SAS, Users would be able to access any desired Python package from within Stata. Scrapy to Scrape data, Matplotlib used for 3-D graphs, and TensorFlow for machine learning are particularly expected to help the users.
- The advanced Lasso tools bring in a wide range of utility enabling the extraction of relevant features from bulks of data. The new tools employed for model selection and prediction include goodness of fit, cross-validation, knot analysis, coefficient paths, and several others. With the Lasso tools, users would be able to demarcate groups and patterns in the data, detect relationships which are potentially nonlinear to a great degree, manage endogenous covariates and unobserved confounders, predict outcomes, etc. The tools would considerably expand Stata 16’s applications in the practical world, especially in the spheres of econometrics, machine-learning, and statistics.
- Reporting is more convenient because Stata results or graphs can be saved in MS Word, Excel, PDF and html formats. This reducible reporting feature would also ensure that all these documents are updated corresponding to any change in the data.

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