Marx Max can work with your locally installed Stata to help read do-files, write and execute commands, inspect output, and revise an analysis. Stata performs the calculations; a computational notebook keeps code and output together. You decide the research purpose, sample, and interpretation.
For an existing program, start with an error or one regression section. For new data, begin with variable checks and descriptive statistics. A bounded task produces something easier to inspect and revise than asking AI to complete an entire paper at once.
First inspect the executed case and complete downloads. It uses the same auto data and specifications and provides code, coefficients, a log, and figures for comparison.
Connect your local Stata installation
Marx Max does not include Stata or replace its license. You need a legitimate installation with a usable official Python interface. Under Settings → Research tools, select the local Stata installation. Newly started computational environments use this choice; changing it does not alter an environment already running.
Start with a connection check:
Check whether the current environment can use Stata. Do not read my research data, clear existing data, or run existing programs yet. Report whether the connection is usable and explain any specific problem.
If the saved location is unavailable, select it again in Settings. Save code and outputs before restarting a computational environment: a restart clears memory, so data and estimates must be recreated. The Windows edition is still in beta; a working Mac environment is not evidence that a Windows connection is ready. See the download page for system requirements.
A notebook places code beside its output so you can inspect, edit, and run sections. You do not need to learn Python first to use Stata this way.
Start with Stata’s example data
Create a practice project and conversation using Stata’s bundled auto dataset. This lets you learn the workflow without sending your own research material to a model. Ask:
Use Stata’s auto dataset for a practice analysis. Inspect price, weight, and mpg and their descriptive statistics. Estimate price on weight, then add mpg. Use the same complete-case sample and robust standard errors in both models. Show the code, actual output, and observation counts. Do not interpret the regressions causally. Use a new practice notebook and leave existing analyses alone.
The corresponding Stata commands are:
sysuse auto, clear
describe price weight mpg
summarize price weight mpg
generate byte sample = !missing(price, weight, mpg)
regress price weight if sample, vce(robust)
estimates store baseline
regress price weight mpg if sample, vce(robust)
estimates store extended
estimates table baseline extended, b(%9.3f) se(%9.3f) stats(N r2)You can also download the complete do-file and run it independently in a fresh Stata practice session.
The sample indicator fixes observations with nonmissing values in all three variables. clear replaces data currently in memory, so use a fresh practice environment. Do not repeatedly clear processed research data merely to rerun a regression.
In a Python notebook, add %%stata as the first line of the cell; the remaining lines are ordinary Stata commands. The official Stata documentation describes this approach. AI can arrange the code without requiring you to configure the interface manually.
Compare actual model output
Both example models use 74 observations. In an independent PyStata run, the weight coefficients were approximately 2.044 and 1.747; the second model’s mpg coefficient was approximately −49.512. These check the example commands. They do not imply that AI always chooses these specifications or establish a causal relationship in car prices.
Check the data, specification, and sample before reading the interpretation. Then ask:
Using the actual output, compare the weight coefficients, standard errors, sample sizes, and R². Explain how including mpg changes the interpretation of weight. Do not guess causes the output cannot establish.
In the second model, weight describes a linear relationship conditional on mpg. A smaller coefficient is not automatically more correct. The example fixes the comparison sample; in your data, additional regressors may exclude observations, so inspect whether changes reflect the sample or specification. Significance and fit alone do not supply the research rationale.
Read an existing do-file before running it
Place the necessary files in the project directory and identify the data, entry program, and task. Programs may overwrite files, process batches, or run lengthy calculations. Start with inspection:
Read analysis.do and identify its inputs, execution order, and expected outputs. Do not run or modify it yet. Check paths, variable names, and required commands, and list unresolved conditions.
After confirming the interpretation, specify the execution scope:
Run the descriptive statistics and baseline regressions. Keep the original program and do not overwrite source data. Save a new log and results in the project’s output directory. Check errors, generated files, and the model associated with each output.
Whether material may be processed by AI depends on licenses, institutional rules, and confidentiality. Local storage does not mean no content is sent during model processing. Read research data and AI processing before working with restricted material.
Fix errors without silently changing the analysis
Use the actual log to investigate:
Identify the failed command and its context. Explain the cause and propose a minimal change. Do not delete observations, replace variables, or change models just to make the program run. Explain any sample or estimation change and wait for my decision.
A path error, a missing command, and unmet data conditions require different remedies. If earlier processing changes, recheck downstream regressions and figures. For third-party commands that need installation, confirm the purpose and source before treating installation as routine execution.
Save work that can be continued
Ask AI to organize code, logs, results, and necessary instructions, including dependencies on live memory:
Put the independently runnable Stata sections into a do-file with data loading and execution order. Explain which program produces each result and identify unverified steps. Do not overwrite the original.
A saved notebook retains code and saved output, not the memory of a terminated session. If Python also processed data, retain that program or the required intermediate files. Regression commands alone may not reproduce the workflow. Rerun from a defined data starting point before using the materials for collaboration or paper replication.