How to Run a Regression Analysis in Stata

Learning how to run a regression analysis in Stata is important for students, dissertation writers, researchers, and data analysts who need to examine relationships between variables. Regression analysis helps you determine whether one or more independent variables explain or predict changes in a dependent variable.

For example, a student may want to know whether study time predicts exam scores. A business researcher may examine whether service quality predicts customer satisfaction. A healthcare researcher may test whether income, education, and age predict access to healthcare services.

Stata is widely used for quantitative data analysis because it allows researchers to manage datasets, run statistical models, check assumptions, and produce results for academic reporting. However, running regression in Stata is not just about typing one command. You also need to prepare the data, choose the correct variables, understand the output, test assumptions, and explain the findings clearly.

This guide walks you through the full process of running a regression analysis in Stata, from opening your dataset to reporting your results in a dissertation or research paper. If you need support with data cleaning, Stata commands, interpretation, or Chapter 4 writing, SPSS Dissertation Help can assist with the full process.

How Do You Run a Regression Analysis in Stata?

To run a regression analysis in Stata, use the regress command. The dependent variable comes first, followed by the independent variables.

regress dependent_variable independent_variable1 independent_variable2 independent_variable3

Example:

regress exam_score study_time attendance age

This command tells Stata to predict exam_score using study_time, attendance, and age.

Step	What You Do
1	Open or import your dataset
2	Inspect the variables
3	Check missing values
4	Confirm variable coding
5	Identify the dependent variable
6	Identify the independent variables
7	Run the regress command
8	Review the regression output
9	Check assumptions
10	Interpret and report the results

For dissertation work, the most important part is not only running the model. You must also explain whether the model answers your research question, whether the results are statistically significant, and what the findings mean.

What Is Regression Analysis in Stata?

Regression analysis is a statistical technique used to examine the relationship between a dependent variable and one or more independent variables.

The dependent variable is the outcome you want to explain or predict. The independent variables are the predictors that may influence the outcome.

For example:

Research Question	Dependent Variable	Independent Variable
Does study time predict exam score?	Exam score	Study time
Does service quality predict satisfaction?	Customer satisfaction	Service quality
Does income predict savings behavior?	Savings behavior	Income
Does leadership style predict engagement?	Employee engagement	Leadership style

In Stata, the standard command for linear regression is:

regress

Linear regression is commonly used when the dependent variable is continuous. Examples of continuous dependent variables include test scores, income, sales revenue, productivity scores, satisfaction ratings, and performance scores.

Regression analysis is useful because it does more than describe your data. It helps you test whether relationships between variables are statistically meaningful. Students who need help choosing the right regression model can also review Regression Analysis Help for more support.

When Should You Use Regression Analysis?

You should use regression analysis when your research question focuses on prediction, explanation, or the relationship between variables.

Regression is useful when you want to answer questions such as:

Does one variable predict another variable?
Does a predictor remain significant after controlling for other variables?
How much does the dependent variable change when an independent variable increases?
Which predictors explain the most variation in the outcome?
Does the evidence support the research hypothesis?

Common dissertation questions that use regression include:

Dissertation Question	Possible Analysis
Does employee training predict job performance?	Linear regression
Does service quality predict customer satisfaction?	Linear regression
Does financial literacy predict savings behavior?	Linear regression
Do income and education predict healthcare access?	Multiple regression
Does leadership style predict employee engagement?	Linear regression

Linear regression is appropriate when your dependent variable is continuous. If your dependent variable is binary, such as yes/no or pass/fail, logistic regression may be more appropriate. If your data follows people, firms, schools, or countries over time, panel regression may be needed.

This page focuses on linear regression using Stata’s regress command. For broader support with statistical methods and dissertation analysis, visit Dissertation Statistics Help.

Types of Regression Used in Stata

Stata can run many types of regression models. The correct model depends on your research question and the type of dependent variable.

Regression Type	When to Use It	Example Dependent Variable
Linear regression	Continuous outcome	Exam score, income, satisfaction score
Multiple linear regression	Continuous outcome with several predictors	Job performance score
Logistic regression	Binary outcome	Yes/no, pass/fail
Ordered logistic regression	Ordered categories	Low, medium, high satisfaction
Poisson regression	Count outcome	Number of visits
Panel regression	Repeated observations over time	Firm-year performance

This article focuses on ordinary least squares linear regression. More advanced models should be handled separately so the analysis stays focused and clear.

Before Running Regression in Stata

Before running a regression model, make sure your data and research design are ready. Many students make mistakes because they run the command before checking the dataset.

Before you begin, confirm the following:

Checkpoint	Why It Matters
The research question is clear	The model must answer the right question
The dependent variable is correct	Stata treats the first variable after regress as the outcome
The independent variables are correct	These are the predictors in the model
The variables are coded properly	Incorrect coding can lead to wrong interpretation
Missing values are checked	Missing data can reduce the regression sample
The dependent variable is continuous	Linear regression requires a continuous outcome
Assumptions can be tested	Regression results must be reliable

This preparation is especially important in dissertation research because supervisors and committees often expect students to justify the statistical method used. If you need help preparing your dataset before running the model, Stata Data Analysis Help can support you with data cleaning, coding, missing values, and variable setup.

How Much Sample Size Do You Need for Regression in Stata?

Sample size matters because regression models become less reliable when there are too few observations for the number of predictors. A very small sample can produce unstable coefficients, wide confidence intervals, and weak statistical power.

There is no single sample size rule that works for every dissertation, but students should think about the number of predictors, missing values, expected effect size, and university requirements.

A simple way to think about it is:

Issue	Why It Matters
Too few cases	Results may be unstable
Too many predictors	Model may become overfitted
Missing values	Stata may exclude cases from the regression
Small effect sizes	Larger samples may be needed
Control variables	More controls usually require more observations

For example, a model with 2 predictors is usually easier to support than a model with 12 predictors. If your sample size is small, avoid adding unnecessary variables. Every predictor should be connected to your research question, theory, or literature review.

Before running the final regression, always compare your original sample size with the number of observations Stata uses in the model. If many cases are dropped because of missing data, your final regression sample may be much smaller than expected.

How to Choose Control Variables for Regression

Control variables are variables included in the regression model to account for other factors that may influence the dependent variable. They help you estimate the relationship between your main independent variable and dependent variable more clearly.

For example, if you are studying whether study time predicts exam score, you may control for attendance, age, or prior academic performance. These variables may also influence exam scores, so including them can make your model stronger.

Good control variables usually come from:

Source	Example
Literature review	Prior studies controlled for age and income
Theory	A framework suggests education affects the outcome
Research design	Demographic factors may influence the dependent variable
Committee feedback	Supervisor requests specific controls
Practical reasoning	A variable is clearly related to the outcome

Do not add control variables randomly. Adding too many controls can make the model harder to interpret and may reduce statistical power. A strong regression model should be focused, justified, and connected to the research question.

Step 1: Open or Import Your Dataset in Stata

The first step is opening your dataset in Stata.

If your dataset is already saved as a Stata file, use:

use "dataset.dta", clear

If your data is in Excel, use:

import excel "dataset.xlsx", firstrow clear

The firstrow option tells Stata that the first row of the Excel file contains variable names. The clear option removes any dataset already loaded in Stata.

After importing the data, save it as a Stata file:

save "dataset_clean.dta", replace

Saving your file makes it easier to continue cleaning and analyzing the dataset later.

Step 2: Inspect Your Dataset

After opening the dataset, inspect the data structure. This helps you understand the variable names, labels, data types, and number of observations.

Use:

describe

This command shows the variables in the dataset, their storage types, display formats, and labels.

Next, use:

summarize

This gives basic descriptive statistics, including the number of observations, mean, standard deviation, minimum, and maximum.

For more detail, use:

summarize, detail

This provides additional information such as percentiles, skewness, and kurtosis.

You can inspect a specific variable with:

codebook variable_name

For categorical variables, use:

tabulate variable_name

Example:

tabulate gender

These commands help you identify possible issues before running your regression model.

Step 3: Check for Missing Values

Missing values can affect regression analysis because Stata excludes any observation that has missing data in one or more variables used in the model.

To check missing values, use:

misstable summarize

To check missing values for specific variables, use:

misstable summarize exam_score study_time attendance age

Suppose your original dataset has 300 observations, but your regression output only uses 245 observations. That means 55 cases were excluded because of missing data in one or more variables.

Stage	Number of Observations
Original dataset	300
Regression sample	245
Excluded cases	55

This matters because missing values can reduce statistical power and affect the reliability of your findings.

In a dissertation, you may need to explain how missing data was handled. You should not ignore missing values, especially when the number of excluded cases is large.

Step 4: Check Variable Coding

Before running regression, confirm that your variables are coded correctly. This is especially important for categorical variables.

For example, gender may be coded as:

Code	Meaning
0	Male
1	Female

Or it may be coded as:

Code	Meaning
1	Male
2	Female

The interpretation of the regression coefficient depends on the coding. If you do not understand how a variable is coded, you may explain the result incorrectly.

Use:

tabulate gender

You can also use:

codebook gender

If you need to create a dummy variable, you can use:

generate female = .
replace female = 1 if gender == 2
replace female = 0 if gender == 1

This creates a variable where female is coded as 1 and male is coded as 0.

Stata also allows factor variable notation. For categorical variables, you can use i. before the variable name:

regress exam_score study_time attendance i.gender

The i.gender tells Stata to treat gender as a categorical variable.

How to Use Dummy Variables in Stata Regression

Dummy variables are used when you want to include categorical variables in regression. A dummy variable usually has two values: 0 and 1.

For example, if gender is coded as male and female, you may create a dummy variable where:

Dummy Variable	Meaning
0	Male
1	Female

If the coefficient for the dummy variable is positive, it means the group coded as 1 has a higher predicted value than the reference group, holding other variables constant.

You can create a dummy variable manually:

generate female = .
replace female = 1 if gender == 2
replace female = 0 if gender == 1

Or you can use Stata’s factor variable notation:

regress exam_score study_time attendance i.gender

Using i.gender is often easier because Stata automatically treats gender as a categorical variable and chooses a reference category.

Dummy variables are common in dissertation regression models because many studies include demographic variables such as gender, employment status, education level, marital status, or group membership.

Step 5: Identify the Dependent Variable

The dependent variable is the outcome you want to explain or predict. In Stata, the dependent variable comes immediately after the regress command.

Example:

regress exam_score study_time attendance age

In this command, exam_score is the dependent variable.

For linear regression, the dependent variable should usually be continuous.

Dependent Variable	Suitable for Linear Regression?
Exam score	Yes
Income	Yes
Customer satisfaction score	Yes
Employee engagement score	Yes
Sales revenue	Yes
Pass/fail status	No
Yes/no response	No

If your dependent variable is not continuous, a different regression model may be better.

Step 6: Identify the Independent Variables

Independent variables are the predictors used to explain the dependent variable.

For example, suppose your research question is:

Does study time predict exam score after controlling for attendance and age?

Your variables would be:

Role	Variable
Dependent variable	exam_score
Main independent variable	study_time
Control variable	attendance
Control variable	age

The Stata command would be:

regress exam_score study_time attendance age

In dissertation research, independent variables should come from your research questions, hypotheses, literature review, and conceptual framework.

Do not add predictors randomly. Each variable should have a clear reason for being included in the model.

Step 7: Run the Regression Command in Stata

Once your data is prepared and your variables are selected, run the regression command.

The basic syntax is:

regress y x1 x2 x3

Where:

Symbol	Meaning
y	Dependent variable
x1	First independent variable
x2	Second independent variable
x3	Third independent variable

Example:

regress exam_score study_time attendance age

This model estimates whether study time, attendance, and age predict exam score.

If you have a categorical predictor, use factor notation:

regress exam_score study_time attendance age i.gender

If you want robust standard errors, use:

regress exam_score study_time attendance age, robust

The regression output will appear in the Stata Results window.

Step 8: Understand the Stata Regression Output

After running the regression command, Stata produces an output table. The output has two main sections.

The top section summarizes the overall model. The lower section shows the results for each independent variable.

The top section usually includes:

Output Item	Meaning
Number of obs	Number of observations used in the model
F statistic	Tests whether the overall model is significant
Prob > F	P-value for the overall model
R-squared	Proportion of variation explained by the model
Adjusted R-squared	R-squared adjusted for number of predictors
Root MSE	Average prediction error

The coefficient table usually includes:

Output Item	Meaning
Coef.	Estimated effect of the predictor
Std. Err.	Standard error of the coefficient
t	Test statistic
P>	t
95% Conf. Interval	Lower and upper confidence interval

For dissertation reporting, the most important values are usually the coefficient, p-value, confidence interval, R-squared, adjusted R-squared, and overall model significance. If you already have output but are unsure what it means, Regression Analysis Help can help you interpret your results correctly.

How to Interpret Regression Coefficients in Stata

The coefficient tells you how much the dependent variable is expected to change when the independent variable increases by one unit, holding other variables constant.

Suppose your regression output looks like this:

Variable	Coefficient	p-value
study_time	2.45	0.003
attendance	0.80	0.041
age	-0.12	0.511

The coefficient for study_time is 2.45. This means each one-unit increase in study time is associated with a 2.45-point increase in exam score, holding attendance and age constant.

The coefficient for attendance is 0.80. This means higher attendance is associated with higher exam scores, holding the other variables constant.

The coefficient for age is negative, but the p-value is 0.511. This means age is not statistically significant in this model.

Coefficient Direction	Meaning
Positive coefficient	As the predictor increases, the outcome increases
Negative coefficient	As the predictor increases, the outcome decreases
Coefficient close to zero	The predictor has little estimated effect
Significant coefficient	The predictor is statistically related to the outcome
Non-significant coefficient	There is not enough evidence of a relationship

The phrase “holding other variables constant” means that the effect of one predictor is estimated while accounting for the other variables in the model.

How to Interpret P-Values in Stata Regression

The p-value helps you determine whether a predictor is statistically significant.

A common rule is:

P-Value	Interpretation
p < .001	Highly statistically significant
p < .01	Very statistically significant
p < .05	Statistically significant
p > .05	Not statistically significant

If the p-value for study_time is 0.003, you can say that study time is a statistically significant predictor of exam score.

If the p-value for age is 0.511, you can say that age is not a statistically significant predictor of exam score.

However, p-values should not be interpreted alone. You should also consider the coefficient size, confidence interval, sample size, theory, and practical meaning of the result.

How to Interpret R-Squared in Stata

R-squared shows how much variation in the dependent variable is explained by the independent variables in the model.

For example, if R-squared is 0.42, the model explains 42% of the variation in the dependent variable.

A dissertation-style interpretation could be:

The model explained 42% of the variance in exam score, suggesting that study time, attendance, and age accounted for a meaningful proportion of differences in student performance.

Adjusted R-squared is similar to R-squared, but it adjusts for the number of predictors in the model. It is often useful when comparing models with different numbers of independent variables.

You should not rely only on R-squared. A high R-squared does not automatically mean the model is correct, and a low R-squared does not always mean the model is weak. In many social science, education, business, and healthcare studies, lower R-squared values can still be meaningful.

Step 9: Check Regression Assumptions in Stata

Regression assumptions help determine whether your model is appropriate and whether the results can be trusted.

The main assumptions of linear regression include:

Assumption	Meaning
Linearity	The relationship between predictors and outcome is approximately linear
Independence	Observations are independent
Normality of residuals	Residuals are approximately normally distributed
Homoscedasticity	Residual variance is constant
No multicollinearity	Predictors are not too highly correlated
No extreme influential outliers	No single case strongly distorts the model

Check Linearity and Residual Patterns

After running the regression, use:

rvfplot

This creates a residual-versus-fitted plot. Ideally, the residuals should be randomly scattered around zero. Strong patterns may suggest nonlinearity or heteroscedasticity.

Check Normality of Residuals

First, create residuals:

predict residuals, resid

Then use:

histogram residuals, normal

You can also use:

qnorm residuals

These commands help you check whether residuals are approximately normally distributed.

Check Heteroscedasticity

Use:

estat hettest

If the test is significant, it may suggest heteroscedasticity. In that case, you may consider robust standard errors.

Check Multicollinearity

Use:

vif

VIF stands for variance inflation factor. High VIF values may indicate multicollinearity among the independent variables.

A common guideline is that VIF values above 5 or 10 may be a concern, depending on your field and university requirements.

If you need help checking assumptions, interpreting diagnostic tests, or deciding whether robust standard errors are appropriate, Quantitative Data Analysis Help can help you review your model carefully.

Step 10: Run Regression with Robust Standard Errors

If there is evidence of heteroscedasticity, you may run the regression with robust standard errors.

Example:

regress exam_score study_time attendance age, robust

The robust option adjusts the standard errors. This can make p-values and confidence intervals more reliable when the constant variance assumption is violated.

Robust standard errors do not usually change the coefficients. They mainly affect the standard errors, test statistics, p-values, and confidence intervals.

However, robust standard errors do not fix every problem. They do not correct poor model selection, missing variables, incorrect coding, or weak research design.

Step 11: Export Regression Results from Stata

After running regression analysis in Stata, you may need to export the results to Word or Excel for your dissertation.

Common Stata export commands include:

outreg2

esttab

asdoc

Example using outreg2:

outreg2 using regression_results.doc, replace

Example using asdoc:

asdoc regress exam_score study_time attendance age

Exporting results can help you create clean tables, but the table alone is not enough. You still need to explain the results in your own academic writing.

Step 12: Report Regression Results in a Dissertation

A strong dissertation regression section should explain what analysis was conducted, why it was used, which variables were included, whether the model was significant, and what the findings mean.

You should report:

Item	What to Include
Type of analysis	Linear regression or multiple linear regression
Dependent variable	The outcome variable
Independent variables	The predictors in the model
Model significance	F statistic and p-value
Model fit	R-squared and adjusted R-squared
Predictor results	Coefficients, standard errors, p-values, confidence intervals
Assumption checks	VIF, residuals, heteroscedasticity, outliers
Conclusion	Whether the hypothesis was supported

Example dissertation write-up:

A multiple linear regression was conducted to examine whether study time predicted exam score after controlling for attendance and age. The overall model was statistically significant, F(df1, df2) = X.XX, p < .05, and explained XX% of the variance in exam score. Study time was a statistically significant positive predictor of exam score, b = X.XX, SE = X.XX, p < .05, indicating that higher study time was associated with higher exam scores. Attendance was also a statistically significant predictor, while age was not statistically significant. These findings suggest that study behavior and class attendance are important predictors of student academic performance.

Replace the X values with your actual Stata output.

If you need support writing the results section in a clear academic format, Dissertation Statistics Help can assist with results interpretation, tables, and Chapter 4 reporting.

Full Example: Running Regression Analysis in Stata

Suppose your dissertation topic is:

The effect of study time and attendance on student academic performance.

Your variables are:

Variable	Role
exam_score	Dependent variable
study_time	Main independent variable
attendance	Independent variable
age	Control variable
gender	Control variable

Step 1: Inspect the Data

describe
summarize exam_score study_time attendance age
tabulate gender
misstable summarize

Step 2: Check Correlations

correlate exam_score study_time attendance age

Correlation helps you see whether variables are related before running the regression. It does not replace regression, but it can provide useful early insight.

Step 3: Run the Regression Model

regress exam_score study_time attendance age i.gender

This command estimates whether study time, attendance, age, and gender predict exam score.

Step 4: Check Multicollinearity

vif

This checks whether the independent variables are too highly related to each other.

Step 5: Check Heteroscedasticity

estat hettest

This tests whether the residual variance is constant.

Step 6: Run Robust Regression if Needed

regress exam_score study_time attendance age i.gender, robust

This runs the same regression model with robust standard errors.

Step 7: Interpret the Findings

If study_time has a positive coefficient and p < .05, you may write:

Study time was a statistically significant positive predictor of exam score. This indicates that students who spent more time studying tended to have higher exam scores, holding attendance, age, and gender constant.

If attendance is significant, you may write:

Attendance was also a statistically significant predictor of exam score, suggesting that students with higher attendance tended to perform better academically.

If age is not significant, you may write:

Age was not a statistically significant predictor of exam score in the model.

Common Mistakes When Running Regression in Stata

Running Regression Before Cleaning the Data

Do not run your final regression before checking your dataset. Missing values, incorrect coding, duplicate cases, and unusual outliers can affect your results.

Using the Wrong Dependent Variable

Linear regression is appropriate for continuous dependent variables. If your outcome is binary, ordinal, categorical, or count-based, another regression model may be more appropriate.

Placing Variables in the Wrong Order

In Stata, the dependent variable comes first.

Incorrect:

regress study_time exam_score age

Correct:

regress exam_score study_time age

The incorrect version predicts study time instead of exam score.

Ignoring Missing Values

Stata automatically removes observations with missing values in any variable included in the model. Always compare your original sample size with the number of observations in the regression output.

Misinterpreting P-Values

A p-value tells you whether a result is statistically significant. It does not tell you whether the effect is large, important, or meaningful in practice.

Ignoring Regression Assumptions

Regression assumptions should be checked before final reporting. If assumptions are ignored, your findings may be questioned.

Reporting Output Without Interpretation

A regression table is not enough. You need to explain what each important result means.

Weak reporting:

The coefficient was 2.45 and the p-value was 0.003.

Stronger reporting:

Study time was a statistically significant positive predictor of exam score, b = 2.45, p = .003, indicating that each one-unit increase in study time was associated with a 2.45-point increase in exam score, holding attendance and age constant.

Adding Too Many Variables

Do not add predictors only to increase R-squared. Each variable should be supported by your literature review, research question, or conceptual framework.

Regression Analysis in Stata for Dissertation Research

Regression analysis is one of the most common methods used in dissertation research because it helps test relationships between variables.

Students often use Stata regression analysis in fields such as business, education, economics, healthcare, public health, psychology, political science, nursing, and social science.

Examples include:

Field	Regression Question
Business	Does service quality predict customer satisfaction?
Education	Does study time predict academic performance?
Healthcare	Do income and education predict healthcare access?
Psychology	Does stress predict life satisfaction?
Management	Does leadership style predict employee engagement?
Finance	Does financial literacy predict savings behavior?

A strong dissertation regression analysis should include a clear research question, correct model selection, clean data, accurate Stata commands, assumption testing, proper interpretation, and a clear connection to the study’s hypotheses.

For students working on a full research project, SPSS Dissertation Help can support the process from data preparation to results reporting.

Stata Regression Analysis Help for Dissertations

Running a regression command in Stata is only one part of the analysis. Many students can type the regress command, but still struggle with choosing the correct model, preparing the dataset, checking assumptions, interpreting the output, and writing the results clearly in Chapter 4.

At SPSS Dissertation Help, we support students and researchers who need help with Stata regression analysis for dissertations, theses, capstone projects, journal articles, and academic research. Our goal is to help you move from raw data to clear, accurate, and well-explained results.

If you need support preparing your dataset before running the model, our Stata Data Analysis Help service can help with data cleaning, coding, labeling, missing values, and variable setup. If your main challenge is choosing the right model or interpreting coefficients, p-values, confidence intervals, and R-squared, our Regression Analysis Help service can support you through the full regression process.

For students working on thesis or dissertation projects, our Dissertation Statistics Help and Quantitative Data Analysis Help services can help connect your statistical results to your research questions, hypotheses, and Chapter 4 writing requirements.

Stata regression support may include help with:

Area of Support	What It Covers
Data preparation	Cleaning data, coding variables, labeling values, and checking missing data
Model selection	Choosing the correct regression approach based on your research questions
Regression analysis	Running linear regression, multiple regression, and related Stata commands
Assumption testing	Checking multicollinearity, heteroscedasticity, residuals, and outliers
Output interpretation	Explaining coefficients, p-values, confidence intervals, R-squared, and model fit
Results writing	Writing Chapter 4 findings in clear academic language
APA reporting	Formatting regression results, tables, and statistical statements properly

A strong regression analysis should do more than produce Stata output. It should explain what the results mean, show whether the hypotheses were supported, and connect the findings back to the purpose of the study.

If you are unsure whether your regression model is correct, or if you need help interpreting your Stata output, Request a Quote Now and get support with your Stata regression analysis, assumptions, interpretation, and dissertation results writing.

FAQs About How to Run a Regression Analysis in Stata

Conclusion

Knowing how to run a regression analysis in Stata is important for students and researchers who need to test hypotheses, examine relationships between variables, and report quantitative findings. The process begins with preparing your dataset, checking your variables, identifying the dependent and independent variables, and running the correct regression command.

After running the model, you need to interpret the output carefully. This includes understanding coefficients, p-values, confidence intervals, R-squared, model significance, and assumption checks. For dissertation research, your final write-up should connect the findings back to your research questions and hypotheses.

A strong regression analysis is not only about producing Stata output. It is about choosing the right model, checking the data, interpreting the findings correctly, and presenting the results in a clear academic format.

If you need help with Stata regression analysis, data cleaning, assumption testing, output interpretation, APA reporting, or Chapter 4 results writing, SPSS Dissertation Help can support you from data preparation to final results reporting. You can also Request a Quote Now to get help with your Stata regression project.