In this tutorial we will cover the following steps:

1. Open the dataset

2. Explore data

3. Make a research question (that can be answered using a linear regression model)

4. Running and reading a simple linear regression

5. Running and reading a multiple linear regression

6. Answering our research question

For tutorials about Linear Regression on R and Stata check the following articles

Back to Python…

1. Open the dataset

We are going to extract a publically available version of the Framingham heart study from; https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset/version/1

On this website, you can also consult the coding for the different variables. For example, regarding gender (variable male; male=1 means male and male=2 means female)

First Download the file to your computer.

Here the Jupyter Notebook version (Dataset downloaded from Kaggle).

Here is the Kaggle Notebook so you can run everything on your browser.

First import all relevant packages

import numpy as np 
import pandas as pd
import statsmodels.formula.api …

In this tutorial we will cover the following steps:

1. Open the dataset

2. Explore data

3. Make a research question (that can be answered using a logistic regression model)

4. Running and reading a simple logistic regression

5. Running and reading a multiple logistic regression

6. Answering our research question

For more on descriptive analysis using RStudio check this post

1. Open the dataset

We are going to extract a publically available version of the Framingham heart study from;

https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset/version/1

On this website, you can also consult the coding of the different variables. For example, regarding gender (variable male; male=1 means male and male=2 means female).

First Download the file to your computer.

Then go to RStudio and open the file via the following options (this is the easiest way).

File -> Import Dataset -> From Text (base) (this is a .csv file)

In previous tutorials, we approached basic descriptive statistics. Now, we will take the next step, inferential analysis using regression to study association.

In this tutorial, we will run and interpret a logistic regression analysis using Stata.

In this tutorial we will cover the following steps:

1. Open the dataset

2. Explore data

3. Make a research question (that can be answered using a logistic regression model)

4. Running and reading a simple logistic regression

5. Running and reading a multiple logistic regression

6. Answering our research question

Before we get started…

For more on descriptive analysis using Stata check this post

Now, let’s roll the numbers (and open our data)!

1. Open the dataset

Let’s revisit the publically available version of the Framingham heart study from https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset/version/1

First Download the file to your computer

Then go to Stata and open the file via the following options (the easiest way)

File -> Import -> Text data (delimited, *.csv…)

In this tutorial we will cover the following steps:

1. Open the dataset

2. Explore data

3. Make a research question (that can be answered using a linear regression model)

4. Running and reading a simple linear regression

5. Running and reading a multiple linear regression

6. Answering our research question

For more on the fundamentals behind a linear regression analysis check this post

For more on descriptive analysis using RStudio check this post

1. Open the dataset

We are going to extract a publically available version of the Framingham heart study from;

https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset/version/1

On this website, you can also consult the coding for the different variables. For example, regarding gender (variable male; male=1 means male and male=2 means female)

First Download the file to your computer.

Then go to RStudio and open the file via the following options (this is the easiest way)

File -> Import Dataset -> From Text (base) (this is a…

In previous tutorials, we approached basic descriptive statistics. Now, we will take the next step, inferential analysis using regression to study association.

In this tutorial, we will run and interpret a linear regression analysis using Stata.

In this tutorial we will cover the following steps:

1. Open the dataset

2. Explore data

3. Make a research question (that can be answered using a linear regression model)

4. Running and reading a simple linear regression

5. Running and reading a multiple linear regression

6. Answering our research question

Before we get started…

For more on the fundamentals behind a linear regression analysis check this post

For more on descriptive analysis using Stata check this post

Now, let’s roll the numbers (and open our data)!

1. Open the dataset

Let’s revisit the publically available version of the Framingham heart study from https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset/version/1

First Download the file to your computer

Then go to Stata and open the file via the following options (the easiest way)

File -> Import -> Text data…

Without a strategy, you lost before you started

In my career in clinical research, I have realized, with the help of great mentors and some costly mistakes, that research is not a random game. There should be a plan, a chart, a strategy, to avoid (or at least lessen) future headaches.

Just like a chess game, there are some core rules one cannot forget. This list could be infinite but let’s talk about the top 8 fundamental rules that I wished I have mastered sooner.

1) Develop your pieces — A single finished project outperforms 1,000 ideas

Moving your Knight back and forth will not develop your game. You need to break…

You have a great research idea and you have a very broad idea of how to get the project done. However, while writing your statistical analysis plan, once again, you end up stuck in the “statistics” section. Then, you waste long hours checking GitHub and Reddit threads and watching jargon-full YouTube tutorials. With a headache, once again, you procrastinate and jump to the next thing.

You are not alone…

This article shares some simple steps on how to plan your data analysis by anticipating what you want to “show” in the final work (manuscript, poster, oral communication, you name it!).

These steps are

1…

The interpretation of p-value is one of the most misunderstood subjects in science and statistics, not only by the general public but also for Academia. This leads to low-quality publications and misleading news, with a consequent distrust in Science. Just stating “p<0.05 = good” and “p-value≥0.05 = bad” is a dangerous oversimplification.

This article tries to debug some concepts, focusing on:

1. Data distribution and description
2. Hypothesis testing
3. The value of the p-value
4. Practical limitations of the p-value
5. How to choose the test to calculate the p-value?

1. Data distribution and description

Let’s take, for example, the height in adult men vs women. Empirically, we all…