🧮 Dive into Simple Linear Regression! Establish correlations and predict outcomes. Import the dataset, preprocess the data, and fit it into a regression model using Python. 🤝🔍 #LinearRegression #DataPreprocessing #Python

https://medium.com/towards-data-science/simple-linear-regression-in-python-8cf596ac6a7c

💼📊 Want to determine the relationship between employee salaries and experience? Use Simple Linear Regression! Learn how to preprocess data, fit the model, and visualize the correlation in Python. #LinearRegression #DataPreprocessing #Python

https://medium.com/towards-data-science/simple-linear-regression-in-python-8cf596ac6a7c

📈 Homoscedasticity is a crucial assumption in linear regression! 🔍 Check your data using the goldfeld-quant test from the lmtest-package in R. #rstats #r4ds #datascience #machinelearning #linearregression

A little lm() for my #RStats readers #R #DataAnalysis #regression #LinearRegression
Post:

A little lm() for my #RStats readers #R #DataAnalysis #regression #LinearRegression
Post:

📢 Are you interested in #MachineLearning 🤖 and #Python 🐍 but do not know where to start? You can check out my free course on YouTube▶️. I plan to release new recorded lectures and Python labs every week.
Please repost 🔃

Lecture 3 - The connection between #LinearRegression, Hypothesis Testing, Confidence Intervals, and Correlation
https://www.youtube.com/watch?v=-PEoLbqgnQ8

Link to the complete course playlist on YouTube
Data Science Methods and Statistical Learning
https://www.youtube.com/playlist?list=PLSkGXOii6-CRlwmik1l1h9pG4Uuq0TgeT

Explain linear regression like I'm 5.
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Linear regression is like trying to draw a straight line to connect some dots on a piece of paper. Each dot represents a piece of information, and the line represents a pattern that helps us make predictions.

For example, let's say we have a bunch of dots that show how old a person is, and how tall they are. We can draw a line that shows the pattern of how tall people get as they grow older. With this line, we can make a guess about how tall a person will be based on their age.

This is like linear regression. We use it to find the pattern between two things, so that we can make predictions. We can use this pattern to answer questions like, "how much will the price of a house change if the size of the house changes?" or "how much a person's salary will increase if they get more years of experience?"

The line that we draw to connect the dots is called the "regression line," and it helps us make these predictions.
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Show me the equation for it, along with dummy data.
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The equation for a simple linear regression model is:

y = b0 + b1 * x

where:
y is the dependent variable, or the value we are trying to predict.
x is the independent variable, or the input feature.
b0 is the y-intercept, or the value of y when x = 0.
b1 is the slope, or the amount that y changes for each unit change in x.

For example, let's say we have data on the number of hours studied and the test scores for a group of students. We can use linear regression to model the relationship between the number of hours studied and the test scores.

x = number of hours studied
y = test score

y = b0 + b1 * x

Suppose the regression model gives us the following values:
b0 = 50
b1 = 10

This means that, according to the regression model, if a student studies 0 hours, their test score will be 50. If a student studies 1 hour, their test score will be 50 + 10 = 60. If a student studies 2 hours, their test score will be 50 + 10 * 2 = 70, and so on.
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#ChatGPT #teaches #statistics #LinearRegression

Feel free to argue with our #RobotOverlords in the replies.

I want to use the #polr function of the #MASS package. What will be the factor of increase in computation time between a normal linear regression & f.e. a polr computation with the same input variables and an output with 15 levels? (Or even an output with 200 levels?)
Or are there better ways to model something with that much output levels?
#rstats #ordinalregression
#linearregression #logisticregression

Cheatsheet for linear regression in r. #rstats #linearRegression #glm #cheatsheets
I’m trying not top post things from the bad place but this one is really good!
Originally posted by Ben Larson blarson424 on the bird site.

#linearregression #stats #normconf
https://www.reddit.com/r/economicsmemes/comments/s3ni9v/how_true_is_this_do_economists_only_use_linear/

Comparing Linear and Logistic Regression.

Discussion on an entry level data science interview question.

(by Devesh Rajadhyax | Nov, 2022 | Towards Data Science)

https://towardsdatascience.com/comparing-linear-and-logistic-regression-11a3e1812212

#statistics #research #data #datascience #dataanalytics #linreg #logreg #linearregression #logisticregression #dataresearch #sociology #psychology @sociology

Comparing Linear and Logistic Regression.

Discussion on an entry level data science interview question.

(by Devesh Rajadhyax | Nov, 2022 | Towards Data Science)

https://towardsdatascience.com/comparing-linear-and-logistic-regression-11a3e1812212

#statistics #research #data #datascience #dataanalytics #linreg #logreg #linearregression #logisticregression #dataresearch #sociology #psychology @sociology

Neelesh Rampal from NIWA teaches us the difference between #linearregression and #convolutionalneuralnetworks #AMOS2022