Something I do when I am free: Welcome to my comprehensive tutorial on #LinearRegression in #R! Whether you're a beginner or looking to refresh your knowledge, this video covers all the essential concepts and practical steps to perform linear regression analysis using R. ?? What You'll Learn: Introduction to Linear Regression:?Understand the basics and the importance of linear regression in statistical analysis. Estimating Coefficients:?Learn how to estimate the intercept and slope, and their significance. Practical Application:?See how linear regression is applied in real-world scenarios. Model Accuracy:?Discover how to evaluate the accuracy of your model using RSE and R2. Hypothesis Testing:?Understand how to test the significance of your regression model. Checking Assumptions:?Learn about the assumptions of linear regression and how to verify them using diagnostic plots in R. Advanced Topics:?Explore multiple linear regression, interaction terms, and polynomial regression. ?? Hands-On with R: Loading Data and Fitting Models:?Step-by-step guide on reading data into R and fitting a linear regression model. Interpreting R Output:?Detailed explanation of the summary output from the?lm()?function in R. Diagnostic Plots:?Learn how to use and interpret diagnostic plots to check the assumptions of your regression model.
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4 Regression Models You Should Know! 1?? Linear Regression Countinious dependent variable | Code in R:? lm(y~x) 2?? Poisson regression Counting dependent variable | Code in R:? glm(y ~ x, family="poisson") 3?? Logistic regression Binary dependent variable | Code in R:? glm(y ~ x, family="binomial") 4?? Cox regression Time/event as dependent variable | Code in R:? coxph(Surv(time, status) ~ x) Which regression should also be included? 5??... 6??... 7??... #statistics #rstats #rstudio #r
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?? Explaining DS For Beginners: Day 1?? ?? Logistic Regression 'Regression' is a statistical method of finding the relationship between two variables, such that one (or more) of them could predict another. For example, if the data has features A & B, and A (called predictor) could predict B (called outcome), then it is a regression problem. Logistic Regression is used for binary classification problems: problems where the outcome is Yes-No, True-False, etc. It uses a function (don't worry about it) to assign probabilities of each outcome to fall under either of the classes. If values Xi has a 32% probability of being True and 68% probability of being False, the model classifies it as False. #DataScience #mle #ds
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Differences Between Robust Regression and Linear Regression
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