# Questions tagged [linear-regression]

Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.

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### Correlation vs Multicollinearity

I have been taught to check correlation matrix before going for any algorithm. I have a few questions around the same: Pearson Correlation is for numerical variables only. What if we have to check ...
• 161
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### Confidence interval interpretation in linear regression when errors are not normally distributed

I've read that "If the error distribution is significantly non-normal, confidence intervals may be too wide or too narrow" (source). So, can anyone elaborate on this? When are the confidence intervals ...
• 133
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### Assumptions of linear regression

In simple terms, what are the assumptions of Linear Regression? I just want to know that when I can apply a linear regression model to our dataset.
• 109
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### How to interpret Variance Inflation Factor (VIF) results?

From various books and blog posts, I understood that the Variance Inflation Factor (VIF) is used to calculate collinearity. They say that VIF till 10 is good. But I have a question. As we can see in ...
1 vote
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### Finding optimal weights for models

I'm trying to implement an algorithm to find the minimal value of a function. Before moving to sigmoid activation functions, i'm trying to understand linear regression. Usually, a gradient descent ...
• 389
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### Linear Regression and scaling of data

The following plot shows coefficients obtained with linear regression (with mpg as the target variable and all others as predictors). For mtcars dataset (here and ...
• 1,456
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### xgboost: Is there a way to perform regression on rates/percentages data?

I have a dependent variable, $Y$, that is made up of rates/percentages data, so each value is between $0$ and $1$. I was attracted to the xgboost library because it allows focusing in on specific ...
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### Implementing simple linear regression using a neural network

I have been trying to implement simple linear regression using neural networks in Keras in hope of understanding how to work in the Keras library. Unfortunately, I am ending up with a very bad model. ...
• 207
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### Reason for generally using RMSE instead of MSE in Linear Regression

In linear regression, why we generally use RMSE instead of MSE? The rationale I know is that it's easy to minimize the error in RMSE instead of MSE by Gradient Descent, but I need to know the exact ...
• 534
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### How do you predict a continuous variable when all your independent variables are categorical

I am new to data science and ML. Recently I have been given a sales dataset which contains weekly sales of a fashion brand. It has information about the product like category(t shirt, polo shirt, ...
• 21
1 vote
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### R-square or adjusted R-square for one variable model?

I have model like y=mx. Since the adjusted R2 tells you the percentage of variation explained by only the independent variables that actually affect the dependent variable and I have only one ...
1 vote
288 views

### Why would it be bad to fit a regression model to a binary classification problem?

Let's say that we have a binary classification problem. Why would it be bad to fit a linear regression and then classify given a threshold? The output would be continuos and it could be out of range,...
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