When exploring regression lines in statistics, it's essential to consider various aspects and implications. Regression with multiple dependent variables? Is it possible to have a (multiple) regression equation with two or more dependent variables? Sure, you could run two separate regression equations, one for each DV, but that doesn't seem like it ...
regression - What does it mean to regress a variable against another .... Those words connote causality, but regression can work the other way round too (use Y to predict X). Equally important, the independent/dependent variable language merely specifies how one thing depends on the other.
Generally speaking it makes more sense to use correlation rather than regression if there is no causal relationship. Why are regression problems called "regression" problems?. I was just wondering why regression problems are called "regression" problems.
What is the story behind the name? One definition for regression: "Relapse to a less perfect or developed state." When conducting multiple regression, when should you center your .... Building on this, in some literature, I have read that a regression with multiple explanatory variables, if in different units, needed to be standardized.
(Standardizing consists in subtracting the mean and dividin... regression - Difference between forecast and prediction ... This perspective suggests that, i was wondering what difference and relation are between forecast and prediction?
Especially in time series and regression? For example, am I correct that: In time series, forecasting seems to mea... regression - What's the difference between multiple R and R squared .... In linear regression, we often get multiple R and R squared.
This perspective suggests that, what are the differences between them? From another angle, regression - When is R squared negative? Also, for OLS regression, R^2 is the squared correlation between the predicted and the observed values.
Hence, it must be non-negative. For simple OLS regression with one predictor, this is equivalent to the squared correlation between the predictor and the dependent variable -- again, this must be non-negative. It's important to note that, correlation - What is the difference between linear regression on y .... The Pearson correlation coefficient of x and y is the same, whether you compute pearson(x, y) or pearson(y, x).
This suggests that doing a linear regression of y given x or x given y should be the ... regression - Trying to understand the fitted vs residual plot? A good residual vs fitted plot has three characteristics: The residuals "bounce randomly" around the 0 line.
📝 Summary
Throughout this article, we've analyzed the multiple aspects of regression lines in statistics. This knowledge not only inform, but also assist individuals to benefit in real ways.