Linked Questions

131
votes
9answers
128k views

When is it ok to remove the intercept in a linear regression model?

I am running linear regression models and wondering what the conditions are for removing the intercept term. In comparing results from two different regressions where one has the intercept and the ...
100
votes
10answers
270k views

What's the difference between correlation and simple linear regression?

In particular, I am referring to the Pearson product-moment correlation coefficient.
111
votes
2answers
54k views

Removal of statistically significant intercept term increases $R^2$ in linear model

In a simple linear model with a single explanatory variable, $\alpha_i = \beta_0 + \beta_1 \delta_i + \epsilon_i$ I find that removing the intercept term improves the fit greatly (value of $R^2$ ...
33
votes
6answers
15k views

Why do we need multivariate regression (as opposed to a bunch of univariate regressions)?

I just browsed through this wonderful book: Applied multivariate statistical analysis by Johnson and Wichern. The irony is, I am still not able to understand the motivation for using multivariate (...
11
votes
2answers
4k views

What does the formula y ~ x + 0 in R actually calculate?

What is the statistical difference between doing a linear regression in R with the formula set to y ~ x + 0 instead of ...
13
votes
1answer
47k views

Test for cointegration between two time series using Engle–Granger two-step method

I am seeking to test for cointegration between two time series. Both series have weekly data spanning ~3 years. I am trying to do the Engle-Granger Two Step Method. My order of operations follows. ...
10
votes
1answer
8k views

Why does statsmodels.api.OLS over-report the r-squared value?

I am using statsmodels.api.OLS to fit a linear regression model with 4 input-features. The shape of the data is: ...
4
votes
1answer
10k views

transformation to normality of the dependent variable in multiple regression

Is it really important to normalize dependent variables in multiple regression or are there any exceptions? My model is providing better results with more significant hypothesis when the DVs are not ...
5
votes
1answer
9k views

What is the point in regression through the origin? [duplicate]

I am doing the Coursera Statistics Inference course and one of the questions is to find the regression through the origin, when the regression line has an intercept. Can you please explain what the ...
8
votes
1answer
10k views

What to do when a linear regression gives negative estimates which are not possible

I am using linear regression to estimate values that in reality are always non-negative. The predictor variables are also non-negative. For instance, regressing the number of years of education and ...
1
vote
2answers
6k views

Interpreting a negative intercept in linear regression

This is my first time of having a negative intercept, so I'm a bit confused. My line of regression is: $$ \text{starting monthly income} = -7.5 + 0.75\times \text{years of education}. $$ How would ...
1
vote
2answers
776 views

Analytical solution of a simple regression with fixed intercept

I would like to know how to find out the analytical solution of a simple linear regression with fixed intercept = 0: $$ s = e^{-ht}$$ $$ y = -ln(s) = h\cdot t$$ Here ist the background: I have ...
2
votes
1answer
2k views

How do you set your own intercept in SPSS? [duplicate]

I am trying to specify the constant in a regression model using SPSS. Does anyone have an idea on how to do this?
5
votes
2answers
733 views

Two simple questions regarding GLM

I'm currently doing a modelling project. However, I haven't taken a bunch of statistics classes, so I have to teach myself generalized linear models. I'm reading Generalized Linear Models for ...
1
vote
2answers
1k views

Negative fitted values in OLS regression

I am running a regression where my dependent variable is a cross-section of variances. Therefore, I require my predicted values (fitted values) to be positive. However, when running a simple OLS ...

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