Why does removing the constant term prevent the dummy variable trap? I understand that if you have a dummy variable with $m$ categories that you should include $m-1$ categories in order to avoid perfect collinearity between regressors. However I don't understand why removing the constant term prevents the issue of perfect collinearity, because all of the categories will still sum to 1.
For example: Suppose we have a dummy variable 'gender', with the categories being male and female. If we include both categories in the model without an intercept, the model is given by $y = \beta_1male + \beta_2female$.
Don't we still have the issue of perfect collinearity because $male + female = 1$? We could rewrite this as $male = 1 - female$ so the regressors are still perfectly collinear because there is an exact linear relationship between them. How has removing the intercept helped us?
(I am yet to learn about matrix algebra, so please provide answers that do not require knowledge of matrix algebra if possible).
 A: When we perform linear regression with the constant term (intercept), we actually are moving the origin (the anchoring point which the prediction line will come through) to the data cloud centroid (the mean). Both X variable(s) and the Y variable get centered.
Let us take your example with predictor gender making two X dummies, female and male. When they are centered (and scaled), their scalar product is $-1$. Below I'm showing the two dummies - original on the left and them after centering (and also scaling), on the right.
  Original dummies   Standardized (centered-then-scaled) dummies
   female    male           female      male 
       1        0            .802       -.802 
       1        0            .802       -.802 
       1        0            .802       -.802 
       1        0            .802       -.802 
       0        1          -1.069       1.069 
       0        1          -1.069       1.069 
       0        1          -1.069       1.069 
    scalar prod. = 0        scalar prod. = -1

   Scaled dummies
    female    male
     .500     .000 
     .500     .000 
     .500     .000 
     .500     .000 
     .000     .577 
     .000     .577 
     .000     .577
    scalar prod. = 0

One of the things we need in regression is to compute scalar product between the predictors.
Scalar product of centered variables is the covariance and of centered & scaled ones is the correlation. And it equals $-1$. This is the mark of their collinearity: both vectors, male and female, lie on the common straight line (and face opposite directions). Since they are collinear, one of them is redundant as a predictor, for them two span only 1D space. (It is like on the second pic here, except that X1 and X2 vectors are directed oppositely in our case.)
But when we perform linear regression without the constant term, we leave origin on its place. We force the prediction line to pierce the anchor there where it was, not at the data centroid. We don't center, neither Y nor X variables.
Since we thus don't center the dummies, we compute the scalar product between the dummies as they were, raw. But look, their scalar product is $0$, not $-1$. If we scale them and compute scalar product - it is then called cosine similarity - it'll still be $0$. The $0$ means the two vectors, male and female, are orthogonal; they do not at all lie on one same line. That means they are not collinear, they form a 2D space.
That is why we may enter both dummies as predictors in linear regression containing no intercept.
