_subfiles/count-regression/_exr-prac-glm-score.qmd:80 declares a column
vector using a row-tuple and no transpose:
where $\vx_{(j)} = (x_{1j}, \ldots, x_{nj})$ is the column of $j$-th
predictor values
One line earlier the same solution writes
$\tp{\vx_{(j)}}(\vy - \est{\vec{\mu}}) = 0$
which requires $\vx_{(j)}$ to be a column vector for the dimensions to work:
$\tp{\vx_{(j)}}$ is $1 \times n$ and $(\vy - \est{\vec{\mu}})$ is $n \times
1$. As written, the declaration says it is a row.
CLAUDE.md asks for dimension compatibility to be verified for every matrix
expression. The fix is $\vx_{(j)} = \tp{(x_{1j}, \ldots, x_{nj})} \in \reals^n$, matching the form
#1138 used for the identical
problem two files over in _sec_pois-reg_intro.qmd
($\vX = (X_1, \dots, X_n)$ became
$\vX = \tp{(X_1, \dots, X_p)} \in \reals^p$).
Pre-existing on main and adjacent to #1138's changes rather than part of
them, so left out of that PR. Worth a sweep for the same pattern elsewhere
in the chapter while fixing.
Filed by Claude Code (AI agent).
_subfiles/count-regression/_exr-prac-glm-score.qmd:80declares a columnvector using a row-tuple and no transpose:
One line earlier the same solution writes
which requires$\vx_{(j)}$ to be a column vector for the dimensions to work:
$\tp{\vx_{(j)}}$ is $1 \times n$ and $(\vy - \est{\vec{\mu}})$ is $n \times
1$. As written, the declaration says it is a row.
CLAUDE.mdasks for dimension compatibility to be verified for every matrixexpression. The fix is
$\vx_{(j)} = \tp{(x_{1j}, \ldots, x_{nj})} \in \reals^n$, matching the form#1138 used for the identical
problem two files over in
_sec_pois-reg_intro.qmd(
$\vX = (X_1, \dots, X_n)$became$\vX = \tp{(X_1, \dots, X_p)} \in \reals^p$).Pre-existing on
mainand adjacent to #1138's changes rather than part ofthem, so left out of that PR. Worth a sweep for the same pattern elsewhere
in the chapter while fixing.
Filed by Claude Code (AI agent).