daviddoji/18065
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

whoops

Browse Source
This commit is contained in:
Steven G. Johnson 2023-03-13 16:27:06 -04:00 committed by GitHub
parent 5ceb4b7a63
commit f9cd16d0de
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 1 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
psets/pset3.ipynb
View File

@ -177,7 +177,7 @@
"source": [ "source": [
"## Problem 7 (5+5 points)\n", "## Problem 7 (5+5 points)\n",
"\n", "\n",
"The most common form of least-squares is linear regression, i.e. fitting $m$ data points $(a_i, b_i)$ to a model of the form $a(b) = x_1 + b x_2$.\n", "The most common form of least-squares is linear regression, i.e. fitting $m$ data points $(a_i, b_i)$ to a model of the form $b(a) = x_1 + a x_2$.\n",
"\n", "\n",
"Suppose the data points $b_i$ have independent random errors with equal variances $\\sigma^2$ (i.e. they are \"homoscedastic\"). (This is the case in which Gauss–Markov says that ordinary least-squares minimizes the variance.) In this case, many sources give simple explicit formulas for the variances of the fit coefficients $x_1$ and $x_2$\n", "Suppose the data points $b_i$ have independent random errors with equal variances $\\sigma^2$ (i.e. they are \"homoscedastic\"). (This is the case in which Gauss–Markov says that ordinary least-squares minimizes the variance.) In this case, many sources give simple explicit formulas for the variances of the fit coefficients $x_1$ and $x_2$\n",
"\n", "\n",