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{
"url": "https://numbas.mathcentre.ac.uk/api/questions/2884/?format=api",
"name": "Quadratic regression",
"published": true,
"project": "https://numbas.mathcentre.ac.uk/api/projects/3/?format=api",
"author": {
"url": "https://numbas.mathcentre.ac.uk/api/users/6/?format=api",
"profile": "https://numbas.mathcentre.ac.uk/accounts/profile/6/?format=api",
"full_name": "Bill Foster",
"pk": 6,
"avatar": null
},
"edit": "https://numbas.mathcentre.ac.uk/question/2884/quadratic-regression/?format=api",
"preview": "https://numbas.mathcentre.ac.uk/question/2884/quadratic-regression/preview/?format=api",
"download": "https://numbas.mathcentre.ac.uk/question/2884/quadratic-regression.zip?format=api",
"source": "https://numbas.mathcentre.ac.uk/question/2884/quadratic-regression.exam?format=api",
"metadata": {
"notes": "<p><b>10/02/2014:</b></p>\n<p>Created. Based on the Numbas question JSXGraph line of best fit plot 5</p>\n<p><strong>14/02/2014:</strong></p>\n<p>Improved display and modified so that $20 \\le n \\le 35$. Also sharpened up the error term to be N(0,20) rather than N(0,100) (see variable r2) so that the quadratic regression would clearly be best.</p>",
"description": "<p>The data is fitted by linear and quadratic regression. First, find a linear regression equation for the $n$ data points, $20 \\le n \\le 35$.</p>\n<p>They then are shown that the quadratic regression is often a better fit as measured by SSE. Also users can experiment with fitting polynomials of higher degree.</p>\n<p></p>",
"licence": "Creative Commons Attribution 4.0 International"
},
"status": null,
"resources": []
}