Задача 4.
Тема: «Критерий согласия Пирсона». С помощью критерия согласия Пирсона на уровне значимости α = 0,05 выяснить, можно ли считать случайную величину X, заданную в виде сгруппированного статистического ряда, нормально распределенной с параметрами x и s, рассчитанными по выборке.
![](data:image/png;base64,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)
| [2.3;2.5]
| [2.5;2.7]
| [2.7;2.9]
| [2.9;3.1]
| [3.1;3.3]
| [3.3;3.5]
|
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAIAAAAC64paAAAA70lEQVR4nO2SqwqEQBiFV9gnUCw+gpem1boIRjH7BIKI2awg+BhWm0ZtavWSLYJFBMHqDi7IIKzrTN6Tzszh4/z/MM9t2x64emKTf3iXbdthGBZF4XlekiTAsCx7Fw6CIIoiwFuW5bquoih939+F53le19X3fYZhpmkiCAJh7LquJUkCJPBd13EchwA3TXMsCTzP82jNgiAcsCiKcCrLcp7nV826rn9827aGYcApTP4eW9O0qqpIkgRHVVWXZcmy7Cs8juPhh2GAI9M04zi+ar4Q+DCnx0eAy7J0HAcTBstjNqdpStM0RVE48GvX6fINrhRp7VJUxuwAAAAASUVORK5CYII=)
| 3
| 6
| 9
| 8
| 5
| 2
| Для каждого из интервалов определим середину. Имеем
группы
| середина интервала
| nj
| xj*nj
| x-x ̅
| (x-x ̅)^2)
| ((x-x ̅)^2)*nj
| 2.3-2.5
| 2,4
| 3
| 7,2
| -0,47
| 0,22
| 0,67
| 2.5-2.7
| 2,6
| 6
| 15,6
| -0,27
| 0,07
| 0,45
| 2.7-2.9
| 2,8
| 9
| 25,2
| -0,07
| 0,01
| 0,05
| 2.9-3.1
| 3
| 8
| 24
| 0,13
| 0,02
| 0,13
| 3.1-3.3
| 3,2
| 5
| 16
| 0,33
| 0,11
| 0,54
| 3.3-3.5
| 3,4
| 2
| 6,8
| 0,53
| 0,28
| 0,56
| итого
|
|
| 94,8
| 0,16
| 0,70
| 2,39
|
=![](data:image/png;base64,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)
Вычислим дисперсию
![](data:image/png;base64,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)
![](data:image/png;base64,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)
Нулевую гипотезу сформулируем как утверждение, что случайная величина Х имеет нормальное распределение с указанными выше параметрами .
Вычислим теоретические частоты, учитывая n=33,
, h=0,2
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAPsAAAAsCAIAAAAsK1lmAAALGklEQVR4nO1de1BN3x7v9hJCyaMokUdRmTzymibK6IHUNJEmxIwUZpLHaCJjmDwKTaYhdTNiikbJWypqRmWEREPOOGISovSgIhy6n2nfu517Hru993mt49fnj2adtdfju7/rs9f6fvd3rZ1+V1eXTi/+h8bGxqFDh/4ze1c6yFSmvvpFIRm5ublhYWGa6v3ixYvr1q3TVO9KB5nK7GX8/6GpqUmDvTc0NGiwd6WDTGX2Mv4PhEKhhYWFBgVA769evbKxsdGgDMoCscrsZfwfFBYWurm5qbqXly9flpWVeXl5DR8+XOLSrFmz8vPz169fr2oZ1ABildnL+D94+PBheHi4qns5fPhwS0tLSEiI9CU7O7ujR4+qWgD1gFhl9jL+D1pbW3V1dVXdy6dPnzIzM2Ve0tPT+/z5s6oFUA+IVWYv4/+Lzs5O9fSSkpLSp08feQV+/Pjx8+dPAwMDNQijOpCsTNUyfurUqa9fv+7o6DA3N/f29j5w4MDgwYMZ8hmQnZ29efNmkUgUGxu7du1a6QLOzs5os729fcSIEf7+/nv27Onfvz97UQUCwciRI2Veunfvnqen5/Lly6Ff/Lx7925AQMD79+/ZN07j8ePHzE1BeNzFhAkTeDRODkhWpsrneIiFm29ubt69e3doaOiFCxeY82Wiurr6+PHjjx49+vbtm4+Pj5OT0/Tp0yXK/Pr1CwVwk1jpdu3aFRERcfLkSfZy1tTUyBsk+ECXLl3y9fVNSEjAU3Tt2rVTp06xb5lTU5BBKBRqO+NJVqZqGQ8KUolhw4ZhIh89ejRzvjwkJSVt374dhZGOiYlJTk6WZjPdJtaNQ4cOjR8/npOob968YQgQurm5OTg4pKWlVVVVBQcHu7u7c2qcfVOQAdMS78YJAcnKVJ8dD4PE0tKSfb447ty5g6WASs+YMYNOy8O/usFJvLdv386dO5ehwM6dO/38/MrLy2GSSVy6ffv2ggULZNaC/wRTkn1TGKQHDx5wkpxAkKxMdTAeUsK6Ak2joqLY5EsDc4aJiQmVNjMzg0KZyz9//nz27NmchGxoaKC7kAZcjqysLCwyHz58kL46f/78379/s+yIuSnIwM+oJQokK1MdjIc3jRkXZsnKlSvZ5EsD5jvtkvfr1w8/mcvD5unxKZIArP8BAwbIvFRbWwvTa+/evVhe9u3bt3DhQirf1dUViw+nXuQ1RWPgwIGQhFObBIJkZaqD8d+/f3/x4gXc04yMjBUrVvSY7+LiUlpaKt5C3759Ozs78Rfp9vZ2KiEPV65cGTVq1Jw5czgJ2dTUBAVJ58M9yMvLg9+sq6u7atWq6Ojo4uJiKprIdYQYmqIBorS0tHBqlkCQrEx1MN7AwMDe3h5z+ZkzZ8SZLS9fgu6AlZUVRKeIjqdW3nsA4NatW6geHx/PVci2tjaZD9LixYuhSiqYMmjQoKCgoCVLlmRnZ+/fvx9inDt3jn0X8pry8vKiy2AF+wsYT7Iy1ee5woDR15fRnbx8ccAop948In3//n2sYjKL5ebm4ioPuut0W4QyYxkShuC/u4FEc3NzXV0dpy7kNSUOyABJODVLIEhWpmoZ7+/vn5qaampqClcGyxaeS+Z8eQgLC4uMjHR2doYFD4tN5ov2lJQUGHYHDx7kJ+rXr1+NjIzYl3/y5Ikir9XkATJAEk5Vegy9wSB0cnISCATSdSU2Aki7jAx1GUCyMlXL+MbGRhsbG9jrMEvAWthbzPnygEk9JCTE0dERIxQbGztz5kw6H5M6laZ2yYkznr3Lr9M9tJxi+xikLVu2sC/PEoaGhj365RLoMfS2e/du+EvyqjNribmuPJCsTNUyvqSkhFM+A8K6IZ6Dx7e6upr+yYnf0hCJRJx2PmGQduzYwSmsywaw8SAJpyrMoTeoGuYsP2F41yVZmZKM37p1a2JiYnl5eVxc3M2bN5GYNGmScuVQFiAnc5hDGhhCLBH5+flZWVkbNmxITk4ODAykLmGm1NPTY99UfX09p65ZAjJAEt7VJUJvmBSOHDkCfw6mDtemeqyrpcqUZDxuEjcAMm3evBkrmre3N4xj8QJcQ2KqAySRt1NUJoRC4dKlS8+fP9/U1IQbLCgoWL16tfggcQ3TqgKYGulB4qFqidBbdHR0TEwMs4FhYWEBI3PMmDFwkJYtW8ayrtYpk4Yk41tbW/Fwx8fHwy6EBy0tN6eQmEoBQnAqn5CQsGnTJldXV6xduIspU6bU1NTQV0kYIaCrq4uWhIeqxUNvRUVFcGGlt9yJA+aEtbU1TJeKigp4SuidYm2PdbVOmTQkGf/06VP4/tR7QEwY9vb2/Doj5J51um+bShQXF1+9ehWJsrIyjBCebfGVF2nQS9z6VOct0EJCBk72gDjEQ29tbW2Yti5fvsxcxdHRkUrMnDkTk3pOTg4Yz6au9ipTkvHPnj2jDXekHRwcVC2f2vD27VvqnG9paSmWYKzLVlZW9FV9fX2JQdIIIEOP0QmZkAi9bdu2DUYpw2kJaWCao95es6mrvcqUMcfTzz0YT69r9LYHlsYlgR9+wjIN8SCYQCAYO3bsiRMn5s2bR1+FwQqbT1xBGrkFcRnY2/HSoTfpiAz4x2wjvXnzBtY8y7pap0waMuZ42n2prq7G40ul6W0P5NjxXOHh4ZGRkWFra4txBUXS0tLEt5LClv3+/TunSVEVgAz0C0GWqpYZepOoKI/uPj4+6enppqamVVVVcXFxVJyfTV2tUyaNHqyagIAA3Imfnx/XbQ8EIjExMTw8PDIyUiQSJSUlXb9+fdy4cfRVapA0KB4FyMDpsKIO99CbeNgO9jco29nZib9g/LRp01h2qr3KlGT8x48f6TS9dQH3xnXbQ4/gfW5Vwjo0Njb+8uULmx7NzMzQaUxMDNw76e+zoR31nEdmBg/Gs1kH6DISYTs2oUCZ7WuvMlk5SUrf9qDIudWwsLDk5GSqTF5eXk5ODqeuKysrPT09pfNNTExI2MKFJ5zhLIXi4BG2Y4A2KpMt45W77UGRc6s03QGYj1goOHWNQZIZRR4yZEhbWxunppixcePGY8eOca0FGTB9KlEMCXAN2zFDG5XJlvHK3faglHOrDQ0NAoHAxcWFU9fyztRhkFhaRzLx7t072F1Yc+rr6ykzABrj0Y4aGK/E1rRRmawYr/RtD0o5t3r69Ong4GBliWRhYdHc3Myv7ocPH7y9vaOjo6Oiompra4VCIe+vSMOEo8J/Wg2SlamZb5Ip5dxqenp6QUGBskSCB9bY2Miv7t69ewMDA4OCgnS6BzsyMlKRQRIP5WgpSFamZhiv+LnVkpISZDIc/+MKa2trHnuYKdy4caOwsJBKGxgYYFriLQZMNa52GoEgWZmaYbzi51bhs65Zs0aJItnZ2Z09e5ZfXVh9VLRSp/v7zsbGxrzFqKurs7W15V2dEJCsTM0wXsFzq/CKioqKpM81KgI8dby/6wtHDYs49Q8CsrKyFPluOiYCrF28qxMCkpWpGcYreG41MzPT19fX0NBQuVLxPiu0aNEiuNERERHFxcWJiYm8F3Sd7sPIvOsSBWKVqRnGK3huFSZNamqq0qWytLTs6OjgGvIE4uLiQkNDzc3NYa3haZw8eTI/AeDS/AUTPAVilamx78crcm61oqJCFSLBy6msrOThOJqamnIN/coELL2/wG2lQKwyCfqPCcoNgPOAu7s7fAMNcg4UwfSmqd6VC2KVSRDjlRsA5wEjIyPNbusXiUS8zV/SQKwyyWK8pkXQmThxogZ7p8/i/B0gU5kEMZ4EyDtzpB54eHhosHelg0xl/gcEsqGYCjfoWAAAAABJRU5ErkJggg==)
группы
| Ui
| φ(ui)
| ni
| 2.3-2.5
| -1,76
| 0,0863
| 2,12
| 2.5-2.7
| -1,01
| 0,242
| 5,94
| 2.7-2.9
| -0,27
| 0,3857
| 9,47
| 2.9-3.1
| 0,47
| 0,3565
| 8,75
| 3.1-3.3
| 1,22
| 0,1872
| 4,59
| 3.3-3.5
| 1,96
| 0,0573
| 1,41
| итого
| 0,61
| 1,315
| 32,27
| Сравним эмпирические и теоретические частоты. Составим расчетную таблицу, из которой найдем наблюдаемое значение критерия.
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABUAAAAUCAIAAADtKeFkAAABJ0lEQVR4nGP5//8/AwWAhRLNxOrX0dG5d++eqKhoY2NjQkICyfp5eXk/fPhw/fp1BwcHcvQfP34cSEpKSoqJiaFJkeD/pUuXTpgwgSj9ampqz549W7hwYXBwMERkzZo1lpaWFhYWROm/devWx48fExMT/f39WVhYFi1aJCMjg6kZn/v5+fmNjIwuXrxobGyMHGb//v0jSj8QADUDjcDUQ5T+PXv2bNmyxcfHB48FOPX/+fOnra0tOjr69evX5Ojv7+8PDQ399OnTq1evSNYPjLm1a9cePXp0+fLlN27cIFl/SUkJMJ0zMzNLSUmRbP/BgweBznZ3dweypaWlSdZfXFy8ZMkSCBuo/9KlS7t373Z1dSVW/5kzZ+BsHh6enz9/kmY/qYBS/QBCG3eOhZYzsgAAAABJRU5ErkJggg==)
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAIIAAAAvCAIAAACg44WaAAAHaElEQVR4nO1ae0gUXxRea6E/0rQiIS0zpIdZEmpWGr1fWqZQWRpKJkiCaA+N6o9MhChTe1CUlYaV+cpALSwrqKCsNHyWb8selpapaZn5+n3twDDMzszOzM7u1v7m+0Pu3Llz7tn7nXvuOdejHB4eVsgwNJSGVkDGH8g0SIyBgQELC4uenh5a/+zZs5uamiZMmBATE7N9+3baWyOn4enTp25ubvqcsbe319LSkmgXFhauXr2aaJuZmXV2dlZXVy9duvT/RcPr168bGxv1TwNMnmg3NzdXVlbOmTMH7aKiIvydOHEiSRIVxkxDYmJiUlKSnifFKj9//pxoBwUFBQQEpKenk2/T0tJOnjyp/pXR0lBbWztSBQPqoFQqR48ejR1pZ2eHxxs3bixcuHDBggUMI/Wum55QUFAwf/58Q2uhwKLn5eXt3r37ypUrkyZNYuRAYcQ0PHnyZM+ePYbWQmFvb3/27Fk0qMfy0NAQbZjR0oDz2cbGhta5d+9euGb47mPHjt25cweNWbNmiZ6CjzRbW9uKigoF09JTYbQ0dHR0mJqa0joTEhIyMjKwdvAS0dHRHh4eCGZET8FHGs6G9vZ2jaKMmQYsAa0TkfvPnz/j4uKsrKy+fftmYmKizRR8pMEUMEyjKKOlgXFRqqqq5s2bh1VDG5mUg4OD+pgHDx6sWrWKUSbirv7+fkHShoeH+ZDNiwbuRFxHmDp1Kk+PcfTo0X379tE6LSwsfvz4YW5uTu189eoV6b7Rxu8iXy1evPjx48dorFixgtuPa5RGigKgw9ixYzWK4kUDdyKuI+Tn57u7u3d3dyP92bJli/qAvr6+srKysLCwFy9eqL8dN25cT08PjQbYL5HTKlQL5+LiQr4iF44/2KRRRUlJA3ciriPAuK5fv+7t7Y1cFDvD1dWVNmDUqFHIDBCPk/c2VMBFvHv3ztramtqJxfL19SXaCKVIk1q/fj34fvTokSANGaVhK2BSMnPGhmZ0VjQIOBvYEnHdYd26dcePH0dcCDJg8pMnT1Yfg8D8/fv36v2LFi3C9kXWSu2kuZFNmzYVFxdj34SHh+fm5gpVj1Hazp07qfpAB+xpjaKYaZg+fXpLS0tqaurGjRuJHo5EXKdALAhDS05O9vLyQkamHvywwdPTMz4+fseOHdTO1tZWso0fSLYR8vOxWRoYpZWXly9fvpzsh/XAjDSKYqahrq6uq6sL3gBmqFQquRNxXePcuXMNDQ3wGH5+frBZnlGmnZ0dRg4ODvK5VsJiRUVFaa3pH4AGMnvH7IiUYNMav2J1SjjcnJycINTZ2Zk7Edc1YAc3b97E2XDr1i1YVmJiIs8PsZMyMzP9/f01joQzEbEbGIEVO3jwILYv2jgheF6ocJ0NkAgmFIZYehoQbIADbEccTjNnzgwJCeHz1bRp0/ikTnfv3kUsPn78eG21VOHTp09kG/sA2vL5ipWG+/fv45cjhJBANQr4J0c0zJgxIzs7Gx4f9oX4lRaJsgHpFfcAUPXr16+rV6/ykSYU6tEdG5hpGBgYOHLkyLZt2758+SKdVn/APzlSx8qVKzds2ODh4cGTAz6or6+XSpQ2YKbhxIkTmzdv/v79e1tbm54V4kBGRsaUKVOCg4M1jtTyskhvIKuTGGhA7JWTk4PoECdMTU2NfhVjxcuXL7NVMLQiOgEDDZGRkTExMfDUVlZW4nbD58+fEWUirLa1td2/fz/1lbizAQIhB8YxYsQIPgr8czVwdBoQnsMXrVmzBm0k5SJoQKgAnwYuGW8+RJwNfX19yMLOnz8/ZswYocpIDrYyJC1BpwGB+bVr14g2aKioqLh37x7NfnFO9vb2IqdDXv327VvQtnXr1ocPH+JIv3jxIuJ6GDUCGwRayOMvX74cGhoaGBh44cIFcSric+RWxH/VGfHx48fo6OhLly6Jky8I1DIkCUGnoaSkhGybmprCEtW/wXJjWFVVlaOjIx6Rrbi4uCDF/fDhg0IVhgcEBDx79gxLn5qaWllZmZKSQr05EIS4uDgEncuWLeMYc+jQocbGRnHyhYJahiQhxPzbp6ysLEEFEIBHpLhY69jY2LVr1+LxzZs3SK/geZqamrCxIiIikpKSuNeRDbdv3wa1p0+fZnz7+/fvurq6+Ph47EupriI0glqGJCFE0lBQUFBUVETcL8IFdXV1FRYWHjhwQKHaQ1i7zMxMuLLa2lqEmBi/a9cuobNUV1f7+/t3d3efOXNG42BD3XdJBZE0zJ07Nzk5uby83NfXF2nt4cOH4aCx9OHh4dgBS5Ysgb9qbm4+deoUxpeWliL2xbYQNIuXlxc44Dn4byhJ0gZiaCCuTbKysohHYhOQQCz09etX9fFC0dDQIOKrfxRGWxKgPaQtauKGTAMrpC1q4oZMAyukLWrihkwDK/iUIUkFmQZWcBQ1SQ6ZBlawlSFRq8GkgkwDK9iKmiTnQCHTwAHGMiQfHx9qNZhUkGlgBWMZEq0aTCrINAgDrRpMKsg0CAO1GkxCyDQIA7UaTELINAiDuGtKjZBp+CvwH+sX1lzJNd3EAAAAAElFTkSuQmCC)
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAIAAAAC64paAAAA70lEQVR4nO2SqwqEQBiFV9gnUCw+gpem1boIRjH7BIKI2awg+BhWm0ZtavWSLYJFBMHqDi7IIKzrTN6Tzszh4/z/MM9t2x64emKTf3iXbdthGBZF4XlekiTAsCx7Fw6CIIoiwFuW5bquoih939+F53le19X3fYZhpmkiCAJh7LquJUkCJPBd13EchwA3TXMsCTzP82jNgiAcsCiKcCrLcp7nV826rn9827aGYcApTP4eW9O0qqpIkgRHVVWXZcmy7Cs8juPhh2GAI9M04zi+ar4Q+DCnx0eAy7J0HAcTBstjNqdpStM0RVE48GvX6fINrhRp7VJUxuwAAAAASUVORK5CYII=)
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![](data:image/png;base64,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)
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![](data:image/png;base64,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)
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![](data:image/png;base64,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)
| 3
| 2.1
| 0.9
| 0.81
| 0.39
| 6
| 5.9
| 0.1
| 0.01
| 0.002
| 9
| 9.5
| 0.5
| 0.25
| 0.03
| 8
| 8.7
| 0.7
| 0.49
| 0.06
| 5
| 4.6
| 0.4
| 0.16
| 0.03
| 2
| 1.4
| 0.6
| 0.36
| 0.26
|
![](data:image/png;base64,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)
По таблице критических точек распределения по уровню значимости и числу степеней свободы к=6-1-2=3 находим критическую точку правосторонней критической области
![](data:image/png;base64,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)
Так как то гипотеза о нормальном распределении генеральной совокупности не отклоняется. Случайная величина Х имеет нормальное распределение с указанными параметрами.
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