Maths-
General
Easy
Question
In a test of Mathematics, there are two types of questions to be answered–short answered and long answered. The relevant data is given below
![](https://mycourses.turito.com/tokenpluginfile.php/c161933dbfaab094c54655ab71e9b8f0/1/question/questiontext/506870/1/977679/Picture283.png)
The total marks is 100. Students can solve all the questions. To secure maximum marks, a student solves x short answered and y long answered questions in three hours, then the linear constraints except
, are
,
,
,
,
The correct answer is:
, ![x less or equal than 10 comma y less or equal than 14](data:image/png;base64,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)
Obviously
and
.
Related Questions to study
maths-
The feasible region for the following constraints
in the diagram shown is
![](data:image/png;base64,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)
The feasible region for the following constraints
in the diagram shown is
![](data:image/png;base64,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)
maths-General
maths-
Shaded region is represented by
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)
Shaded region is represented by
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)
maths-General
maths-
For the following feasible region, the linear constraints are
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAASMAAADhCAMAAAC0qSMUAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAALiUExURf////v7+/r6+v7+/vz8/Pn5+cDAwGZmZlZWVpKSkvHx8cvLy21tbVpaWo6Ojvb29t3d3RwcHJ2dnfj4+M7OzjY2Nk9PT729vbm5uTU1Naurq6SkpF5eXsrKyuHh4To6Onp6etjY2E1NTaysrPX19WJiYltbW3FxcX19fff396GhoVlZWXx8fOTk5NTU1OXl5SEhITg4ONLS0uzs7HBwcJCQkMjIyCsrK7KysiAgICwsLNfX15+fnzAwMKioqO7u7mhoaAAAAIyMjP39/UdHR9PT0yYmJgEBAQMDAzQ0NMbGxu3t7UVFRTMzM0hISDs7O4iIiI+Pj0BAQD8/P5SUlLe3t6qqqmlpadDQ0PDw8KampmxsbJWVlbi4uLOzs+/v79zc3JGRkeDg4Ovr67W1tbq6uoGBgfT09Jubm7S0tLy8vHl5edbW1vLy8uPj476+vvPz8+bm5pOTk+jo6Nra2uLi4urq6piYmIqKind3d7+/v25ubt/f32pqaomJia+vr97e3n5+fj4+PhgYGIeHh3JycmRkZOnp6SoqKoaGhmtra5ycnLa2tmdnZ7u7u4uLi3t7e+fn53Nzc9vb266urnR0dNXV1WNjY62trdnZ2ZmZmV1dXYODg8zMzHV1dbGxsYKCgs3NzcnJyaOjo8PDw2VlZXh4eJeXl7CwsM/Pz6mpqZqampaWlldXV8HBwXZ2dn9/f0NDQ0JCQsTExJ6eno2NjdHR0W9vb8fHx19fX6enp8LCwqKioqWlpVNTU2FhYaCgoMXFxYCAgIWFhYSEhFhYWFVVVWBgYFxcXBERER8fH0lJSRkZGR4eHhISEkZGRlJSUj09PRYWFi4uLkFBQURERDw8PCgoKAwMDBUVFQYGBgoKCicnJyMjIy0tLSQkJDc3N05OThAQEBsbGxoaGiIiIktLS0xMTCUlJR0dHVRUVFFRUTk5ORMTE1BQUCkpKTIyMi8vL0pKSjExMQ8PDwAAAPPJbvwAAAD2dFJOU///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////AEo/IKkAAAAJcEhZcwAAFxEAABcRAcom8z8AAA1mSURBVHhe7Z3ZkeMgEED1xy8ZUQ5AVXyRi5IgAKpIhyoHQAjKY/sA3bbkY8dXP2kk2R7v2m+6uYTlRhAEQRAEQRCeh1c5mCaqZMsdwgqdzqFpYpt9uUNY41vXNC7pclPYwLRdozN4Ei5iVNJKCqPruDZ1NdOiKQfCjNi3VZGRgNom9l05anzfS9m9RcCKjTDpdJJA2qLLNXZ8fzq1sdwQRqA4KlqMym1uJZDW2Jwt12ZeeRWckkBaYUyp702IjQqNl+bkZQwUS1j3a2kjXQXiSNhB2pD7SBztI3G0jzjaR3JtH4mjfcTRPpJr+0gc7SNxtI/E0T7iaB/JtX2SONpFcm0fybV9JI72EUf7iKN9pDzaR+JoH3G0j+TaPtLO3kdybR/JtX0kjvYRR5uY6VxaybVNTDf5FITUa9v4bpy5Jrm2jQlhyDbJtQvobpjeJ3F0CT98FkIcXcSWmbXi6DKmZtukPBrm2wpMrdvGODJa5tcuKHXb4MhoWETSjFK31Vwz6EgkLYhUt5V2NvgBQyJpgQlYt3GuoR1SJEXSHI39Nso1iiKOI5E0xytNcYSKKIz4pzwqEFC3QRxR+CDFlUiaoq1TFmq0wZAE0hpv21AUAdWSSJoRzpb0DIJ4Vx4VEJ0VSpmCgSSSpqjWFzcllAiRNEX1NhYxCAYRbcrDAqBUF4qfGSJpRAWvxmxjpNyeA+1s202zjZGm5ARoZ0fJtutgf81hts1iSbJtCvb7jV01kgiRxND4kVbY2l5FkhRJDI+xUbYtLAHiiODxbIN127p2k0giOI4g26Bu29AkjoDiCLLNoRPUNBUlgQRwrgG2G8ujiSWRNMbRULctE04cjXHUuOSrnpknkTTEUa3bBj+w46OflzQ6qnUbWyqiiPL4zzLmWq3byA5uhoNfL7dn82on2TYIQn7c0STXcJqEZT91g+DutyVNc437beSH18HTb0uaxRFkm4o11YoePvruIsmHq99tsHCEdRvHD7kpnoDvdmS7LviLb3Gea1y3TRXxMUZS+YXvJAbWtPkul58XmbQkyRCuSPz6Iik626kQNzQtco2ybZSESwmoX2glRW/VhqZlruHMLazbhiBiQbj5fkeA9jYliJJyk1jFEZ4BoACiDS118xOSGgOaFJRNo6a1IxolmQjCH15/RBIAmqAId+UNbzhqfKp1Gyy8K+vvfFwCoqkjTXC8Lo+4bhtSjdcftAQeMJo6t/0dLHAveqmacCkr8UOaNDYI+m7rDVO/DdTwOiy8IUs/pMnnrKxfF8aB67bZMtv8UjSpLoZurSlSS3IWRKwG7eAP8ivRhGW2Zk2zr9DyypGYebrVHf0Qpvl+T6XMNjEoZd1EU6fADwhZxhL+8K54+v5omtRrEToroybD2UYuZp54LYKYL9c0ax9BNHVd1QTZhlGEzCXhwpr4iPhmTcv2EWiynYW+L9RtHXduYRnWspQVGSxhRfednjbakMbTgJOmMwBjFLEdkkO7yX7gO6Npqy+C0eSgcEqp1G2oqhyMrnABJoaIL9S02RdBjHe2zVA4gQtcx4BCLbRZbCvm2xoEFx0BJqqMVZ2vbspusvBmaoj5qrJpO9cqULe5TkFV58EFrrwrC2/KljcTvifprsURgFdK0KBJ2QCaUAfrqQtvgA1N9FmBb/C046hcl4Q04fglOplpmh4Ac0vAN0TT9VyDxne95lbRBIJQSTWDS91sGELA0odr2okjyLbxF2jACaKpeBoWoNyzrQmT7pM17TrSdvKVmoY1YU03KqJDXIsqPizQJ09w/8Ga9nINh5LmE9nwRF2aNAjKwptt+CM6H6tpN46w37Z8azranDDp2ExdpgdLOJ4+U9MBR7PrSVaMt4nKJnjngx9yw354u4YsfZin/Vyb1G1zoO+LNR32VshQXXizAX2aGfcfpulAHJWrAG0B0aTwTB2JKSsLWnqqZfcnlk1HHDVmWrctwBN10AxflU3A3BLZgR+OJmoPfIimI7kG/bbhepJboKaE51YQFDNoWliqVFsfoulQHG3VbXOgbCJNWDYVMzWaJqCbwRDf+gRNBx0N15O8jI6ugwbBUIQDQzyNsKYqiRa09NaejuUaZtuBb/mnE3VF01rPhCGMytF7azoYR9hvO/Qm8ERdwnMrKIDjCA5ntkhOjaNh88Y5d9jRgWyraNCEZ+rQxWWqqpmnt4ymo7l2sSW5DWjC4csSRMtIKiws4e4to+lwHDXGHcu2Cp7PVF0tm+qmQn5mu2rq/aLpuKPplcmPQecza9KtAomcFC+DIVrfLZqO59p6lOQIoAnKcI6m7YxjBlW8eytPN8TRfktym+hsSnSibs2opR6Um7B5G0s3ObrWb7sGTnmmmu56HNGOjvAqzGzpHcqmW3LtYEtyE5zLi+0mGnCaQ3IQslN08TEsb2DppjhqmhvrtjmgCRoEeAZqpAiqasbDsuD6ak83Orq5bltQz0CRjYEiajAzHMCCK0p6Ibfl2n112xxzSVM1RW5oM1nKk1/CjXF0uN92lXKiblU0sY8tSeWJr+FmR9Bve8Yr1o6GCKCmG8KngE7qrhh6cSPg1lzbG5O8gUjnDBwW4Rw8Za2CaMuOyjNexM1x9JxsK2gfErTCoUHAbgpVEK+wlF9/EXc4umGU5AAmYiscGgQkZdiWQ5ZUfvVV3J5rkCR3tyS3AU1Y0w3RVAzxBhS93NHtcXTzKMkB6AwUTXAiUNAg6dWK7nL0cEtyE4gmOlE3GnqTVLsr13Ce5HOzrYBnoFSylHMoiT2VB1/HXXH01LptDp+oo6TDMEJZ5ZEDLH7V5hSaCDXCg6/1TkfPrdvmUDSBJi7Cb3l/cfKBc8Cfet/ENl+94MoB7su159dtc0yETh3XdOWeQ5jQ0QepK6rVeCm+cutu7nXUuPG7Sf8LBocI+MM9xzG2TZNYctnF9HjAp+SAcMvKhNb6xQMPriuCzedzKo+EAwv8Vns6pyGWTM7pvrJkRmhzC+stS/39c1/vec4yPWhzAY770+lMt8vDV5fz6XRKQyTZU1eOHqLUsTdQn4FXAfoLcBIBj6RQPXcV155zGBQZdX5CGD3C5jzJ/0N01oKl3bIppokhKJ1STv+31NyFvy3xj6iXcCo3N9F2YgjKEaXD+aEi+wmjxP+tJblN5GsTlVsbzC+JGtrY6P6BAslAWOb0YBtn9hmAPwGvTYQfGC83r2Bcrwy0IKEZeScm5C5G9aikp41JHmd9CadtYteCo5Dbu9txnq7F4s73tiArf5xtjMGRlF1NVJQ8UKCYDK10cN0/WjX+z37bVegSTvYppx8u4M9UlPmH4+iBs9sPQxPo/9//bs9UkoX+8Sh4SbZVzG6xdDdGQbU47B7j3rkk745OGf/4+gwl/8O8oG77C4wiR/YJqQa8NNv+G0b18Ld3XHA/zMvqtv9L7JULT+vvPT6X5C3xSqmn/fUvf75NGDD2z0ZJPpe/+Q4SnN3VXRgdMa5eR11fGxn4elQONm/2401U2MvHo5AfGzL6WKhjajL0LksvakEMoaW549oH1f9QHOHJNU1n2IwlMXgI3XBj8E7aVKB3nziOdOPvHzH6OLRqvbE0MqFTDR7T5djonEFFm7TxBBaGJtXPIPj2hyqQ2KfG0hvWLRcxOmQ6Md1hqOSkYyJoFK3EETh6QRxpF9x2hkPyX6xDIBVoD88Ok6S4CdsHq6HGCt25o6//8Cm32BRzYE4nP50DMcYRPvjHhNSpnLeqCmyHJnrNa3zX09CUS6rbfvYBIJDgT6Ctanto8fL/Y8/wH0b4x7mIGhnj6M8dGQU9Ft+rcrOAL8fYNjaxpQHNBXiVDaqB4dkRnn1fpweqcX6i6cb/XuN9ug84tqfxMul4oXS4/6VxFPFPiS+sjg/DfZra1Ro36hynjzAGSlMeU8BnN7kdHzqOscHxPwLvH3caBx1Nhs4hbHDUCvMY4MbjWGa/pl5zLSSOg1caucx049hcwukrLVQ+FrIOP9xRZpz7UswC5q7Os+laA8/EKNWcrJB6WneozaRlK8nEFio8PDCYjff8TR4i2ow1h4bGGdW6UEbR8BMSW3ixkHCQj/DSLM1coPcVx7Epj4Zvxvfwh7B8KX0u9E1IbeZySdHLGNFd7vsMv2s8FF7t87rrB4mqzVQwgw+sZ7SH6iVygFNpBX9WcOZADX0Cm6eejXGk8bXfjsE+IG1GcEYE7qNapBNPlcD/Zjz6U7Quf84O65TGqdS3iV4lBA4FuIWE6+bBMsSRUU8eXdI63j2x7D+iE8a26bi3aHwO3DQJZR4d1HAOLEJzKVhoDuFdNY5M9+i54AUQtM+Yd/VMDE0K6yBQjLWJC19fZmF6LEuxJ+D6gHGFQxc4eoGP1TgijUfOzx/FhD8vbfaIWAQ7KJm16prA1arlIpt6UQbnG8Z+VSw7PgUcIeSg+HhuKL0bUOQkhbEB/QFou1EERQ4j12doaGO86DJ4MwBV4bnFKVwWSi6Vn3Aa760x0dEQX8ROG/exC9rjXE58zHHRPQIPwQK/i09206+b+FHMqioW5sTULb9TUlgQ073d+h/i58saQRAEQRAEQRAEQRAEQRAEQRAEQRCEpmmaf97j1jGbUgCtAAAAAElFTkSuQmCC)
For the following feasible region, the linear constraints are
![](data:image/png;base64,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)
maths-General
maths-
The maximum value of objective function c=2x+3y in the given feasible region, is
![](data:image/png;base64,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)
The maximum value of objective function c=2x+3y in the given feasible region, is
![](data:image/png;base64,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)
maths-General
maths-
For the following feasible region, the linear constraints except
and
, are
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAWYAAAD6CAMAAAClKx2oAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAMAUExURf///97m5t7e3ubv787Wzs7Fzvf397W9vcXFxa2trYyMlAgACKVjWr0QWlJaUhkhISkpITExMYycMZzvpYzvY4wplEre5lLOY1JK3r1KlAje5kqc5gic5lKMY1II3r2MY4ze5ghaWhnOYxlK3hmMYxkI3pzOpYzOY4wIlGNrY3NjOu/eEHNjEEIQEGtaY+9alO9aQu8ZQu8ZlO+cQu+clO9aa+8Za++ca8VjWr0xWr1a773vY73eMcXmpb0Z772cMUJrGb0plBAZY5Sc7xBrGb3OY0JKGb0IlBBKGUJCQr2UnO+lve9Cve9zve8QvUpKUu/elO/eQu/eaxAhEIwQGWsQGZylpZSclISEe++l7+9C7+9z7+8Q7zoxOkIpWkIIWnNza8Wt5lLvEFJrjFKtEFIpjIwQWqVjKb0QKRnvEBlrjBmtEBkpjIxrzozvEIwpzoytEMWM5lLOEFJKjFKMEFIIjGsQWqVjCL0QCBnOEBlKjBmMEBkIjIxKzozOEIwIzoyMEHtze4wxGWvvpSnvpWutpSmtpUrvpQjvpUqtpQitpWsxGWvOpSnOpWuMpSmMpUrOpQjOpUqMpQiMpYStc+/Ove9rEO8pEO+tEL1rzr3vEL0pzr2tEBApQpStzoRSpYStUu9KEO8IEO+MEL1Kzr3OEL0Izr2MEBAIQpSMzoRShGvv5lLvc1Jr71LvMVJrrb1rpSnv5lKtMVIprWut5imt5owxWlKtc1Ip772tc63v5sVjKb0xKRnvMRlrrRmtMRkprSlrWhnvcxlr7xmtcxkp74xr74zvMYwp72vO5lLvUlJrzlLOMVJKrb1rhCnO5lKMMVIIrWuM5imM5msxWlKtUlIpzr2tUq3O5sVjCL0xCBnOMRlKrRmMMRkIrSlKWhnvUhlrzhmtUhkpzoxK74zOMYwI70IQOu/vxc7m70oxEIRzpYSMWu//Ou//ve/W98W1nMWcvUpra8XFpc7F5s7vzu/O5u/O3vfv5ub39/fv9869veb//97v5t7O3gAAAMDhtPMAAAEAdFJOU////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////wBT9wclAAAACXBIWXMAABcRAAAXEQHKJvM/AAAVSElEQVR4Xu2dz26jzNLGSbcBA1IDl8HGd/BuP4lsWCAZNpZ8K1zEucqzsPxmJDp2bH1V1Q3G/zJmxnbmZOqH4xA7mZEqT56u7q7udhiGYRiGYRiGYRiGYRiGYZjH4brTCXwSrutK8wpzf2RWxiHE101L5drXmDsx0K1fJlv4JJLUNy8w90I0wt5BxGcxxrfKOcr3Jowqe+c4mzAKQdPp4RXmPog0Slt7D64cK08WzcZ+ydyLKooGctZ54i4VZxn3BsQcRenBneuyKDjJuDthVMJ1kPM8KjHXYO6KyPM6CfMFdkqIeTnz7C1zL7wqn/rJdFr0cq6jV3vH3A3R+M4WUmW/tn68mZWcMt8dF6Lrx/DkWtcQ6aA5ZO7EO3z4CerX5HCbKsKgM/fHp2EMg5/HacZZ8yNAb+7YSC05yg/hjW3iGUwHamYeRpByt+8J+KzmZ+An7M1PYJhpMA8jGOTNzMNgNT8FGtNgHo0Z02AezJrD/AymHOZnEHDe/Ay4F/gUhuPNzMPgvPkpsDc/BVaz43iToApGqU1OA5qbln4VmPkmD+4+ma7246m9+2uRdVFXKg/slzfg1nWIMdVhFs6o5F6GszCbXVcs9wKdNlbSccuF/rAv/Iwgr0i4XrjYOm6Ms9Tz0ncmi+tVnmvONN4b0ONGnQzueFjFchE3Dak0+UPn6t1xsnLq6FmhHaeJ3+gbLsC9QINcpNoRVf3DmzYhBvg9yeB52lTS0RX8IqTUO3w4UuXWhN/i0MNyzzlYbwNfV2Vo3jgnSP56bya2JQRKNlHtTCOQJ1oJVha6aSlw7VPghUWh4KrQJfTExUjPEwzra1k7a/iAr+P6zDXklF7iXiAhlFqBMlGpcwU26oH+KDBNuXd0nUmvWtZN3TQ+aHY2D5WCFjMk9QZx7cxxNQncnS90sNMmPBeIyKYwf9RBki3hLsiyvFRZuANLcJ0fs2EWsoyWrnTz1LVhRsOYw18BtnMNOPQxIs2who7zZkCHhY3CJEn3oOW5UmU8U+ABFaQQFeQSwt/C5YMmC6qaraIK7MKYRghhRocJLnlzSHLmuUAg6JJmXS0SdF3PW6WN3oAF+MncXUJcwziN0yRZSmdJwgzAJoKkxiawDMDZl57z8Voe1j30yAUmfKxmCKXC8MynjqzqIDaKnCYm8m4KXRHI9yYg5qmPSV5I76Ca92kBd8t474hFjgkdGMk5FWaKPBfo6CyH+AVJoJtiD55Axd6BjUtbxOqwog9p05l0VgorwRvQqEgxl6vBSUR+sZxWoJy5FwgZWoSUfhBBRhxGsx2+2LV6OaQaxwSqqTLqYk+yIlzO0GXkLA+bzObTJ9Qgch5vdnbz1/l8XgWtmAfC0YHpSVum+bnfTl7hm+lOB+GbUfD7uhtEOsPNG/bmT5Cu3KoLycNI3ut8wnnzdba5ys8z4fG4acgFBNfx87y6YgTjaPKKxzSuosVdggwGn+bszY/HW/IStWfglryu9QnIWcRhfgIBDeExD8YfbsLDPIptSQP/zGPx0yTFoRLmofhpwxuiPZ632M8KdudH46dTl6bKmUeyTlwvK26ua2J+Daw6CtidH00QTx2p2J0fDM0FvtppXOZR0OyJKC7OyTJ3w8wFBjHL+aGYtScixypT5mHYitCK3fmh2DoNuVD0JfMYuqqjkGdRHsnazmyLdEafmYfQ70PHcn4k/SrXNs1+7+gCPvjgEw41dK+jKjY+tOuaapGNdsVkLyo8AAgQ9mVmyGGV626R3VwvJt1aqTzN8C9BKtyfP4ooVZFVnuf0MjNkUN8c3n5GkkyjaiUarAd2dBGrmcrUHN8Iy1AHcc5xPmGwA4Gb1rf6q1Ch4+FxHvCLkQWWq5uf3JaYr9QRLktkBgzU7IXphbJFGVTuxvHh2b6AbDT5r4reUM0YZsIzRy/5Zc72fMyw8NZNbTM2RGdRofGAlLUj251Y7dodLlRERBr/QDUvpWhx8ZvTKqphcmPeov+EozXbzSVTXSnIqKsofH9v0gSvOLMVB1W5hOhqtQgzleE/s0sj/AfanHPwE7oVQ8Q0vVTpFZS1P6s8R1cqI0KTkUxTs8botfa3VZKsQcZJiTLeLWL4ghlwvJLq4so2WcQ5RPkUoezxVTRdG0TpzhFxiS+t8vLqjgd/KccrqYKE0rIT6uhCCiKLvD9jCZAYYZN6OCLnoukTTlZSZRcar20RLbFpnIZhBVdIC7P08ni3+A22fjLH1MOZ8Ck1p5zs3OWfl8b4KszQS+SSVilCdw9/IqRpLQ3hNGd4yATU7DVU9+hT9swMOF0XmC1OhOgXoTPHPT2cdjIRcE3wG9bQ2/vQLxn8Uszq/QDTPmgtMWMOuYzpFD8+XuXq26XhHUEJKnXj+O3InXd5BB3sWR6BpJeR8t0gJw/RWVm7r2m2Mt/GdBxnGsCMdjroqROIn25SdVSdu1VxAleMoyBurWYZbfYBrOo8+7dmZz6lmz3peTvae2Mj9ziuKYU4UjN0+6BDCE/4qpb7SdsV4W1WE65gOufsFJ/NmTszv8/5HqF+/Mr1offmws5dXLh4fy7sp+Gnl7qCx8i3F3vH3MKF/TRk1m0beJ3p7TMtDHDuzY7zmv600is4/+0wn3BBzc5OqZ/J2S95qHMMF/dvrn5qCbzn5TjOeoHIKp/ReNB1ttf30GUucPks1/nJGMYZ7M3juLzXkczV52H2eRpqFKcjdJbwJ/MfPk+qjuJKW6Zxh8ZPuJSgMNe5tg9dSJOnVwku/xH8Ip8b1HfgYqYBtEn2WYHW9teXXnnvq65mVEvEtQeGvLff98z6q/s3V5/awi+reSOqZaYUHRsiwzQBStMZ8pdK4ZkN35KrJivT2Sd1uNtfbQK9ogz9rYrQ+vUyXuRFvqCSMjed+UF6xcL+57m+R+inycbNefOp7ZpFAaLEzrrMGqk3eoPupPN0Av9n9E3XJ17vNevLuz4btjfkzcJ14R+Qrvsy/LMwvhzjZO1mtqSXkGmEReyuKVv6fqzjeTuZ7CeucCeDh5iIH1niww290L9PN/BmFc93l94b/GK8IMVfoQCX2DjQ0mF718XbLXFiF9S8erc/YUp2Zf5Nt8Wblqm6Qh5dfQvfm9nbIcsjLTYUugaEW83UDB+NCaow+yttsqIKl42Lwc/MBmKKfuT74ZezZgieoUIXoMoZPP+3Dmt8DG7CcFZmeGdepDt6tzoq0KgirCNVSjpNmiMphlkHTWoHtF+z+bzOcThQKnQRDPP33K7NTyopgJ2QQsudXskVJbPAxl3gqTQeNGN4DW48Z12it8J18u5Rrh2Ujefscdd0kyAD8KoIM7s4yCHHCGIsHaPKGgwznnHx/VjHgdRC6pXeSRNhT28Mjmyuzla93ZJpBPEShHzakHpSipAWBxmkwl+ZdYtvaxrJXO5aVDNEWmtsqDbwvNHeRuMmdfbbTrlphM5dKGe6gN+HGxDzbRdxraL+X9ZU32iKHJ3CiPrb8RYHq53YtTt4AuOgfEBupCP1h3bk7NqOizeN0Ik8F6oGvc5sLemhHneQIMtZOUcfx869wAPNviPrtNIU5Va28kWDpCVpegPdBgmivdIi3TRCJ1WyVJBGeFbNges57b+v+BaZg59hj12k2A9yY+yeQIMMNv8N8cGb0TJWu53ereADDRoiDHoG19DZYti1OLAubxnTWEaL097GtEz8dleVWHITRsrV7pION/Tqshb70yMovw0+dE9AzeAY8KHbHaqZ7BkfIOdLh4YBt025mj7HEbIu/s0atURvmGS5Wjaqot/kqimy7OjEuO+Enxg1g21I/MCEA+IMev6AdhDd+aJZ3pRpCEWLKY7RYhusXVOltxF+4Hf/PliL/22ryrYxeDPlzcagdy2kzkbQ+gP82Y8v7lY+vaFOQ9a0fIIB3uL5CqLcQpCFbN+NmDGjw3wDA12k54q8YYQunLtNcc8Zlv9tpvHrDrWMD42Sxs4gdlPQncGcobG71GH4qTenUZLfYt9/CUEcoGeQoAXpWUI3BYMMioaPKxuI/izTkGG3FpZBpuDNE4wymTMKWkNqR/aMvqExd74gZy7uGse6VzNGGjqEEiTdZxse2EarLmzvfFOmwfSAmoVwTZRBzcabIciQP+PYBjx9vKXnuTP8duwdcwsHb0ao3w29bo0Zhxnb2GxeipPTTAH/pl4g0+EnoGZB7oyQP0tMoKk7iJezqc7Lytmbx4GdbQjy3gYavfmlpXyOzBkfzv58e2cuIx/HFtXcBxl7KdATFHq105hugG1AvvH+eubEXEM3joBmtk2UAWgEMdMwo3XGNTaeN8lPt6i7bw3d92eaVC8QZawZsIGGIOO+URpaQkycwTWkE56682/U0P2VUJ1GH2OBmbNJNzDVsAbteeJ0A1HONMYBefPuB4m5dw4aeQYtt332/KFPK73W7M2jCOJX3ImEorzv4gxqFjjyjGom43Dc5Li4jdeejGMLvUBTr7Xv9IxqxoFn+UIzg6hncOfjOVb25nH4EGbaXMcdpBvYDLbvWL6xw6wOOoXOPj7av4i9eRzYC6QiQ4iy9WeaGcSxOgl6xhhj7uyFRzbBvcBxrOPXCQXZxpiAQOP+LysNOR0GGruCIhkmG5w3j4NG6Gw17SCx27Uv0EWhHgr4MzaDIOdBZNmbxxFgEwh6xhBDkLtWEOKMs90v2Bc0vuFs0/ow7LzmdYGjwEzDmAZtMmeTOrTnFbaEOLixolxDynCwjyJ78zh88OYfIGaI9B6CjJE2gUY9Q6axw3wD9bw5Klx848XEo4CEzgQZnGPQDIKczSA/jmygnrEtbA5z1T7vQDAKzJsP3mwdGuMMl3ghQbcoZ0g33rdpP1vNne1x+EmISiZBk0cP9Iy9FAnZM9ozGsd7k/ywP3ZL1RFzIEiqPUbZBBql3DsHTcCClE3VM+R2g7N1g3jKm9WNADINCjGpGVpACjKlG9RHATUPs41+e2f25nGs44GaKakbZhs4aQVBxgEO0LPegPbNj/Fc4DigF9iruW8HezljQ4iFSGTPKOhuA1HOm8cRxK8uRRkujHc/+GwCDRdIGh3aFG50qyS4FzgOzJsnrok0xpcSOohyN7qxE3L3AmqGXAMeWs5ikjOreRwQ5l7NGGkSc6dmI+cW4rzDOiQc2XjFZU/YC+QRujFgpuFO+zhPXMw2JuDNJtJozji2sXp5QXcG21ApVnpxDd04AlLztAsyqBkTDZK0AdUsoR1cgaIhzPADKGeePRmHn6CaQc6ddXRbOdhAk5yxsA7SOjM1qHAdMHvzOCBvNmo2QUYxU17X2zMlG9IMPaM96zXmzpw3jwMzjekU9Gz9GYLctYOdcUCXG3uDGme6Ic47XCXBNXTjeItDF5pANGcKM1wQXnSN3p3BNqykIcjQGZynAXvzSMACQM0Y5y7dwAcqmi4LdgQxe6aZQaGUZDWPg/JmNI0u20ApGzFDkM2UFZ1ygudeStm+g6LD1OetWMcBveeDmm2gUcx9pBEztIH1uGgbjltkAefNo/CTENs/DHQv6MmeEo5DtgFqfhE7jckGOXSVLi9tYs5cBXuBlGmgc3RqhguiDGLu6+og3XjBZbBY8yy9fRHz+ZajCCDT6LQMF4YYY0x9wd40aFZwh4sGpcZ0Q4cRnY3L3MrWZBrEwZ4p0saeu+QZPjReKGc9WeA2UMzNQBNovJmS5y6pg0DbgY2BPUOsX9CesaQA9/BjbgeawF7NYBy9P/dqpmADNLSxsnp2guiTDUSZM3CEzoYYxNzZM152bOOgZ1yWAvZMdaJB9E33TH0Q2yQ0UYYY/4Nx7obqUMzwgOzORhlNAz5RnagXxBHuycXciF9ab+6zOhI0RRodA1K6zjbQn6Ud2wji4tLJ58wVIG/+jw0zgUG2IcYHPlk500S3GaoD00iq8xXGzFVs3mwwerZxNr5BjaC1565uA9VcrqvvukH7IwA19yEGqJtiBW0VbfqCffqscdGgDMppm9vdmJmf0+fNHYPcGS6TbvTJBmXPQkg9j30nZHe+mW0JavZtiAH3H3y2UbZDSBDlLtSUPevVZh6/OZO0+ZZ7LT8C8GYKb88PMOfBGBJGen9QsxAv1A7OcS6wxt1TmVvwE1Jzp2cyEOp3myijbaCUqVYUAC23mNG94uzJJL+2vTNzAnjzfzDGB99wsZvSiRkCfTJWR7YBTSCo+eOGs3WfDnSaPLjg05/UQG9LMI2DmhHsoxwaQhzcICn3eR2mGmb2ZJtUf5w7u5nKQ5BPVpj9+/8MsE7Dn26PmkHTSTGBNjGmrM6CyYZ8pYrQW46KfjZCReXrB/Ruo+bKTupfwRTUvL0g576XQuu5rT9jqLEFbME0aGbbvbZb+RfixrikYJ3Wf5Jp+Ng9OZIzjotCE/hP3wjS3KDxDGsbO9MEghFe2638acizLfJkBhJwi+XRESxfDXnzqZxR0Ad7hiCbfAMM2lhHaxI6wI0HK4y/hOn8NKucR3Wr1O74DJYvhjINCPG5cUCUTZjN0PNFNTtOnn6xnHd1Xh3bwz5Pi+IPazN8Gm8+lTINhvZZHbozStmOa2BGZ70Zfv7i9s7PRDZlOj+KahP9Ef0m4QdVt6U9jtD5/tbHj5NoA6hnCrVNOcg9UNP92Zh6tniBJHUDWaqmj2ffOU6bRVFuDypF3peRsrdfiZ/lapYXJk5umahC5ebjGHgNr0tkeZQvs2yWZXBXwPNXslR4dE0edNZR5fEfUEgZxLjn/VtscgQxSxfpAh5Jniz+L70IvEvf0j3hc74wby0WcRTjPb0F17Pv4D4uIcwxHVUKf15VEVSHo5i+Cjem4fiNPTnOedFC0woeeKwk1uTrd7yoHoMetCiQFgZKKjqCW/Oggmf5ltbwvML7pz6Zh1xJ0URRYk4ddJyPNfQBW0qdvxLZnWGWDY5+6XMykwPh80k2dOGljg0eCEjQNzztyeA5XlimtT1IBRyRJhuyry4j2ZaJCYo9bvIOBOfbOz8RWeW1+26/cKY5/a02UfGleaZsInNcTK/q30eozw/yfyieHw5GiLZJhFVnb3EUfWm3qT+TVqT3O5x2ntCSzC9B6oGDQKrq+5Ayw6e3H18p521pFgM7QRTfTYJtcbq989/OuszNkAqYx1AGvwcu+mEOfAS2bXCTck+v3AWdn2zv/LfjlubYUMgz7idmxwl5jcQRWuEIpgjL+1ZmicXx9s5/PW6ehmGWNnc+ALHibUyOEVWzrO4+GdnGS3vHPJKKF6M8A7ng0phn4LI5MwzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDMAzDII7z/zmjZDQfVYmNAAAAAElFTkSuQmCC)
For the following feasible region, the linear constraints except
and
, are
![](data:image/png;base64,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)
maths-General
maths-
For the solution of equation f(x)=0 by the Newton-Raphson method, the value of x tends to root of the equation highly when
is
For the solution of equation f(x)=0 by the Newton-Raphson method, the value of x tends to root of the equation highly when
is
maths-General
maths-
The formula [where f(xn–1) and f(xn)have opposite sign at each step,
] of method of False Position of successive approximation to find the approximate value of a root of the equation f(x)=0 is
The formula [where f(xn–1) and f(xn)have opposite sign at each step,
] of method of False Position of successive approximation to find the approximate value of a root of the equation f(x)=0 is
maths-General
maths-
By bisection method, the real root of the equation
lying between x=2 and x=4 is nearer to
By bisection method, the real root of the equation
lying between x=2 and x=4 is nearer to
maths-General
maths-
A river is 80 feet wide. The depth d (in feet) of the river at a distance of x feet from one bank is given by the following table :
![](https://mycourses.turito.com/tokenpluginfile.php/c161933dbfaab094c54655ab71e9b8f0/1/question/questiontext/506492/1/977531/Picture29.png)
By Simpson’s rule, the area of the cross-section of the river is
A river is 80 feet wide. The depth d (in feet) of the river at a distance of x feet from one bank is given by the following table :
![](https://mycourses.turito.com/tokenpluginfile.php/c161933dbfaab094c54655ab71e9b8f0/1/question/questiontext/506492/1/977531/Picture29.png)
By Simpson’s rule, the area of the cross-section of the river is
maths-General
maths-
If for n=4, the approximate value of integral
by Trapezoidal rule is
, then
If for n=4, the approximate value of integral
by Trapezoidal rule is
, then
maths-General
maths-
If
![](https://mycourses.turito.com/tokenpluginfile.php/c161933dbfaab094c54655ab71e9b8f0/1/question/questiontext/506491/1/977520/Picture28.png)
Then
If
![](https://mycourses.turito.com/tokenpluginfile.php/c161933dbfaab094c54655ab71e9b8f0/1/question/questiontext/506491/1/977520/Picture28.png)
Then
maths-General
maths-
The coefficient of correlation for the following data will be
![](data:image/png;base64,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)
The coefficient of correlation for the following data will be
![](data:image/png;base64,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)
maths-General
maths-
Two lines of regression are 3x+4y-7=0 and 4x+y-5=0 Then correlation coefficient between x and y is
Two lines of regression are 3x+4y-7=0 and 4x+y-5=0 Then correlation coefficient between x and y is
maths-General
maths-
If the variance of x=9 and regression equations are 4x-5y+33=0 and 20x-9y-10=0, then the coefficient of correlation between x and y and the variance of y respectively are
If the variance of x=9 and regression equations are 4x-5y+33=0 and 20x-9y-10=0, then the coefficient of correlation between x and y and the variance of y respectively are
maths-General
maths-
From a random sample space, 5 students has been selected. Their marks in Maths and Statistics as given below
![](https://mycourses.turito.com/tokenpluginfile.php/c161933dbfaab094c54655ab71e9b8f0/1/question/questiontext/506373/1/977504/Picture26.png)
The rank correlation coefficient is
From a random sample space, 5 students has been selected. Their marks in Maths and Statistics as given below
![](https://mycourses.turito.com/tokenpluginfile.php/c161933dbfaab094c54655ab71e9b8f0/1/question/questiontext/506373/1/977504/Picture26.png)
The rank correlation coefficient is
maths-General