Maths-
General
Easy
Question
If f(α) =
and
are angles of triangle then f(
). f(
).f (
) =
- I2
- – I2
- 0
- None
The correct answer is: – I2
Here
f (
) . f (
) . f(
) = ![open square brackets table row cell cos invisible function application left parenthesis alpha plus beta plus gamma right parenthesis end cell cell sin invisible function application left parenthesis alpha plus beta plus gamma right parenthesis end cell row cell negative sin invisible function application left parenthesis alpha plus beta plus gamma right parenthesis end cell cell cos invisible function application left parenthesis alpha plus beta plus gamma right parenthesis end cell end table close square brackets](data:image/png;base64,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)
=
=
= –I2
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The coefficient of correlation for the following data will be
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAACFCAMAAACAPRrmAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAALuUExURf////z8/PHx8e/v7/Dw8NPT01VVVT09PUBAQEVFRUFBQUJCQkRERENDQ0dHRzs7O1FRUdTU1MDAwGFhYX9/f0pKSmVlZYCAgFtbW4ODg3d3d1JSUmRkZH19fV5eXoGBgXp6ejg4OGpqanZ2dmhoaE1NTVRUVH5+fj8/P3Nzc1lZWcjIyP39/cTExLa2tqCgoN7e3rCwsLS0tNfX18PDw7y8vJqams3Nzejo6P7+/tjY2OLi4qqqqpmZmbi4uIqKitra2tDQ0K6urpiYmNHR0b29vaenp/Ly8rm5ubKyspOTk8LCwvj4+HV1ddvb28zMzNbW1qGhoZKSks7OzvPz8/f39+7u7urq6vv7+ysrK76+vm5ubmlpaSIiIiMjI93d3SkpKc/Pz1xcXCgoKPn5+SUlJd/f38rKyunp6cbGxuzs7Ofn59nZ2fT09MfHx6ysrObm5vb29u3t7fr6+j4+PgkJCaWlpcnJyUhISKSkpLOzs3JyclpaWpycnHFxcW9vb2ZmZmNjY2tray8vLzExMSEhIVdXV+Hh4cXFxTY2Nh8fHzIyMhwcHPX19YeHh3R0dGxsbHl5eV1dXXBwcEZGRjQ0NJ2dncvLyxAQEL+/vzMzMycnJ+vr65ubmxsbGxEREdLS0klJSY6OjpCQkLW1tTc3N5WVlZGRkZSUlKampmJiYq2trRgYGLq6uuTk5IiIiKmpqRQUFKOjoyYmJmdnZ4mJiYyMjDAwMI2NjZ+fn9zc3NXV1YKCgiAgIIaGhp6enl9fXxUVFY+PjywsLE5OTpeXlxoaGlhYWCoqKnx8fBkZGW1tbaurq8HBwbu7ux4eHk9PT+Pj4x0dHeDg4KKiolBQUK+vr1ZWVi4uLiQkJA4ODi0tLXh4eDU1NQ8PDxISEgwMDIWFhQQEBEtLSxMTE+Xl5Xt7e7GxsVNTU6ioqAgICAsLCwAAAAcHB7e3txYWFhcXF0xMTDk5OYSEhIuLiwMDA2BgYDo6OpaWlgAAAKOraKEAAAD6dFJOU////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////wDeYEUjAAAACXBIWXMAABcRAAAXEQHKJvM/AAAPz0lEQVR4Xu1dwXnrIAzuiRMTMAvMwI2bP1ZhDl8yApswSYd4Apw0gORAUue5Lb/T1E4pERK/JIgDHxMTExMTExMTExMTExMTEz8RZggjxQfKDklxkAxHyXuOttXQfOX9j3x0Po4pCo/R0v2P5gXq0byw8xiqdrTq3ofd7H1DcN6L7Xh84rm7v9w/Yex6dnuJPGG53ng8OoF6i+vdE+56SuWTtb9t9/I++g/H06/thf0Tz9n1avu1c7LJm48HJ0xv9r5Bh+2kD7LpOTS03E46EEbqHRBZNQ2mIVp/SGJAXjOghg810DY/oDPZ6CGMiDWmyIGqTSsYjTCinBHDjxizv16rBjqUGjDHSNtaeUdoCTiF4QdEHmGQO4Hh/TGGR+QdZfyAezmD4X8z48VLhj8H4xFXROMgxpuDDK/lCOOPCWPfwPhjDHQY46erT/h7hh9x9QcZfojxv8PVq+3kMQ4z/AjjzxDjX2K8CdQ7IfJOxl9xWIx/03DOSLZQUiHyzuHcDUfF+Pe4+uDccqGkQuSdjL/iFIx/3tXrYNlCtQCRd8b4G86Q1b80c+dGXP1k/A0/bTjXZPUzxn/hp2X1LzF+yPCT8VeYM4zjR2bumo46Y/wXXlIkjRF5T+rqfznjB1z9Kebq3+fqZ4y/4W8ldy8z3pLNqqo2NujeGGR00L31AgzV4JIVUYKgJXHnCsJ4Y4nWlfIaDdUGrXEpmnF81APVuIrxUDOth4rxxq4XidxVGYEY/kXGa8/JSF7GeCmcYIzo/ZUilROCO4qsjeGN47jOK8Ybt6zLemFE96sZb4JjhPsv5dXrBepdLg5tXDVzZ70TUC8hcGl4HXXmqLaVdRjp+eoIj4wY/iXGG8WWC1lBUbVdV23CuuDWLAWTF2GNurR3hmY0IqvPlShaMZ5xpTzl/WvGG78w2cV4uQqllFhwTZSMt2wBWnqik5SuXnMedcbxohXjwTlY8A89HTWhZHx8C5OeCZSG116LXsO7eEWVrhgUO7nh1OCkNrwGIjft2lAx3m9nKCrG+5UuXcmbTKsIT1LGeJ1MbjghccF4n7qSvxA+lfIaCBDDF1rUjukPeXUumP0rxl+FQ1EaKNXmugyfyuqVYQIAKsMbplwn40cMLxexnSEo5U1yWirmlVl9SIa3K+HO7hlvcrIWKEGqGL8HxPAF4424+A+95IhpGJLmNjFkz/CNJiwjOnojmJWOjG2V4RUDp0yVLcbxxjGpiCgIKFy9Ac8dyMKIIhXV+UrGG7eCq1ec6IIF4zfDU2HhGxkP3ZYbv918bxekKY0mqMgGqAwE8CtRuBZMuoVycHW9mskP0enqvVCecUqGop/bdfExXcILt4q0jPImVVZv+SLY4rarGkVHFUkH/pMo/I2MB0Ut4trxjSolThhifP0XSUbNWjAbIG30eEcv6zUC1CIWS4xjqkwOmqbJhKBgPBSDfwXvh9bbKlKRfa/K6iUTUjKKAUVyBw7SS8UuhKv/TsbH9rY0v8MrjJf0yA8RDChHNKyoV118CNBJRGnhKxBWZB61KBkfPpO6HX5vQyOv5RSHK8aHJaYu0KFwNVfDOS98oOT9VsZDtCQn+hOGGF8KrBlFYVSwD/ZJ1FwY3q+c8+WyctzwpatP7+8hj0FRMv6SLEkMQxp5VfQPOMoYL9O7G3YhOmrzdpAUEAb+RsYbr+7csUW6wPOuXrNIoa5xpkmFGHUjUSly/AqwW6j5rYIVIc3ZkUPKwvCQd8cqiQ+8akUazrazFmVWL5PjJseq1cwdQC1UfPw+xkO8tDDC3ESyDBmYtq6e4k9teLGoECSRApWCyUjfQI14qnojRF9WD2EBGkzWWw5i/AIOKyx4Rl0rUlIxG1AyHnK72Dii3prxxvqVKIkwnshzAIjh77Ro/Cf0cvG5KQCy+rai0vBWueVzFUgSGFEYSC+fy7JcPnsMHxh3DhtMZjSGD8sn9U3XwtVDvQzqbXSQUc3cWcGc4+VrN1SKNIyaXQNUWX1gUC81FVwxHoZ9O/GxYrx2wlHeHzH8PeODDAbKbK7JaOQ27dLwRkslpcTDVRnjoVoYQ0viu3eVYDZApWSDG8NbunSZ1e/XW/U0E5SkIkityEAoPKK+yzYJsZ03qBiv976OXVrZchbk2n4PPgExfOs3d9G4+h0MVI0IRmNE5JE4SLEQwYi8FeP30cZ4GkXbjIghhJreReStxvGP8NMMX43jd0HGlhYj8r7n83iTVjvRF3yeG5F3Mv6KwxhfufpdPM14u8RxBZW5IvIeyXhi4gHBcYb/K4zXC4dnGCaigiHy/nLGn8DVvyfGh0tkPDU/gMg7Y/wNB7n6oe/Ovch4e5uFKYHI+8sZPxLjfzTjbfo4CSI9+maIvL88xp+B8e/J6tOtDoH4JA+RdzL+ilN8k+ZpxqfPnvJgHgEi7y+P8X/nmzTWrUJQN/Yg8v5yxg+4+p89cxdlUqRUiLwzxt9w1Dj+LVn9PhB5fznjR2L8D2f8HhB55zj+hh89c7cPRN6RFYEBBy1pOray5YDIA6w4LrnbTnpwEOOxRYzVR9zpoHOzA3izrtKpDMT47XIPuTpg5uNaMz6kjM/54gFiVr+dPobTXWWh0IdRSQ89AA3bzrZBscj4x4WTCJHxHfWmsiDvZu8bAgew+Og5+NJXOpVY19s1fWzVrev2T48PqDf+/nph71h6ZMhHbNv9NXUkiZO8969SByDq7O4V+oBSIG9H4SQCX7p0lsu23yrVzAkXH/npwY/gLD7DRX7afeY8n+8CiqSy13of/MRfjKdf+WkH8e9Zhlzy0fMaZUhXez/5mX+pbRfx665R3K//pJ/hl4jypqv4RP2kZxFlyCflT/kcf8GjNfxIIAaMxPiRvHFk54uRGD+SjI7sUDEg79BwbijGD+js9eRuQJFnyOpHEqAzZPUjhn8tqz/HcO6wrP4UH9JsJz0YGc69No4/0NVPxiccxvj3Gv4YAx1m+J82V3/Qh0oIYefM3Q1nmKt/X4z/5YwfcfWnYPwxbUPkfeunc+TSXYcZ/qfN1f/GGG+CIFclO8zwI8o5LKs/KsYPtK2V920x3np2Ib/Uepjhfxrjzzuce9pAUmni3l/AYYYfifEHGf6we+7Oy/gixkPr2X8w/DHDoxF5/zrjQQP/wfAj4/gfx/iX5B1lfP+bNQbKt36jOIPhT8H4047jR2JmY/hzx/hzzNwd1LZW3jeO4/8H40di/Jy528FLrv4/MH4kxk/G7+CV5A4Yf2bDz5m7PTSG32lUXbUlvrUPaASDakkXOSJyrRyzI29jeLrsa4bfkeGHzNxZ5b1wVI8uY7xVYrkwomGlYMYzAHKDYEbToRS50HWpSCO99+SSYFVWb6TwgirbKtIKal2yyvBGCRCCCpmFq7eOc9ADteIZyvi47HerY8TwrzA+rnKuJafWtiyrtlIBCKZUhl/XeJsgdedKXa+70MuwFq4+rFxqQa0CXo7jjVihbSvR+RpFGv9JLa9fjuNTvcFRa9AXrl4uDNTAyCVYEdk041inQgz/EuPjytEf8pPQ5ECfqgxPr3YcUdarHHQ9RAUZpeEZXJm0tCSGgvE6rSyeV55t0SgyrMR6UzXjdVq6IG7Usr1QomB8SLd/SsrzIYy3HF9TFzF8ocUQ7Wrkzi2kpeFTxFTU6srvMTzI4GjGF8pJa+Ru+z60KLP6tKJp9woTNm60hJYEUxeGz9sOkAOcgvFJvYZcCL8dhZADZsTwBeNDXE3buGxJI5BlEkvDA0xg1GKr7efxJGrDe0vv0NV2KHq/NSRZ2lYnRlC4+mx4ykC1IiHLcCTjC1cPnIR/DStxL3eb1ZOER1x9WIQBzW1Xd0AMX2gxD7hcXivNrMjy0ZXhreALtZvXC4xn3HtOrM6JGp4SARnOOWKB84rxeZVpal35SpGBB9PJeIjCq2OU3ZFxPCNv929cPeRcTnmH+EvE8GWMdxD/7HVZBdX6ktrwkCe7lRr/Pm34D62NldRa/m299L7preHVSvqhMrljiw9SEClb3VEF/JCMrwzPmfKc6n0N4wO9LnbLeHYR1mok5UEMX2rRg+EluU42oHH18P55L4cWzxs+gVrLf4jxNSskL81wh2rmTgvnle9ZTMj4VdoAtkTHV1VWr3mMNIHykzXj6f6EMX5NgUl9Nn0b0W/JeLXIvEEcBcTwkBjgsj0d4zN81w4VEXvJXdl5wl7aiHguKnEtDe84jLqWhQhOJeNhnACXhtqFoWZ8oPeBaBm/ZSQx0ucXbkD0W2pRLt7fpguwWa7C8PFLuNB1qIHM04zP66W772B86ep1imLU7pFtZCO3hyvltQDw4MStpCXjZVqxn1p6tGY8JNhk0xDG55gnn2B8WNfbO6GboZSGd7FrKWqXpMZAWE/KKAWTC+gqp78YWsNTm5hUho970IKJKJdWM97omPKgaBXZG+MhXYR/DdRuihXjQxpbEGhjfNokcTN/AcTwpRb13dZPhj/K6qFrOC94Z3JnvaD2sqgEg+4HWT2V/1T1Qn55+WRtwzIKVsjLZV3XC7UpatUOiNpxwgdFq0i5UJ2kyurVyrxn1JClDE2gYJrwLePBQXABWX3bPsTwJeOzL8wwofBRGWWMt0FJRW75UM3VOybJDVZKweCdycldQGX4WJgsXShSQ7YGoObJK1evFT2R1SpSk7tS1h826dg4qubS1Zud/TQwxoMyoG6kAyKGL7RoPDlo3EC1DkNpoLToYufesvuoPMku2nE8iZ+9lu0+EHm/GG+s2fmYa8PThs87KORpkRZnMPwp7rlrJ3BoIIyngMh70yKMRgUZ02542vB5A7+8z2KLwww/woqjGI9ETBL/gfEmuI61NYYMfx/j85adeXHtFscZfjI+ApF3RIuApxkf8vi1f88UGiMij8T4gwx/+hjfhRcMH1099dHYGQyPzdxRGJF36C7bX8j4FON155aduxgReYQVk/E7eDrG59nPQNxydJzhT8D4vx3j09T7yA4KNEZEPkOMP3lW34fnDa9XHhQnJojOYPjJ+D08b/gPLRj5ie9hhp8xPgGR910xHpA/xUVxnOFnVh+ByPs+xu/hMMPPcXwCIu/bYvwuzmD4vxbjBzQDeOXbsnsYMvyAyAOsGJmyHZHXjlBrpKO+tnq1jreww6PvsEJeS+88clGrfG/FVnt5d7l/WKWuMjw8QN67qwcHCz3VxkeSt1cG6cPd1f5hBejs7nrvsG6gbS0BrI9faevGSGk/UPiosmKkbG/roNiADCPiDsrbDezmk7hviY2PvuNaMj+w4+vkdpaPu9P7Iz8lfL1IHfmpp2Q+UtHb1f6RcP8CdcRHKrpd7x/dBeOR603/8PgBPz1HfAykGRMTExMTExMTExMTExMTExMTExMTExMTExMTExMTExMTExMTExMTExMTE38LHx//AC+DzSaMfVzCAAAAAElFTkSuQmCC)
The coefficient of correlation for the following data will be
![](data:image/png;base64,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)
maths-General
maths-
For the following data
![](https://mycourses.turito.com/tokenpluginfile.php/c161933dbfaab094c54655ab71e9b8f0/1/question/questiontext/503587/1/975057/Picture22.png)
the value of r will be
For the following data
![](https://mycourses.turito.com/tokenpluginfile.php/c161933dbfaab094c54655ab71e9b8f0/1/question/questiontext/503587/1/975057/Picture22.png)
the value of r will be
maths-General
maths-
The coefficient of correlation for the following will be approximately
![](data:image/png;base64,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)
The coefficient of correlation for the following will be approximately
![](data:image/png;base64,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)
maths-General
maths-
If correlation between x and y is r, then between y and x correlation will be
If correlation between x and y is r, then between y and x correlation will be
maths-General
maths-
If r=-0.97, then
If r=-0.97, then
maths-General
maths-
If r=0.2, then linear correlation will be necessarily
If r=0.2, then linear correlation will be necessarily
maths-General