Mathematics
Grade-8
Easy
Question
What type of association is in this scatter plot?
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAOMAAADBCAYAAAA96gdxAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAFxEAABcRAcom8z8AADTBSURBVHhe7Z13dJTXtfb9170rN7krvuu7xUnc4sQtceI4sR0nTnEc2ySOwaZXUU2zwWDAptgYjMF0AUIU0TsCJIzoIAQS6g313nvvBSQBz7efPRoiKwOIIjQSR2v91sxo3ve8Zc7z7n3O2WefByoqKmCL8vJyVFZWoq6uDpcuXcLFixcNt0htbS2qqqr0XpaWlqKsrEzf19TU6Hd8b/1cXV2t95vv6+vr9X1JScm3PrMM/jbW7bgPy+f/WDa3J/w/y+d2hO9Zhq1zNNgPRoztDO9da270HbF+19DQ8K3Pd4L1mAb7xYjxHnA9Udj634240bbWsm6lPIN9YcRoMNgJdyRG65O4sbFRMaI1GG6fOxaj9b3pIDAY7ow7EqO1x66goAA5OTm6X8tOB4PB0HZuS4xNTU0KBXj+/Hns2bMHu3btQkJCAi5fvqzftdzeYDDcnNsS49WrV1VwFOL06dPx1ltvoXv37jh06JBaxitXrhjraDDcIrckRr4SCo6u6apVq/D888/j3//93/Hwww9jwYIFSElJ0X1oIY0gDYa2c0tibKR7KiLLzc2Fh4cHBg0ahO985zt44IEH8N3vfhfvvfceNm3ahNTUVLWcFG3rAxoMBtvckhgvi/tJfMQ9HT16NH7xi1/gwQcfxH/8x3/g+9//Ph555BH07NkTR48e1X2tVrT1QQ0Gw7/SZjGSetmhpKwMp06fxtSpU/Huu+/ij3/8o7qq5I033sDEiRNx4MABJCUlaYwkxx9tHdhgMHybNouRFq5CPqeICxoWFoZTp05h7dq1mDx5Mnr16oV+/frB2dlZO3W8vb11GwY20121WlaDwXB9bskycqZAUVERCgsLtd14/PhxzJ49GwMGDMDw4cNx5MgRnT3A77Kzs9UyGjfVYGgbt9RmZJQNP1utHa3gF198oVZx5MiROHPmjA5r8HsGBLQ+mMFguD63JEYrHGdkW9DX11fF2LdvX4wYMQJeXl76HWm9j8FguDF3VYxWy2jEaDDcOrclRgqObUGrm2pLjC23NxgMN8eI0WCwE4wYDQY7wYjRYLATjBgNBjvBiNFgsBOMGA0GO8GI0WCwE4wYDQY7wYjRYLATjBgNBjvBiNFgsBOMGA0GO8GI0WCwE4wYDQY7wYjRYLATjBgNBjvBiNFgsBOMGA0GO8GI0WCwE4wYDQY7wYjRYLATjBgNBjvBiNFgsBOMGA0GO8GI0WCwE4wYDQY7wYjRYLATjBgNBjvBiNFgsBOMGA0GO8GI0WCwE4wYDQY7wYjRYLATjBgNBjvBiNFgsBOMGA0GO8GI0WCwE4wYDQY7wYjRYLATjBgNBjvBiNFgsBOMGA0GO8GI0WCwE4wYDbfMJSJ1okFobLDS8C/1xHBrGDEabhmKsOGSvF6sl9d6ixgbG6ROXDKCvAOMGA1txloPaqVOFJZUICopC34RKQiITkdsWh6Kyyrlu3rL9kaUt4wRo6HN0BVlfcjILYZXSBJWuvrg07XHMHP9CTi7+8PnQiqy80tRX09r2WCzDMP1MWI0tIlL4pZeaWpAaXkl3H1iMG3tCfT+fBden7wRf528CX2/2IMZLqdwLCAeVTW1uNrUqG1LW2UZbGPEaGgTKsbGi0jPLcRnmz3xm/EueG7EavxyuJOwGj+X15c/3IiFu73FXS2HbKz72CrLYBsjRsNN4e9PYTVerEdUcjYGLXTH93suweMDHfHCSGflkf7L8V+9l2K802Hk5BcCly37GOvYdowYDTfFIsZLKsbolFw4LDqIB3suxkMivp8MWoEnhIf6LMMPBzhi6vrjyC0ouiZGW+UZbGPEaLgpauGEJvnN03OKMXerF34/YSN+Ju7pUw5OeFJ4/v11eGfWLqw/FIjSsgoRI8cdbZfXGtYvDotYxyw5dNK6zt0PGDEa2gTdTYqxtKwKp4ISsHDXOYxf4YGB8/cLBzBp9TG4iBCDYjJQU1uLy40ixlZl2IJ1q0m2vXq5CVelnXnlsoXLTY0qSlv7dFWMGA1thlarrq4e2QWlCInLhLtPNDYfC8UW4bBvLGLEhS0qrdRtaela798aq+Xk+GRKViEik3MRlpSDJHlfUFyu45ltKaerYMRoaDNWS1dffxGVVTXIEVEmZxWIkAqQW1iK6pq6f257ExeV31PYFK9vRCrWHPTHZ5s8MX2DJ5zc/HEyMA7ZUibFyGgfW2V0NYwYDbdMo9QHiqS+vg41NbXqlrKetLWtx325HcV8JjgRX+84i16zd+PVjzbh9xM3o/vMXZjhchLnwlNQVlkt+9TfVNy3Bc9X6jG5SGxtcw8xYjTcNqwXLbG1jS3YRqQgw+IzsWDrGfSYsRM/G+6Exwc44scDV+DHg1bijanbscEjGEkZBWJxazX29Y6HSXieFF9jIy41NVlC9urrcVEeJvVSz/W9rf3uEUaMhjuCArlVkWjnTOMlnA5KxJAv9+GFUWvwtMNKPOOwCs8OXYWH+izFL99fi7mbPREQIdaxogpXtEPHdnlthmIUITZIHaUYa8rKUJmbi4qsLFQXFqJeRKlW0ta+9wAjRsM9p6lZjGfDUjB6kTteeH8NHu63DI8PXI4nh6zAf/VYiCeHOYkYTyEkKhXl4qpaeldtl3cjKC6KrxGwCFDqdHlKCgqk7qbt3YPk7duRcew4iqJjUFdV9U+LaaOs9saI0U5QC8Mn9zVsb9cV4BAJA8ljU/PgfMAXPaW9+PjglXh0oCOeEuv4g77L8MqHG+ByyB/pWfmora1pu5vKusr7J+WrleNn+X9dTQ0qcnJQEBSE5M2bEf7JJ/AfMAD+Um+jnVYj2y8AteUVKlwjxvsYCo/DBnz6W2kUy9FVBalzIaX+FJVWICg6HY4H/DBggRu6f74bvebsQT9xXWdsOInzF5JQWcGhknoRVtsEYnVDm2gJL19GdWkpisLDkbF/P+KXLkX4lCkIkLrqK0L0GzQIoVOmImnXbuRHRqFWLGODsYz3J2oB+SqwR7JU2kZFZZUoKa9CtTzJb7Ut1pngg4adJiXllQiKy8KO0xFwOhiA1e7+2HEyDF5hScjj0MalG/Sk8v6R5g4ZvrLdV1tSgsr0dBSFhCDz0CHEOjoi5IMP4d+nD3y7d0fAsGEImz4dcStXIt3NHQVh4ajIzTNtxvsZ3lfO/auQNhEHvX3EEnB8zSssEXFpuSpOjsVZRWurjM6M9ZrYJszKL0V8eh5iU3KRnV8s94TXLnWvxfb/Au+L1ENFxFhfXY3y1FTkenoiUYR2YdIkBIr1O9+3H84PdkDQ6DGInDEDyZs2IefcOZQmJaGmuFjbivwdbB7jHmLE2EGwkl0S96uguAwBkWnYfjwMc7Z4YtqaY/hs0yls8AjEubBk5BaWyb22xGzaKqczQ4tnSeFhedjQO6iupkdQj8vipv9LOJxVfLSCUs9oCdkjyg6ZQj8/ZLm5IdHZGRGzZiFIrJ9/794IEGsYMmECopc7ImWvK/LOnkVZQgKqRYT1Uqa1Z/WStEm/dawOwIixA1BLJ+5XXV0NIhIzsWyPN4bOP4A3p27DHyZuxOtTtqL/XFd8udUTQbEZWmk5NmerrK4Cr5H3xSrMa99ZBdgM/1cvLjwtWnlyMvKkDqbu2YOYuXMROno0AsQSkmB5H/HJJ0iUdmKGuzsKIyJRmV9gGU9kOVZaHquDMWLsAFSM8vSvqqrEiYBY9J2zFz/nBN0RnKRr4Wfy/u8zdmDf2UhUibWgZWz9O3QlrGJsfY0UYMPlywrFUyUiLIqMRMa+fYibPx/h06YhaPx4BAwdiiAHB4SJFYxZsABpIlAKtZxWMD9fO2doCbV9SShEO7ufRowdgIaDySt7Ct28IvDqxE347/cW48eDHPHUkBX4qbz+b+8lOi1pw5FglJVXaQhaR4qRx6bV4kOBqAVrh/Nhmdc6ZNgOFNe1qrAQpYmJKBBXNP3gQcStWYOwyZMRIPWOwxMU44WZMxG3fDkyXF1R4OuL8rQ0FSB7VOmKNkiZ9ia+1hgxdgBWV6yqogoe56Pw1rRteKSfZcD7qSEr9fXRAcvx6oSN2HEiXH4HWkZx0zq0MlmObbFgls939XxYVjNqterrUSN1sFRc0SxPT8SvW6fDEkHvvw+/UaPgL23CYHkfNWcOkrdtQ47UvZK4ONQUFakbq6FtLcpUbB3XjjBi7AD06S8VmgPRgdGpmL7uBP72yXa8PG49fjlyDX4zZh3ekHbjhJVH4BWaLL9BfZsHvVk2hdvU1GAZs5T97lTIPG5tbR0Ki8uRnl2IzJxifc8eyMtynNsum/dBzk3dUKk3tIi1UufKMzJQEBCA9L17EbtsGULF6gWMGQP//v0RKPUsbNYsxK1ahXRxVfPFWrIDp1pEyPvJMlieWtc7uOaOwIixg2AFv1Rfi8zcIhzxjcXCnecwcqE7+nyxFw7zD+CLTZ7Y7xWFlOxivdcMArBVTkusVqtGhFMp7UwOmXCqE4Vk/b71PjfCun2puMkM2PYKTsT+0xfgdiZCZ1ukiDArq6tVlG2t+HqOfDhQLM2uI13RmpISlCUlIU9czNT9+xEjIgwZN04F6CdtwUBxRSNmzEDC6tXIOHoURRERqJK2IDtkVHx0ayls3iu60nK/1J1udqltnYu9YcTYgVCQ1dW1OpUoMDode6Wib/QIEtc0DN5hyZqftLKqts3tM0a2cEpTWlYhQmMzdJ5gsLymZhfpDH3dRipn6/2uBytzXV0tIhOzseVICGa6nMCoxe4YKXwq1nzr0WAExaQ3x462zXKz3lwSi822XL1cU3VZGUpiY5F17BjiHB0R+vHHCPzgAwSwZ3ToUISKCGMWLUKqWMkCf3+1ghQuraC6olZh87VZfE0UosBX9kJ3vIvfNowYOxDeV1Zg9vIxEiVVRBSfloekzELN2E331NZ+rbE++cukjPjUXOz3vIAlu87p0Mji3eew7VgYfCPTUFRW8S/73oimxotiXSvhdjYSwxa44bXJm/Hb8S54cZwL/jx5C/p/6YoV+32RkV8CXG2yaYH0Guk6Sr3Q8DS5ptL8QuRHxyLX2wc537gjed1aRMyejcCRI+FLV3TcWFyQtqBaQbGS+RRhaipqpV5ebC5LraHUQWtbkA+ieimbnV2ZeaWISctHTHo+MvJKmjOd/3Pis71ixGgHWN1LdV1Zefna/H9b27eE2zVJBa2rv6hRO3tOheFDx0PoNm0bXv94s7RFt2ms59K9PppmsbKqWve5kYW0nI/FvcsvKsUyV2/8fKQzHu23HE87rMJTQ1bhx4NW4JGBjhj09QFEyQMEuKIWyNpZYrFUls86pCDUlJUjLyEZccdOI2DxKvhMmILzw4bhTP+BODHQAT6jx+HCZ58haeNG7ZApS05GrdRDWkAtr7nsa7Q8Z7kehhGGiSdwQNz7FW6BWCnslQdTQFSaBk9wXmRb7mlHYcRoJ1Ac7KRRt0poqzvJynX1cqNWtBP+cZi2+gj+IhbsmWFOeHbYajw91AlPCYPm78chn2hk5RVLG+/GQQRWIVIE6TlFmCXt1//psxT/r+ci/GzoKjw3TMQ4YDm+987XeGvGToQl5ogYr15zB2kJOWWpQaiV9mB5ZoZGyLBDJmLJUniO+wg7/toTzi/8Fct/8xd8/VovOA6bjDNrt6AwKBBVaSn/nF/YPMSh7UuKu9W5Ws+zUtz9yOQcOO33E1f6ILp/vgfdP9uDUYvcsXy3t7Z3s8SC67XZKMceMGLs5FCMuNKEiqoa7JK25sDZu/GLEc54uP8yzWf6o37L8J/dF+G1j7di05EgJKbntUGMlkrO7TJyS7BglzeeGbEGjw1cLmU74VcjnfDc8FX46ZCVGLrooLiDBSI7oOmquKJSL+qqq1EjYtIOGR8fpO7ahah583B+1Bi4/70H1r70OmY/+xd88Gw3OLzYB+++OQ5Dxi3BwROB+hDin7VHtLUFbA0fXHRBM3KKxZ2OwpD5B/DL99fgWQZOCC+NXYd+X+zBop1nm6OZLmn2OVtldTRGjJ0cqxjZ0eN6+gIcvtyLX41y1nFLipGi/K/3FuNNcVu3HwtGUlvESOQ35ysTRu0Tt2/YwoN4bdJmvCyV++Uxa/FXsb7DvnbDOo9gZBdXiGEUN1XKrCkt1Q6ZTHd3xH79NcLZGTNgIM707I09b/XAvBffwrgXemDQn0fhH90/wav95+Opfovwaylz47FQNDbRpl5Vt9PWubWE58fhG3oFoTFpWCrt5N9P3IiH5AH0jFjwn4t3wPmRvx6zDv3n7cfB87F6XaJG3ddWmR2JEWMnh2JkheRwhnd4Mr7a6okeM3fihdFr8av31+J5sRK/Hrce46QdeTooXtuAbMNp+65VWS1hZW0QamrqEJ2Shz2nIzBnsyfGLjukfL7ZC64+cYgQ17A4NxeVifHI8/LSMLT45csRNnGiTlkKeLcHggYORMDHU7Hr49lweG8KXnlzCl7qORfPD1yKJ0UsPxy4Es+P34ANx8JU0Gx/8rpsnVdLuA2tXJW4qEzPMW/zKU3X8Z89FumD6KeD6RksxdPirvf8Yi/cvGPU4otf36by7zVGjF0ADkHQ2qXnFuFkYAK+2u4llswNA+buU7dt4qoj2Hw0BMlZ+Tozgvu0rbJbhgqqRJDZhWXwjU6H27lo7a319I9BSnIGShKTkON1BokuLgibNg2Bw4YhoH9/+EudCHRwQNS0qUh2ckLm8RM4cew8Bn61H8+OdsEzI9eKSJxUKHxo9BH3+qBPjF4LxWLrfGxxWVxZWsbIxEw4Hziv7viPBjhKO3kVnhSYQeAPYtGnrDmOs+EpIkZpY4snYausjsaIsSvQLKxqEVqOiOa8WIm9nhHYciQYO6UdSYHGpOZpu1I7PFrvfwO0F1MsL4cUymvqkZdfjPSoeCSfOYvk3XuQ4OiI8OnT4T9qFM6LJWSsaJi4pnFLliDj4EEUhYaiKjMTF8tKEZ+Wi6VuARi0+Bu8PXMX/iqu8z/EitPSrnbzw4XEbD0/Jp+ydS62sIyFynkVlsAzJAHT1p3EO5/tRrdPt0n5W9FNyp+y9gTcvaN1vJV1+UYuekdixNiFYMWkxSsuq9A1MRKkfcgkwxyzpHVTS3czMcr3FgFaejH5vzqpBzX5eahKiEeZny9y3Q8gaaUjwj76CP5i/fyHD0eQCJDDEvGrViHzwAEVYWVhoYqYKTCuCmXVNQiQtt32kyFYuPMs5m7x1CGXfV6RCE/MQmFpRdvOsQVWC6+dOHklOBoQD+eDAZgv3sHcrWewxPU8DvnFIk3uB91Za3u4dTn2gBFjF8NaOdkuZJha/UVL4MBNKyC/b0bHBwXOgK/MydEcMpmHDyNh5UqdIxgi7cFAxooOHYrgceMQ/fXXGiFTGBKi29fLfhodw3Ip7OYyeU6cNpaZW4iQmHT4R6UhMikHWQUl6mpaz/V24HUz7I8PopTMAgRL+Yxqik7JQW5RGeo4Vnmze9DBGDF2QVqOWRJ+vm5FpFDEejXyNxX4ubqkBCXx8cg5fVpTVEQvXoywmTM1QsZ/4ECEfPghIufPR+LGjcg6ckStYEVGhgZ5a7QNhyakrNZDE7RKjSIaxpOWV1RpzCtjaCkUWvW2jq3agtdHi0pYZ5lrlWlLOBeUDyV2WN1J+feC+1KMluxklh+fP549dnO3K7xmWqxmsVActIIMvC6Ni0OulxeStm5FJEPUxAWlBQwYPx4hkyYh8vPPr+WQKUtL0zFFZlRT8UmZLcV3PVifrOIj131Q3Aa0kPxNWb71GHez/PbkvhOjtSLwSWl5Wgod9GOx4vB8FBvftws8Fq+5eaaDTllKT0deQADSmM5QrGAEZ8+L9aMI2SHD4G3OJ8w8cQLFUVGozM7WMDVNYcHyRNStreDN0AeiXr/t7+8EHZZpx/Lbi/tGjPqElFe2K8oqqpFXVK4Ul1boWNq9dGOuWYQW/7M8JNrhHJrFR+FRgPzMDpmq3FyUiLByRGDJW7YgSkQYOnUqgoYMUThMEbVggSZ4yvTw0FQXnHHP/WkFG+U3/pYArW1Ew21z/4hRroHtB/aqBcdk4kRAPI77xeF8WLJ2eddwhkSr62wv2EZiW4m9nByKYBAzg5wtnQy297lleC1W5LN25jR3yBQyqe8hD8SvXInwyZMsmdSkPcgJvCFjxyJGflNO3C1ghwytoNQDlnHNDW1VtuHu0OXFqK6gvHIsiotw7vOKwIIdXpi8+ggmrvDA7I0nse/MBSSk5+u8PG1DtlMlo9WrlfPI4mKj8Znw8IvFHs8IuHpF4kxIMpLl/NixwePftuvM86fbyKEJsYTswawuLkFpfDzyPD2RsnkzIubOFTd0IvwchsKvVy8EDR6MyDlzkLRtm84r1GGJzEzLlCWWx7LILbqihlujy4uR7UOOQXExz2P+cZi48jDenLYVr07YgN9+4KLTjD4UUW4/HoLEzALtfWSqCltl3Sm8jxxLOx2cgOWu5zHB6SgcFrpjxJKD+HzjKbieCkd0Us41t9lWGTahYGR7FaG8si3HfKIVzKotVjDH8zSSt25FtPxWQWIBfXr2gs/AQQj4cAIiZs5E0qpVyDl1yjJlSX57dWmbBWjEd+/o0mKkRWTcJiNPwuLSsWyPJZD44f7L8aTDSp11wNjIF8esw/Cv3eAZkoQrlxvuergU75+1vRqbmoOvt5/VnDeMG/2pHJ/Tnf4k5zVusZuGmhWWlKsVtVWWLfT3oRgFZkRjDpl8X1+1gtHzvkT4J9MQNHYM/AcNgl+/fjpEEbloMdIOH9G2YLW0HynCax0yzWUZId5burYYxUVlIDHHnLxDEzBr/TE8JRX/wR6LNIj4SRHj4wMd8Vh/R7zx8VYc9ImV7Zt0FoSt8m4XWueL9RdRUlaBs6FJGLrgAH4sx320/zJ5XY4n5P2Tgxzx+/HrsWinF9JzCkW8lvZj63tPKJRrwwnyyuEFtgWLL1xAzvHjSNqyBZHiijJO1K+3uKFDhyBs0kREz5+PJBcXZH7zDQpDw3R9Cf6+DfJ7WcsyAuw4urwY1TJKWzAwMgULtnniN+PW44f9luG54U74hSYMdsLzo9ZoIqjjgQkaFyk72SzPFrR4FtFYsLWNupz19SgoKsMxvzj0nL1HFwT9yWBHOQcn/HzYKhXjcyOc8IW0YVOz8qVcLsvNMpvvPV9ZDpHPusCL/E4cGyyOjkaWiJChaBeYQ0Ysn9/AgfCT3yVY3sfMnYO0nTtQGBSki4PyXJhH9FsW0Iiww7kP2oziuol7mJlXhANeERgsVullsUAvjV6LF4U/ins47OsDWLX/PKJSci1txjYGKvO+sHxrpItGu9io1FaXkxbaLyJVA6OZNZzW+aeDVqhl5Az6N6dsxuoD55FfWIymBrGMsg+Pcc0SNk+41TmDMTHSFvREyvbtiFmyBKHTplnc0B49dLbEhRkzLOkrTp9GqWzLoYy6inLL+Ug5LSfv8hg87/bsvDLcnC4vRsu51+vUIc7Lc/EIxqTVRzF43j7BFZOcDmPz0SCEx2dqXCOF05aeTJbL4QKmQiwpq9S1Bik29tq23p/bUlhMmJScXQhnd38M+Wof/jJ5i85Ef0keDt1n7sDM9cdxMiAOldVVch+b0EjBiAh5nFoRYEVmJkrECmaLwBLE3bzw+ecIGj1aE/py1kTw+PGInDULSWvXIvvECV1licMSOi4Iy8q9aglbnBtd6CaB56Yu6yVLaJoR5b2ny4vRCisYO3ISMwpwKigBGw8HYvORIHEbY3QpMk7OvZ6baYvqmjoUlFRoGsNzocnwCknU4OTs/DJNv8ghBevUJqKdSWI5maslODYd246FaA/qhys98NHqI1i297yucZ+ey9WRxOLSOsv+tdIe5Iq7zJCW1pzUN4yr7or7eb5/f/jSFRURRnPK0qFD6rIyoe9F+d302HwQyG9lyx3lOdFlrZJjZBeUaA7XUvnduVKw1SpbtzW0P3YpRh7vbrtMtHisYBw2KCgu13SIHNfLk3ZcVY1lmeq29GByG96TtOwieAYnwemAn1i0E5i57jgW7zyHvWeiEJ6YI2XWqvhaClwtjrxygJ/rMQZGpYiQE3E+KhXR8pAoqqpDdb2Uz8H5zCwUhYUh++RJJIkrGvHVV5YQtcGDddpS8EcfabA2l8SmFSyOiND2oGbVFouqETK0hNe5h2yPclpVYmY+zoQmwfVMBPacDscJ/1hcSKSXUHlHsygMt45difFaRwg/yxObr3c7vpBioijYUcPEu9rOa4MIrTQ1XJT7U4mTgfFi2U6j9+w9+POkTXh98ia8M30Hhiw6iDUeIcgRkV/R1PfN12NFPjdKW61Jjn9JyuLScHQNG+pqcLGiDJVMbR8cjHQ3N03qG/7ppwgUV9R3+HD4iTXkjAkmd0rZuVOtZWUec9rI/vxN6Nbyt7rBQ8z6WzLZMTOCbz0ZhonOxzBcznv4Incdc13l5ouQ+Ax1v7XsW7g/htvHbsTIY2ilratFlVgGxoxWaI5PVta7J0iWQ6vLjheKkhWtLWWrqASKMU9cuqV7vfH7CRt0jPBnw53wwihnTYL0w4ErMFgq9oXkHBWZZb8W94/HpxgvN6GJ6Q3lYVBfXoaK+FjkHjuK5PXrLREyH3wAP7GCPr166drz4dIWTNy6VccPuS4hlzljjCnFci3ixuqKtjjv1vB4PCeuDnzYNxqDFrrhMYfVeELOnSkwnnBYhXdm7cImceNjUnI1YogdWt96oBjaBbsRIysVM2KnZOZJ2ytVXKdEBESnITmz4NoT2tZ+9wreAz4UKLD07AJ8Im7pI/2X46HeS3TVqGelMj/cdxm+8/Z8/H3mLpyPSJV2qLiMKj4RgAhGezHZISP3lD2iDDkruXAB2cePI8HZGaGcOT+Qy1731dnzDNyO4tjghg3aaVOSmGiZsiT399rYYBsE2BJrSosQabdyTPN3Ezbiu90X6VDLI/2X4fvvLsIv3l+DT5yPwlOsPyf9XpUHB6+jdVmGu0uHi5Fl8xhMbx+RmIXdJ4KxcMcZzNxwEvO2n8WW46Ga71LbMPKU7qgntFWMHP/joPzszafxsxHOmln72aFOyk/k/WMiUIcFbvCPTkd1bR0u0vqKGDnoXy9Wv5bLXqem6upJabt2IW7RIoRPmqQiPPuPd+D9bk8EjxmjLmrWqVOWHlHZRy2gnMO134Kv1vdthPeOAQ3sXPIMSsDHqzzwygcueHiAJVM4h1f4/lej12LMYje4e0k7tKpG9zFibH86XIxss/EHj07KwubDQXCYtw9/mrgBrwp/mLwF781xxYoDfriQkKVDBzxu6/O5VzB1Id3UopIy7Q0d+OU+/HniJrw0dj1eHLsOr4x3wVtTtupDJCWvRJP6NohVoTUrz8pGcYi0JT0OI3njRkR++SWC5J75v/su/Lt3R/CIkbiw4Gsk796NAh8fXXe+qqDgWlZt67jgnV67NYFvYFQqvt52RtP//2TISh3vfGrwCk1x+CdpA3+2/oQINl5nujA80Iix/elQMbK9xmiXopIKHPKOwkcrPPDLkc54qNdiPD5wOX4gVuZxBycMXeiOfZ4XNNU8xct2j63y7gVc2YidHyFirTceCsKna49jxCI3wV1TIi7c7YNjAfEoKSlFQ3kpKmgFg0OQdsgDcYuXIGLCRASPGoWAYcN0cD5UrGDMzJkaR5ofEorKkhLtCdX0+BQg3VAe+y656bx/tJCp2YU46B2Nj52O4vWPt2h87ovyUPnTR5sweulB7D4Zhtg0aTOKdWdHl62yDHeXjhcjLuuSaGvd/NDn812a65JtMWaD5lOai6v8/ZPtWCztm6ikbO2hbGuETHtgvRccnuCYpWdwAlw9w5Vj/rGIFAuenZaFotg45Es7L8XFBZELFiBo6jT49ekL3791Q+CA/oiYPh0p27Zph0wll7yWNqSuuCvlqwUkFOIdWsLWWM+f3giHd/aficCcTacxZtk3eF9EONPlJLaL1Y9JztFJ2MzHeqfW2NA2Ol6MVy8jr7AMWzwsLupzo5w1eJvxmgwXe8JhBd6dtRMr9pzT3j0+pTtSjCoW0ijup7wWSls2LSsfyYlpSI2IQa6fP7KOHkbCpk0akhbIGNFBgxAwbpwO1jPlfZqIME/uFXtFW0bIfMsS3gMYgMCIIGZp44PkiG+0djzp3E4RItu5tvYztA8dK0Ypl7MkSsqqtENh3tYz+PuM7Xhh9Br8+v01umbEHz7agI9XH8E35yKRmVesbhaxVV67wnvQEv5PLFltcTEqU1OkPRiMLHc3xLNDZuoUBHzwAXwHD9bJu1zwk8teZ4mlLG1eZ9Ai6Paxfm1Fz0Haghzcr6mpRk11lVhCy+/d+jc3tD8d3oHDsT7GjVJoDFP7atsZjFjohr5z9mCIWMppa45i7+lwfVpz9oWOC9oop11h5ZTr1bE8EVCdnG9Vfp6GnuUeO4bUTRsRu2QJwiZPgl/P9+Dbq6dGyETJ/3TFXa47LyJkCkTeTyb2ZZTMtQ6ZDqr4PDbvJ9vg/B0aGzgOyf9ZOsnM2OK9pcPFaKVOnsg5haXqJrl6RsDlUKC0XUJxIjAeCRn52qtna7+2opVLsf39t7BuS7E0C4ZhZjViBculfce0FBmHDyN+zRqxglMRNHy4LnvN5a85Vhj91VcqwvzgYFQWFGh5dEM1RI2W0NYxDfc9diNGPoU1X0tNrY4p0lIye5s1ye2ddq3rNVBgtyJGuUYdoBeLdm2twd27ESuuqE5ZYrD2e+/pakvh0h5M3LJFQ9Q4u4LRMdayrLPwO9IlNdg/diVGukxcVaiJFZef5bjsrLGGcNna70ZwHw15k1deRx1nZjSH11kC0Zu35Xs5pnagCBxg1xwyWVkoYoTM0aNI3LBBw9QCR43SdiCHJsJmzNDB+fQ9e1SozCFTI/eO7iyt4O1EyBjuX+xGjK1Ry3QbAmwJRVUtlrWotBxp2YW6BkNeQTHKKuS6RJRWN5SWixaQQwt0RdUKMkLG3R1xTk4IZ1Jfri3x/vuW9SU4e37BAs0nWtocoqYD8xQghcg2mLS9LA+CO78Ow/2BHYvRYi1tfdcWKER2z8el5sIzOB67ToVj+/FQHPaJQEhsGvJLK1B7ydKRwigXTj8qiYxE7okTSFy/HhfkugImToTvoEE4/847Gi0Ts3w5Ug8csHTI0AqK+6pJnChqilGFKJZdxMjpU4SibMsqvAaD3YrxdrGeK/OPcuCasw84m3/A/APoPW8/Ri79Bkt2n4N3YByyktJRkZKC4sAApO3bh9ilSzVOlPljOHGXHTLhM2cibuFCpO3cqVObONGXQtcpS3KdFCHd0IYGiytcKu1dJiXOyC/V/KhMVFxZVW05txbnaTC0psuJkS4iO4IycovwzTmuRX8Az4xYjceYjW2Qo86w6DV5PdY67obfpt1IlbZgwrwvETR6DHx698a5f/wDPj16IGTcOM0hkx8QoPljaD31GHLdej+aocCsFBSXISAqVcPMtp2+gN1nInAqIAEJabmoqrQsk2ZcVsP16FJipCAYCF1XfxGhCVlYvt8Xr32yEw8xT2qfRfhNzy/xh7eno3+3D/DlWw5w/Vsv+L79DwR0e9PiiooljGKHjJsbCgP8dWyQHTlsV2p7sHlssGWHDNuEHCgvK6+Af0QKvtzkqZnm/vLJdvxt5i6MX3YIu46HIDUjF9ViIS1tyG/fT4OBdB0xspKLVbxy9TLqLl6Cf2QqFmw5hdc+XIcn3/kCr7/5MQb/aQTG/vpdTH/6j1jy4xew94XfwVesYPiokYiZMwdp+w+gMDZO885wtV1r6oqW4msJxc/2IcdAOetknbsfuk3dhkf6Lcd/916Ch/ouw6/eX4Mpqzxw0jcKufnFsE5qtlWe4f6ma4iRQqT7yM6TpkbUiAW6EBoDl/VucHCYg7deGoxRT3fD7EdfwuL/fRoujz4L99/+EQETJ6sV5GpMnDmvWbU5ZYlliQhvNizB+8LpRRwX9fCOxMQVh/Dzkc74QZ9leNphpc51fEzoPn07Vuw+iygRrHbudGRsrcFu6bRitAqGPZhsx3F4gYKqSEpGfmAgona74uhnX2H12/2x4Od/wMonX4bTz17F0t++g21DJiBw5Rpkn/OW9mCelCMipiVksDZFeAMBtoTbceItU4QcPBuBsUsP4smhTvgfsYqcqMuAd1rHv0zeLFb6tKaD1F7WOxAj3Vyrq2van12LziPGaxVQzqX5fNirqSvuFhRoUl9mSWO2NK4xH/bpJwgeOwaB/frA9913cW6AAw5+8CnWz9+Ig4f9kMaUig2WMUa1gK2P1wYoBga6cwjl/IVkzN3iiV+Pd8EP+y/Ds8NWqWV8VOg1ew9cvglAfFqeuqm3Mz9Qh3qaxadpIPm/FrTc1tA56VxipNViB4p8tqavKAgK0sF3zooInjQJPn364Nzf/w7/IUMQ+flnSNu8EXknjou1DEJOTALSUrKRU1SO6ktSDjtkKMRW13crsP3He5RXWAp3n2j0+2o/XvzABa9O3ISXP9yIP0zeis83n9FpSrSgGph9G8ej4BigzvQkmjC5skpDB5mg2Yixa2C/YqT45BjXImSarWC1WMGyxEQNP0vetg2RX32l6QuZVZtTljSRk4gybsUKZB47pjlkWIk5jZmuKP+uXrmsorB53FtELVOzxUrILIDLkRDM3Hgan64/hU9dTmH+Lm8cD0rU8UZeg1q4FvvfDD1P2a9Mfo+UrHz4RaTAKzQJAdHpiM/IE4GXa+YBbms9D0PnxP7EyGM1Y7VanICrqyxFROgqS8mbNuninswjerZHD5x96y1N7Bu1aBEyjhxBaUKCZtXWmfNSprq3FLbS7Oq2Pu4dwjJpqZiZOzolFwFR6QiVNiJzk9KaWbaxve/1oGjZvmRvbXhcOvacDMH8bZ6Y4XIC83ec1YiigMhk5BQwCzmFfvevy3DvsB8xUjBiBXUoQcrWDpniYlSkpaEwIADp+/cjWsQWNH68Bmr79uyJwHHjcGHuXA1fyxIRFolYuSoTy9PZ83Iet9IhcyfwGGr1BOaNYTuyUq6BbiStG7nV82BZ7PBhyv+Vrj7o/8Vu/HXKZl1j8o+TNqPfXFfM23IaZ0MTNUU/18ywVY6hc9BxYtTKK0g5agnlf9Zlzhgnyom7OVJe6s6diBVXlLPlmb7Cu3dvFWOYuKbJW7bouvM6MC+i0xC1eyS+62ERpUV87PXU67Sx3c2wWHBLO5FWdtC8ffhR32V4tP9yPDrAEQ/1WaY5gihOl2/8UVJaJsKVY9ooy9A56FgxUogCg63ZIVORkYEiEVfWoUOId3JC2LRpKkCfN96A35tvInTCBMSLFcxmPlERqy5zRuvDsihC0kLcHYXFQlrEZOv7tsBeV7YxC4vL4HE+Bm9+uh3f77FIl49jsi7mOeViq8xovmC7JwoKC3G5gdPDeNyOvX7D7XHvxMhKIvtoh4zA7vnaCktbkMMS1om7MfPmIXTsWPgPGKAB2wHvv68LgCaIi5otIi2R9iAjZBrQPC4ox1MBSsW9W+kM7QFLBxM7bip01sl7X+zBD/ouxzMOK/HCSGf8auRqHcv87bj1cHT1RnFxCa40ykPARlmGzsE9FSNf1QqyfLFqhRGRuqR1orMzotgrKpaPM+d9unWDv7iikTNmINXVVdedZzp8LnNmtaYKy2x1bl0Fq2W93FCHhLQczNxwCn+avAW/+2A9fjdundJtyhaMX/oNvvGO1ED0K02yj42yDJ2D9hdjs2CYQ6aqqEjnAbJDJvXgQUQuXQb/wUPgIy6or4iQae2j5s5FEl1RDw/NwE3Lqcuc8ZjETlzRewHFeEXagSViHU8HJ+oajhOWH8KQL10xdP4+fLHxFA6ei0JiRr4Gq5sOnM5N+4pR9tMcMlz2Oj4eWSdP6ipLMVxxV6ygz9BhOl2JVjBi6lQkb9iga1BUZmdre0nFZ50p0eL49xO8z9p25MKsSTk6LWzrkRBN1nUmJAmZ+SW6ziItYsNFI8bOTPuJUb6nBWNPZ3FUFFJ27ULIlCk4//bbOPfbl+H9xhu67mDi2jXIO3dOhzDYiaMdMmz7cf/mAf/7wQq2BQ6ZMF1laUWVUI0qrpDchdrJ9zvtJkYKiVaNPaQpe/dqzyjXGgwYMACRYgWTnJw00ROjaShAdsgwQkY7ZChCI8BvwV5SjjkyMJ1Z2MnVy40ajmfaiV2DdhMjFwSlsJgxLWnbNlyYNQthH3+s0TPsPWUqQ4pV3VCr9TMCvCkUnmXoxPLe1jaGzkm7u6nacyouKAVYEh2NyqwsS6wohUgL2nT9ybsGw/1E+3bgCBQkRacZtemGUoB3wQ2lZaDrRjdNUyISI2pDJ6bdxUjRqRvajL5vUdadwPNqCdfLp0htbWsw2DvtL8b2oJ7JiWtRWMq0iOVKQUkFysqrUcOs4VZx2trXYLBTOpUYeR7syi8tr9JZ80f947DjZDh2nLoAt3Mx8A5LQVpOsWxjDSezXY7BYI90GjGy55Bd+1U1tYhKzsHmI8EYNM8Vv/vABa98uBFvT9+JyauOaFA1V9zlct+mt9HQmeg8YpRz4BgbU05sPRYCh6/24fkRq/Gj3kvwoz5L8Vj/5bom/ZwtnohKytFlss0MBkNnonOJEVeRkVeCj8QCcsbCsw4r8cvhTirKRwcsx//1XYZB8/fDwzsaObIde1lNvKahs9DpxMhZ76OXHcLjAxzx00Er8NywVfiFCJKW8Yf9lmPAl67YdzoM6dmFYhkbdHk5W+UZDPZGJxPjFe05XbjbG29M2YrnRjrjp0NW4WkHTrZ1wm/GrMPU1UfgFRyH/MISOUfLGKSt8gwGe6MTiVGOe7kRFZU18I1IwRIRZPfPduNX0k78zfgNeO3jrRi+4AB2HAsSF7UAF+tq9Lxbn7vBYK90GjESWrq6unrNPxoen4VtJ8LESvpg/s5zcHb3xyHvKMSn5qC2pgaNZvDf0MnoVGIkHK7gsm8X6y+ipKwSWXklSM4oQE5+iYiwWs7VpCw0dE46pRgtbcEGOS9Lin/O82NUDntOdUqREaOhE9LpxNgSnhfHEk2QuKEr0KnFSGgpzdw+Q1eg04vRYOgqGDEaDHaCEaPBYCcYMRoMdoIRo8FgJxgxGgy3ALXQWg93CyNGg+EGWOt+dXU1qqqqrumhPUR518Q4cuRIeHl56Xf8a3nCBkNnhfWcGsjPz0daWhpyc3NRWlqKWqYbbbFdS33cLncsxtmzZ3/LMtIqGsto6CpcvnxZrWJkZCQ8PDywY8cOHD58GOnp6ahpXqb+bnFXxNizZ08MHDhQT5b7cP/CwkKDodNTVlam1vDcuXNwdHTE4MGD4eDggA0bNsDPzw+pqalqKamTlhq5HW5bjI2NjQgICMDcuXPxzjvvoFu3bnqyPEFfX191WQ2Gzs7Zs2f1lV6fk5MTXn75ZXz3u9/FY489hvfeew/Ozs4IDg5WQVr1cbtu622JkaabljEmJgbr1q1D//798corr+hT47PPPsOsWbMwffp0g6HT8+mnn2LmzJlYtGgRJk+ejCeffBIPPPCA8m//9m94/fXXsXLlSiQmJn6rHdlSL23ltsRIq0gxslHr7e2tnThvv/023nzzTX01GLoS73Tvjt69e+Ott97CE088ge9973tqHfn6yCOP4MMPP0QIF/YVvVAr1EZLvbSV2xIj4f/YgM3Ly0NgYCDc3Nywa9cu7Ny5U+F7g6Gzs3fvXuzdtw+7d+9Wo/PMM8/gwQcfxGuvvYaxY8di8eLFOH78OLKzszvGMlrh/2klrTQ1NRkMXYorV6+i6coVFBQWagdlnz591EIuW7ZMjRB7Wjl6wPp/uyK0ckdiJDTJRoiGrgj7RvhXV1+vwnN1dcWBAwe0aZaZmameoXXbOxUiuWMxGgxdGQqN1i86OloFmZOTo3rgHy0i39+tpdyNGA2GNkLRUQv0Bvl6t3VhxGgwtAGr23o32obXw4jRYLATjBgNBjvBiNFgsBPaJEZbOxoMhrvLAxSdLRitTlEyqsC6MXuTDAZD+/AAxXY9rC6qNcLGYDC0Hw9wOtT1sE4UNhgM7c8DGkpg/syf+evwPyNG82f+7OIP+P/7Pyw01/0TJQAAAABJRU5ErkJggg==)
- Linear association
- Nonlinear association
- No correlation
- Negative association
Hint:
check the resemblance of points on the scatter plot with straight line
The correct answer is: Linear association
The given picture is in the linear association.
step 1 . A scatter plot shows the relation between two variables. It uses dots (.) to show the relation between two variable .
Step 2. In the given diagram we can clearly see that the points are
Conclusion ;
By the definition of linear association 'we can say that the above scatter plot diagram shows linear association
step 1 . A scatter plot shows the relation between two variables. It uses dots (.) to show the relation between two variable .
Step 2. In the given diagram we can clearly see that the points are
Conclusion ;
By the definition of linear association 'we can say that the above scatter plot diagram shows linear association