[list][*][size=150]mean of 65.3 degrees Fahrenheit[/size][/*][*][size=150]median of 63.5 degrees Fahrenheit[/size][/*][*][size=150]standard deviation of 9.3 degrees Fahrenheit[/size][/*][*][size=150]IQR of 7.1 degrees Fahrenheit[/size][/*][/list][br]Recall that the temperature [math]C[/math], measured in degrees Celsius, is related to the temperature [math]F[/math] measured in degrees Fahrenheit, by [math]C=\frac{5}{9}(F-32)[/math][br]Describe how the value of each statistic changes when 32 is subtracted from the temperature in degrees Fahrenheit.[br]
Describe how the value of each statistic further changes when the new values are multiplied by [math]\frac{5}{9}[/math].
Describe how to find the value of each statistic when the temperature is measured in degrees Celsius.[br]
[img]data:image/png;base64,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[/img][br]Give an example of a box plot that has a greater median and a greater measure of variability, but the same minimum and maximum values.
[size=150]One dog’s vitamin C level was not in the normal range. It was 0.9 milligrams per liter, which is a very low level of vitamin C.[/size][br][br]If the value 0.9 is eliminated from the data set, does the mean increase or decrease?[br]
If the value 0.9 is eliminated from the data set, does the standard deviation increase or decrease?[br]
[size=150]6 6 7 8 8 8 9 10 10 12 13 14 15 16 30[/size] [br][br]The median is 10 hours, Q1 is 8, Q3 is 14, and the IQR is 6 hours. Are there any outliers in the data? or show your reasoning.
[list][size=150][*]mean: 15,976 followers[/*][*]median: 16,432 followers[/*][*]standard deviation: 3,279 followers[/*][*]Q1: 13,796[/*][*]Q3: 19,070[/*][*]IQR: 5,274 followers[/*][/size][/list]Give an example of a number of followers that a very popular band might have that would be considered an outlier for this data.
Give an example of a number of followers that a relatively unknown band might have that would be considered an outlier for this data. [br]
[size=150]The weights of one population of mountain gorillas have a mean of 203 pounds and standard deviation of 18 pounds. The weights of another population of mountain gorillas have a mean of 180 pounds and standard deviation of 25 pounds. Andre says the two populations are similar. [br][/size][br]Do you agree? Explain your reasoning.
[size=150]The box plot represents the distribution of the amount of change, in cents, that 50 people were carrying when surveyed.[br][img]data:image/png;base64,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[/img][br]The box plot represents the distribution of the same data set, but with the maximum, 203, removed.[br][img]data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAeUAAAB7CAYAAABKK5jlAAAavElEQVR4Ae2du8tdRRfGA1op+KlBvERRbIMkGhsRL4VaagoxamOsRCQYGysxKawEUQuj2JhOUUHBQpt4qSwDWlhYWFkoEf8Ai/3xe3mfuLIy+5yzL3PevbfPwGH2mcuaZ34zs9be572cfY2TCZiACZiACZjAJAjsm4QKizABEzABEzABE2gclL0JTMAETMAETGAiBByUJ7IQlmECJmACJmACDsreAyZgAiZgAiYwEQIOyhNZCMswARMwARMwAQdl7wETMAETMAETmAgBB+WJLIRlmIAJmIAJmICDsveACZiACZiACUyEgIPyRBbCMkzABEzABEzAQdl7wARMwARMwAQmQsBBeSILYRkmYAImYAIm4KDsPWACJmACJmACEyEwOCifP3++4VU7vfnmm81VV13V7Nu3zy8zGLwHTpw4UW3L/vXXX825c+d2XtUGsWETMIFFEhgclD/44IPmxx9/rA7n2LFjzd1339288cYbVV6vvPLKjqN/5plnqtivpdt2u++Hl156qbntttuq7VmCMjePH374YbUxbNgETGCZBGYXlGstw6+//rrjSD///PNaQ9juRAg4KE9kISzDBEzgMgIOyrtIHJQv2xuLLXBQXuzSemImMHsCDsq7S+igPPu9vPEEHJQ3RuWGJmACWybgoLwL3EF5yztvD4dzUN5D+B7aBExgJQEH5V08Dsor98miKh2UF7WcnowJLIrA4KD87LPPNq+++mrz/vvvV30dOXKkOXToUDX4DsrV0E7OMEF5//791fbrW2+95d++ntyqW5AJzIPA4KB89OjRHQe0jb8fvuuuu6pRdVCuhnZyhgnKV155ZfV96z+JmtzSW5AJTJ7A4KDMU8E333zTXLhwoeqL4M/fKddKDsq1yE7PLkH5wIED1far9pKD8vTW3opMYOoEBgflbf/zkFpA5Uj9d8q1CE/Hrn+mPJ21sBITMIFLCTgo7/JwUL50Yyz5nYPyklfXczOBeRNwUN5dPwfleW/kLuodlLvQclsTMIFtEnBQ3qXtoLzNbbe3Yzko7y1/j24CJtBOwEF5l42DcvsmWVqNg/LSVtTzMYHlEJhdUL7nnnuaGq+DBw82V1xxRXPnnXdWsV9Ds2323wu1vyXquuuu87dELcdPeiYmsDUCg4PytpR+9dVXzZNPPumXGYyyB86cObOtretxTMAETGBjArMJyhvPyA1NwARMwARMYKYEHJRnunCWbQImYAImsDwCDsrLW1PPyARMwARMYKYEHJRnunCWbQImYAImsDwCDsrLW1PPyARMwARMYKYEHJRnunCWbQImYAImsDwCDsrLW1PPyARMwARMYKYEHJRnunCWbQImYAImsDwCDsrLW1PPyARMwARMYKYEHJRnunCWbQImYAImsDwCDsrLW1PPyARMwARMYKYEHJRnunCWbQImYAImsDwCDsrLW1PPyARMwARMYKYEHJRnunCWbQImYAImsDwCDsrLW1PPyARMwARMYKYEHJRnunCWbQImYAImsDwCDsrLW1PPyARMwARMYKYEHJRnunCWbQImYAImsDwCDsrLW1PPyARMwARMYKYEHJRnunCWbQImYAImsDwCDsrLW1PPyARMwARMYKYEHJRnunCWbQImYAImsDwCDsrLW1PPyARMwARMYKYEHJRnunCWbQImYAImsDwCDsrLW1PPyARMwARMYKYEHJRnunCWbQImYAImsDwCg4Py119/3fzzzz+TJXPhwoUGjVNOv/zyy+Q1fv/995PXyDp/++23U17qHYY///zz5DX++eefk9c4ZYF//PHH5M/LTz/9NHmN586dm7xG/M4PP/ww2nYcHJRPnDgx+aD82muvjQashiGC8rvvvlvD9Gg2CcqffPLJaPZqGPr0008nH5Tfe++9ZupB+fXXX2+mHpRffPHFGltoNJsE5VOnTo1mr4YhgvKZM2dqmB7NJkH5s88+G81eDUMff/yxg3IXsDwpOyh3IVZu66Bc5tK11EG5K7FyewflMpcupQ7KXWi1t3VQbmdTrHFQLmLpXOig3BlZsYODchFL50IH5c7ILuvgoHwZkl4FDsodsTkodwTW0txBuQVMx2IH5Y7AWpo7KLeA6VDsoNwB1oqmkwvK9913X3PgwIHm1ltvneTrlltuaa655ppJahOzm266qbn22msnrfGGG25orr/++klr3L9/f4NOcZ1izjrfeOONk9bIeeHcTJGfNF199dWT1nfzzTdP3u+wD+fgdzjXWvcp5vjFSf2iF0H5hRdeaLhzneLr+eefb44cOTJJbeL19NNPN/fff/+kNT7xxBPNo48+OmmNjz32WPP4449PWuMDDzzQHDt2bNIa77333ub48eOT1njw4MFJ64MfHHXGp5g/9dRTzYMPPjhpjZxnzvUU+UnTI488Mq2g7N++XvG5xoZV/u3rDUGtaebfvl4DaMNq//b1hqBWNPNvX6+A06HKv33dAZaaOiiLRP/cQbk/u9jTQTnS6H/toNyfnXo6KIvEsNxBuQc/B+Ue0FIXB+UEpOdbB+We4FI3B+UEpMdbB+Ue0ApdHJQLUNYVOSivI7S+3kF5PaNNWjgob0JpfRsH5fWM1rVwUF5HaLN6B+XNOF3S6vfff5/8f/RC45QT+qxx+AqZ43CGWIDj1P+j19TPC0F56hp9XsY7L2Ou9eB/sznOtGzFBEzABEzABEzAQdl7wARMwARMwAQmQsBBeSILYRkmYAImYAIm4KDsPWACJmACJmACEyHgoDyRhbAMEzABEzABExgUlM+fP998+eWXDfkU0t9//93wxQm8fvvtt6Kk7777bkdzW32xU6VCuJV0TEGjWLK+WWOs43ovEpq01iUNlKGdNqX6WpoZq+08oFmaSuNTf/bs2R3NpfqxyhiHVymx92DWNgfKt3Hm285G1MwcSjq3qXHV3tI5LmlUXds6xHkOuWackkbKdH5KGlRfOv9D9MS+jCsNqzSioU99HKvv9TqNrK3mUBqD/qvOfKlP76D83HPPNbfffnujnP/1upfpiy++2Pnn6vyPZjT973//a95+++2LkljUo0ePNocOHWpoQ/1HH310sX7bFxyWffv2XfJF6Gg8fPjwRY3wZV7bTnDhH9U/9NBDO/qiBjYhdTAUx5LTqakZfazfyy+/fHH/tWlkDnAsHeqxNbKm4pZtSzN7kz3IOkdNqoep6rONMd5rbU+dOnWJOdbwjjvu2FlzuMKM8xKTytFIfa0zj7Z8NqIOrnF2JdbyR9IYfUC2MeQ946CRNc9JLFlHmEUN8YzLT7EmYyfGYf1KGnWGGV9rWtLI2al1xjUuGjRGZCmNbRpUT1/a4A8oGzNpD6GVteR8sO9I4ss5iPXxTDMf6rGjMx3r27T2CsoaTAOQM7gEtw1Ws5wFiePzng2pxKYDjBL1LKTmoPJt5OLFhozOURtUGgg0cN1mEhfyUmKDRc1wpWybCSYxCOPUOJhK1Ecngz4OTs2EBo0btWhM9lp0OmiSRvYDezUyx8bYzlrOgzyuIRrREPVpj6pM+4JytWe+UbPmOiSHCy/mnzVGu9Qzj8haayCN+AO4Rr8QbfS5xjY3VJxVxhYf2aKem4W2tWNOUbO4qv8YOfMlgLC/SkEZ7fE8iJPGzn4IO8x5zAQ3rRN2GSNyYQ/E9aeeMiXaUqZEW3SPmfIaYj+OEX0Q42bNnPnYJte3af03arW1KJSXACA2Qix023pRdHR5ERFDkM7gtyGSxWFDwSsyy8EGLTXuAFfNcd06ZifHwaIsHrBV9seoy5udja8DjZNDT0w1HF+0z7WcDLm0qA1lrG1M7Du14zreMNIuO6HYt++1NOZ912YPfTof9IlOkT449uik2ux0Kdd4jB3PRrQhNpk1+mKwoY/OWuw/9JpxSWjUtWzGdVVZzCNTlbM3sh3V9ck5iwoGnIVsGyaRrc6wxir5nHzu1XasHI3xjOTxpJE8Xmv8fGOh8jFzmLF+bSmeEeYDx5hYE26W1qVLvde61rv1pQMTBW1opmqzDKW08fPmrCpo13g8tDDjpZQ3IuWlg6/2NXJxgt/p06d3fh6icShDY06UUbetBEOCGGPy4slEToj3pYNT0l1Db2l81jhrop00tdWP/XSi+eZ9p/KY4/jgqqfM0lkp6Y42hlyXfAz2uMFij6Ivs6aP9oHG3mSuats1Zzw0xCRO6HznnXd2fkcArUqls9I2V/UZkpfG000qv7/A+rLP4lOn9mUctzTXWD/0mnXSDR76Sho0l3h24rjUa7/G8rGu1z2wwEgco5/X+JveOFzuYWVhRV7aRDUP6AopxSoOQWmj5QNU88CWhLEocijU5/FLG7HEumR7rDI04Fh44tCa6meLbYdBAXIsDZvY4YCglRcHQAmNMMuJdjUPrMYrjS+OakMeD2jeB9S3sY42+l6Xxsu24CsnSV1pH5bmmu30fV8aD1txr+Xx6UNZTMyV/VwjlcajjPMiB87YPB0pMLMPs0a1raGxNB7jcGao4xV/N6AtIDKveM7G1MqY+EVyEnzQlZPWnnquc2qba27X5z1j8uSrdcw2YJN9O8xyKs3rsja5YJP3DMZmj4n3JRGxzbaus0NhXIDlw6DAsy1d8Il38tk5smB50Uusa+pFQzx86NGBaTssbNbMtqZG1hcuBDZ4ok/7ER2lfci8phSUI0u0M6eYYn0sH+M677tsk/XH6cW9WAoctCuxzvb6vC/te3THj6fzWtOHspjoU1NjHo+xoka0EJj1BMU+LPVBZ41UGk/riw5eaNaNNxrok1OJbW7T5z17jJuW6HPabgzkZ9DMpzg5leaa2/R5n28aso1SfelsMNcS22zvcvq5ReF9KZixqbJjKXStXoSGkg42VQyICKGs1mHIE2WR2Eh8JKzXww8/3PDiPam0qXR3mO3Veo+G6IwZJx7I0qaiTHe5tXTJLuPkO1Y9daKbA5t/bqN62aiZy8nFMdrKmAcJhw3jmNirpaeB2KbvNXu+bd/LYec9QPscbCjLuvtqyv3y2WTd2WcnT568eH54wmOtOT+sce6DzZKvymP1fc94rG1MlGU/E3m39VHQjrbGuIZZ1qjgFu1zYyvd9MnrX3qoif37XDMGn2jGgCw7aMiJMvZB23mmnroxE+PpoaRkt60e5vnHT5SV5pXtXj7z3KLwHoh5QBxIrY1VkFAsagvINC45ldLmLBoeoZAF0eFUzgHlxXsS+iNDbb6xN9qq6XBXnw9JPJCssw4vdpiXgssqu2PVMV4OutiOBxI9kZkCzVgaVtlBH2saE84HfdHRseb6WBWtmSHBpHRzGe32vdb+y/3FKepUG9Y8n/n4BKh2Y+XxXGATRtKtHD7sTd5Tz9kRU+mgPu5XlY+Ro5H1jgkN+eYFnWgkUZfr2Rs49xoJ21ljPCsak7no3GcfANu8f9Wvb74qIGMz+xn4xDOSAyVrTNmYqS3gaoxV9cwv+yH2RvYNshXzXkEZAwDgFxlYcHIElA5zHKzmNRsfp6E/5FbOhiKRo5FfbkAzd9m1nkQ2naeci9qjC438sTnXPEXXcswaM+dZA08mkRMHV5xpy7UcTrZV6z17T09HrGteS/Sgi0PDYY2/CFZLk+zCpHTwWEd0Us/6oolrJfrE+pIzVduhed532GNd0aR/dKDzA0Ml9gHc0a0zr/OlNmPl8Fi3rzJrOUL5Jfbu2I46zg+NcQ2pixpglznJD+mMs+al/RLHGXJd2kcEXT6uRh96s0bOjPwQbThL+UZiiCbGxCZz1z5TTh2J/cjNN+Uwzn6GAEeZ6mm7br900cw6cR5gI23K0QiXtnqNAzN8OPp15je5QewdlBHN4rKhyBG5lwkdpZfu/tAGHGnGSWoD7JVutEV96GDRNA822V5oXMcJzVHjtvmx91g/aWDzZ06wU31mXFMv56DkwNBHuTTlw0l9nFOuH1Nzad9FbdJIHucSudc+84y7bt1KrCmLZxzNtRIaS35vHSfOlxjX9kOMkzWy1+L5gFfmVNMPoUfzz3nUus7PxDlwPWZapzFqy3OQjsx50zPdOyhrYOcmYAImYAImYALjEHBQHoejrZiACZiACZjAYAIOyoMR2oAJmIAJmIAJjEPAQXkcjrZiAiZgAiZgAoMJOCgPRmgDJmACJmACJjAOAQflcTjaigmYgAmYgAkMJuCgPBihDZiACZiACZjAOAQclMfhaCsmYAImYAImMJiAg/JghDZgAiZgAiZgAuMQcFAeh6OtmIAJmIAJmMBgAg7KgxHagAmYgAmYgAmMQ8BBeRyOtmICnQjw/4/5B/d9Ev+nOP6P4D423McETGCaBByUp7kuVrVQAgRTvl2Gb+HhG3zWfelCCYP+EX+pbs5lsOHbf+aS+IKBPus3l/lZ594QcFDeG+4edSYExna6+gajIdNfYlDmG3X4+r29DMpdv2mITyy4ueJTDycTGIuAg/JYJG1ncQT09XVjTgwn3tX55/GXGJSZU83vPs4M83ue0vnkomvi6xu5mXAygbEIdN+FY41sO4shgEM7ffr0zosvBeepJyaeNnmaoJx62p49e/ZiO+rUv+2pg6eSUl+NQz02yHPC4cdy9PCel2xm3dTxBeU43HXaNF7kwJea50SQx/FjF5vrnsIjF/RpDjEoY0P6VB/HhTms1YbrnLBHO/qrHeOVUtSktuR53Zir6ku6su22mxX6ao2wB+OYYn1pbpuu9dGjR3fWRprjfCLjuA7oYHzWlPk6mcAYBByUx6D4H7bBx404VD6W5amBpx1+ZhoDM18CjtOjnHZ8sTt9Dh8+vPNxJX0oow4Hlz/CxCnS99ChQzvtyHkfHTROlL7RmWpZcjl6jh8/vqM16iEAS7fmgk59iTk62hLtxYG5MCfmJ3v0xQ5aqOOaPm0JG7TV2MxZjp8gim1elGOHnPZqI7v0F19xZy1ioh88on6tT2zHuJQzHtcak2txZ77cdDAm7eBLn6wr2tXa5eBNH7QxjjgwByXtC7FkTMaOiTrtvdgu7lF00jfy1lrTR3NhbLREDYxVKosafG0CXQg4KHeh5baXEcAJK/BQyTVOOAZWHBsOT46bdnLEOLzYH4cXPw6kDgca7dE/t5O9OIbE5rHRg8YYBBiHdnEcgg1t1yUFj3iTgD3mlh04Y2B3VVo1F/rRv2SHAJj1Rrb0JdjQN5bzPq8Dc6E88uS9ghW2xCwGXLRlW5TFIJjnTj3rERO26ZP5qY3q43pRhh3sKWnv5bXJ7ejD/GISA+yuSgT1uGdXtXWdCawjcOkuXNfa9SawAQEcYXaMPE3khBOM7ahXgFNbnG52ltThKClX0FgVyGI7+qKv5OyzbrRRti7RhoCYkwJgLC/NOdZz3WZP7RRAcrCgnEC2KunjVnGjbZsmyhX02vjGNtgi2KmPdCi4xTFVR166mdA+yHNUv7Z9kW3BsrSGlMU9IKayTy5WfKS9KpX6rmrvOhNYRcBBeRUd160lgNPkZ3l8/MlHh7wIDDgqpTbHiEOPT160l/NXX+yUnCr1MZioX8nx0y6WYy/q01i5fNXY6kPODUfJnjTFp7SoOdqI12321Iax8pMldZRjPyYCC0FFa0OeefA+rwM2stYccBVs8/z4WD2OVxozaiztj7Y5qh/17LM8Dk+s8cYE2zzJ5pTHLLGjD1yYN3bhWLpJ0DrnMfzeBPoQuPQE97HgPv9ZAjgo/VwT54Vz4pWDSnaAApaDA+XZweFQ6V9KOEucaexH/5jQmMfBnvrFtrmcNm1jx358XFuyp7nkoFVqG+1lfrGO6zZdlDNXJdaE9zwRooWXnjAjp8xH/SmPWhWgFAgJfvGpWKwp03gxLwU0xoJx5sy4qz4SZl/AKdqP15pDXtNYHsfM7NSOHN3MnXVmzlzHpKf6WOZrE+hL4N8T3NeC+/1nCeB8CYzZ2eYgVXK6QCsFAxwr5UoKInqvXAFAP8/UUxv9Y5K9WL7KUccgxHV03NFuvKZN/ChUdSVnnQOd2sYce6WPw9WmTVcOLAStrKvEo7QOjJW1whibzAs78Wfy0kafGKhVvipHY+asG4q8t2SnbV+oXjl245rG8jhmZqd2OUdrfBKnftO+2Zbfm0CJwL/er1TrMhNYQQBnlB2UnH50hDi/6ABlshQM1F9t9HO97Oixn28IsJefYnCieZxVjjrq5nrV05o0KoDEJ2LqGCcHV7TEMWQj5m321Ib+JZ6UY1+JAJo/uuV95pHfq3/WypjZntoqpx5mbcFU7WKedVNHf9Y3r7v6sS9W1avdqrWODMVc/dry0o0WewzWTiYwBoF/T/AY1mzjP0VAT6f8yQk/bzt58uSOo8yOkPfRAQpSKRjkoExbOUx+bs045PSlPCY9xaCDduhi3DxO1icbuTzPD21tKY/Nx/oEjfw0mQNdmz20cMMT56JgvmlQpl20wcfOpZuUzEeaslYFJMp5EXyxSbkSwZQAFcdlvfLNm9qTa83zTY3WPe4v9CupHg2sNy+4ixPt8pqqL+W8lHTzp7HQxAt7ss1aMI98Y8InQ7lMdp2bQFcCDspdibn9JQRwXDgkHBw5TlkOTQ1xnjmAUofzzEGL99GpykYeh/elxDg4bp5Q9ZRFHsehTal/qZx2slfqEzVobFgwZulpkbmtsyOb2BPbyIr+1OVEeWZHO1jwUp/MI9qONqNWAqaCD+NIg4J85IsNxpB28hi44xi6lm29V8646ND+YtyYVK855rkx59yH/pSLh+zRjvkwFtesH21WzYPx225qZNe5CXQh4KDchZbbmsB/lICCYmn6YwQlgilPoaUbmdKYUylTEJ+KHuuYPwEH5fmvoWdgAtUJtAVlPSmSD01tYwy1W6s/T9PckIwx91oabXd+BByU57dmVmwCWyfAx7gEIP1cXz+zpyx/DNxXnH700bf/tvvxkb0D8rapL388B+Xlr7FnaAKjECAI8TEzP+PlxfXcPm4eBYSNmEBFAg7KFeHatAmYgAmYgAl0IeCg3IWW25qACZiACZhARQIOyhXh2rQJmIAJmIAJdCHgoNyFltuagAmYgAmYQEUCDsoV4dq0CZiACZiACXQh4KDchZbbmoAJmIAJmEBFAg7KFeHatAmYgAmYgAl0IeCg3IWW25qACZiACZhARQIOyhXh2rQJmIAJmIAJdCHgoNyFltuagAmYgAmYQEUC/weEvAYqkCvsnwAAAABJRU5ErkJggg==[/img][br]The median is 25 cents for both plots. After examining the data, the value 203 is removed since it was an error in recording.[/size][br][br]Explain why the median remains the same when 203 cents was removed from the data set.[br]
When 203 cents is removed from the data set, does the mean remain the same? Explain your reasoning.[br]