IM 7.8.19 Lesson: Comparing Populations With Friends

Dot plots, histograms, and box plots are different ways to represent a data set graphically.
Which of those displays would be the easiest to use to find the [i]mean[/i]?
Which of those displays would be the easiest to use to find the [i]median[/i]?
Which of those displays would be the easiest to use to find the [i]mean absolute deviation[/i]?
Which of those displays would be the easiest to use to find [i]the interquartile range[/i]?
Which of those displays would be the easiest to use to find the [i]symmetry[/i]?
Your teacher will assign you either a [i]problem card[/i] or a [i]data card[/i]. Do not show or read your card to your partner.[br][br][table][tr][td]If your teacher gives you the [i]problem card[/i]:[/td][td]If your teacher gives you the [i]data card[/i]:[/td][/tr][tr][td][list=1][*]Silently read your card and think about what information [/*][*]you need to be able to answer the question.[/*][*]Ask your partner for the specific information [br]that you need.[/*][*]Explain how you are using the information [br]to solve the problem. Continue to ask questions until you have [br]enough information to solve the problem.[/*][*]Share the [i]problem card [/i]and solve[br]the problem independently.[/*][*]Read the [i]data card[/i] and discuss your reasoning.[/*][/list][/td][td][list=1][*]Silently read your card.[/*][*]Ask your partner [br][i]“What specific information do you need?”[/i] [br]and wait for them to [i]ask[/i] for information.[br]If your partner asks for information that is [br]not on the card, do not do the calculations for them. [br]Tell them you don’t have that information.[br][/*][*]Before sharing the information, ask [br]“[i]Why do you need that information?[/i]” [br]Listen to your partner’s reasoning and ask clarifying questions.[br][/*][*]Read the [i]problem card[/i] and solve the problem independently.[br][/*][*]Share the [i]data card[/i] and discuss your reasoning.[br][/*][/list][/td][/tr][/table][br]Pause here so your teacher can review your work. Ask your teacher for a new set of cards and repeat the activity, trading roles with your partner.
Is there a meaningful difference between top sports performance in two different decades? Choose a variable from your favorite sport (for example, home runs in baseball, kills in volleyball, aces in tennis, saves in soccer, etc.) and compare the leaders for each year of two different decades. Is the performance in one decade meaningfully different from the other?
A college graduate is considering two different companies to apply to for a job. Acme Corp lists this sample of salaries on their website:[br][table][tr][td]$45,000[br][/td][td][br]$55,000[br][br][/td][td][br]$140,000[br][br][/td][td]$70,000[br][/td][td][br]$60,000[br][/td][td]$50,000[br][/td][/tr][/table]What typical salary would Summit Systems need to have to be meaningfully different from Acme Corp? Explain your reasoning.
A factory manager is wondering whether they should upgrade their equipment. The manager keeps track of how many faulty products are created each day for a week.[br][br]6 7 8 6 7 5 7[br] [br]The new equipment guarantees an average of 4 or fewer faulty products per day. Is there a meaningful difference between the new and old equipment? Explain your reasoning.

IM 7.8.19 Practice: Comparing Populations With Friends

[size=150]An agent at an advertising agency asks a random sample of people how many episodes of a TV show they watch each day. The results are shown in the dot plot.[br][img]data:image/png;base64,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[/img][br][br]The agency currently advertises on a different show, but wants to change to this one as long as the typical number of episodes is not meaningfully less.[/size][br][br]What measure of center and measure of variation would the agent need to find for their current show to determine if there is a meaningful difference? Explain your reasoning.
What are the values for these same characteristics for the data in the dot plot?
What numbers for these characteristics would be meaningfully different if the measure of variability for the current show is similar? Explain your reasoning.
Jada wants to know if there is a meaningful difference in the mean number of friends on social media for teens and adults.
She looks at the friend count for the 10 most popular of her friends and the friend count for 10 of her parents’ friends. She then computes the mean and MAD of each sample and determines there is a meaningful difference.[br][br]Jada’s dad later tells her he thinks she has not come to the right conclusion. Jada checks her calculations and everything is right. Do you agree with her dad? Explain your reasoning.
The mean weight for a sample of a certain kind of ring made from platinum is 8.21 grams. The mean weight for a sample of a certain kind of ring made from gold is 8.61 grams. Is there a meaningful difference in the weights of the two types of rings? Explain your reasoning.
The lengths in feet of a random sample of 20 male and 20 female humpback whales were measured and used to create the box plot.
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Pvkk0/uzWHQaFNAV0NmUPHRj340zD///GGfffaJNBhYvOc97wk33HBDj4byHMl+V4BuNXRM7mjokl9deZDOmtyb0EnTcO89h46FBkBP86r7vooPnQsvvHDMALreGx8T6dprrx0tcTa8zbUXoFsesoaeNXRbH5pejzkNnW0srDj/8Ic/HA455JCoTdPhXXvttWGSSSYJM8wwQwR59thiRmWUt+qqq0agvuWWW3pyxjzPKBANTe7666+PtNEIrEsBnWeY/FdcccUw/fTTx728in///feHqaeeOuZpzbhenbvy8fa7AHTkawEdDR0Li5xkgq9rPSvzpaErvvyy+IPCKX+0K1tWg9IMem4BQ/zJH5TWPrdpxoqGbt8ZWWgO3XNRHFYj6mYTB3/iUT50soaeAb1JfUrTjElARxNHU7cLHJhjm2KKKcJMM80U57OtoKTVs1I4ddAA9NmCwCh7sskmiyZ85sbVYIsA/cEHH4wDiGWWWSaw1cD+5plnnoBVANP/aHHDBehtV7mzh1irqFU+bWWMNm0Hdm3pAehYErz4gw6A7r3KfSQvipPsaJ9MmWHi83JMldkTvjzoZkDPgO5Rj8YcoHMIBYB+ySWX9DpMGj979wB0zObcq0NAyLvssktMw8IiOZ4DwpjemfPGFIzZfdJJJ43z6VZjKwL0s88+O9Kce+65w8477xx22GGHuE+Y6w996EPhXe96V08LsLwo/5HmdwHoAKXV0LGosG6BsmJeB1+/9L4oDmGcbMbCQ65ZEKl08kWvqs97c2AQ6ZvSSPOCDus7bHgT+pYfpoE4VMbTjWRA13sC6Kuvvno8WMbKs801Zc50GTQkY/mD6JaVI3WIqQFPl1e5jz/61UOmeZW7z5ZklYXbHLoA3YIzmdAQAXQWwVF41hUBOqZbNHcWw7EobvPNN4/z7ZjzOZlqEKBzQAoDi9lmmy12OHQ6+q277rrxkAkOXBgtrgtAT03urDNgCxIHzBx00EGNfqyd2HvvvVvRSPPmZDMGc2l403sWVjLAU/o27wsN0rPgaiwCOpYTrGC0Ycmzrb/vvvvG0+ya0lF5Wv+LX/xiXITr2d4zoGdA96hPDEJH7KK4foA++eSThwUXXHAgoDOHxjGN0047bTwsBk0PTZIXhwYgD6BLsy7S0DHfA+ibbrppNAGzlSr9eZpxPQq2H40uAB0zpDR0ZMnRr6yBQHtFQ2ryO/fcc+OCQ9ZINEnPrgSlgw/uv/vd70a/KU3Rw4fGTTfdFLDgpPTSe5uu3zXpWOzpDehMU2HiG+Qm9D50BtxMOTSVn5UtNNCmKR8b3uaaenTmmWfG1fiDZFnneQb0DOh16ktZ3BEN6Jz1DJAWaehVAB1gQZvHJA74C7QRBlo74ax+t2BcBOjMwbEAb6655oqm+zJhWvplcUZCeBeAjoZut56wyv2MM84Y8rp15UPl5PhGL0f+lLuno+4wLdDWSTb4l112mTugj0STu95ZssPkjimbA1E8HPQZrHtaz6BJHRoNJ8Xlg2XywTIe7cjN5A6gYxZnG491gPSUU05ZqqHbNIAXK9HZysY8L1oKDfyUU06Ji+LYioaJgobKrwjQWVjDxy0YRKA50YFzUA1H7NFooGvBzPI6Eq+7AnS7Dx1At6vcm8jBbltrkj5NA2B4b1vDKkH5y1GH6rii+GP5YJkuTorDeiRXJG89q+rnbWt5UVzVutIv3phbFAd4okWnK9YBdECafeB2Dp3GipnXpqHD/dSnPhWmmWaasMoqq4Svfe1rYYsttohnRi+xxBJhqqmmih9iYWsb6Y8//viY/phjjhlSFuTJCtwZZ5wxHnYDYBEHHtEqAMnR4roCdJnckYPHwTI6Kc6jE4anLgCdgVzTbVHwlL4b92MN0CUDaegj5aS4svbM4UQcj+vpssl95Jvcva17Yw7Q+YwkC4RY7WwdX3jaeOON4+cc7YI2Oga2StHY7rrrrl4SOlw+CwmA82MRE1oVB8Jw6tuSSy4ZP7dHgmuuuSamv/zyy3vp1eEwv8iCqhVWWCHOvbOgjjk/6MHTaHHDAejS0CU7ZKNr+YPklWroVdOV0QUw0NbsILAsbtVwHSxTNX4ar+idxso+dGRh35/yGUlHv6ZlJX6zhp419KK6UTdszAE6JlwWn6UdMA2f/clF56iThi90WaBH0GiPmNoZ/ds5czRvAA6zOg6f9LovKiTAG7DB9M558+qUUr8o7UgI6wLQGSBV0dAloypySAHdpqlDR+kod2+TOxq63efchC/xp7Rj5aQ4va/evytA954SyYCeAV11to0/5gC9TFh0BPqVxWkTnnY0TWh50GiSb5U0XQB6um3Nw+QOoGOK9HCUB4CBuTQdILahD6BbwGhDS2m7MrnbDxopr9Svs8qdg3/Sj+ek9OrcU0ajQUPPB8tkQK9Tr8vijnlALwPJuuFFAi6jURR3tIcNB6BjctfHWZrK1hPQKTP4QLuyFpq2ZdnW5C6+LB9dALpWqtp8iq7rAjrWrKYurRfcsx5lJB39yrulfGYNfewBOvXcWuKa1nmbbrQA+nPPPRenlEfl99CtwN+p1wA6UwVorF4/tBamO6BHB8hHcAB00SdM11V9GtBrr71WO10ZfZncmU4pi1M3XIvi6qZL41v5AOicQghtBgxtf9BheojpppSmpq7IgykTFv6w+JP7NK7lg2eUObs9+sWzaQZdw8tqq60W+Uzl0/Qe3rAeWfk2oWXTA+is07FhTWjaNNRzBrA2rO21tq1ZOnV5Vnz5TFuyK8jSbHtN+VBObemQnjZO+6aMPOiJBpbCLsqHwavysD7vYe+rXnfRt8Mj08wZ0EfoiIBCZ9RFJ+L14zv1FDr0qPwA+oknnhivCeP8fP2q5vnEE0/EbYGkE42qaRXP5sk2w0cffTRuN9TzNj60eWf4FI916Nk04hPZcaAOOzOuu+66+ONjRG1+LP4866yzwjnnnBMXghbRIi/CWYi69NJLx08QK6wsPkcocyxzGi+9L0qfhvGxpKuvvjost9xycaBQR4794lI+1E0r637x02eko0xsej7K9MlPfrJXt9M03Nv4Rc9tGHH5FgQDJBve9ppzHJgWgX4dftJ8bXqml7D2pHGa3IsnyofpIJuPnlWhq7jyGWQ+9thjrd6ZfEUPn6PDma6z4VV46xeH9VsMoIviKO+iZ2VhpPEsH+VDHaIuZUAfoYBORWKkjd/2h4kWGg899FCkyT0Nnl0CdOyM7hRHeaX3Ck99wJfKpPjyiWev03T23sajUo4bN653preN1+Qa2mi+Dz/8cJSBzasKPcXH1zXyuvnmm+NxpRxbmv44Ule/9Fl6TzyFcZwqJy/aMJ7pHp8T6vA5/0HpbJz0mt0de+21V4xLOv1s2n7XNj7XHKPLcb8AG9aEKjLsFweZUj7UTeJJxv3SpM9UNkoLX9Sho446KtJTuPw0fb97Sxvt75FHHunx2YRemhdyRFMlXHml12maQfcAJaAxKF6V53pHygclI+WxKg3RkQ8tyoj0CrO0bXi/PJSWOAAv76609lk/Gv2eUTYMEkTT+v3Spc/ECz48YtlM4zS9hyYKC3UpA/oIAnRMZnJUeHuYDs+a/KCndOmiOOVl44gHpRnk04iKFl0NSqfnad6YrWhELIpTnLY+Zmo6uJROmnf6PL2HNzksCTRAT1d1Dr1OnpSPvoZXJ11ZXGRCZ6QdKqmMmtwzDVRUPnVowa/ic039aUtT9ORjBUCeurd5KqyuL5O7TdeWLoNi6qel2fZaJve2vIkPygcAok0prKkvnigf6iZ0FNaUptIxh84AUfTk63kTn/Lx6Ntt3uo7MqDHohp5fwA6jdLT0SjpPD0dWksRoDfNgwZOQxdgNKVj0zEvTEP3dDRIysjTofmnq9xptG1c0Sr3ujTT+JRPvy2jdfllwEXd9HTwB5+eToDuSVOA7kmTevnKK694kozlwxy6lxOgp3WrDX36IQZcnq6rRXHefTvTs/QdGdA9S9+RljR0R5KxUdp96EW0aWBqZPKL4ilMgF6Urkp66Nh4FtBtuPJr4lsNXemb0FYa/K4AnQ7E05VZUJrmwbtbC0pTOkoHPQaZAnTJmOf2WvGLfOIprnwBuu5FT3HlF9GzYYqHL0DnWvRs3CbXAvSmNMWf5cdTGRBflA8DYzmbr8Kq+qQVoEtDt2mVpw3rd634ADr9kZzCdd/EF6BDqwm9NA33aOisIUifNeFPabKGLkmMUH9CAXpdcdCA6Oi8HJUcwPDctoZmgfnV03UB6GqUnnx2AehovnTIXk4DLi960BGge9IUoHvSFKB70aT9eAK6+KL9dKGhA+hebjRp6PQfnk59R9bQPaXqSGu4Ab3paFEaOq/elIYVGw0cQPc0uXcJ6B7vrPfH5D7SNXR47QLQvc3jAnTP8hlOQK/Kd1G8LgCd8hkNgG41dLWrNr409DY00rSsvekC0OE1A3oq7RFyP9yA3vS1LaA3pWHTjTZAt7y3vR4NgA6AdAHoMrm3laHSj3ZA13s08bsAdMonA3qT0nh7Gi2Ke/uT5iFZQ28uu2FJmQH9P25y7lJDd2MyhLh9cKRr6BnQfRddeZvcqY8Z0P1aZRcaegZ0v/IZNZQyoGdA96iso2UOPWvoHqU9nkYGdD9ZZkD3k+WYppQBPQO6RwPIgN5u258tg+GcQ7f51r3OgF5XYuXxM6CXyyY/qSGBDOgZ0GtUl9KoGdAzoJdWjhoP8hx6DWENiJpN7gME9E58nAE9A7pHvc6AngHdox5lQPeQ4ngaGdD9ZDlqKGVAz4DuUVkzoGdA96hHGdA9pDieRgZ0P1mOGkoZ0DOge1TWDOgZ0D3qUQZ0DymOp5EB3U+Wo4ZSV4DuuZcUYY6GfegcWdnFSXF8Dc/TsQ+dbUyezrt8Rtu2NU9ZdnESWVfb1kbLWe7eJ8VVPVim6ECeorpStiiuavoiml0AOme5w2s+WKZI4iMgDEDnvF9Pxyh70FnudfPzBgyOfOXgEs+T4roCdMrI03UB6N4aOh2w98EyDDKpmx5OHa0OlvGgKRqscudjN54OQKcz9nSjQRlQ+Xge8Uz5VAX0VN6qN2k4IKmvraXPmt53Aej0HS+++GIG9KaF0nU6z0apyoqW2gWgv/7661EcyqepbEjPr4ujX0fj19baypNysAOulF56X6XcSOMN6PbjLFV4KItj30eAYcPK0lUNbwsYRbxYDV3P5VflK42H5aju17zI0+abXqdnudvnaf72viweA3bqUdlzS6PftU3f7+MsNt6ga54rDoDOKWxtnGjJ7wLQ4bFTDV3MtxFEF2lHKl/pu3oCumiPBg1dGqCnht7FSXFYT7w1dDVKlZeH762h037oiAFML8cg00tDhyd4hD8Ghp7tXfvQvWhCRxq6B03RADDqArotS9GRzzPvvsN+bc3m3eY6BfRBtPR+8oviUz5WQ+8Xtyh9URhnuXtbX+k73DT09CXT+6KXmhBhI5WvIll4ATrvrPemI7YausKL8q8ahgYoDb1qmjSe5YNr+PQ0xQnQbT4pD/3uSZem7eJra3Qc9nvoaZ79eCx7hom4rHzq0ld8OnfPr615aehWBvCHVime7bOm113MoXtqgHov+o5XX33V9d0pc30+1UOmDNgpH485dPFDPS8zuRNH8SQn65c9pz12oaGzxqEfP5a3Ktfw6P49dAqJkTGF5MlslReqG8fyZwvThtel6RmfUfbLL78c55KRq8ePBvTGG2+40BI/mLJfe+01V5oA+j//+U83mjR0OiQGCeK7rU/ZYNpsSwfgEQ3mwZ5//vnevX2mOHV9OjhbPh4yQJZe5cM7Uicp87rv1i8+g7hHHnnElSaDuMceeyy2SzSstj/qEBYU2pClRT72vuo19Ej7xBNPxO8CVE1XFI86o/pHnaHMKSfCPOoQ9cerzMUnPCNL6kVVHnknlBzSiI6tVy+88EJ45plnevWoKI6NX+Wavv2ll17q0aySpiyO3hNAx5rgsiiOwqFiXn/99eGqq64Kd9xxh7tJoS1YWrbpT9YAACAASURBVNAWLTQDGuhTTz2loBHjU+gyo2BK8fjRwQEYHrREA/lpQYbCGCnqepCvuPhUcvxHH300+oPSVn3OoiP4VF5KR366ruqLBmYzTLpV01WJR2dEXawSt2qcJ598slX5FOVD+dDRFT2rG4Y8qZPQrJtW8VUm3FOm/BjInHjiifFa8Zr4og3oPfjgg2GfffYJZ511VvjOd77T+nfmmWeGb3zjG+Hoo49uRfPII48Mp556auQH3o444ohw3HHHBeg34fOMM84I3/72t2N7UZuk76AdIUPJpIk8lR4aaucpvfS+aj70lwxmyuKndCnTa665Jjz++ONxkFaUjvaIIqRnKQ2F1/EZyNB/1ElTFlf80HegYLgAOgC+yiqrhI9+9KNhvvnmCwsuuGC49NJLRww4ljGCAOadd97w2c9+thdlJGnogDqNyOtHo2S0SQcKTflt6EOzLZ+WD64ffvjhCEJt+LJpeWcNZmy4zdeGV7mmodN5VIlbNQ4NnYap+G34Ew0GMm3LR7TED+VD58lPz9r4TDWofJRHVXpFfKCp3HrrrWGNNdaIA4+qtPrFgyZgufTSS4cTTjjB7Qf4Hn744XHw0ZTuPPPMEw488MAeT4ccckg45phjevf96DLoSZ8zyFh44YUj6Kg8KB/KSTKqU/aiIR8aKnPR8/CZamCQUJUW0x2bb755uOiii3prGdK0tEcGxWl4m3v6DabC2tBI06rvaA3oLL6gQs0yyyzh6quvDuPGjQs33HBD5X2/XQLoINqYkSaaaKLYSHuIPkIuvObQ7evwvnYO3T5reo0mxNyil6PMqJyYkryc5tC96EGnizl0LB10Mp6uq0VxmB69XBeL4uiIN9hgAy8WIx2sjzvttJMrTczkbct8o402ioM2McYAlgV8TR3lsfrqq8dpFdFAS/XsO2jf9EeD+mjlX8XvN4deln677baLg7+y55QN7dLTMcCm//B0DLDQ1lsDOoUy6aSThuWXX96Tv2GhRSUF0JdbbrnC/DwrW2EGfQLHKqCz/qKLbWuUtafLgD5yAZ1ypg55Ajp9wfe///2www47eFajnmbYhuj6668/ZH88fUebVe4M0FddddU4Zy6+aD8MjJs69aX4/JgTZuBuF8UpTp08bBq7aNGG96P3xS9+Mdx8882lUcYUoDN3fuedd0ZQXGuttWKBU+gUVupYsICZgDmZImEzYmOBgt0Oo9FrOjIkHnN4zIGkTrShQ6XGzMP8AxqFnskXoC+77LIpmV5cOm5oeI+o3pZhEpAB/e11KBFR5dusoftbUEaShq72bCuEN6BDuwtApx9Du2rjvAEd7T4FdMA37Yfb8Ez9gWZR2TWlC6CXrXIvo5kB3UjmwgsvDFtttVWYeOKJw1xzzRUOOOCAsP/++8e5RUVj4dm1114b9tprr/CFL3whmqxYdAGwW4eZiLkfzFpsuTj33HPDzjvvHL70pS+Fr3/963Hul8K/9957w3777RcoiN133z3GtwMI4jB/duyxx4bddtstbLvttnFUffzxx8cBhSoQvgC9SENnMHDBBReEXXbZJfK96667xgbN+4iG5d/7OgN6BnSPOjWWTO62XQrQCfP4URYAurfJnbn5IkC37zKoHmCJsCfYNTlYxuaRaujwQl9ZtG1tkGyhW/QuAnTtiBpEp99z8W4BvV98+2zMATovX+ZYzLHMMstEDf1973tfYKS43nrrxVXupEHr/u53vxsWXXTRsPLKK0fwZ6HK7LPPHhdxAI5yt99+e5hmmmnCpptuGg477LCA1rzOOuuExRdfPLz73e8OX/3qV8MPfvCDsOaaa4aVVloprL322uG9731vWGSRReKCGtFBe994441jvC233DJsvfXWYamllgozzzxzHHBYE08ZoDNCZXDCgjneh0ELNOaff/4I8pKJfOXt6WdAz4DuUZ/GEqBbeaH9eZrcoX3FFVeE7bff3mbT+roM0CFctX+h37U7dZijbXKWu/JLAR1e3ska+k033VRajmPG5E7hMyoEsNHQAeAHHngg3H///T3tm9WBH//4x8OSSy4ZAGy0cLZ+YM4BjFlAJ4dWzXz2bLPNFoEcLf6hhx6K2j1xAWTM+mjtFAB0ttlmm5iGAYAc5lW2cDAvAn/keeONN8b0s8466xDTeRmgX3LJJWHGGWcMDAhY0csWA+jNOeecYaGFFio09St/Lz8DegZ0j7o0VgFdGnpbGQrkoIOGvuOOO7YlOSS9B6CnGjp9h+ccOjJgrZQ09CEvUPNG8rQaek0SpdGbLIobcxp6qfT+/wMADyAuGg1jGucZAEtB6sfeSMLR8OVuu+22GDbHHHNEAEa7V/wlllgiPmNBCnNOcpdffnkM//znP6+guMjCzsPzADrQYPGeHckWATrrAlZbbbUwxRRTxMGJeMBnymCSSSaJloJehh1dZEDPgO5Rtd7pgE67lLPXXoAu2vho6F0sisPkLt7l23wHXWNFtCb3poCuvKWhWwCnr/ScQ9eiOOU56B2rPG8C6Ezp3nLLLaXkx4yGLgmglQPO6667roKij2mble88+/KXvxxOOumkuNfy5JNPjvNQhGNel8OcThgDA2sW5zkmdp4xf24dWjPhn/nMZ2xwvGYJ/3333Rfn2NG4MZ8DxpiO5IoAnQJEO5966qnDUUcdNYRvzPzkx7t07TKgZ0D3qGPvdEBPZSSAANABOoCj7Q/lAhqcrdEVoKfvUec+Nbk3BXTyRH4AOueKMPUo2WFtJVz3bX0UJ/Zjo6m3paX0TDNgSdB9FR8lLV3lrjqEPMYcoGNiB+RSQEdLxtyOOZ55drRe/QBo5sZZcCYnQMfMnToBOo3UOkzv5L3FFlv0gsmXRXiYxjg0QIvi3v/+94fJJpusL6BTkIx00eSnnXbaODUgntmXyXvANwdMdO0yoGdA96hjYwXQabu2I+ZAHdbXnHfeeS6/888/P4I5/QnO5tWmnPqZ3KvS9V7lzjokLJpMe/Le/DgAh5PnuK4rU6VhoTNpuT/77LMjzXPOOac2vaL8oQm/nLqn/Iri2TDirbDCCllDtxXNArqt5IyOmG8G0NHKOXRGP47bu+666+IcuWgJ0C0465kAnSP6rCsCdEZbLMLDOsAJSORz1113xcZdRUNnzn3KKacMLPK78sorI8+svId3+OZ4W1aRdu0yoGdA96hjYwXQU1lxstkCCywQjzDlGNO2P6YJWbODRufpRiKgY7oG0DlFTnI76KCDhtwTfvrpp/eeK15Vn2lYaJ522mmNadi8AHP4hSZlVYU/4nHyH2usytyY1NABbWnoAnXM5hypyjNGYYMcgE5ctOrUCdAx+1iXAjp5Y8YHuFnAgqkMR7hoSMsnTCZ3uw+dyjz33HOH6aeffog2b/MdjusM6BnQPerZWAV02jm7ZGjPHj+0VraxjlSTu8ccuuob8mJXEluLJTvWSnHuB3JQWBsfcz6LnjHjt6Fj0zLNytGvCqvCK7ugUpO75IA/JgEdszfzValDw8V0zT7v9BAB5lDYby7XT0NfccUVo2mdORfrLKAD0PxYUQ+gc+AN94A6wP2xj30s0mDkLidAt6fckYZRHoMLVkAygrZOlYR4XboM6BnQPerXOx3Qy9ohgF60ULepTMmny0VxTfkinbfJHZBlqjFdFNf2DA5bVlhwPY5+tTQBcuo7zob3k21e5Z5Ih4VqgB/7y1NBcjoc8+QzzDBD3BuOeYWV6cxd8IUgTDZyWuC2ySabKKjnA7jkkQI65m/CN9tssxiXQuSwGQYYNGZMKnx1iHkvgJ64HHyj0SwVirg8w6kSAKY0Eva/U+BYGC677LI470N6eFXcHpPOFxnQM6B7VKmxBOi2TXoDOmUx3NvWqpZ/F4DO1mIAUo6+ss3Rr6KjMupi2xoDkfSkOOWn/FM/BfQ0/pjT0DGDM99iF7hZoTEnjcaLlv2JT3wirp6ksrDIjK/6yLGvHDp8njB1zFvxTECs58zfE77vvvsqKH4Zh0EBX37jGZYD9qkzkFhsscXij4UROPaXs8iNbW9pQWIOwrwGzxwqA8/82AvPvHzXLgN6BnSPOvZOB/QyGXkDOv0DgD4WTO7S0LFGymFh9QB00esK0KWhK59+PmUKoOdta0ZKbBVAU2aOpcyhqfOJVTRdFkMAqAiRs93lmJ+BTrrwjeeAPc+Yd7GOs95tGoEypvSLL744rsrELK/tFwAxWjtgjWMESnq23uGUPt6EEM3tfDmO1Z0ssuAze3fffXejU5hEs6qfAT0DetW60i9eBvR+0qn2TP3CWAJ0lBcBOu/vpaFL4p6ArvJJNXSFK88iPwN6kVRqhFURsiVXNz5pSdMknc13Ql9nQM+A7lEHxyKg0/a9NXTKYqwCOu8OoHseLCNAt/20va5T95UuBfRBNEiXmtxtGp6POZO7FUC+9pNABvT/uA3KRtvX1tRBedSmsQjoyG20AHrR19bqln8Xc+gcLCMNHXl6aui8nwA9PUSsTZ2vC+jkNQjQWTk/Wr6HzldFW38PvU0B5LRvl4AaM4De5jzmt1MOcbU/uws8HYBhF8+0pc370xmzCtbLoVkwDePpKBvWYHg6Og4apadj3YntmD1oM5/qWT7USQDD0/30pz+NC1u9aFIvOYvC+2trbA2zx1k34XfDDTcMfJAFB5/PPvts3A7WhBZpWM3OKnd7hDbtx0NDV//mtcrdviP1PF0UZ58XXW+33XaBY8fLHBp60dfwyuJXCaff8O7b4ZG6lAG9SglMgDgAOpWJUafXjwWMfNfdix506NwBIdEE3Llm3YLC+vnES+OyjoJ1F/3S1XnGOzNIqJNGcVPeFM76D0BD92198uHAIhaRtqVl0wOUtnzss6bXlA9rZ5qmt+l4b9bCsIPFhte5hgb1Tj5pWSejfeh1aJXFhT5raNg9w84e1tLcc889tX5FaTjVkoOroFmXnuKzcJc1Q9DnuGt25LATh2vFqeMDcCwEtn0F7YdyKpNPnXDKifpDmXNdJ22/uNTzOu2cMt16663jgWHQTesQYQyOaJf98q37DCXo+eefd6XJN0rgNQP6BADrKlky4uZHwXv8ACAWLgIYXOvXljYHOVDhoQNNSy+9t8+KrtFUSAOfaKt10xfRhAYjYs4fED35RfHTMOIqvq7x0XzpPNL4de8tbQZcaEJ1afSLzxGo1CPl0y9u0TOlsz5giUagsKJ0VcOgQZ2kfEjDvX5Vadh4pKUeAWZsYdWzIpqE6XmZrzjQ5KRIDr5id843v/nNyr+y+ITzmWY+YmXpEa6fDS+6Jh7fsmAn0bHHHhvT7b333uGQQw4ZQrMobVkY37DgM7G0G8mNNmnvy+SVhkt+osNzrmnf0LTP07RV7i1dlCAGm4No6jkKE0fFMvhR34Nv82XQjtKiNOLfxhl0LR7lM5ABgAelq/ocugzcsU5kQK+CrhMgDp0wh9owgm370wiSiqRRt0bGbWlT2TH1iB55NaEpHkkLCKGhN6FTlIYdFDR08Sa/KG4aZvnime5p+DT2NH6Te9GkkdPR6b4JrTQNAwTKRzTxiSM/jV90r7jyqUccCqX7ojRVw6BBnbTaWtW0Np54wUfT4p05rErhNm6Ta2gy8ECbZkDDzhs+/4w/6JfG414/wISdOJam4svvR584ACPpucZnaoBdRaLZL72eKS98ftQb5CT5UT60I+4VVkWONr7S4VN/GMDaPBRXfhX6Sk8aNHRoKr18G4cw0cVET5mSjvK1z5SG57TLomdpmOha38bRNcoAYG3jtbmGLjwyyM6APgHAelCWzDN1MYfOKM5jHszyz6iQCuXlWCQDn55ztLwz4CuneTzdN/EBIcrI09EgWYTj6bpY48AgjkVNXo45WwDE01F/bJl70KbTZ6Dt6TBjoyl6Og0K29Ckjdh24t13UD7UI5tHG35JSz9Efa/ibL72Ok1Le0QD9nRYOug/PB08Uo8yoHtKtQGtssoEWHgWOvl4N0pe1wJ62bv0E0uaBkDHlO0J6Okq9zTPOvwpLotaPADd8kJH7N2508F5Drjg1xvQGXBRNz0dC7q8AQMtqipgVH2Xth9nUf2RT76eyoDoevcdXa1yt4M48V61LIri0R5pl21cygeDQs++Hd6kDGRAb1NSHaalUWLi8nRoQV1o6HR0Xg5ApyP2BnTb0D14pWw8AN3y0gWgY94D0NNOxeZb5xo6GdDrSKx/3CJAr1NWRXGpl1UBoyh9EccAuj0prmq6IlqEAejQ7GLb2iDeBj0Xz8RLAZ2wqulFJ/W76NszoKdSHmH3dRplVda9R9nkazX0qnz0izccGnq//Ks+o8McDYCeNXS/jyiNRA29qL5SL723RaWAXpRvnbCuNHT6I0+XAroH7ayhe0hxlNHIgO63Dz01uXtUhQzofnPo2eT+W48q2aORAb0nitYXGdBbizATQAIZ0DOge7SErKFnDd2jHmUN3UOK42lkDd1PlqOGUgb0DOgelTUDegZ0j3qUAd1DiuNpZED3k+WooZQBPQO6R2XNgJ4B3aMeZUD3kOJ4GhnQ/WQ5aihlQM+A7lFZM6BnQPeoRxnQPaQ4nkYGdD9ZjhpKGdAzoHtU1gzoGdA96lEGdA8pjqeRAd1PlqOGUgb0DOgelTUDegZ0j3qUAd1DiuNpZED3k+WIpFR0UMFoAXQObCk7iazovQYVQN6H7nsMqAC9SVkUlRV0vA+WYVshgOHp+CRrFyfFcVCPpys6WKYtffqOvA+9rRTHp+9i21oXfXs+WManvDuj0kWh02lybracRydfdrBMHdo2bheA7rnPWbyO1X3o1B1vQKd8oOnpujz6VXXAg98U0D1od9V3MPDy4A+5eR8sA191znKvWnYAOmAp1+b9lTZr6JLmGPEp+K4aZRdHv6Yauipuk+ISoL/11ltNkhemoSPyPvq1C0Dne8ZdfJwlLZ9CIVUMpGw9AR161EmPj7PYemcBw4ZXfM3CaF0ABoDe9qzwlFn6Dm8NnfJ5880306wa36t8vMoGRigf73behYaeAb1xtRm9CSl070ZJR2w1dA/poKF7n+XOx1lo8F7OnhTn1YEA6JSRp6NjHy2Ajgbs5awFxat8utLQvU3uAEbR17zayOGXv/xleOWVV7yKJ9Kh76AdeTmVDwN4LzdaAL0LZQ0rAp+BzR9n8apNznRolN4fZ7ELW9p0GPZVy0zuNk6da2no+F48WkCvw0u/uN4fZ+Fdu/g4C3PofPazrVNZ4NO5e348xwJ6Wz6V3gKGeNezpv5o0dC7UAboO6yG3lam+nyqN6B3cZa7Nbk3rTs2HeXj3bfDI4PDDOhW0iPouotRXDqH7vG6o01D93hnaHRhcs8aulfp/N8crR/FEC1RDJA8HSb3Ig29TR5dmNytMtCGN6XtyuTeBaB7T4kA6PQfng5Ax7qXAd1Tqo60KPTXXnvNkeL4L6N5mrJhDktCG1NcOtLnns7Dc+TOO7c1lcKX5ZWyeeaZZ1qXj6X5wgsvhJdeemlIPm0zoHzsNIvNz15XzYc0zFM2SVuWB+UjoPSiyxoMj/lUyw8aKmCJs+Fl71UlnPJGsypydfJQXHzWYnhNg4kuQNm07xAN+46077J6VBTfpi27xtLz1FNPlT1uFP7yyy8H2mWRq8IncdJ4DBBeffXVIpK1w0QbMIfXDOi1Rdh9AgqJRShXX3216+/SSy8N119/vTvNa6+9dgjNq666ash9nfe45pprwiWXXBJSmnVopHGvu+66wLun4W3vL7/8clea3//+98OVV17pSpP3RqZF71oWXhRXYaSBpnf5fO973yvkUfnW9eGPetTkHcvyguZll13myucVV1wRKHebp3iWb58VXaftjXpZNW0RvaIwypx2VPSsbhi8Ict+dbMuTcXvonwoI9H38OHRu3yoQw888EAG9O7huVkOjOLOOOOM8K1vfavW79hjj43x5dv05513XjjxxBNr0bPpjzvuuLelhebJJ58civKzacuui9Kdf/754fjjj39bXmU0BoXzzvAp/tM8FZ7SSePZ56effnr47ne/68YjtM8666xGZW75Sq9571NPPbWQz37vl9Kx95TPCSecUEjTytJe2/T2Gh4oH2ja8LrX5KUfaeEPmpaHpu8rGqeccko499xzG/EpGvL1frTxM888sxJN0lZ5h7PPPjucdtpplWiKj0E+9aiszAelLXqu8il6VifMypN+qE75VJFlnfIZxLd4pXzoPwbFr/qc96DvuOuuuzKgN4Pb7lNxMAbzIk8//XQ07Vqfa36Y5XWd+piD0zDMMpjj0vA692me2tYhGnouX+F1fPisE39QXG0HI16RXNL08F7Evw1jwMX8Z5q2zf3zzz8f51Or0rD8lKXRtqiy503Cq5ZPFVmTvy2fJvyQpkgWKZ9FcZRfv2eK00WZM3+OSVd54FeVm02ja9LWKXPeW/nh23vRxK/Td0iW8i0de52Wj32WXsOb+Eyfca/n3m2yqHyK8q8SJnnAo/p2pev3borTz6fvwIyfTe7dY/PAHDQPQkR7rYQKk18WT/GrPLdxq1zbvG18G26vbRxd2+dl18TlmX2u9N5+1TxsPHvtyU9VuoonfxAPxKsatx+tujS88u3HU9VndXlP6Sq9/PT5hL4v46ssvIxfxZdfFq8svCxdWXgZneEKb8NXnbRFcYvC9N79nilOmZ8BvUwy78DwNhVlkDhEW/6g+GXP26ZvQpc8la/8Mjpdhg/KO32e3g8Xb8pXPvna6zZ8tKFTNW3VeG3ew6Ylv0F5lsUpSlcUZvOrcy1a8vulLYtjw+11P1p1ng2iyXP9oFsWX+Hy6/DgFbdq3nXi2bgZ0L1KqgUdWyD9yNh49rpfmqJnddMWxS8KK8qrTphoeq5wL8pf+fDMXhfFHa4w8SG/LN9Bz4vSNUlTRKdOWN0868avw8uguFXzJl7VuIPyLHvuQb8tjUHvWUa/LLyrd62bn+WjTVpLp8k1eRflrzD5TWhnQG8itRGepmqFqBpvOF93uHhSPvLTdywLT+NNqPvh4i/Nh/s0DBkUhTWRjRedNG9L116n8YbrfiTw4Pmu/d6n37M6PIiOfKXVvXyFj0S/Do914updM6BLEhPIp9A41Yr9kzfddFPcVvbYY4/FsCKWOPXrnnvuidse2K9dt9Drxi/iQWEcNVh24hHb7tiawrt0rXGLnyKfE6lY2GL3YhOPPcXwxja+u+++2/2ozCJe+oWxoIXFMv1k9cYbb8R38Tznvh9PRc9YfFO2x5lFYzfeeGOU6ZNPPhngt4pTnZRfJU2/OOxHpsyLTrODp/vuuy/WTY4Y9sqzHz96xnkN8IWcWBSFz4/2nzr2fbNN7NFHHw0q7+HilTZt+eOatm4d7/LEE0/EsmZ1tfcx1Tavomv2xCNLyyey7FfnJH/Js4iudxhtxZY5/NKGinggHhhAv/n444/3fRfLp60XGdCtZCbQNY129dVXD5tssknYeOONw3LLLRd23nnntx0+AEhuscUW4ZOf/GSMu+KKK4avf/3rjQ98aPO6HASy6qqrhoMPPngIGSrqSSedFFZbbbWw6aabxvc67LDDCjutIQk7umF7yNJLLx0efvjhXg50PltttVVYb731wmabbRbWXHPNsMEGG8SOvhep5MI2npIotYM5pOYzn/lM/AmE0nzo9HfdddcoT+9Tpqoy/IMf/CDWTbYwWccg5Dvf+U5YY401wqc//elYN6kb7C1v41IZVKFF/dt7770jn+mBLQyaP/vZz4YNN9ww8rnCCiuEI444ovO6qfdg4LjMMssE2u1KK60Ufa7tYSjwz5Yz5Kf2c+CBB8YP2Oj9RU/33v6+++4bll9++R5/8LjLLrv0smHwuc0224R11123135oS3feeWcvTnqR8pzep/F1XxaPnQHIiDKEP/0YoFun9LSrr3zlKzEeh/nUcaJRJ43innPOOb0yF4/0NRwjLAd94lkMoP9kC15dlwG9rsQ6iM/IksMhGPGi2bDfdYYZZogHLyg7GjpgvtFGG0VwIh77FN///vcHNI3hdIyC6WymnHLKCEI2bzS0j3zkI4G9lvDIoQwf/ehHY4W18YbjGk1szjnnDJNNNlm49dZbe1migbD3Gc0CHu+4444I+gykPM4972VkLso6BcLpsKebbrow//zzl4IL+2FnmmmmMOOMM7p/wMWwWXqJ9gAYvetd7wpHHXXUkHgckjHXXHPF8wiQJ/WYeoDfz5XJpF+afs+gd9FFF4X3ve99YZpppolbmWz8z3/+8xGEHnrooVjuDDyJawd7Nr73NYcGUc4ANp21fla7vf3228Mcc8wRvv3tb8d2jWw//vGPxwGTNz9l9NZZZ50IlOzjh0fAhvKUg1/aDwBOeTNQAViXWmqpIUqId/kqf3y2gE077bThoIMOimdMiE9ORixyvMN73/ve2K9Sl4fLHXLIIWH22WePcmQgDJ8MdNmWLIdWTj/F2RvCAMKwINZ1GdDrSqyD+IwebQEzgqRDOvroo3u5oaHNNttsYZ999umFMbKfYoopwr333tsLG44LNG5GmyuvvHLUdJQng4611loraucCRkzdW2+9dVhsscWGfNxBabrw6UgwEdLJfOELXwgzzzxzuOWWW3pZIUs6JfjFIX80NcCqrEPoJW54Uda5MeBZeOGFo+Y4zzzzFFpbxo0bFxZccMGw3XbbhVlmmaUTQC/jj9dFXoAhlqH55psvHHrooT0pUL4MhNZff/0hRxWTht9wOga2yHL77bePAx/27cpRxgDjbrvtFs3svC/73xmUAqLD4ShrBmW0b6Z8+GEGtvUQGaMdS4OjX+B9Flhggc4Gm+m7Y7HadtttezyKT+IhN2Rp2w/8f/Ob34wDZ6yIw+EAdPpITkeDP5QMfHhL6zIW0IUWWii2H0B9OAGdAcciiyzyNlmKR9oImjkyt1/II9xiQlWZZkCvKqlhjMccyrvf/e6oQargaTSbb755mHfeeeMcOnNIRx552AMpJwAAG69JREFUZFh22WU7n78SD4jg5ptvDnPPPXfsBDG70QHpOQdFYFmALzmeXXjhhREsPc7WFt3UJx/9MAHvuOOO0aROngA6fPdzBxxwQBwcDWdjZ0BGR4P2jdaGJSNtxJzPjHl2v/32i/NrXWnoKsNURoRjMWJAgfbwiU98Ygigc0Y+oIhFhk4VjY33Ekil9LzvxTcAiGb55S9/OWqMtB8L6MT73Oc+F0EdAKfj54Qt3iedH/bmUfTQ0N/znvfEtRKpfOAPyxF8Uxet41hPrEyDLB42TZNryZKpEwZwrEWo6hgQwyMHpMiJnu7b+CktBt4AOla2fgNHQBLz9Z577hluu+22KH/msIfLUZa0nbK5feQ11VRTxTZm2w/10zq9v3z7zF5nQLfSmIDXaBfHHHNM7JCYm+JYUUac1rFQZoklloimWTRP5gIfeeSRHqDauF1cs6ADTRuTK5oZ89Cf+tSnelmxkGOiiSYKF1988RCeMMlNPPHE4YYbbhgS3kvofAG4YL5GNjRoRuVo6GWNgSkPTMbMr/brHDzZpGwZDCFD5tAxscKDzZ9Of/fdd48dEoMlTJwAgjcAlcmF90UDwnLAuegMlBZffPEhgM5AiTI//PDDww477BDXgGC5Yb5yOOf6GfBgKWBAhqkSc6wFdN4FEMAsjJWB9sO8L+b3fu/vWeZMqwFCWLEAzAsuuGAIaKLd0k44xtM6LDSEk344HPUSMzFtm3IcNCUBQFJHWD9h668nr5SRLSfKlsEPVjimIjFXMyCycbj+2te+Fi2JLDh98MEHo8JBPzZcDuWGNsu8Oe2DhY6WR6b7KFvWIu20005x/QntB0sSlhwbtwrPGdCrSKnjOBQajYbKx6I4OhzmVdE6VKD4mNbR6JhH/+AHPxjn2vhYQOqIq3Tps6b3jNYxnfNTR02nxEI+HPkB3HTuaCLW0SERzkcJunZoMYAO81SMcpEhgM7o3DrJBwCgsdHJtv0im6Xf7xpgZDEjmjdmXxyLyphHE1+EMR+86KKLRoAinN0NaOjWNNcvn7bPGDig3aBlaAoF2QLecsiZsl1llVUi4ACQzBPOOuussQ7zrl065MI8M6Z2Bh846gCWovQrWTxHnpQ301cf/vCH44dRrMy75BV+mJemrLEgMQWASVbaGH0AsmRAbB3vQzhph8PRjhlssEZn7bXXjrK1C96svABK+gDqSZcWuPS9WT2OnKhrTEOiBbOw1H6hEssG5m7qJA75Ui+weg2X+9GPfhTLje8poDBQ5nbAxiCNsmVgwkd14JVypn7utddeQ/oD8WzlrzD5GdAliQns02GiKbJ6nIpKgdLpy9FwMA8C9Jg0qazMY1NBhmMOnQUd008/fRxlspiIDyEsueSScYDBSnKAUx0PnbytdNLQ0xWoejcvH6sB1g0GO8zpwScyRCti10CaPxob8bfccsv4dTvLsxdPRXToWD7wgQ/EeWfM2ciS1fYs0ELToB5Q3gA8K/Qlb8zJU089dZzSaLJgpoiXfmGsdkZ2+++/f+QBPqiXyIwOH7Bm8EaHhHWJ+WAcpkM6V7S8IlOjp5wZdKAdMtBloRayZGDMNAALktDKcMRDg6fzp9zpSNGEmOZAS8KJL/kx0PEP4EZGtBXKGC2MwSadPg4fWWJhso53QItjDr4rp3fGZ9oHCxIDR6ZQ6GOwZmjgIR7Qklk7Qd0dzi2A8MiP9k6/SVuhD6Ku0vfgGDx97GMfi4N76gV1d4899ojthzVAAnm9S1c+U6OUOQMN6h1ypM5pUIHVkjJnXYossvgoS/QRdT+zmgG9q5JsQZcCpSPHvK65NkZtVFh9jxmNmcUerHJn0Uzq1EDT8Kb3aBWMwpnHBwAxc1Hh0MSYS6eCajEfnbt1DFDokNg336VDbvAlkOaaDmfSSSeNgx++diS5oJkzV8ioGZkq3Ju/IroMcFgIg4lSsgSQMBPTOTJ1wdwa/GGNURwAiXlKplpYZ9G1w4xNmSt/yh4eWaBF50jdlPUFk7x1TMswNyhrjn3meQ0wokUiE8qSMgeoKXPqgfhC+4Efpq1wgBYDUOovVieVk3xPHstoofXSmQuEaENokHbRIfwwaKL9DMcgLuWVQRtyZR2K1X6ZAmLNAgM3gKpIbkVhKf2692U02aLIYJe5clxR+0EBov3QJ6AQDYdL+WXASVlS93D04dQBVuFbx8AeXtOtlzZO0XUG9CKpTOAwGhGmTcyIMlkCRhSwXbTFM0ahzHl17dBwsB7QIWJa4wfAsDqTcObO4Ie9oYRZx2iTaYQibc3Ga3tN/mgN8MN8JDxiamf1Og1GsmNkzyCE9QDpPGtbHvqlV+PGXJjKkn2+mIDhGzkxYLNxCKeR02lhOu4aKHkP5MU0hMobn0EcYK5OHD6wiDDXbx2DTOomsrZOMrBhba4BZnhEVuITk+bkk08eF25KE2LRIR2pLW94wVTLdMtwO/KWuZV5VRwDJAYhAI+VE+Z5ZKlpj+HmFZ4Y+MgCQ1unfJEdq81HgoMPBnFYMHHUO+qF7a8we7MriKkr5tsnhGOwNskkk8T6Sv4MkrB6Mb9uHdYbwqW122f9rjOg95POMD3DpMZ8KR0SWiwrRmVaFQta0AEQMVKnA6ODZ2GIHd2pI5Cv9F34zJuiGeGUH/tVsRqwyItGhmkWELA8dsFLGU14oCO329aYomD6AAsI32VmsMQPs5zdE1xGs4tw5qnpNFOzps2LOgJQMbiaEA7wBLytBskgivpKGTOfyXTQpZdeGt+F+jmcTnWQqRXKXOsT4AFNiPUHrHTnms6e6Q7aD3V1OBwm9fvvvz8OKjgXASsclhmrhXF4D7JEk8NyhHULmWNSHg6HiZhBMANIKyOmKihrHFYZLAkoHLb9YNoWUKos2vJcRofBJrsVaN+Yz7EUsCUQ3soclg7AdDhXuTP4pr+GT6xEnNGB2V1Azfux24LpNgaitB+mVpiustuWy94pDc+AnkpkAtzTAaElMBKmsDG1Ms9jO240NjpMnvMDSDHH0qDK5lnKGkOdV7Q07DU0mNNlntU6KiojYb0LZjnmsRmJpultuq6uabwsOLENnTlTtvthTWCBFCY4fixIHM7Gbt+ZKQ3y7wforChnEDWhBh3wRp1L53jRgqkHmOcxf1Pm1N/hsCJYGXJNHaMTRcu1QMlghO2TlDPth3KnDdGZlvHpWV+hBTjTtsmf9s70ANuuBJTwTzunLtj2w0ppa+5O39nzHu2b8w4oR3jA2sYaCuap5TiwifZDXbTth7rBFIin3JRn6rODBVlS1/jBB9MryK/MIWsWojI1OFyOskOG8IiPZSOdemQQxICe/khyZ7GkrEt1eM2AXkdaHcVl8QmjOICGSsf8SlEnA1iixWMyIh4j/uFq6Hp121hpvEVbQDApMw/MCJr3sgBk04tmlz4dlMzYygf+ZA1hMY9+NDQ6/gnh6GQwY9vOHT6svMR3P9Dvknd4QYOQFmbzYvCJpoR2hwZcFMfG7/Ia0zTlS9lbx1QGJljaDusYhrv9ICPmzWkXDDow/6MRp45FcyxGIx6yLOoL0jRt7m0d4xq5kDf9EX2RBRaewx9tivaitkMaroer/cADAwv4pD/EYqkpgTJZlNWLsvge4ZQxcoRPFsNiWbDyJg/ukTGDFNoP8azM6/CRAb2OtDqMS6Eyh6ZFcGlWthLQ6aedun2eph2O+6L8y95F/BSl0bMJ5Y9EniaULOrkK7ml9bIOjeGIC5/8BtXNLnhR3shI1/3yKeORtF078rb52zz78W7jdckj+dAPMiAq47PL/OvQhj/bLvrJyMark4fiZkCXJEaQ36/Ai9isG7+IRtWwKnlViVM1P494lh97PYh2nbiDaA16rrzkp/HLwtN4E+JevMmHB3s9oXgq40Hh8sXfoHvFa+OnFpiqtFLeqqarEs+DtgeNKrz2i1OFB+JUidcvn5H8LAP6BC4dVS75Yie9J7woTPEnlF+FpypxJhT/Nt+RzudI58/KkuuRwq/lw15bfhUu3z7zuq5Cu1+cfs+8eCyjQ97KX35Z3AkZ3o+3fs+Gk+cyPgjXryk/GdCbSq7DdGUFriwHPVe84fKL+CkKgx+Fy++ax7J86oZPKD67zjfTH14JlNW7Mi7qxi+jUzV8uPMbxNdI42cQv1Wed/lOGdCrlMAEitOv4PVM/gRicWC2I52/gS8wAiIgwwkpxwmZt7f4q7xLlTjefGV63UtgLJRrBvTu61HOIUsgSyBLIEsgS6BzCWRA71zEOYMsgSyBLIEsgSyB7iWQAb17GeccsgSyBLIEsgSyBDqXQAb0zkWcM8gSyBLIEsgSyBLoXgIZ0LuXcc4hSyBLIEsgSyBLoHMJZEDvXMQ5gyyBLIEsgSyBLIHuJZABvXsZ5xyyBLIEsgSyBLIEOpdABvTORZwzyBLIEsgSyBLIEuheAhnQu5dxziFLIEsgSyBLIEugcwlkQO9cxDmDLIEsgSyBLIEsge4lkAG9exnnHLIEsgSyBLIEsgQ6l0AG9M5FnDPIEsgSyBLIEsgS6F4CGdC7l3HOIUsgSyBLIEsgS6BzCWRA71zEOYMsgSyBLIEsgSyB7iWQAb17GeccsgSyBLIEsgSyBDqXQAb0zkWcM8gSyBLIEsgSyBLoXgIZ0LuXcc4hS2DYJPC///1v2PIaroz0TvKL8u33rCh+kzDlIb8JjZwmS6BLCWRA71K6mXaWQAUJVAWINF563y+rruKmedbJJ01b5b5L+qL9r3/9K/zlL38J+AqT34/HKnH6pc/PsgTaSiADelsJ5vRZAs4SuO6668Kee+4ZHn744R7lfmChZ/J7iZKL4Xx+9913x3e4/fbbEy6a3w7ivznl8SkB8HPPPTdsscUWYa211gqbbLJJuOCCCxqThV/Ls67lNyacE2YJlEggA3qJYHJwlsCEksD+++8f3v3ud4cLL7xwIAtVwKEoTlFYWWZ14orGKaecEt/h2GOPHQJqel7H/+EPfxh23nnncPHFFxcmgz/xKL8wYp9A0l100UVhjjnmCIsttljYdtttwyc/+clw8MEH90lV7VEdnurErZZ7jjWWJJABfSyVdn7XUSGBr3zlK2GiiSYKZ5999tv47arDr0JXceSj0b766qvhrbfeinwqnJtvfetb8R2OOuqoIe9g4wx50Ofm+uuvD9NPP33YZZdd+sRq9+g///lPWH311cPkk08ebrjhhvDb3/42/OpXvwq/+c1vSgmXvYvC5RcR0LO//e1v4a9//WtvQFIUN4dlCVSVQAb0qpLK8bIEOpSAOniy+OpXvxrB8JxzzinM0cYtjJAE1o2fJI+3oiH/v//9b/ja174WNt544/Dyyy+/Lclxxx1XCOhvi1gh4LnnnouDm/vvv78wtnhK/cLIBYGke/3118OHPvShMMsss4RXXnmlIFZxkPIsfto/9IUXXgibbbZZ2HvvvftHzE+zBCpKIAN6RUHlaFkCXUtA4ACgTzzxxKEM0P/5z3+GZ599Njz99NPhzTffLGTr73//e0D7k3vjjTdimiLwVRzl/7vf/S7SZmEYDk2cfNBicfholQsttFCYZpppoiZLXuQpJ0A/+uije9onAIbm+49//EPRKvn//ve/oyXAvg8JCYcvfBz8wzsDAHiu6kj//PPPh/e+973hgx/8YHjxxRej7ERDcsEnP+SO5k459HNYL379619H2gyArCPPRx55JEw55ZRhhRVWiHTtu9i4+TpLoKoEMqBXlVSOlyUwTBLop6GzYG7llVeOwINGudxyy4WrrrpqCGcAzac//emw5pprRiDEdL/44ovHNPPOO2845JBDCsHoxz/+cdhyyy3D7LPPHuMussgi4fTTTw+HHXZYWHbZZcM999wT8yEez6aYYoowySSTBGguvPDC4TOf+UwQcAnQMb0DajybddZZI9111103PPnkkz2gH8J8wQ35LrPMMuHrX//6kKfnnXdeDEcmTz31VNh0002jlg0ob7TRRuFnP/vZkPhlN2eddVaYb775wqSTThre9a53hfnnnz++j50uAMxZMLf00kvHd/jABz4Q5Xvvvfe+jSwDm2OOOSYssMACgXi89+abbx55VGTK5OMf/3i0Ykw77bRxcIQMjz/+eEXJfpZAbQlkQK8tspwgS6BbCZQB+tVXXx21yCWWWCKceOKJcZ567rnnDu9///vDAw880GMKQJ9rrrnCjDPOGPbYY4/wvve9LwLcTjvtFAFvuummiwvAeglCCL/85S8jqEw11VThi1/8YgDkDj300AhgM888cwS7a665JiZBy95tt93CTDPNFCabbLK4gIx8AG9pswL0z3/+83EwseCCC4Ydd9wxgiDz1KutttoQjd7ykl6TL2C73XbbDXl05JFH9sKxFjDI4B1XXXXVONhg4GCtAeJtCJEQwq233hq23377wLtjcYAG7/P973+/F/W0006L8mSQdOqpp4YjjjgiAvvHPvaxOGBRRNYTsENhhhlmCJ/73OfCGWecEadQAO0VV1wxbocj7m233RY++9nPRkBnAEKa3XffPVx77bUilf0sgdoSyIBeW2Q5QZZAtxIoAnTmddEiAe+f/vSnPRPtjTfeGEEVcJADxAB0zPYf/vCH4+rwP/3pT1FbB5hYcGfjA3Rf+tKXIjgCZJiK0TKZV/75z38eAZk0DChwgNZrr70WNVlMxpiguSe+nAB96qmnDttss00EPehiEoc3tHu06ioOQCd/AN2CMoBOOHnssMMO4Zlnnol8YD7nvQknbJBjAPSHP/yhZ3JHVryPBgO8H3PrDBqgLdmceeaZ0UJx0EEH9bK444474qCAFfJ//vOfo+meqQvkivZ/ySWXxLjk+dhjj0X+sbKQH/HstEWPaL7IEqgogQzoFQWVo2UJDJcEigAdszJaKiZxmbXhBxDFpAuACVABojnnnDMCCPPwAibijxs3LoIIGrIcYAZgoVX+4he/UHD0yQstG+CUhs4D5oAxTaPVAnKpA9AZULByHIC0jryhBy9VHAMJ4jPosA4tmfD11lsvAqLAHh+Q5NkTTzxhkwwZENgHgC9z6ExjSI56zhY83uWAAw5QUPSZR8fawHSEyoRBB/kKuJWA/fjpOzB1AV00d/Gu+NnPEmgigQzoTaSW02QJdCiBIkCXNop2u/7660cQw6QMmDGPjembxWA4aeiY1tGIrXv88ccjsKyyyiq9YA6w0Vw4mmMKLmjYgJGdq2dhXBGgK6009MMPPzzmo3Bu4Bt6dpqgx0zBRRmgSyaY+nE2D8zu5IEWXMUx6GBqQYBuabEnHVrMiW+wwQZR5pTBGmusEQEZk7kGTcyxExeQJy7x+GmAYeXO/nrisihOzuarsOxnCVSVQAb0qpLK8bIEhkkCRYDOFjG0OYEKYLHhhhtG0MDnVDOtYAdcPvKRj0SN+6WXXhoCdEWAfuedd0ZgWXLJJXuapn1VAZpM7jyTho7JnTn11ElDZ3FY6hiEAGRegM56gtTJClAH0KWha3W/aLLAEH6Rj5U7QI3sGfBgKkdLp3yIy4CCuPop7r777iuyQYC+/PLL98LyRZZAGwlkQG8jvZw2S6ADCRQB+je+8Y0IFCyc+uMf/zjkB5CjYeqAFwAdkzsaOluwrNZXBOiEMVhgEAAw2fi8njR0TO56ZgGd7WgKlzikobNtLXUAOvlVBXQsA4BkanKXhg6gp/kL0Hm3QY60RRq6aDI/T/7kk8qee9Y3KC7gTNxbbrkl0kzj28HCj370oxh3pZVW6rEoOr2AfJElUEMCGdBrCCtHzRIYDgkUATpbt5hD51jSItC1fAnQOV0NQLcuBXQABEDC1Mx8MNq6dYAux6ECUtLQSQOgo42SRhq6wAh/EKBDryqgDzK5S0NX/vAvQG+iobPH3jqO4IVftO1BjkNiiGs18bI0P/nJT2JczPSW97L4OTxLYJAEMqAPklB+niUwzBIoAnRAep111olz5SzO4hAS6wBuHYRSB9ChAZhAE62Z7WXskUbDZPU28+RsjQOk2FJlgYf98ITbxXLiqQtAL9q2Rv4CdOWN7wnoaO9siWN6gS1rDGZwyILf73//+551hB0IzMVjvmcho3VaTa8wLBtsZ2OvOjSyyxJoK4EM6G0lmNNnCThLALM6QMW2KOsAC+ZxMaVj2mUrFPuXOUSFQ0kECgC6NG5WsFv36KOPRtos0rIO0Npqq62ixs3+dUCJ/esnnHBCz+QOyFt30kknRVqY6uGZezlNEXAoTepYTMb73Xfffemjwvsrrrgixme1vRxAyj556DB4SB0LzXjGaWxVHKZx5MreemsWJy15cYAMe875aA570Rl07brrrmHttdcOmMyROY5pDz4iA6Ajw0996lNhn332iXvwiWe3CzIw4DkLElnpvtdee8Vz5MkvuyyBJhLIgN5EajlNlkAHElBHjobMYisOH7EOsGBPNEAGoKM5L7roolFzJwytHRpo6miznBPO3m/r2JcN7XQLFnEwvfNhElaNs1WLs9OhyYABcHzooYcsqXj8K6epMcjgtDj78RS0dtJdfvnlQ9JwA8jzjD3uVdyDDz4Y45988slDol922WXxXVJNmEgHHnhgfIa8qjjM7ICtFrgpjcoE8GXOGyBfaqmlwjzzzBM44If3YNCj9QukA9yZIuGAHqZIKCfScFrepZdeKtKxrOCPOXosI1gBzj///N7zfJElUFcCGdDrSizHzxLoWAKAKBojJtrUATCADyZ29kGzB5xFceyd1l5o0rCvmp8ASXTYboY2nmqhes5ggHPfOTcdkIIXNFO0V1kAFBefuPDC2fIMCOSY5ycfaa4Kx4d/nulsePssvdYABXmQl3XwCB3ySh3vVzUP0pIP8rIL3FKayBe67BzgfTGZkwcysg5avBuHxSAz4mIpIW56Hj00GXTpnPuUlqWbr7MEBkkgA/ogCeXnWQIdSsACrr0mS+7TMMuKfaZr+TZelWtA+a677hpyqAoAt99++0WTMNvirBYqmoPyK3peFCZ6Xn6TPMrSKNz6uk75LQu38frF6ffM0sjXWQJFEsiAXiSVHJYlMIIlUKXTrxLHviKmdubMMeGzmpujSzEDo5ljNuZUs340+z2z+SiefPus37XiyyeuvVbaojA9a+NXoas48ovyS5+l90VpcliWQFUJZECvKqkcL0tgBEkAICgCgzQsvS97BbaecQjMFltsEdZaa614MApz8PpamjXnl9EYFF6Vl5RO03QpnaJ7S5tre18U34ZVjVs1nqWdr7MEmkggA3oTqeU0WQITSAICB/lt2LA0MKczR808L3PyzBNrfph4Nm5RnnouvygOYYOee6cro1c3XHzLT9OXhROv37OUTr7PEmgjgQzobaSX02YJDJME2oJC2/TD9Jo5myyBLIEWEsiA3kJ4OWmWQJZAlkCWQJbASJFABvSRUhKZjyyBLIEsgSyBLIEWEsiA3kJ4OWmWQJZAlkCWQJbASJFABvSRUhKZjyyBLIEsgSyBLIEWEvh/g567YOiltfsAAAAASUVORK5CYII=[/img][br][br]Estimate the median lengths of male and female humpback whales based on these samples.

Information