IM Alg1.3.8 Lesson: Using the Correlation Coefficient

Match the variables to the scatter plot you think they best fit. Be prepared to explain your reasoning.
Priya takes note of the distance the car drives and the time it takes to get to the destination for many trips.
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[/img][/center][br][br]Distance is one factor that influences the travel time of Priya’s car trips. What are some other factors?
Which of these factors (including distance) most likely has the most consistent influence for all the car trips? Explain your reasoning.[br]
Use technology to create a scatter plot of the data and add the best fit line to the graph.
What do the slope and [math]y[/math]-intercept for the line of best fit mean in this situation?[br]
Use the applet to find the correlation coefficient for this data. Based on the value, how would you describe the strength of the linear relationship?
How long do you think it would take Priya to make a trip of 90 miles if the linear relationship continues? If she drives 90 miles, do you think the prediction you made will be close to the actual value? Explain your reasoning.[br]
For each situation, describe the relationship between the variables, based on the correlation coefficient.
[size=150]Make sure to mention whether there is a [b]strong relationship[/b] or not as well as whether it is a [b]positive relationship[/b] or [b]negative relationship[/b].[/size][br][br]Number of steps taken per day and number of kilometers walked per day. [math]r=0.92[/math][br][list=1][/list]
Temperature of a rubber band and distance the rubber band can stretch. [math]r=0.84[/math][br]
Car weight and distance traveled using a full tank of gas. [math]r=-0.86[/math][br]
Average fat intake per citizen of a country and average cancer rate of a country. [math]r=0.73[/math]
Score on science exam and number of words written on the essay question. [math]r=0.28[/math]
Average time spent listening to music per day and average time spent watching TV per day. [math]r=-0.17[/math][br]
A biologist is trying to determine if a group of dolphins is a new species of dolphin or if it is a new group of individuals within the same species of dolphin.
[size=150]The biologist measures the width (in millimeters) of the largest part of the skull, zygomatic width, and the length (in millimeters) of the snout, rostral length, of 10 dolphins from the same group of individuals.[br][br]The data appears to be linear and the equation of the line of best fit is [math]y=0.201x+110.806[/math] and the [math]r[/math]-value is 0.201.[/size][br][br][center][img]data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAPkAAAJ3CAYAAABWavcRAAAgAElEQVR4Ae2dwYs1S3nGZ5OFf0AW2QmCiyQwkqxciK5mEoggZuG4URFkwIVZxIEsQhazMwERosgV1IUjuHChoCtFQWehi2SuOwUX4XLJIkwIgTghUdPhPeZ38n5ldZ0+fbqr663zNJzbfbqq3nrf51dPdc/c7373YtAhBaRAtwr89Kc/HS58db/4xS8GfaSB1kAfa8C8nTW5N72u4ypgRtVxvgrAXybveA0AueMSVVpBAfjL5AWRojcBOXodyn+eAvCXyefpF2IUkEMkqyQXVwD+Mvni0rYTEMjtZKRMaioAf5m8puqV5wJy5Wk1XSMKwF8mbwTIGmkAeY3Yitm+AvCXydtnNTtDIM8OoIGhFYC/TB4aYzl5IJd7qbVXBeAvk/dKeBh2f3Kx4/JU2gEFZPIDAvXQDOQealENxysAfz3Jj9cuzAggh0lYiS6qAPxl8kVlbSsYkNvKStnUUgD+MnktxTeYB8gbTK0pG1AA/jJ5AzDWSgHIa8VX3LYVgL9M3jank7ID8klBNDisAvCXycMiPJw4kA/3VI8eFYC/TN4j3f+rCcgdl6jSCgrAXyYviBS9CcjR61D+8xSAv0w+T78Qo4AcIlklubgC8JfJF5e2nYBAbicjZVJTAfjL5DVVrzwXkCtPq+kaUQD+MnklICb05eXl7mPXNQ4g15hLc4wr8Pz8PFxfXw8XFxfD4+PjeMeFW+Avk48Iu7Qpl443kvYrt4H8yk19WVUBM7GZ2Uxt5rYD9nb/4eFh1fl9cPg3YfJf/epXw69//etdfv/zP/8z2PetD8DY09euTz2WjjclHyBP6TuljzHiM6X/OfbJmXwrHeC/qcnN0O9+97uH3/3d3x3+9E//dPjHf/zH4YMf/OBuJ/zoRz86WPuh4/7+ftffdkj7+Fci/5pk930bcYFC++3t7fD666/vXqu5Z2d25rH5cnP5XbsHk//N3/zNTsP3ve99wxtvvIGEwze/+c3hIx/5yP772AV8vK52bZp/6Utf2sW277zSek1NdzvQkRivvfbajhV8mBtO9GO8j2FjvvGNb+znJYbP04+zsb7NYjPG2tK1ZO1W25tvvpl9XX95edm1k6OdqZ06TjlvbnIz8F/91V8NX/7yl3dPh69//evDH/zBHwwf//jHh7/+67/eCT/liZ6KjlDpYvBC2hg7cn0OmXxsvnRRMV8611JvBlPgA3lK30N9/vVf/3V417veNfzXf/3X8Pu///vDV77yld0Q4/gnf/Inu3uHYuS0M51SI2AsTINmOV7obDHMNDnjpH385kFb6ZwyTPtidPL17WltmDhXC3Ue0nFqO/xtrt/6f6FNDXJKPzPwn//5n+9f0+0p8Yd/+Ie7V/W//Mu/HP7iL/5i31aaxy8cBLT+mA4AHj73GMt3Pw8QUuEZYyD9fLZwbA4O5mfxjcWj/xpnIC8R+2//9m+Hb33rW8N3v/vd3Qb8ta99bRf2P//zP4e3vOUtO17HzIOOXl/uoVn6HU3h5ZkyxhsNPsSBmTc5G0rax8emj9Xn32BgSlxrZ35ytHt+PnKiFl//MfpN6Qv/zUxuSfJzuJ3f8573DB/72McmvaL7AoEzJqoHBACE9ZAMlI9BG32ZMzcfbYCzWHxYfGPxGLvGGchLxDZG9tT+xCc+Mbz97W8f/uM//mMX9jvf+c6uVnvtnXrAwZvDxnqNnp6e9q+y1t8bxRhwwAOdYeBZpmP9d0xHTn5cGtvnCF/OpTjpfP67r4WaljrD33Td5EnuC7Fd83d+53d2r+7+/pTrHAgvYsnkFt/3NWBA9gvOrjly8/ldH+icWXxj8Yi7xhnIS8W2t68/+qM/Guytiw367//+73cm/+///u9J06CD6ZMucK/jpz/96R0LeHhOflzKY02TsxHA1p9l8gx+WySf//znhx//+McDT4Of/OQnu54/+9nPdk92++LBe8MSMoXM/RS2j+MXjt23wy8+A5Z+J25uPvr6p37aL9eHmGudlzb5P/3TP+0M7X8e/9CHPrQzPqanbjT2tXmj5lha39RI9Mvx8/fYTP14jEdOZkq75/OgD+N83owjNmuKnEpx/FpI+/m8fT+v1RLX8Le1t8mT/Itf/OJuwXzuc58b/u7v/m53bU8KeyW037TbBmAHAo2JkYJAHEzld1uuActY7tsZyMxLG6AZw3efI339mX7kM1YHeS95BvJSMf/lX/5l+L3f+73hs5/97C6kPb3/+I//ePebdeNmB0YwnfzhF7bXx67R3PqnusPK2jBiOt6+o3Npninm9LmkrPmem5884Uwfm9PXNNbP+i+9NuC/mcl//vOfD29961uHd7zjHYM9DT784Q8Pn/zkJ4f3v//9O8OzaAALRL9w7Brhc+1e3JyIjAVIKjJzWzvxGcN38vF9rT+LnUUD/HQOxq9xBvJSse1p/ZnPfGb4sz/7s+Gd73znjp/Vaq/sdqB3rsaS+dCIPNEu1djavc42zl7rPZ80hrXZx7hxkKfdt3h2ENfnkrJOa7i5uRnsk8ZnnN0fM7nN6fOwvj4fcj3lDP/NTG7J25PbXtFt8djnH/7hH3b3ePWzPggGjFOKPrexQF6q7u9973vDL3/5y+Hf/u3fhh/+8IfDV7/61eFtb3vb8M///M+7KdjIbGHPPYiRW/BmMjMGhzfdKXMSr7cz/Dc1+SFR2elyO/qhsWpf9n+u8O///u+7P7Rk/xbEDmNjb2H2W3U2ZTNa7ik+hYU3rBk8x9xvADz57Dx3zil5Re4TwuSRBW4hdyAvkYs9wc1Q9vT+wQ9+MLz3ve8dPvWpT+0NvsQcvLXlDG7xcyb3r9dL5NBTDPg3/STvSfAtagHyUnPbn0q013Mz+/e///0m/huDpWrrMQ78ZfIe6f5fTUBeskR+IbpkTMVaRwH4y+Tr6NtEVCA3kYySqK4A/GXy6tLXmxDI9WbUTC0pAH+ZvCUqC+cC5IXDKlwQBeAvkwcBNidNIM8ZqzHxFYC/TB6f5WgFQB7toIauFYC/TN4xZiB3XKJKKygA/0VNbn/iyf4dakt/xNAKtD8RdeyfimLc2B/MKGh7chM6nvpHeYF8ckILBTj0h11sGv8n36x/6UCnY9cbeRyKX5p7Tht/grPWH+CBfzcmH1scmPVYk9vCqQUjXTDUcur8QE7jb/UdU5bqonZ7WGDCMYbEO8bkGO2YMUvqRS015oe/TJ4hWBNEZvrdLZ42pzzNgTw2R4v31zb5Erqeohv1lTa6U+L7sfBf3eTsnLYz28e//mImK9j/rZnWL13c9CWO/1s67W/DtLi0cbYYjLMnuc1hZ2svPdltnPXhSQIYu2cxLN/cHHbPw2PutD76sOBsXLqzk0N630M8dA3kQ/3mtJOf5W7XdlAPOXv21ka7XwNeW4t1d3e3Z2n90dDa+KCfzWP37Mw138dqsn6ePfGPZUT9VoutRXKjNp+P1eEP2tDNty15DX+rcbG/NILk7WwHAiIAZ4Twi4A2zoAsxbG+Fsv+JlE7M5aziTiWA2NtkaUHi9FDoDZil85T6suN94uBvImV5jjlO5Cn9D22jzcneaMR7KiB7+hKTT7GmB7E8O3EYz7fxrVnR23Mx3i7X1qDxPJnas3l5ful1z4fdCAW+S19hr/luprJgUAxXlAr2n+3vnbYfQRCGOIAB1jWL7dgmM/ieRjct7ON9Tu6F9jmS9vSHHxccicueebqo4/Nb/X5WohjuTCW+nx+U6+BPLX/sf3QxM7k+4EPfGCvHbVSF9+pyWtobXaU7h1iQg6mLXP6mmhnfmvjnh9DniVGuTzRgzx9bOqzOVnj/p7Pc6lr+Fuuq5jcF2hipR8r0PfB0Ok9/92LYtcWE2DeLL4fMBDeBMzd88IaLN/f2gDI4vF5kXsaN9cHwGwEFjutxe4xlvp8flOvgTy1/7H9qMVytL9d1TTjxyirCc3gkdbJd69FjmOqK3kSHyZ2P3eP/jlNuYehrS91+bzIFR65nNI+Y/kQ3/qvecDfcpXJE6VtocjkiSiZrxjEzGB/DZNpZgvYvvOztdcxNQHfvZlk8ozQM2+tbnIPy++0Pl8Wid9F03s+DovB32Nn9ff8fLkdN3fP58XiswXLkT4h0jytXxo314ddnFpsHPNRi4/l75HL1DOQp/Y/th+am5Gvrq52b1VWs+Vs3+2+zz+tEy2Mv7XZgYZj90pMbHzKyddEvl77uYzIs7SJjeWDDtTsc1zyGv6W6ypPckvWQzRofACfEzh3byyOxSOWF9TuI34ORu6eF5f5PIR08eTyTOPm+hDbLzSg+1ro53PwOU65BvKUvnP7kLtpzubq7/n8uU+dmI51kZ4Z63W0PoxPmVgNuXu+Nmtnbdh9H9s0twPtS4xS1jYurc/u5fJJc9hNusI/4G+5rmZyyxsxPEDEywmcu2dxEN7i2Pjc39LpxwKS+fnuc/L3vMaMMRgcKSw/F4uDccTN9aEONLD4ucXBPWKTxzFnIB8z5ti+1GxcyJUa0YGY1IRJ7X5qdGvjX0l5/Yl5qsnJgVznMqJuXyOxfX3puqFezx99lj7D33JdzORLJ0k8E8ZgcCCUAfcLgfYlzhbXA1wi5tQY1HfqQgDy1HnPoR+m9kasWTebw1rr1tcC/xAmRxgztf+saULm3GIxsPvztPHgjrkG8jFjzqEvT1w71zzYYE7dvKfmDP+wJq8l1FRBW+wH5BZzU07rKwD/ECZfX44+ZwByn9WpqkMKwF8mP6RU4HYgBy5BqZ+gAPxl8hNEbH0okFvPU/mtowD8ZfJ19G0iKpCbSEZJVFcA/jJ5denrTQjkejNqppYUgL9M3hKVhXMB8sJhFS6IAvCXyYMAm5MmkOeM1Zj4CsBfJo/PcrQCII92UEPXCsBfJu8YM5A7LlGlFRSAv0xeECl6E5Cj16H85ykAf5l8nn4hRgE5RLJKcnEF4C+TLy5tOwGB3E5GyqSmAvCXyWuqXnkuIFeeVtM1ogD8ZfJGgKyRBpDXiK2Y7SsAf5m8fVazMwTy7AAaGFoB+MvkoTGWkwdyuZdae1UA/jJ5r4SHYQByxyWqtIIC8JfJCyJFbwJy9DqU/zwF4J81uTXqIw20BuKvAdsesiaft29oVGsKsJO3lpfyqaMA/GXyOnpvMguQN5lck26uAPxl8s1RrJcAkNebQZFbVgD+MnnLlE7MDcgnhtHwoArAXyYPCnBK2kCe0ld9+lMA/jJ5f2z3FQF5f0MXZ6UA/GXyjrEDueMSVVpBAfjL5AWRojcBOXodyn+eAvCXyefpF2IUkEMkqyQXVwD+Mvni0rYTEMjtZKRMaioAf5m8puqV5wJy5Wk1XSMKwF8mbwTIGmkAeY3Yitm+AvCXydtnNTtDIM8OoIGhFYC/TB4aYzl5IJd7qbVXBeAvk/dKWH9pRMdkp5Umk0/TKXQvIIcuQsnPVgD+epLPlrD9gUBuP1NluIYC8JfJ11C3kZhAbiQdpVFZAfjL5JWFrzkdkGvOqbnaUQD+Mnk7TBbPBMiLB1bAEArAXyYPgWtekkCeN1qjoisAf5k8OslC/kAudFFTxwrAXyY/A8gdl6jSCgp0afKHh4fh4uJi/7m/v39FgpeXl+H29nbffnl5ufs7qel0qJ1+Uc5AjpLv1Dyfn5+H6+vrHUdjPnbQz5gbWztsTfg1wrXFs/49HfDv5kluhdzd3e1hAhijY2C+G0xbIMA91B4RPpAj5j6Ws3G9ubkZnp6edhv2mMk9T2/yXFxbE35d5PpEvAf/bkyeg+BNjOkfHx/3Xa34q6ur3dP8UPt+UKALIAdKeXKqmHjM5HbfjGvnksn9Gpg8eZCO8O/e5B6wQefJbZzs+zHtQdju0wTy/kZHFyWT20YO50MmtzVgnx4P+Hdr8rFFYAuAn8NycA+1R1oMQI6U89Rcx/jyOm8L246SyXt+ilvt8O/W5Dm4/snNImHHN1EOte9WTaB/ADlQypNThZ9x5sjdy60D+hvv3EZPe/Qz/Ls0uYH15jVY9oROf5vOz+HW/1B7ROBAjpj7oZxzhvZvYbyt+bO1c+R409bLGf7dmTxncINmUFPjs1BsNz/UHhE8kCPmfihn2Bnv0mHt/vcu1pexPT/FrU74d2Vyg5YamQXAU9uD9bv5oXbiRDoDOVLOU3PFqHNM7rlPnS9iP/h3Y3KD7V/NuPav6Bg512YQD7VHAw3kaHlPyXeuyWHsN/sp80XsA/9uTB4Rwto5A3nteRS/TQXgL5O3yWeRrIC8SDAFCacA/GXycOimJwzk6SPUsycF4C+T90Q1qQXIyW19PRMF4C+TdwwcyB2XqNIKCsBfJi+IFL0JyNHrUP7zFIC/TD5PvxCjgBwiWSW5uALwl8kXl7adgEBuJyNlUlMB+MvkNVWvPBeQK0+r6RpRAP4yeSNA1kgDyGvEVsz2FYC/TN4+q9kZAnl2AA0MrQD8ZfLQGMvJA7ncS629KgB/mbxXwu4/Ney4RJVWUEAmL4jTSxOQe6lHdRynAPz1JD9Ot1C9gRwqaSW7mALwl8kXk7S9QEBuLzNlVEMB+MvkNdTeaA4gbzS9pt1YAfjL5BuDWHN6IK85h2K3qwD8ZfJ2GZ2cGZBPDqQAIRWAv0weEt+0pIE8rbd69aYA/GXy3si6eoDsbunyjBSAf9bk1qiPNNAaiL8GbE/LmvyMNruuS2Un77pIFTeqAPxl8lGJ4jcAOX4lqmCOAvCXyeeoF2QMkIOkqzQXVgD+MvnCwrYUDsgt5aRc6ikAf5m8nubVZwJy9Yk1YRMKwF8mbwLHOkkAeZ3oitq6AvCXyVsndUJ+QD4hhIYGVgD+MnlgiIdSB/KhfmrvUwH4y+R98t1VBeSOS1RpBQXgL5MXRIreBOTodSj/eQrAXyafp1+IUUAOkaySXFwB+Mvki0vbTkAgt5ORMqmpAPxl8pqqV54LyJWn1XSNKAB/mbwRIGukAeQ1Yitm+wrAXyZvn9XsDIE8O4AGhlYA/jJ5aIzl5IFc7qXWXhWAv0zeK2H9H1Q6JjutNJl8mk6hewE5dBFKfrYC8NeTfLaE7Q8EcvuZKsM1FIC/TL6Guo3EBHIj6SiNygrAXyavLHzN6YBcc07N1Y4C8JfJ22GyeCZAXjywAoZQAP4yeQhc85IE8rzRGhVdAfh3ZfKHh4fh4uJi/7m/vx/lRN/Hx8ddHxPi8vJyP9bHsb4RDyBHzL2U8/Pz83B9fb1jVWJDv9vb2+Hl5WUX0taEZ8u1xbP+PR3w78bkVsjd3d0eJoBzRjdj39zc7D6YPAfXYl5dXe3+cvpce+v3gNx6nsfkZ1yN3dPT02DmHTO5mdrajb83eW4u65NbJ7m+ke7BvxuT58S3BZDu0CwSM7e1lUweHT6Qc9pEv4eJx0xu942fnUsmj76RlzjCv3uTe8B+YfCkHzN5D/CBXFoIUds8y7QGNnBjfMjk0TfytHb/Hf7dmjy3CNjdTYhDJu8BPpA9+F6uc3zhaq/ztrDtKJm8h428xBP+3Zo8het3dxbD2Ot6L/CBXFoIUdtyJs/dS9eBr7eHjdzXk17Dv0uTG1j/szjw+U1qes690tsCiH4AOXodufxhaqw5bCNP2frv/kczu7Z/m2IG6PWAf3cmTw0+BnDsdb0n+EAe0yDy/ZzJc/XknuSM7WEjz9XMPfh3ZXKD5p/gFJs750zeG3wg5+qPfg9W/kmeqyln8p428lzN3IN/NyY3mP7VjOuxV7KcyS3GWH+Ei3QGcqScp+Y61+Rw7/0pbjrCvxuTT10c59QPyOdUs2r9fwXgL5P/vybdXQG5u8JU0CQF4C+TT5IrZicgx8xeWZ+qAPxl8lOVbHg8kBtOUamtqAD8ZfIVRd46NJC3zkPzb6MA/GXybfSvMiuQq0ymSZpTAP4yeXNolksIyMtFVKRICsBfJo9E7chcgXzkMHXvRAH4y+SdAM2VAeRcm+71rwD8ZfKOWQO54xJVWkEB+MvkBZGiNwE5eh3Kf54C8JfJ5+kXYhSQQySrJBdXAP4y+eLSthMQyO1kpExqKgB/mbym6pXnAnLlaTVdIwrAXyZvBMgaaQB5jdiK2b4C8JfJ22c1O0Mgzw6ggaEVgL9MHhpjOXkgl3uptVcF4C+T90rY/c0gHZeo0goKyOQFcXppAnIv9aiO4xSAv57kx+kWqjeQQyWtZBdTAP4y+WKSthcIyO1lpoxqKAD/rMmtUR9poDUQfw3YZpI1eY1dRnOsrwA7+fozaYYWFYC/TN4inYVyAvJC4RQmmALwl8mDgTsmXSAfM0Z9+1EA/jJ5P0x/qxIg/1aDbpyFAvCXyTvGDeSOS1RpBQXgL5MXRIreBOTodSj/eQrAXyafp1+IUUAOkaySXFwB+Mvki0vbTkAgt5ORMqmpAPxl8pqqV54LyJWn1XSNKAB/mbwRIGukAeQ1Yitm+wrAXyZvn9XsDIE8O4AGhlYA/jJ5aIzl5IFc7qXWXhWAv0zeK2H9pREdk51Wmkw+TafQvYAcugglP1sB+OtJPlvC9gcCuf1MleEaCsBfJl9D3UZiArmRdJRGZQXgL5NXFr7mdECuOafmakcB+Mvk7TBZPBMgLx5YAUMoAH+ZPASueUkCed5ojYquAPxl8ugkC/kDudBFTR0rAH+Z/Awgd1yiSisoIJMXxOmlCci91KM6jlMA/nqSH6dbqN5ADpW0kl1MAfh3ZfKHh4fh4uJi/7m/v88KZkVfXl7u+vk+Ly8vw+3t7X689bG+UQ8gR81/LO/n5+fh+vp6x8mY+8N4+jXAtfW3cXZ4zp6/j9PDNfy7MbkVcnd3twNogFgIKUT77oEDE/C+vy2gXF/GtH4Gcut5HpOfcb25uRmenp52G3Jq8lwsYwpXOD8+Pu7ucT83Lvo9+Hdj8hyQ1KT2fQwqm4LB5zBxrq6uwj7NgUw9PZ0x6yGTlxh68/ekDbXAv3uT2+u3LQieAN/4xjf2r+r2KudNbdD9k9u+Mx7hIp2BHCnnqblONXnJyKW2qXm03A/+3Zo8XQRmZjO1N609BdKfu+lnfW0RRD6AHLmGsdxTvrl+pae49ZfJc6oFumcGTg3tn9JWCgsFM9uZMbSlYwJJsPufVkbK95hc4VN6XT9k4kPtx+TTYl82+S6f5AY+NWfuHgvFYNsTPH2q83N6aSG1CJecgMz3ns6wG2OT45nWL5OnigT5njOzpZ57dfMLxRZFujHQbosh4nGuJp/KTSYPuKoNWmpUX4a18zpu9/2GwFPbG3rK08DHb+36XE0+lZtM3tqKPZCPGZY/+ODP6Su4gaU93RAwOu3p2AMpNNd8jiaHod+sx8DI5GPK6H4YBXo2eRgIGyYK/y5/8bahrk1NDeSmklIy1RSAv0xeTfL6EwG5/syasQUF4C+Tt0BjpRyAvFJ4hW1cAfjL5I2DOiU9IJ8SQ2PjKgB/mTwuw4OZA/lgR3XoUgH4y+Rd4v1NUUDuuESVVlAA/jJ5QaToTUCOXofyn6cA/GXyefqFGAXkEMkqycUVgL9Mvri07QQEcjsZKZOaCsBfJq+peuW5gFx5Wk3XiALwl8kbAbJGGkBeI7Zitq8A/GXy9lnNzhDIswNoYGgF4C+Th8ZYTh7I5V5q7VUB+MvkvRIehq7/+qeOsS1Wmky+mJTtBgJyuxkqszUVgL+e5GuqvHFsIG+chqbfSAH4y+QbAagxLZBrzKU52lMA/jJ5e2wWywjIiwVUoFAKwF8mD4XtuGSBfNwo9e5FAfjL5L0QzdQB5EyTbp2BAvDPmtwa9ZEGWgPx14DtZVmTn8EmdxYlspOfRbEq8rcUgL9M/lvS9HMDyP1UpEqOUQD+MvkxqgXrC+RgaSvdhRSAv0y+kKAthgFyi7kpp/UVgL9Mvr7Wm80A5M0S0MSbKgB/mXxTDOtODuR1Z1H0VhWAv0zeKqEF8gLyAqEUIqAC8JfJA8KbmjKQp/ZXv74UgL9M3hfXV6oB8is39eVsFIC/TN4xciB3XKJKKygAf5m8IFL0JiBHr0P5z1MA/jL5PP1CjAJyiGSV5OIKwF8mX1zadgICuZ2MlElNBeAvk9dUvfJcQK48raZrRAH4y+SNAFkjDSCvEVsx21cA/jJ5+6xmZwjk2QE0MLQC8JfJQ2MsJw/kci+19qoA/GXyXgnrf67QMdlppcnk03QK3QvIoYtQ8rMVgL+e5LMlbH8gkNvPVBmuoQD8ZfI11G0kJpAbSUdpVFYA/jJ5ZeFrTgfkmnNqrnYUgL9M3g6TxTMB8uKBFTCEAvDvyuQPDw/DxcXF/nN/f7+HYYVeXl7u23w/G2fHy8vLcHt7u+9j/W1c1APIUfMfy/v5+Xm4vr7ecYIdfY25Z8u19bdxh9qJ08MZ/t2Y3Aq5u7vbGdUAsRC80VNwNubq6mpnZAzu+9sCYnGkYyN8B3KEXKfmaFxvbm6Gp6en3YacmjwXx5h6rmmfQ+1p/yjf4d+NyXPCHzKph8um8Pj4uA/lN4H9zUAXQA6U8uRU2ZQPmfwQw0PtkxNqsCP8uze5vX7bgkiPHFwzvX9y2/ex8Wm8Fr8DucXcTs1pqsn9Rp6b81B7bkyUe/Dv1uSHFsEYXHuS83Oc9Yl8ADlyDWO5H+Jr43IbuY93qN33jXgN/25Nbq9xY0/hMbhmasawiPyTPRpoIEfLe0q+8Cm9ro9t5MQ/1E6/qGf4d2ny0s/iLI70KW1P8PS36fycXlpILS8AILec49zc4DjGJsfTz3Wo3feNeg3/7kxeMrjBGoNr99OnNgsp3RCiQAdylHyPyRM2OZPTNsbtUPsxebTcF/5dmdygpkb1EEpweWr7hTG2IfiYLV8DueUc5+YGy5zJD3E71D43p9bGwb8bkxtsfmHmz/4V3Pr47ykUjM74Ut90bIvfgdxibqfmNGZyGPrN2s91qN33jX4N/25MHh3IGvkDeY3Yitm+AvCXydtnNTtDIM8OoIGhFYC/TB4aYzl5IJd7qbVXBeAvk/dKWH/9U8dkp5Umk0/TKXQvIIcuQsnPVgD+ejj4dIcAAB1ASURBVJLPlrD9gUBuP1NluIYC8JfJ11C3kZhAbiQdpVFZAfjL5JWFrzkdkGvOqbnaUQD+Mnk7TBbPBMiLB1bAEArAXyYPgWtekkCeN1qjoisAf5k8OslC/kAudFFTxwrAXyY/A8gdl6jSCgrI5AVxemkCci/1qI7jFIC/nuTH6RaqN5BDJa1kF1MA/jL5YpK2FwjI7WWmjGooAH+ZvIbaG80B5I2m17QbKwB/mXxjEGtOD+Q151DsdhWAv0zeLqOTMwPyyYEUIKQC8JfJQ+KbljSQp/VWr94UgL9M3htZVw+Q3S1dnpEC8JfJO4YO5I5LVGkFBeCfNbk16iMNtAbirwHbA7ImL2wOagqkADt5oJSV6oIKwF8mX1DU1kIBubW8lE8dBeAvk9fRe5NZgLzJ5Jp0cwXgL5NvjmK9BIC83gyK3LIC8JfJW6Z0Ym5APjGMhgdVAP4yeVCAU9IG8pS+6tOfAvCXyftju68IyPsbujgrBeAvk3eMHcgdl6jSCgrAXyYviBS9CcjR61D+8xSAv0w+T78Qo4AcIlklubgC8JfJF5e2nYBAbicjZVJTAfjL5DVVrzwXkCtPq+kaUQD+MnkjQNZIA8hrxFbM9hWAv0zePqvZGQJ5dgANDK0A/GXy0BjLyQO53EutvSoAf5m8V8LDsPs7ATouT6UdUEAmPyBQD81A7qEW1XC8AvDXk/x47cKMAHKYhJXoogrAXyZfVNa2ggG5rayUTS0F4C+T11J8g3mAvMHUmrIBBeAvkzcAY60UgLxWfMVtWwH4y+RtczopOyCfFESDwyoAf5k8LMLDiQP5cE/16FEB+Hdl8oeHh+Hi4mL/ub+/f4WdFXt5eblvt77X19fD8/Pzrt/Ly8twe3u7b7e+NibqAeSo+Y/lbbyMm/Ez5mOH8bc+KcfHx8c9Y2tP18lYvGj34d+Nya2Qu7u7wYxqBwvBAzS4ZmL6eGgY3Pe3BeQ3Ad8/wjWQI+Q6NUfjenNzMzw9Pe1Y5kzOZp5rszXgTc86yfWdmlOr/eDfjclzQqcmte/exH4MsG0RcJg4V1dXYZ/mQKaens5syqk52QSMXe6w/ulGb2tibF3kYkS5B//uTe6BGkh7PeOTLhBr909u++7HR4FLnkDme0/nMZOzkXvWnqEteHuSw57vfnPvRSf4d2vysUUAQIOa+3mM+7k2xkY5AzlKvsfkmePLPWOHaXlDM9Nz+H7+1Z32Xs7w79bkudeyFJ71GXtysxB8ezq+9e9Abj3POfnBhyeyxcDQ/p7dN8Nj5vTJzaaejpmTU2tj4N+lyVPzjonv4ftr+o8tGtpbPwO59Tzn5DfH5K+//vruxy//VLe5p66XOXluOQb+3Zn8GGC+r5k8fWqzkNJFsSW4Y+YG8jFjovSFjTH0h7FKecH2zTffzP5GPrfB+5hRr+HflckNbmpUANmisCczB69tLBKe2n6BRIcPZGru6TxmcrgaOzvgCmc78+pu7cTx3HvRCf7dmNzg8VtzfwaoQff3/S9ngMqCoB9jaY92BnK0vKfkizkxrx+D0eGY9knXSo8GNz3g343JPWRd/0YBIEuP81QA/jJ5x/yB3HGJKq2gAPxl8oJI0ZuAHL0O5T9PAfjL5PP0CzEKyCGSVZKLKwB/mXxxadsJCOR2MlImNRWAv0xeU/XKcwG58rSarhEF4C+TNwJkjTSAvEZsxWxfAfjL5O2zmp0hkGcH0MDQCsBfJg+NsZw8kMu91NqrAvCXyXsl7P7EU8clqrSCAjJ5QZxemoDcSz2q4zgF4K8n+XG6heoN5FBJK9nFFIC/TL6YpO0FAnJ7mSmjGgrAXyavofZGcwB5o+k17cYKwF8m3xjEmtMDec05FLtdBeAvk7fL6OTMgHxyIAUIqQD8ZfKQ+KYlDeRpvdWrNwXgL5P3RtbVA2R3S5dnpAD8ZfKOoQO54xJVWkEB+MvkBZGiNwE5eh3Kf54C8JfJ5+kXYhSQQySrJBdXAP5Zk1ujPtJAayD+GrCdI2vyxbcUBdxEAXbyTSbXpJsrAH+ZfHMU6yUA5PVmUOSWFYC/TN4ypRNzA/KJYTQ8qALwl8mDApySNpCn9FWf/hSAv0zeH9t9RUDe39DFWSkAf5m8Y+xA7rhElVZQAP4yeUGk6E1Ajl6H8p+nAPxl8nn6hRgF5BDJKsnFFYC/TL64tO0EBHI7GSmTmgrAXyavqXrluYBceVpN14gC8JfJGwGyRhpAXiO2YravAPxl8vZZzc4QyLMDaGBoBeAvk4fGWE4eyOVeau1VAfjL5L0S1v9BpWOy00qTyafpFLoXkEMXoeRnKwB/PclnS9j+QCC3n6kyXEMB+Mvka6jbSEwgN5KO0qisAPxl8srC15wOyDXn1FztKAB/mbwdJotnAuTFAytgCAXgL5OHwDUvSSDPG61R0RWAv0wenWQhfyAXuqipYwXgL5OfAeSOS1RpBQVk8oI4vTQBuZd6VMdxCsC/qyf5w8PDcHFxsf/c39/vVbFCLy8v922+n42z4/Hx8ZX229vb4eXlZR8j2gWQo+V9KN/n5+fh+vp6xwp2fkzK0a8D34840Tn7mvw1/LsxuRVyd3e3NyUAxwCbGDbm6upqd7b+BtvOdpi57XvkBQBkDz76tfG5ubkZnp6edmxSk5vBbTM3tnawDtJ+8LX1EZlxiSf8uzF5rlgDazs+xk37GODSJpAumHR869+B3Hqec/LDpKl57Xtq2hxn62f3c/3n5NPiGPh3b/IUODD8U5x76dlMXtok0v6tfQdya3ktkc+YyY2rPckxP9+NJYfnKpOjSsDz2CKglNzuThvnKX3o2+L5HE1uHGBvv3fxr+7Wxuu+md8OmXwnQ8x/lOCdw1PcqJ2jydMntz21zey2HjC/XXOU1gl9op7hb5pc+CJo8PeiXRu4sddsQNtTeuywhZE+Acb6tny/B5Zj+sLRG5Z7KVvWw7e//e1X/u2J/zcsdu1f6cfmjXQf/t2ZHKBjv2w7ZOBD7REhR8p5aq4YOmdyf8/ilZha37Hf20zNpdV+XZrcdvCxJ7iBYGGkOz2QDHgPT3DqATLfezrDMjV0ypB+JeYyeZCVYXDT1y/77k2bLgBfmu32ufGRX+PO0eTGNF0LYwanr0zunaDrUAr0bPJQIDZKFv7d/Uy+kZ5NTgvkJpNTUqsrAH+ZfHWpt5sAyNtloJm3VAD+MvmWFFaeG8grT6PwjSoAf5m8UUBLpAXkJWIpRjwF4C+Tx2M3OWMgTx6gjl0pAH+ZvCusrxYD5Ffv6tu5KAB/mbxj4kDuuESVVlAA/jJ5QaToTUCOXofyn6cA/GXyefqFGAXkEMkqycUVgL9Mvri07QQEcjsZKZOaCsBfJq+peuW5gFx5Wk3XiALwl8kbAbJGGkBeI7Zitq8A/GXy9lnNzhDIswNoYGgF4C+Th8ZYTh7I5V5q7VUB+MvkvRLu/O946xjbYqXJ5ItJ2W4gILeboTJbUwH460m+psobxwbyxmlo+o0UgL9MvhGAGtMCucZcmqM9BeAvk7fHZrGMgLxYQAUKpQD8ZfJQ2I5LFsjHjVLvXhSAf9bk1qiPNNAaiL8GbMPKmryXnezc62AnP3cdzrV++MvkHa8AIHdcokorKAB/mbwgUvQmIEevQ/nPUwD+Mvk8/UKMAnKIZJXk4grAXyZfXNp2AgK5nYyUSU0F4C+T11S98lxArjytpmtEAfjL5I0AWSMNIK8RWzHbVwD+Mnn7rGZnCOTZATQwtALwl8lDYywnD+RyL7X2qgD8ZfJeCeu/J++Y7LTSZPJpOoXuBeTQRSj52QrAX0/y2RK2PxDI7WeqDNdQAP4y+RrqNhITyI2kozQqKwB/mbyy8DWnA3LNOTVXOwrAXyZvh8nimQB58cAKGEIB+MvkIXDNSxLI80ZrVHQF4C+TRydZyB/IhS5q6lgB+MvkZwC54xJVWkEBmbwgTi9NQO6lHtVxnALw15P8ON1C9QZyqKSV7GIKwF8mX0zS9gIBub3MlFENBeAvk9dQe6M5gLzR9Jp2YwXgL5NvDGLN6YG85hyK3a4C8O/K5A8PD8PFxcX+c39/vydghV5eXu7bfD8bZ8fj4+Mr7be3t8PLy8s+RrQLIEfL+1C+z8/Pw/X19Y4V7HJjjL9xNu7Gn8OYGltr82uE9l7O8O/G5FbI3d3d3pQshBJEG3N1dbVbANbfwNvZDhZCZKMDuZdFa3UYn5ubm+Hp6WnHK2dy42rGzrXB1TZ0Wxul9RFdN/h3Y/IcEINsOz7GTfscgmwLIX0KpDFa/g7klnOcmxtmTY3MJmAL+9BxiP+h8a23w797k489ia1wnuJjsMzkpU1ibFwr94HcSj5L5jFmcjM95uVHsrE1QL8l82opFvy7NfnYIgDCFMBT+hCvxTOQW8zt1JxyfLln5rYN2g57sttGbSzTIzrftJ70O/y7Nbnt6GM7+Dk8xQ04kFP4PXzH0P51HUP7e1br2I9dMnnglWCQx16zWRy5nZ2SxxYF7VHOMvlvSI3xlMmjrOQkz5LBresYcMIcaqdfhPO5mdyY5IxrTHObfq5vBK5Tc4R/V6/rBi0HE1EOPcVtg4j823Tq5Axkvvd0hmX6am4L2hiase0Ye4W3Npk82Iow2Pw21Z+9aUsmtkXhx/lrFkwwSc7uZ3L4YHQYphsB/WRylNA5rAI9P8nDQqmYOPy7el2vqF+IqYAcIlklubgC8JfJF5e2nYBAbicjZVJTAfjL5DVVrzwXkCtPq+kaUQD+MnkjQNZIA8hrxFbM9hWAv0zePqvZGQJ5dgANDK0A/GXy0BjLyQO53EutvSoAf5m8V8Kd/9n1jrEtVppMvpiU7QYCcrsZKrM1FYC/nuRrqrxxbCBvnIam30gB+MvkGwGoMS2Qa8ylOdpTAP4yeXtsFssIyIsFVKBQCsBfJg+F7bhkgXzcKPXuRQH4y+S9EM3UAeRMk26dgQLwl8k7hg3kjktUaQUF4C+TF0SK3gTk6HUo/3kKwF8mn6dfiFFADpGsklxcAfjL5ItL205AILeTkTKpqQD8ZfKaqleeC8iVp9V0jSgAf5m8ESBrpAHkNWIrZvsKwF8mb5/V7AyBPDuABoZWAP5Zk1ujPtJAayD+GrBdKmvy0NuXkt8rwE6+v6GLs1IA/jJ5x9iB3HGJKq2gAPxl8oJI0ZuAHL0O5T9PAfjL5PP0CzEKyCGSVZKLKwB/mXxxadsJCOR2MlImNRWAv0xeU/XKcwG58rSarhEF4C+TNwJkjTSAvEZsxWxfAfjL5O2zmp0hkGcH0MDQCsBfJg+NsZw8kMu91NqrAvCXyXslrL93vWOy00qTyafpFLoXkEMXoeRnKwB/PclnS9j+QCC3n6kyXEMB+Mvka6jbSEwgN5KO0qisAPxl8srC15wOyDXn1FztKAB/mbwdJotnAuTFAytgCAXgL5OHwDUvSSDPG61R0RWAv0wenWQhfyAXuqipYwXgL5OfAeSOS1RpBQVk8oI4vTQBuZd6VMdxCsBfT/LjdAvVG8ihklayiykAf5l8MUnbCwTk9jJTRjUUgL9MXkPtjeYA8kbTa9qNFYC/TL4xiDWnB/Kacyh2uwrAvyuTPzw8DBcXF/vP/f39KwSs2MvLy3279b2+vh6en593/V5eXobb29t9u/W1MVEPIEfNfyxv42XcjJ8xTw/j7tdB2i9dB7kYacyI3+HfjcmtkLu7u8GMagcLwRv98fFxZ2L6eHAY3Pc3+H4T8P0jXAM5Qq5TczSuNzc3w9PT045lalA4GuvcYff95s06SePkxka7B/9uTJ4DkJrUvnsT+zHA9ovDxLm6ugr7NAeyr7OXa8ycmpNNwNilB2PSNZCuk3Rc1O/w797k9vptcO0wuP41Ll0g1u6f3Pbdj48GG8jR8p6SL4ZNGdqC9j+SeZ5jY9Kn+5T5I/SBf7cmHwMKHANrhk93de7n2hgb5QzkKPkek+chvhaLPv713DYF/z3X55g8Wu4L/25NbjAPPYWtj9/p/ZMb+L69ZaC53ICca4t+Dz7pkzytix/DfD/jzBudGf611157ZR2kMaJ+h3+XJk/NOwbJv6b5a/rnFghtEc5AjpDrsTlONTn90jc25qP90AOB/pHO8O/O5FMNbrB8XzN5+tRmAYwtkNaBA7n1POfkBxv/hM7FObRR8+OZnXs74N+Vyc2MqVEBZ4vCgHNY4faqxiJhMXhD557ujI9wBnKEXI/Ncczkb7zxxiuhSmvC2NtrO2vglYEdfIF/NyYHGD9rceaXLOzY3LdzuntjdPowNipvIEfNv5R3zuTcg5+d09dwz3jsgVCaN1Ib/LsxeSTxa+UK5FrzaZ62FIC/TN4Wl0WzAfKiQRUsjALwl8nDIDs+USAfP1IjelAA/jJ5DzRHagDySLNud64A/GXyjkEDueMSVVpBAfjL5AWRojcBOXodyn+eAvCXyefpF2IUkEMkqyQXVwD+Mvni0rYTEMjtZKRMaioAf5m8puqV5wJy5Wk1XSMKwF8mbwTIGmkAeY3Yitm+AvCXydtnNTtDIM8OoIGhFYC/TB4aYzl5IJd7qbVXBeAvk/dKeBgGIHdcokorKAB/mbwgUvQmIEevQ/nPUwD+Mvk8/UKMAnKIZJXk4grAXyZfXNp2AgK5nYyUSU0F4C+T11S98lxArjytpmtEAfjL5I0AWSMNIK8RWzHbVwD+Mnn7rGZnCOTZATQwtALwl8lDYywnD+RyL7X2qgD8ZfJeCevfk3dMdlppRZNboz7SQGsg/hqw7UBP8mmbYshe7OQhk1fSJysAf5n8ZCnbDQDkdjNUZmsqAH+ZfE2VN44N5I3T0PQbKQB/mXwjADWmBXKNuTRHewrAXyZvj81iGQF5sYAKFEoB+MvkobAdlyyQjxul3r0oAH+ZvBeimTqAnGnSrTNQAP4yecewgdxxiSqtoAD8ZfKCSNGbgBy9DuU/TwH4y+Tz9AsxCsghklWSiysAf5l8cWnbCQjkdjJSJjUVgL9MXlP1ynMBufK0mq4RBeAvkzcCZI00gLxGbMVsXwH4y+Tts5qdIZBnB9DA0ArAXyYPjbGcPJDLvdTaqwLwl8l7Jay/NKJjstNKk8mn6RS6F5BDF6HkZysAfz3JZ0vY/kAgt5+pMlxDAfjL5Guo20hMIDeSjtKorAD8ZfLKwtecDsg159Rc7SgAf5m8HSaLZwLkxQMrYAgF4C+Th8A1L0kgzxutUdEVgL9MHp1kIX8gF7qoqWMF4N+VyR8eHoaLi4v95/7+fo/QCr28vNy3+X42zo50/O3t7fDy8rKPEe0CyNHyPpTv8/PzcH19vWMJu9wY42+cjbvx53h8fHxlHfh1Qp8ezvDvxuRWyN3d3d6ULIQSQBtzdXW1WwDW/wtf+MKe7ZTx+86NXgC50fRmpWVcbm5uhqenp8E24ZzJjasZO9dmBvemh3Ou76wEGxoE/25MntPWwNmObyBzh20ApU3A2iI/zYGcqz36PXvDypmcTcAWdu6wNZEyPbQOcnEi3IN/9yZPgQLHCucpzj1/5pXOzlEPIEfNv5T3mMnNxJiWH8n8GjDu/inP98icx3SCf7cmH1sECMJC4LudGWOLo/QG4Me0fA3klnOcmxus/Gs294wfpuV13Hhz+H7+1Z32Xs7w79bkudcy4FnRpae49WOH94uD8VHOQI6S7zF5YlRvcgzt71lMMzxmhiubgJ1tU0jHHJNLq33h36XJDdjYk5jFMcW8fnG0CrKUF5BLfaK2wdGb85DJX3/99d3P4yn70nqJqo/lDf/uTH4I2DHGPaZvi4sByC3mdmpOOZNbTDNwamLjaJv+m2++mf1lXXTOY1rCvyuTG9yxJ7gJwcJIF4G12VPgZz/72V4vngq5vvtOjV8AufE0Z6UHS/8kt0Dp6zgc6WdnXt2tP3Eicx4TEP7dmNzg8dtUf/ZAU8BeHNvN/Ti7jg4eyL7OXq4xJ+b1dWF0eKZ90rUSnbOv3V/DvxuT++J0/RsFgCw9zlMB+MvkHfMHcsclqrSCAvCXyQsiRW8CcvQ6lP88BeAvk8/TL8QoIIdIVkkurgD8ZfLFpW0nIJDbyUiZ1FQA/jJ5TdUrzwXkytNqukYUgL9M3giQNdIA8hqxFbN9BeAvk7fPanaGQJ4dQANDKwB/mTw0xnLyQC73UmuvCsBfJu+VsPsPFDouUaUVFJDJC+L00gTkXupRHccpAH89yY/TLVRvIIdKWskupgD8ZfLFJG0vEJDby0wZ1VAA/jJ5DbU3mgPIG02vaTdWAP4y+cYg1pweyGvOodjtKgB/mbxdRidnBuSTAylASAXgL5OHxDctaSBP661evSkAf5m8N7KuHiC7W7o8IwXgL5N3DB3IHZeo0goKwF8mL4gUvQnI0etQ/vMUgL9MPk+/EKOAHCJZJbm4AvDPmtwa9ZEGWgPx14DtHFmTL76lKOAmCrCTbzK5Jt1cAfjL5JujWC8BIK83gyK3rAD8ZfKWKZ2YG5BPDKPhQRWAv0weFOCUtIE8pa/69KcA/GXy/tjuKwLy/oYuzkoB+MvkHWMHcsclqrSCAvCXyQsiRW8CcvQ6lP88BeAvk8/TL8QoIIdIVkkurgD8ZfLFpW0nIJDbyUiZ1FQA/jJ5TdUrzwXkytNqukYUgL9M3giQNdIA8hqxFbN9BeAvk7fPanaGQJ4dQANDKwB/mTw0xnLyQC73UmuvCsBfJu+VsP4PKh2TnVaaTD5Np9C9gBy6CCU/WwH460k+W8L2BwK5/UyV4RoKwF8mX0PdRmICuZF0lEZlBeAvk1cWvuZ0QK45p+ZqRwH4y+TtMFk8EyAvHlgBQygAf5k8BK55SQJ53miNiq4A/GXy6CQL+QO50EVNHSsAf5n8DCB3XKJKKyggkxfE6aUJyL3UozqOUwD+XT3JHx4ehouLi/3n/v5+r4oVenl5uW/z/WzcofZ9oEAXQA6U8qRUn5+fh+vr6x1LYzd2GH/jbNyNb3ocak/7R/sO/25MboXc3d0NLy8vOxYsBG/0FJKNubq6yi4A63uoPY3X2ncgt5bXKfkY15ubm+Hp6Wm4vb0dciY3bmbsXJvNfaj9lPxaGgt/q/fCJ0aDvxf12iDbjm8LI3fYBlDaBA6152K2dK8nlqmutpnnTM4mYAs7dxxqz42Jeg/+3ZvcFgJPdw/LCu/5KW61AtnX3cv1mMltY2dz5kcyvwYOtfeij+ffrcnHFgEQWQh8T8+H2tP+LX4/N5PD3Mz9+Pi4Q2JPbnubM56H2ltkeEpO8O/W5LZj+x3ci3UOT3GrF8i+9l6uMaxx5sDQ/p61meHtZ/Qf/ehHO8OPtdu66OmAf5cmN4hjP4uzOGxnzx2H2nNjWr0H5FbzOyUvOHnDyuSvKgr/7kxeMrhJwK4+tmsfan9Vxra/AbntLOdllzO5Rcr9mGVM2fQPtc/Lps1R8O/K5AYQmDnZWRjn8BS3+oGc0yL6PVj6J7nVZAvaXs3N2HakT/dD7dF18fnDvxuTG2x+m+rP/g9CWB//3Qti14fa0/6tfwdy63nOyW/M5BYLI7MOxjaCsfY5+bQ4Bv7dmLxFkbfOCchb56H5t1EA/jL5NvpXmRXIVSbTJM0pAH+ZvDk0yyUE5OUiKlIkBeAvk0eidmSuQD5ymLp3ogD8ZfJOgObKAHKuTff6VwD+MnnHrIHccYkqraAA/GXygkjRm4AcvQ7lP08B+Mvk8/QLMQrIIZJVkosrAH+ZfHFp2wkI5HYyUiY1FYC/TF5T9cpzAbnytJquEQXgL5M3AmSNNIC8RmzFbF8B+Mvk7bOanSGQZwfQwNAKwF8mD42xnDyQy73U2qsC8JfJeyXc+X9q2jG2xUqTyReTst1AQG43Q2W2pgLw15N8TZU3jg3kjdPQ9BspAH+ZfCMANaYFco25NEd7CsBfJm+PzWIZAXmxgAoUSgH4y+ShsB2XLJCPG6XevSgAf5m8F6KZOoCcadKtM1AA/jJ5x7CB3HGJKq2gAPyzJrdGfaSB1kD8NWB7wG+ZvLAxqEkKSIGACsjkAaEpZSlwjAJm8v8FF/2xNrlZcZMAAAAASUVORK5CYII=[/img][/center][br][br]After checking the data, the biologist realizes that the first zygomatic width listed as 147 mm is an error. It is supposed to be 180 mm. Use the applet below to find the equation of a line of best fit and the correlation coefficient for the corrected data. What is the equation of the line of best fit and the correlation coefficient?
Compare the new equation of the line of best fit with the original. [br]What impact did changing one data point have on the slope, [math]y[/math]-intercept, and correlation coefficient on the line of best fit?
Why do you think that weak positive association became a moderately strong association? Explain your reasoning.
How does changing each point’s [math]y[/math]-value impact the correlation coefficient?
Can you change two values to get the correlation coefficient closer to 1? Use data to support your answer.
By leaving [math]\left(288,180\right)[/math], can you change a value to get the relationship to change from a positive one to a negative one? Use data to support or refute your answer.
Close

Information: IM Alg1.3.8 Lesson: Using the Correlation Coefficient