SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.0557586537 0.0031090275 -0.0058719723 4.3428216036 225 ANOVA df Regression Residual Total SS Coefficients Intercept Number of Outbreaks by County Coded Location North = 0; South = 1 MS F 2 13.0579153301 6.5289576651 0.3461783259 222 4186.9420846699 18.8600994805 224 4200 2006.99605203 0.2072684973 -0.0461419155 Standard Error t Stat P-value Significance F 0.7077675005 Lower 95% Upper 95% 0.4238425501 4735.23965829 0 2006.160782381 2007.83132168 0.2490969238 0.8320797148 0.4062581101 -0.2836286525 0.6981656471 0.582977363 -0.0791487258 0.9369856252 -1.1950197161 1.1027358851 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.0565085098 0.0031932117 -0.0103381022 4.3524521153 225 ANOVA df Regression Residual Total SS MS F 3 13.4114890361 4.4704963454 0.2359868159 221 4186.5885109639 18.9438394161 224 4200 Coefficients Intercept Number of Births by County Number of Outbreaks by County Coded Location North = 0; South = 1 Standard Error t Stat P-value Significance F 0.8712206237 Lower 95% Upper 95% 2007.0000367067 0.425782605 4713.673159329 0 2006.1609229681 2007.8391504 -2.9309198E-006 2.14534904E-005 -0.1366173882 0.8914576033 -4.521052E-005 3.93487E-005 0.210548464 0.250801084 0.8395038038 0.4020938628 -0.2837193449 0.7048162729 -0.0211697565 0.6121955917 -0.0345800538 RESIDUAL OUTPUT Observation Predicted Year 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 Residuals 2006.9962558202 2006.9737759425 2006.9945910577 2006.9980554049 2006.977521658 2006.9785504109 2006.9994974175 2006.8192197474 2006.9948108767 2006.9951068996 2006.9421952814 2006.9712260422 2006.9765280762 2006.9948841497 2006.9700536743 2006.9968888988 2006.9740690345 2006.9945470939 2006.9981345398 2006.9775685527 2006.9785885128 2006.9994944866 2007.0263859299 2006.9947053636 2006.9954263699 2006.9432855835 2006.9713784501 2006.9766423821 2006.9949251826 2006.9700741907 2006.996774593 2006.9738521464 2006.9945646794 2006.9979967865 2006.9774395923 2006.978582651 2006.9993625952 2006.8129358553 2006.9942246927 2006.9952475837 2006.9423447583 2006.9709388121 2006.9766335893 2006.9944152025 2006.9698660954 2006.996373057 2006.973720255 2006.9944093407 2006.9980085102 2006.977594931 2006.9786119602 2006.9994036281 2006.8059426806 2006.9937791929 2006.9950072483 2006.9413541074 2006.9703115952 2006.9765456617 2006.9946145051 2006.9695261087 2006.9961209979 2006.9735619853 2006.9940957323 2006.9980788523 2006.9775451054 2006.9785679964 2006.9993625952 2006.8014437187 2006.9936150614 2006.9947932912 2006.9406770649 2006.9698748882 2006.976498767 2006.9941836598 2006.9691421582 2006.9962763366 2006.973682153 2006.9939697027 2006.9981345398 2006.9775421745 2006.9785767891 2006.9993186314 2007.0070184117 2006.9934802391 2006.9944591663 2006.9408353346 2006.9681954711 2006.9765779018 2006.9938378113 2006.9692183622 2006.9965518431 2006.9735678472 2006.9939931501 2006.9980817832 2006.9772842535 2006.978544549 2006.9993655261 2006.7849572947 2006.9928031966 2006.9945353702 2006.938042168 2006.9657745314 2006.9766599676 2006.9930611176 2006.9690366451 2006.9966690799 2006.9734154393 2006.9937879857 2006.9980026484 2006.9771611549 2006.9784624833 2006.9993625952 2006.7856284753 2006.9928881933 2006.9941396961 2007.1489745825 2006.9633770389 2006.9766218656 2006.992970259 2006.9693355989 2006.9964873628 2006.973646982 2006.9942188309 2006.9979938556 2006.9769794378 2006.9784829997 2006.9993157005 2006.7951949976 2006.9932926602 2006.9943389986 2010.518614628 2007.1726183127 2006.976533938 2006.9935417884 2006.9690131978 2006.9963965043 2006.9734564722 2006.9944855446 2006.9979733392 2006.9769765069 2006.9784859306 2006.9995267267 2006.8098613204 2006.9935300647 2006.9944884755 2006.9412339397 2006.9633066969 2006.9766365202 2006.993996081 2006.9693883555 2006.9968156258 2006.973646982 2006.994834324 2006.9980729905 2006.9773135627 2006.9785592036 2006.9994505228 2006.8199055826 2006.9941103869 2006.994945699 2007.1537490509 2006.9642416603 2006.9768358228 2006.9947112254 2006.9694030101 2006.9968976916 2006.9739898996 2006.9948460477 2006.9982195364 2006.9770908128 2006.9785181708 2006.9995325885 2007.0330186015 2006.994286242 2006.9952241364 2007.1546136722 2006.9653642026 2006.9768328919 2006.9946760544 2006.9694938686 2006.9973109513 2006.9738726628 2007.4161833112 2006.9982517766 2006.9773282173 2006.9785328254 2006.9994387991 2006.8192050928 2006.9949486299 2006.9952505147 2007.1546078104 2006.9652205875 2006.976900303 2006.9948138076 2006.9697195494 2006.9972523329 2006.9741481693 2006.9952593074 2006.998307464 2006.9771083983 2006.9784859306 2006.9994505228 2006.8210427795 2006.9949369062 2006.995493781 2007.1543469585 2006.9655019558 2006.9769618523 2006.9947024327 2006.9697224804 2006.997431119 2006.9740719654 2006.9951068996 2006.9981404016 2006.9771025365 2006.9784566214 2006.9994329373 2007.4486171239 2006.9948958734 2006.9953824061 2006.9442058924 2006.9657071202 2006.9771113292 2006.9943565841 2006.9699481612 -6.9962558202 -6.9737759425 -6.9945910577 -6.9980554049 -6.977521658 -6.9785504109 -6.9994974175 -6.8192197474 -6.9948108767 -6.9951068996 -6.9421952814 -6.9712260422 -6.9765280762 -6.9948841497 -6.9700536743 -5.9968888988 -5.9740690345 -5.9945470939 -5.9981345398 -5.9775685527 -5.9785885128 -5.9994944866 -6.0263859299 -5.9947053636 -5.9954263698 -5.9432855835 -5.9713784501 -5.9766423821 -5.9949251826 -5.9700741907 -4.996774593 -4.9738521464 -4.9945646794 -4.9979967865 -4.9774395922 -4.978582651 -4.9993625952 -4.8129358553 -4.9942246927 -4.9952475837 -4.9423447583 -4.9709388121 -4.9766335893 -4.9944152025 -4.9698660954 -3.9963730569 -3.973720255 -3.9944093407 -3.9980085102 -3.977594931 -3.9786119602 -3.999403628 -3.8059426806 -3.9937791929 -3.9950072483 -3.9413541074 -3.9703115952 -3.9765456617 -3.9946145051 -3.9695261087 -2.9961209978 -2.9735619853 -2.9940957322 -2.9980788523 -2.9775451054 -2.9785679964 -2.9993625952 -2.8014437187 -2.9936150614 -2.9947932912 -2.9406770649 -2.9698748882 -2.976498767 -2.9941836598 -2.9691421582 -1.9962763366 -1.973682153 -1.9939697027 -1.9981345398 -1.9775421744 -1.9785767891 -1.9993186314 -2.0070184117 -1.9934802391 -1.9944591663 -1.9408353346 -1.9681954711 -1.9765779018 -1.9938378113 -1.9692183621 -0.9965518431 -0.9735678472 -0.9939931501 -0.9980817832 -0.9772842535 -0.978544549 -0.9993655261 -0.7849572946 -0.9928031966 -0.9945353702 -0.938042168 -0.9657745313 -0.9766599676 -0.9930611175 -0.9690366451 0.0033309202 0.0265845607 0.0062120143 0.0019973516 0.0228388451 0.0215375167 0.0006374048 0.2143715247 0.0071118067 0.005860304 -0.1489745825 0.0366229611 0.0233781344 0.007029741 0.0306644011 1.0035126372 1.026353018 1.0057811691 1.0020061444 1.0230205622 1.0215170003 1.0006842995 1.2048050024 1.0067073398 1.0056610014 -2.518614628 0.8273816873 1.023466062 1.0064582116 1.0309868022 2.0036034957 2.0265435278 2.0055144554 2.0020266608 2.0230234931 2.0215140694 2.0004732733 2.1901386796 2.0064699353 2.0055115245 2.0587660603 2.0366933031 2.0233634798 2.006003919 2.0306116445 3.0031843742 3.026353018 3.005165676 3.0019270096 3.0226864373 3.0214407964 3.0005494772 3.1800944174 3.0058896132 3.005054301 2.8462509491 3.0357583397 3.0231641772 3.0052887746 3.0305969899 4.0031023084 4.0260101004 4.0051539523 4.0017804636 4.0229091872 4.0214818293 4.0004674115 3.9669813985 4.005713758 4.0047758636 3.8453863278 4.0346357974 4.0231671082 4.0053239456 4.0305061314 5.0026890487 5.0261273372 4.5838166888 5.0017482234 5.0226717827 5.0214671747 5.0005612009 5.1807949072 5.0050513701 5.0047494853 4.8453921896 5.0347794125 5.023099697 5.0051861924 5.0302804506 6.0027476671 6.0258518307 6.0047406926 6.001692536 6.0228916017 6.0215140694 6.0005494772 6.1789572205 6.0050630938 6.004506219 5.8456530415 6.0344980442 6.0230381477 6.0052975674 6.0302775196 7.002568881 7.0259280346 7.0048931004 7.0018595984 7.0228974635 7.0215433786 7.0005670628 6.5513828761 7.0051041266 7.004617594 7.0557941076 7.0342928798 7.0228886708 7.0056434159 7.0300518388 0.972445819 -1.2276580576 1.1853185445 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.2491834041 0.0620923689 0.0536427506 4.2123863102 225 ANOVA df Regression Residual Total SS 2 222 224 Coefficients Intercept Number of Outbreaks by County Coded Location North = 0; South = 1 Multiple Regression equation MS F 260.7879492874 130.393974644 7.3485412814 3939.2120507126 17.7441984266 4200 Standard Error t Stat P-value Significance F 0.0008123959 Lower 95% Upper 95% 6.8550569675 0.1095092061 0.4111125712 16.674403674 5.131617E-041 0.2416153753 0.4532377375 0.6508202454 6.0448743831 7.6652395519 -0.3666440009 0.585662413 2.1184783077 0.5654678196 3.7464170979 0.0002286881 1.0041066938 3.2328499216 Y= 6.8551 + 0.1095x1 + 2.1185x2 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.2493260621 0.0621634853 0.0494326728 4.2217457745 225 ANOVA df Regression Residual Total SS Coefficients Intercept Number of Births by County Number of Outbreaks by County Coded Location North = 0; South = 1 MS 3 261.0866381118 87.0288793706 221 3938.9133618882 17.8231373841 224 4200 Standard Error t Stat F 4.882915813 P-value Significance F 0.002625952 Lower 95% Upper 95% 6.858719342 0.4129961377 16.6072239308 9.611501E-041 6.0448046139 7.6726340701 -2.693850E-006 2.08092312E-005 -0.1294545768 0.8971157076 -4.3703773E-005 3.831607E-005 0.1125238702 0.2432694004 0.462548393 0.6441431915 -0.3669008261 0.5919485666 2.1414305752 0.5938110481 3.6062491295 0.0003840291 0.9711737287 3.3116874217 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.2573260232 0.0662166822 0.0492388037 4.2221762671 225 ANOVA df Regression Residual Total SS Coefficients Intercept Number of Births by County Number of Outbreaks by County Coded Location North = 0; South = 1 interaction MS 4 278.1100653325 69.5275163331 220 3921.8899346675 17.8267724303 224 4200 Standard Error t Stat F 3.900174112 P-value Significance F 0.0044187928 Lower 95% Upper 95% 6.8982092403 0.4150104099 16.6217739972 9.819827E-041 6.0803044035 7.7161140771 -2.887376E-006 2.08122954E-005 -0.1387341478 0.8897871411 -4.3904364E-005 3.812961E-005 -1.9466662311 2.1212157852 -0.9177124952 0.3597743785 -6.1271701632 2.2338377011 2.0972798195 2.0866046736 Interaction does not work. P-value > 0.05 level of significance 0.5955877379 2.1352711725 3.5213616498 0.0005218636 0.9772082818 0.329539368 0.9234921748 3.2710674641 -2.1215996944 6.2948090415 Number of Out breaks by Count y f(x) = 0.0142857143x + 0.0234920635 R = 0.0027944641 Number of Out breaks by Year f(x) = 0.015x - 29.9672222222 R = 0.0030808966 Year County 2000 Apache 2000 Cochise 2000 Coconino 2000 Gila 2000 Graham 2000 Greenlee 2000 La Paz 2000 Maricopa 2000 Mohave 2000 Navajo 2000 Pima 2000 Pinal 2000 Santa Cruz 2000 Yavapai 2000 Yuma 2001 Apache 2001 Cochise 2001 Coconino 2001 Gila 2001 Graham 2001 Greenlee 2001 La Paz 2001 Maricopa 2001 Mohave 2001 Navajo 2001 Pima 2001 Pinal 2001 Santa Cruz 2001 Yavapai 2001 Yuma 2002 Apache 2002 Cochise 2002 Coconino 2002 Gila 2002 Graham 2002 Greenlee 2002 La Paz 2002 Maricopa 2002 Mohave 2002 Navajo 2002 Pima 2002 Pinal 2002 Santa Cruz 2002 Yavapai 2002 Yuma 2003 Apache 2003 Cochise 2003 Coconino 2003 Gila 2003 Graham 2003 Greenlee 2003 La Paz 2003 Maricopa 2003 Mohave 2003 Navajo 2003 Pima 2003 Pinal 2003 Santa Cruz 2003 Yavapai 2003 Yuma 2004 Apache 2004 Cochise 2004 Coconino 2004 Gila 2004 Graham 2004 Greenlee 2004 La Paz 2004 Maricopa 2004 Mohave 2004 Navajo 2004 Pima 2004 Pinal 2004 Santa Cruz 2004 Yavapai 2004 Yuma 2005 Apache 2005 Cochise 2005 Coconino 2005 Gila 2005 Graham 2005 Greenlee 2005 La Paz 2005 Maricopa 2005 Mohave 2005 Navajo 2005 Pima 2005 Pinal 2005 Santa Cruz 2005 Yavapai 2005 Yuma 2006 Apache 2006 Cochise 2006 Coconino 2006 Gila 2006 Graham 2006 Greenlee 2006 La Paz 2006 Maricopa 2006 Mohave 2006 Navajo 2006 Pima 2006 Pinal 2006 Santa Cruz 2006 Yavapai 2006 Yuma 2007 Apache 2007 Cochise 2007 Coconino 2007 Gila 2007 Graham 2007 Greenlee 2007 La Paz 2007 Maricopa 2007 Mohave 2007 Navajo 2007 Pima 2007 Pinal 2007 Santa Cruz 2007 Yavapai 2007 Yuma 2008 Apache 2008 Cochise 2008 Coconino 2008 Gila 2008 Graham 2008 Greenlee 2008 La Paz 2008 Maricopa 2008 Mohave 2008 Navajo 2008 Pima 2008 Pinal 2008 Santa Cruz 2008 Yavapai 2008 Yuma 2009 Apache 2009 Cochise 2009 Coconino 2009 Gila 2009 Graham 2009 Greenlee 2009 La Paz 2009 Maricopa 2009 Mohave 2009 Navajo 2009 Pima 2009 Pinal 2009 Santa Cruz 2009 Yavapai 2009 Yuma 2010 Apache 2010 Cochise 2010 Coconino 2010 Gila 2010 Graham 2010 Greenlee 2010 La Paz 2010 Maricopa 2010 Mohave 2010 Navajo 2010 Pima 2010 Pinal 2010 Santa Cruz 2010 Yavapai 2010 Yuma 2011 Apache 2011 Cochise 2011 Coconino 2011 Gila 2011 Graham 2011 Greenlee 2011 La Paz 2011 Maricopa 2011 Mohave 2011 Navajo 2011 Pima 2011 Pinal 2011 Santa Cruz 2011 Yavapai 2011 Yuma 2012 Apache 2012 Cochise 2012 Coconino 2012 Gila 2012 Graham 2012 Greenlee 2012 La Paz 2012 Maricopa 2012 Mohave 2012 Navajo 2012 Pima 2012 Pinal 2012 Santa Cruz 2012 Yavapai 2012 Yuma 2013 Apache 2013 Cochise 2013 Coconino 2013 Gila 2013 Graham 2013 Greenlee 2013 La Paz 2013 Maricopa 2013 Mohave 2013 Navajo 2013 Pima 2013 Pinal 2013 Santa Cruz 2013 Yavapai 2013 Yuma 2014 Apache 2014 Cochise 2014 Coconino 2014 Gila 2014 Graham 2014 Greenlee 2014 La Paz 2014 Maricopa 2014 Mohave 2014 Navajo 2014 Pima 2014 Pinal 2014 Santa Cruz 2014 Yavapai 2014 Yuma 2015 Apache 2015 Cochise 2015 Coconino 2015 Gila 2015 Graham 2015 Greenlee 2015 La Paz 2015 Maricopa 2015 Mohave 2015 Navajo 2015 Pima 2015 Pinal 2015 Santa Cruz 2015 Yavapai 2015 Yuma County Lable 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Number of Births Number of Coded Location by Outbreaks by North = 0; interaction County County South = 1 1290 0 0 0 1737 0 1 0 1858 0 0 0 676 0 0 0 459 0 1 0 108 0 1 0 184 0 0 0 54470 0 1 0 1783 0 0 0 1682 0 0 0 12512 0 1 0 2607 0 1 0 798 0 1 0 1758 0 0 0 3007 0 1 0 1074 0 0 0 1637 0 1 0 1873 0 0 0 649 0 0 0 443 0 1 0 95 0 1 0 185 0 0 0 55624 1 1 1 1819 0 0 0 1573 0 0 0 12140 0 1 0 2555 0 1 0 759 0 1 0 1744 0 0 0 3000 0 1 0 0 0 1,113 0 0 1 0 1,711 0 0 1,867 0 0 0 696 0 0 1 487 0 0 1 97 0 0 0 0 230 0 1 56,614 0 0 0 1,983 0 0 0 1,634 0 0 1 12,461 0 0 1 0 2,705 0 1 762 0 0 0 1,918 0 0 1 3,071 0 1250 0 0 0 1756 0 1 0 1920 0 0 0 692 0 0 0 434 0 1 0 87 0 1 0 216 0 0 0 59000 0 1 0 2135 0 0 0 1716 0 0 0 12799 0 1 0 2919 0 1 0 792 0 1 0 1850 0 0 0 3187 0 1 0 0 0 1,336 0 0 1 0 1,810 0 0 2,027 0 0 0 668 0 0 1 451 0 0 1 102 0 0 0 0 230 0 1 60,535 0 0 0 2,191 0 0 0 1,789 0 0 1 13,030 0 0 1 3,068 0 0 1 0 808 0 0 1,997 0 0 1 3,318 0 1283 0 0 0 1769 0 1 0 2070 0 0 0 649 0 0 0 452 0 1 0 99 0 1 0 245 0 0 0 62232 1 1 1 2237 0 0 0 1903 0 0 0 12976 0 1 0 3641 0 1 0 781 0 1 0 2115 0 0 0 3292 0 1 0 0 0 1,189 0 0 1 1,808 0 0 0 0 2,062 0 0 667 0 0 1 540 0 0 1 110 0 0 0 229 0 0 1 0 66,160 0 0 2,468 0 0 0 1,877 0 0 1 13,929 0 0 1 4,467 0 0 1 0 753 0 0 2,380 0 0 1 3,354 0 1149 0 0 0 1860 0 1 0 2132 0 0 0 694 0 0 0 582 0 1 0 138 0 1 0 230 0 0 0 65931 0 1 0 2439 0 0 0 2012 0 0 0 13798 1 1 1 5285 0 1 0 766 0 1 0 2411 0 0 0 3252 0 1 0 0 0 1,211 0 0 1 1,781 0 0 0 1,985 0 0 0 697 0 0 1 0 644 0 1 131 0 0 0 246 0 0 1 62,667 0 0 0 2,301 0 0 0 0 1,944 17 1 13,503 17 1 1 5,731 1 0 1 796 0 0 0 2,216 0 0 1 3,362 0 1242 0 0 0 1846 0 1 0 1894 0 0 0 704 0 0 0 645 0 1 0 130 0 1 0 174 0 0 0 57663 0 1 0 2220 0 0 0 1893 0 0 0 12840 0 1 0 5309 0 1 0 761 0 1 0 2061 0 0 0 3234 0 1 0 0 0 1,099 0 0 1 0 1,781 0 0 1,775 0 0 0 670 0 0 1 530 0 0 1 105 0 0 0 0 200 0 1 54,236 0 0 0 2,022 0 0 0 1,737 0 1 1 12,169 1 0 1 4,990 0 0 1 0 693 0 0 1,817 0 0 1 3,229 0 1071 0 0 0 1664 0 1 0 1771 0 0 0 620 0 0 0 606 0 1 0 119 0 1 0 172 0 0 0 53361 1 1 1 1962 0 0 0 1642 0 0 0 11874 1 1 1 4607 0 1 0 694 0 1 0 1829 0 0 0 3198 0 1 0 0 0 930 0 0 1 1,704 0 2 0 0 1,689 0 0 609 0 0 1 525 0 0 1 114 0 0 0 204 0 0 1 54,475 0 0 0 0 1,736 0 0 1,633 0 1 1 11,876 1 0 1 4,656 0 0 1 671 0 0 0 0 1,782 0 1 3,121 0 950 0 0 0 1610 0 1 0 1630 0 0 0 590 0 0 0 600 0 1 0 130 0 1 0 200 0 0 0 53848 0 1 0 1740 0 0 0 1550 0 0 0 11965 1 1 1 4560 0 1 0 650 0 1 0 1820 0 0 0 3120 0 1 0 0 0 889 0 0 1 1,636 0 0 0 1,682 0 0 0 647 0 0 1 602 0 0 1 0 140 0 0 206 0 3 1 55,237 3 0 0 1,754 0 0 0 1,588 0 0 1 0 11,826 0 1 4,490 0 0 1 599 0 0 0 1,938 0 0 1 3,043 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 2 1 2 0 0 0 0 0 0 0 1 0 5 1 5 0 1 0 0 0 0 0 1 0 BUS The measles virus was declared eliminated in the United States in the year 2000. However, measles can, and still do occur, with the risk of cases and outbreaks remaining from imported virus transmission through unvaccinated and sometimes fully vaccinated individuals. The recent outbreak of the measles virus in California has spread to 17 states and 2 countries (Mexico and Canada) and totals 121 cases thus far. Every state in the country allows exemption for medical reasons which may include: HIV/AIDS, any other disease that affects the immune system, treatment with drugs that may affect the immune system, any kind of cancer, treatment for cancer using radiation or drugs, low platelet count, having received another vaccine within the past 4 weeks, has received a transfusion or received blood products, or any reason recommended by a licensed physician. 48 states allow religious exemption and 20 of those states allow philosophical exemptions. There are two states with the strict requirements; Mississippi and West Virginia, who do not allow religious or philosophical exemption. The last measles outbreak in Arizona was in 2008. Purpose The measles virus was declared eliminated in 2000, yet there are still confirmed measles cases. For the purpose of our model: North region of Arizona by County: Apache, Coconino, Gila, La Paz, Mohave, Navajo and Yavapai South region of Arizona by County: Cochise, Graham, Greenlee, Maricopa, Pima, Pinal, Santa Cruz and Yuma What is the prevalence of measles in the North and South regions of Arizona from 2000 - 2014? Is age a contributing factor among the confirmed cases? Were there confirmed cases of measles in each Region? If so, were there any patterns in the age of the confirmed cases? By providing evidence of the existence of measles, we can encourage the residents of Arizona to get informed about the measles virus, transmission of and vaccination in order to make informed decisions for themselves and their families. Method Our data collection will be mainly from the Arizona Department of Health Services website at http://www.azdhs.gov/index.php. The Office of Infectious Disease Services compiles weekly, monthly and annual - data, statistics and reports. We will use the Data and Statistics Archive; Communicable Disease Summary by County. It is likely that we will also use the Centers for Disease Control website at http://www.cdc.gov/ for further information. From this data we will use multiple linear regression analysis. Using the dependent variable, Prevalence of Measles and the independent variables Age and Region (0 = North and 1 = South). We will also provide a side by side bar chart to display the Prevalence of Measles by Region and a pie chart to visually display the results by County. Project Plan Interpreting the Results - detail results from the multiple linear regression supplied by Tony. What are the regression coefficients, is there a significant relationship between the prevalence of measles and the independent variables (age and region) at the 0.05 level of significance, at the 0.05 level of significance, determine whether each independent variable makes a contribution to the regression model. Indicate the most appropriate regression