define Better from ordinal Improved
Arthritis$Better <- Arthritis$Improved > 'None'
simple linear logistic regression
arth.mod0 <- glm(Better ~ Age, data=Arthritis, family='binomial')
anova(arth.mod0)
## Analysis of Deviance Table
##
## Model: binomial, link: logit
##
## Response: Better
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev
## NULL 83 116.45
## Age 1 7.2852 82 109.16
plot, with +-1 SE
plot(Better ~ Age, data=Arthritis, ylab="Prob (Better)")
pred <- predict(arth.mod0, type="response", se.fit=TRUE)
ord <-order(Arthritis$Age)
lines(Arthritis$Age[ord], pred$fit[ord], lwd=3)
upper <- pred$fit + pred$se.fit
lower <- pred$fit - pred$se.fit
lines(Arthritis$Age[ord], upper[ord], lty=2, col="blue")
lines(Arthritis$Age[ord], lower[ord], lty=2, col="blue")
# smoothed non-parametric curve
lines(lowess(Arthritis$Age[ord], Arthritis$Better[ord]), lwd=2, col="red")
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAPACAMAAADDuCPrAAAAYFBMVEUAAAAAADoAAGYAAP8AOpAAZrY6AAA6OgA6kJA6kNtmAABmOpBmZjpmtrZmtv+QOgCQkDqQ29uQ2/+2ZgC2Zma2/7a2///bkDrbtmbb////AAD/tmb/25D//7b//9v////VPUvSAAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nO3di5LbxpZgUcq21CP1jDxj9rjaKsn8/79sgu9XVQEHiUQecK2IG1dmqAAmidpKPAiuNgCErOZ+AgBZCShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgS1HdAVQDHlE1V8iQXN/WoDy1K8UaUXeGN9euafh//wBP9gAE8rWUBfbuo/tKECCpSTKqA/v9zNn3/7e9ASBBQoJ1NA//3zupi/vm3/+49/hixCQIFyMgX09S6XXVK/D1mEgALlZAro+n6HfTsJHXQYVECBchIFdDvd/Hr34MuwfXgBBcpJFNDtbPN+d/112GkkAQXKEVCAoEQBtQsPtCVRQJ1EAtqSKaCPL2O6n5W+Q0CBcjIF9OGF9MM+iiSgQDmZArov5rVPfw1agoAC5aQK6OWtmDLdTKT4ja8eLbDvY6XX29LyRq2kypPpud7eb+aYH874WHObebKAbjLezu70hEut/NEC+z5Wer0tLW/USqo8mZ7r7f1mjvnhjI+1t5nnC+gocwT09EaUeocfLbDvY6XX29LyRq2kypPpud7eb+aYH874WIObuYBOv8qLt3qqBfZ9rPR6W1reqJVUeTI919v7zRzzwxkfa3EzF9Caqyz0Dt//ue9jpdfb0vJGraTKk+m53t5v5pgfzvhYi5u5gNZcpYBOuLxRKxHQBI+1uJknD+jPL+9dCLp6oOKTOz6Jx38uusAGt6zqyxu1EgFN8FiLm7mATk1AKy1v1EoENMFjLW7miw7oPQEtu96WljdqJQKa4LEWN/PkAR1KQMuut6XljVqJgCZ4rMXNXECnX+Xq/MepFtjg9R3VlzdqJS5jSvBYi5u5gFZY5+rmDxMssL0rjOsvb9RKqjyZnustfl35Uh5rcDMX0BorLXwG69EC+z5Wer0tLW/USqo8mZ7r7f1mjvnhjI+1t5kLaJW1lnx/31hg38dKr7el5Y1aSZUn03O9vd/MMT+c8bHmNnMBBQgSUIAgAQUIElCAoEQBffCFHp3WP4kELJaAAgQlCmj3wXcBBdqRKaC7Oeigr4G/I6BAOakC2hV04PcY3xBQoJxcAe324v/4Z8TPCyhQTrKAbl5Wq+8jflxAgXKyBXS7Ez9mCiqgQDnZAjpyCiqgQDnpAjqOgALlCChAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKEBQ2oC+rrY+D/0pAQXKSRbQl201f/t7s/n3z9Xe92E/L6BAOakCesjmp78269UqVFABBcpJFdBjNv/47+3//tnsJqTdfLQ/AQXKyRTQ1302f37ZNvTr6aFBU1ABBcrJFND1ftrZFfQ071wPO5EkoEA5iQL675+Heef2D6dqvhyi2pOAAuUkCuivb4fd9VNJN90+/KCDoAIKlCOgAEGJAmoXHqjj955/L1FATyeRXs8XL122tA8BBT70+xIDeriMafchTpcxAbPLFNDT5zdPF9K/upAemE+mgHankfYf5Tyl1Ec5gTJ+/733nvtJqoCebyZySKmbiQBF/P77EwT07MXt7IAyYvXcJA5ojIACd27y+WOr3w8KKMCFHz8E9A0CCuw93mv/8UNA3ySgQOfxYc8fw/qZPaCXd7a7t3qg4pMD2vTGSaOh+RRQ4PkUyufCA3pPQIFHIvlMH9ChBBSe1XuXesbyKaDAU3jvWvloPgUUWL5e9RyeTwEFlm+ifGYM6Pp0Qn3wJ+EFFLgwMp/pAvpyc1HS0IYKKHAwOp/JAvrzy91lncPupyyg8Cw+usHS+HpucgV0dxvli2Lubgo66DvlBBSewe9Hb/6NIvnMFdDXu1x2SfWdSMC1KrPPTqaAru932LeTUN/KCQxRLJ+pAnr6XvhLvhceGKJgPlMFdDvbvN9df/VZeGDz8W77XtF8CiiwAB+eNNornM9UAbULDzw0y+yzkyigTiIBYRPkM1dAH1/GdD8rfYeAwlOaJJ+5AvrwQvphH0USUFiKAV/lPlE+cwV0X8xrn/4atAQBhWXoddJob7J8Jgvo5a2Y3EwEntaAek6Zz3QB3bidHdBIPjMGdBQBhScycT4FFFiqyfMpoEAOAw57dgrcLbkHAQVa9/H9PW/UyaeAAu1rcvbZEVBgUerlU0CBRalYz42AAo0attu+VzefAgo0adhJo73a+RRQoD2Res6QTwEF2pMknwIKLMA8+RRQIL258imgQCsCZ9078+VTQIE2RM4bTXPV/O5umX3/arnVHpZYeoElCSg0aOAn3U/K5/N8t/aef73Uik9LLL3AkgQUGtRIPq++7qLnTxRa9XmJpRdYkoDCQhTO5933rfX8qSIrv1xi6QWWJKCwBFPX0zHQhwQUWhHabd8pms8H8ezdTwEF5hA8bbQpm89R9dwIKFBfiXqOzufYeO6XMfZZ3C2x9AJLElBoQDCfjdVzI6BADj+K5bNQPPeLGvdUHiyx9AJLElDI6EexfBas50ZAgUrCZ92L1bNsPPdLHPPTD5dYeoElCSjMI3reqOF6bgQUmN7c9ZwknvsFF1jI9RJLL7AkAYUZjPyoe6P13Ago0KQy9Zwynvvll1vWYYmlF1iSgEICOeq5EVBgIiXOuodXXiGe+9UUX2LpBZYkoFBHgfNG4XVXqudGQIHywp91H5/PevHcr634EksvsCQBhQrmmnzWredGQIE2jM1n9XjuV1p8iaUXWJKAQotS1nMjoEAhBW7xGcvnTPHcr7r4EksvsCQBhWmM/mLiWD3nmnqeVl98iaUXWJKAwgRK1DOQz3njuX8GxZdYeoElCShMIJDPH/nruRFQoL5FxHP/RIovsfQCSxJQmNti6rkRUCAqcNhz7I77zOeMHjyf4kssvcCSBBQKGX7eaElTz9NTKr7E0gssae6XGxbh9+Gn3Rc29Tw9reJLLL3Akhp4xSG/gflc2o77mYACU1psPDsCCkxluVPPAwEFehl41PPHJPUcvJRpCSjQw6DTRtPEs8HfXgEFPjKknrfxXObU80BAgY/0zef4eGaq50ZAgTKeLp4dAQXGKhDPhPXcCCjwhp777SXimbOem3oB/ffPw2vyxz+lVzhEivcEGtDrvNFTx7NTJaC/vl29NDM2NM37AnPqU88i8Uxdz02VgN7kc9aEZnprYDbv5vOunNF4Zq/npkZA19fJPOzLfy692n6SvTvQlEfpfN54dqYO6M8v21fl+81fedk+9umv0ivuI+E7BPN7XM5wPBdSz83kAX19o5RdV2+zWkPONwnm8mY54/FcTj03Uwf017c3M/m6+u3v0qv+WNr3CSZ3feBzgnJ2FhTPzsQB/c93zhb9l4BCM04n3qeYdB4srJ4bF9IDXT3fy+b4cu5kqmff205VCeh6jr31hxp+x6C+D7JZqJydRPHcdP+g9PyLNQL6758zf/7orO13DSqqlc5NtnoOUSOgv77NddnnneW8cTBCrXJ2stRz+Nc0b8xA4dnUyeZBknr+fjT0B6scA30tdtHnr2+Hw6ndtfiBi/EbffugjkrZ3Et00igUz06ds/A/v5SZg65X+6tHX4/vx9CzU+2+gzCxu3iGitHX8q5YeqjSMdBr0ZPy68Os8/W8qIGT0EW+h/Cx23jGJlw9pahneNZ5KVNAu89/fj4sb7eI7sYkw5bV4NsI06u2257kg5rhY563MgV0fTiU+nK+mdN2Lvp10JNr7p2EyVWLZ456bgrNPjuJPom0nW9+Pvz/ucDrYQdXm3wzYVIXu+3VD3su/RcuUUCPdybZ/v85mi/DprOLfz/hxmU+Jwxo+/GcZPw5A3q+Ln/gTZ1ae09hWnVmn83Xs9gxz1vVAtodCP301+YlfjnTdtf96+H/BRR6qHPSvfV6biaafXYqBXR/HqkL6Ihr6teHcK6vduEdA4WHZrpafsq1NadOQA/n4bcB7S7ljBb09XDV588vp0UcZ6W9n9xzvbs8swr5bLqe0x7zPagS0O56zT/++fml6986fh19t5jdHPR85mg98Er6pt5gmM70+Wy5npMd87xV67PwX7uJ4y52LwMv3byw+4a6bpf90M3uE0nDltXSewyTmfzTmi3Xc1Np9tmpc0PlXfUOAR1zb6ZdQa8MvE9eY28zTKL2HZae99eq0u3sunniIaBDL928tr5+34YeTn3id5rnMWU+H9SzkV+qarPOS5U+ytmF7hjQkd/Hef5gaGAi28p7DdOZLp+N11NAp9bK2w0T2Bdkon4+imczv06zxLMzwy78wI+vF9XOOw6FHadgU/Sz6XrOqdJJpG7OeQjorN+Q5E1nmc47sOX72Wg9Z5t1Xqp1GdPnY0C7izkLfb9HQAvvO5R3iknpfjYcz6cJ6P7q+V1AX2Inf97y88t7B1TbfO9hOmUPgDb7G9REPDtVP8q5V/IUkoDCpZL99OvzsUo3E+l23Cfo5wcBvWcLYEHup2Hl+tlePJuZdV6qdju7Q0Lnu4JpZ/6tAAp5cBCwVD+bq2czxzxvJbqhcgmzbwhQxMOclDkA2lw9N43OPjsCCgk9DEqBfrZYz5bV+STS1T3nXEgPUxjdz7bq2eys89IsAR11IPR8O5HA9fgCymKN7GdT59ybPeZ5q35Af30bEdCXm3d4aEMFlMzeLcqogLZUz02S2Wdn4oDeBm8vugt/fzvQoS0WUNL6YEo2op+N1TOTiQN6dQX9SfCO9LsroS6KuVv4sBjbMsjpwz3acD+bqWeaWeelqXfhX8v1s1vWTS4Hf7BeQMnpo7gEr2BqpZ5pjnnemuEkUtiDs09Db+0koCxTpJ+t1HOTdPbZSRTQh19h7HvhIdDPB3uGUz23RUt0If3hxvbXBt7e3lZCIr2nZQP7+ejERPApjpB21nkp0YX0AsozGXBQcNAB0CbqmfaY561EF9Lbhed5DMpL734+iucsvxKLiGcn04X0TiLxNIb0pWc/m6nnkmS6kP7xZUyDLoqywbA4vfrZQD0XM+u8lP5C+mGzWQFlaXocAG0kngLaa4lX/1XyQvpHOR54gZSA0q5QYT7s5/z13Cx09tlJdB1oZ32zIbiZCMsQnaJ91M8W6rlkyQK6cTs7Fik4RXu/n/PGc7GzzkuJLqQvQUBZkvf6OWs9F3vM81a1gHbHL7vvhZ/vbvQdAWVB3u7n3DvuTxHPTqWA7k//dAEdePukwgSUhoyszFv9nLuez6ROQA+nz7cB7Y5gzlhQmxLNGLuP+7if89XzaWadl6oEtLuA849/fn7pziWtZ/1ueAGlDeOPED7q52zn3J/mmOetKgF93V37uQ9otxMfvhB0NAGlDaNj86CfM+66P2U8O1UCur//0iGg2+morzWGce766cDnLGoE9HAbpUNAt1PQ+fbhbVUswk0/1XMudS6k3503OgZ04C08i7JhMZ9y+7lX/Zz1uGedVbVLQKGComdZLvo5Uz2f9qTRrRl24aN3pC9BQJlHydqc+umSpdlVOonUzTkPAR16D+SiBJT0Dv2c7ZIlLtS6jOnzMaCDv8q9KNsYNU0xTdv3c4Z6mnU+UOeTSLur53cB7W5RP+PH4QWUaqY5SPi4n6XXcssxzzdU/Sjn3owfRBJQKpmqN4/6WXwl98TzDZVuJrL7No75+ymgVDJNcFb3/Sy/Egaodju7Q0JnzaeAktj21+e2n9Ou0KzzY26oDAnsenndz4nX6JhnHwIKpUxVnGMyL/o5xWouqGdPNQP688vs+/ACylQmO029uutn+XXcks+epg9od+HS7gr647fBzXcZvYAylcnreepn+XUQN3VAjxcwfd2FdPaC2vyYxtT1PB4ALb+SM9PO4SYO6Pnypf/zbf8JpPVhPjoPASWL62s9p++nw54REwf0dbXL5r6j+09wuiM9CzFhcVa3Ju6negZNHND1IZv7b0XaPbTdqXc3JrKb8rONd/VcvfsF8CXIZ9C0AT1/fcd53ukrPViAyWZs9/VcvfcF8Mxr2oCeZ5uv53swrd1QGR57VM+p+mmvvYDJA3o45S6g8IHH9Zykn+6uVIiAQk+TFuetek4z/xTPQgQUeph2yvZ2PTeHgE6yWkYTUOih5iVL1xtpsX6adU5AQGFGH9SzVD8d85yIgMJcHl6ydK3QAVDxnMjkAX1EQMlg4up8XE8XgDZPQOGBqfd5+9RTP9snoPDATJcsXRvZT7vt03NHeqiqbz3H9tNJoxoEFE4mL07/eo7rp3pWIqCwN3l0htRz5PxTPisRUNhM+I1GRw/OBbz7950/SmHik0j/+c6N6/5rhlNJAspjTU0+O5F+mndWN/lZ+O9v/LXXWc7FCyj1Da9nqJ+Oe85g+q/0ePgNSN03HM/xxR4CylmV4ETqGeines5j6mOgu++Cv52Fnr7puDoBZa/Oh8Nj9YwcAJXPeUx/Emn/dfCnb/E4fE/nTF9tLKDsVZiwRevpBFIiFc7CP/g40mxfiiSg1BGvZ/9+2mufX5XLmG4SOt93ygnoU6sWnPt6DtnuevXT/enaUOs60MOe+6z13Ajo86oYnFH17Dv/FM82uJCep1ArOCPr6fhnMgIKpYyu50f9NOtsjYBCEQXq+X4/HfNskICyWBWDU6Sem3cuoHfKqE0CyiLNetIovJW9Pf9UzzYJKIs030mjEduY80fpCChEFa2nE/AZCSiLUfUo4aN4jtu67vtpv715Asoi1P1kTvl63vfTaaMMBJRFmPWM+/h63vVTPXMQUBhgknhuHs4/SyyWiQkoiVXOzFT1dP4oLQElpRnuRjRZPfUzr2oB3d2afjXLFyFdENClqJ3P6eK5OfXTYc98KgX09bzdzfNdHgcCSsCk9Tz201n3jOoE9OVq43vrizorENDM5gnMxPW87GfhBTO9KgHtdt8P++4Xf5yDgGY10x3YJ6+n45+5VQno+nLW+TLbN8ptBDSvOWafFeqpn8nVCOi/f14lcz3j13oIKD1NeM79ZPtvgn7mViOgv75dHfZ8nXEfXkAzmfGkSo3J5+/6mZ+A0qQ5v3WyVj31M79Kx0A/v/lfYdssfx38QwKaxWyzzyoHPg83WtLP9GqdhT+37rXQlaACSnGV6nmgn/nVuQ709XT9/DZ7ha6kF9DFmflC8rr11M9FmDag28g9EjsGWmJhAtqqOY95dqrU82p8+rkEAkoTZp19PtisJljL9T8Q+rkIiQJ6+YF6AaWYGeqpn0uR6nZ2XY9PF+E7BppeCzfPqFLPzd3XG+nnQqQK6O5DocdzUAKa2tzHPHdq1fOOfi5FsoDubkay76aApjZ7PGtdLv/oYf1cjGoBPRwOHf0hpH//POzGCyhxler5OKD6uRyVAnpxNml0Qrubi34X0ITmn3Xu1aun+efS1Qno1dn40RfSd7vxnwU0lyaOeXbu6znJNvH2UPVzSaoEtNvtPtxPpLsUafTd7Lrl/fb/BTSTJuI542mjE/1clCoBfb2IZhe/8d/psf+OEAFliPnrqZ9LU+luTBcHPre73gXuxrT7kk8BbVsjs869Fuqpn4tT6Y70l6l7KXNH+rWAtqyZY547ler54Xj1c2ncUJlJtBPPmvX8YMz6uTgCypJVOuf+zkWfF/RzefLuwocI6JQamnXuPKjnZO9/j5Hr5wJlPYkUJKBTaeuY56ZuPXvRzyVKehnT0c8v7x0PaO53aMGaj+fc77x+LlLKC+nPBJRb9eo55B8N/VymGT7KWfIU0vsBvSegJTU169yrVs9hhyx+6OdCJbyZyBgCWkpzxzw31U8a9R+7fi5WutvZjSOgpbQWz5aP1+jncmW7ofJIzfxOUVa79XT4c9HqXMY087zzrKFfq4Sam3UeVN1xH/wz+rlklS6kL3jifX36LQlcTiqgUS0e89ypOPUMjV8/F63SRzlLXTr/cvO7MnS5AhrVZDyr3mIp9gro57KlmoHu7mF3beDBAQFdkKqHPYP/gOjnwtX6JFKJDx/trse//lDo0KvyBXSINmede7VPGsVeCf1cujpn4bdTxwJz0Ne7XA7+XKiA9tXsMc9N01csXXH50vJVOgY6ar/7aH3/g0MPr7b5i9aiVuOZpp76+RQSBfTmrnh7A++N1+rvGv1Urueof0T08xkkCujNfZn3Bt6dWUDf0+ysc6/23HPcIQz5fAqJPokkoFNq+ZhnJ1c99fNZJAqoXfgpZYvn1G/kyJdDP59EooA6ifSU8pw0uqCfz2L6gO6vfh/+DcT3Hl/GNGjJCX73Kmp51rmXsp76+USmDuju4vfOp79GL/nhhfTDTkil+PWrovVjnptZ6lnm9dDP5zF1QM+3/ihQ0LvT+YOXKqBHCeNZ47BniddEP5/IxAHtvgKpO0jZpa/E5+HXN79SbiayRPPV0/yTYSYO6Pr4Ucvt7neBKehhkcF6bp49oI3POvdmOuxZ7KXRz6cybUAv7sP0UvLbjMOeN6AJjnlu0p40uqCfz2XagF5cZfTzS5Ez8SNl+3Usp/14LqCe+vl0qgW04F2VR8j3G/kk5jjsWXC//Ug/n42ALliCWefOjIc9y74++vl0BHShchzz3Cyonvr5jAR0oVLEc8bDnhO8PPr5hAR0SVJE82iew57T0c9nJKALkWaXfW9p9dTPJyWgC5G9nlVWPOFLpJ/PafKAPhL8TqQST26xAc1ixqnnlP/G6OeTEtDEEk069xZaT/18XgKaVa5jnpu5D3tO+lLp59PKdEf6ApYTUPFsh34+LwFlasuup34+NQFNJNesc2/2HffJ16Gfz0xAk0h2nefO3FPPKq+Xfj41AU0iWzxnr2elV0w/n5uAMoHZ61nrHxz9fHIC2qiMu+x788ezU+eV089nJ6CNShrQNupZi34+PQGlmOeqp34ioE1JOOM8aSKedV8//URAW5F0l32viXrWviGVfiKgzUgbz+esp36yI6CM0EY8O7X//dFPOgI6k9S77Hvt1LM+/WRHQGeSPaDPXE/95EhAGaydeM7zD9AP/eRAQCvKPOM8aaqec7yeP/STEwGtJPsu+15r9az+av74oZ9cENBKxLOsWSef8smBgNJHW/Wch3xyR0AnlH/WufMwnk2/zVOQTx4Q0Iks45hnY1PP2V5P9eQxAZ3IYuM572HPOS9bkk/uCGhBS4jmiXoeyCdvE9BClrLLvtNYPDvzvLQ/fsgn7xHQQhYTzxbrOYsf6smHBJQL4rn344d80oeAjrCcWedOe/Wc+Wp5+eQjAhq0qGOeLcZzlvNG4skwAhq0oHiq5554MpiAPrkW49mZ5w7z4skwAtrTwnbZ91qoZwutEk+CBLSnxQU0Gs+7w4QNGvRCxH8SBPQpfVTPmcJXTt8XIvRDcCSg71jUjPPkHMyZ4lZHj1di6N+HWwL6hsXsss/UrwYMeFkqvRcsjoC+ofl4zhOlyWJT8uX+8KnWGBBPQUAbU7mGb5n7ZRjpvXEsaqDMTEArqRzAfuZ6MSp4Y4xPM37qENCSKvevnwau9XzLpIdJTi/A/SNXj0KcgA5Xu4Bv6DXcduNZ4dOaVy/V0NcOehDQN9TOYfHf7cfxbObdqXOJw6SvMAjoG9oNYx+Nx7NT6yIH9WRKAvpYgkq+4Y14Nv22TKqtt4dlEdDHWs/kY+L5UOPvGokJ6GIkqGfrH06AgQR0ERLEc85vJoaJCGh+GeKpniySgCaXIZ4d+WSJBDSxLPGEpRLQrMQTZiegGSWZejrsydIJaDaZ4imgLJyAJvLG6fYmxySePAMBTeKteKYdECyAgCYgntAmAW3cm/FscSR223kyAtqwVPH0YSOekIA2Klk81ZOnlCygP79sC/LHP6f//vfP1W9/D/j5VvNzLVk7d+STZ5QroOtDSD4fH1heQDPGE55VqoCuTzU5TkKXFVDxhFwyBbTbf/++/f+Xc0EXE9Bshzz37Lfz5DIFdL369NfuD9tsHgq6iIDmjKfzRpApoNtYno59rg8FzR7Qt9vZ2BO9o56QKqC/vu124PcOBc0c0Lzx7Mgn5A1oV9DPaQP6TjvbeIJAH2kD2h0H/ZoxoJnjadoJVxIF9PIY6P4/V99zBTRzOzcOe8KdRAHtLl+6mIJ2V+93/dAAAA9OSURBVDV9+n9pApo8nuoJD2QKaHcd6NeL/37d1af9gGZv5458wr1MAd1dQX+5G//afkAXEU/gsVQB3RX0cg7azUmbDah2wtLlCmh35ujr1QMvTQb0vXYmiqfDnvC+ZAEda/p8LaSdvlUTehDQoktfRjw74gkfE9BSC15OO4GeBLTAMsUTnlPygP788t5JpOmDtqx22m2HYQQ0alntdNIIAhYd0Htlhruwdu6IJwyXPKBDjR/uEuMJxAjokB/WTuCCgPb8uUW20247jCKgH//IMuPpDnUwWr6Anr8c/vPHf/nWwOEutp3qCSUkC+jLTcSGNrT/cBfczh35hPFSBbS7e92NQRcxDRjuouMJlJEpoN23IF0W89e31eHr4XvrO9yFttO0E4rKFNDXu1zuvlhuyCLCAR2yklY57AmFZQro+n6HfTsJHXQYNBLQIctvl3pCeYkCenc3+s7LsH34ocdAByy6cfIJ5SUK6Ha2eb+7/trkV3oAT0FAl8y0EyaVKKBVd+GXwGFPmFiigNY8iZSfesL0MgX08WVM97PSdzxXQOd+DrB0mQL68EL6YR9Fep6AAtPLFNB9Ma99+mvQEhYeUNNOqCpVQC9vxbQ33c1E8vGdRlBbsoBu6t7OLhH1hPryBXSURQd07qcAT0dAAYIENDF77TAvAc3qdyeNYG4CmpR4wvwEFCBIQAGCBDQRu+3QFgFN4ncnjaA5ApqEeEJ7BBQgSEAbZtYJbRPQRjnmCe0T0EaJJ7RPQAGCBLQhZp2Qi4A2wjFPyEdAGyGekI+AAgQJ6IzMOiE3AZ2JY56Qn4DORDwhPwEFCBLQisw6YVkEtBLHPGF5BLQS8YTlEVCAIAGdiF12WD4BnYiAwvIJKECQgBZkxgnPRUALscsOz0dACxFPeD4CChAkoEF22QEBDRJQQEABggR0ADNO4JKA9mSXHbgloD2JJ3BLQN8hmsB7BPQNdtmBjwjoG8QT+IiAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKEBQvoCuV0efh/+wgALlJAvoy+ra0IYKKFBOqoD+/LK69dvfg5YgoEA5mQL675/Xxfz1bfvff/wzZBECCpSTKaCvd7nskvp9yCIEFCgnU0DX9zvs20nooMOgAgqUkyig2+nm17sHX4btwwsoUE6igG5nm/e766/DTiMJKFCOgAIEJQqoXXigLYkC6iQS0JZMAX18GdP9rPQdAgqUkymgDy+kH/ZRJAEFyskU0H0xr336a9ASBBQoJ1VAL2/FNOXNRI5LDzzBUR6tt/dzqfGkS69j1PL6vlp9VzJmeaUf6631F6HvOnqrsZkPkSygmxq3szutoPK79Gi9vZ9LjSddeh2jltf31eq7kjHLK/1Yb62/CH3X0duoH55CvoCO0me4pzen8rv0aL29n0uNJ116HaOW1/fV6ruSMcsr/Vhvrb8IfdfR21y/m28T0Pu/crFNTPpkPl5v7+dS40mXXseo5fV9tfquZMzySj/WW+svQt919DbX7+Y7BPS9v1LzXXq03t7PpcaTLr2OUcvr+2r1XcmY5ZV+rLfWX4S+6+htrt/Ndwjoe39FQCdcx6LbIaAD1tGbgJb288t7F4KuHvhwkQJaaR2LboeADlhHbwJamoAO/IsjCGilx3pr/UXou47eBLS09wN6T0DjBLTSY721/iL0XUdvAjo3AY0T0EqP9db6i9B3Hb0J6Nx6BXR1/uOkT+bj9bqMacgPt3QFj8uYBqyjN5cxza3PcOe6WHfUZdY1nnTpdSz6GvLSz6/4ky69vDHr6G2u3823Ceijv9T7jFNRj9bb+7nUeNKl1zFqeX1frb4rGbO80o/11vqL0HcdvY364SnkC+j0n4XfzPUePVpv7+dS40mXXseo5fV9tfquZMzySj/WW+svQt919FZjMx8iWUBfVtemuRsTQB+pAvrzy+rWsPspCyhQUKaAPrwj/aDvlBNQoKBMAX38nUj3X3X8DgEFyskUUN/KCTQlUUB9LzzQlkQB3c4273fXX4t/Fh6gJwEFCEoUULvwQFsSBdRJJKAtmQL6+DKm+1npOwQUKCdTQB9eSD/so0gCCpSTKaD7Yl779NegJQgoUE6qgF7eisnNRIC5JQvopsrt7AD6yBfQUe4OAQCMULxRpRdY0twvNrAsxRtVeoFPbzHHHQykNQbSnuWMpBWL2TgMpDUG0p7ljKQVi9k4DKQ1BtKe5YykFYvZOAykNQbSnuWMpBWL2TgMpDUG0p7ljKQVi9k4DKQ1BtKe5YykFYvZOAykNQbSnuWMpBWL2TgMpDUG0p7ljKQVi9k4DKQ1BtKe5YykFYvZOAykNQbSnuWMpBWL2TgMpDUG0p7ljKQVi9k4DKQ1BtKe5YykFYvZOAykNQbSnuWMpBWL2TgMpDUG0p7ljKQVi9k4DKQ1BtKe5YykFYvZOAykNQbSnuWMpBWL2TgMpDUG0p7ljKQVi9k4DKQ1BtKe5YykFYvZOAykNQbSnuWMBKAyAQUIElCAIAEFCBJQgCABBQgSUIAgAQUIElCAIAEFCBJQgCABBQgSUIAgAQUIElCAIAEFCBJQgCABBQgSUIAgAQUIElCAIAEFCBJQgCABBQgSUIAgAQUIElCAIAEt4eeX1dbXi0d+fbt9JI/taL6f/iPlQP79c3VyHErKgWy9Xo1ik3IgL6srh+eecCD3BHS886/raTNfHx749NecTyym266TD2T/q3n1nuQcyOGf5q3f/j48knEgDwOacSD3BHS0B9Od8wZz2u7zWF/8S5B0IK+r27ck6UBO/Vyt/vhn90jKgTwKaMqB3BPQ0V4Om0Q37dlv5d1m3/2p+z3+PPOzG+z14h+CrAN5udrn3aQdyGmTOm5jWQdy9nJ43ukHciCgY20noIedkO3mvv/T+rhNbLeSbDso+73f835vyoGsb2c1SQfycpp4vh7+lHQgJ8dxpB/IkYCOtd0Ajv+CHiY+2wYdf39fsh0k3/5z8Nv/PQY060C2gzh05yDpQM7/Nh//mHQgJ6c5RvaBnAhoQet9eS6Suv3j9a9y67p/A047wFkHsv3tvN4rTDqQi6d9/0imgZysj7HMPpATAS3n+K/qxT+p3YQu0yHy7cb89XwEMetAtvuJ33eHIo7zt6QDeb2bmyUdyNHr5bmwzAM5E9BiXo7HDi9OYVzshGWw3/e9DGjKgbysPv3H1bnrpAPZPe39RR7fLx7ZyzSQg4unnHsgFwS0jPXFJTPrq+soM20c+2f7kn0g69XZ8dRLxoF0T/V4tc9+kpZ0IAcv5xPuuQdyQUDLuA7oeYNItXEcdhlfkg+km7Ptn+zr4eqfpAPZPtX/fX21ZNKB7J3OIG2SD+SSgBZxvJj+MN+53Dhurkhs2HYDP150+CigaQZy8Yv688suPDkHst+odm/J8WrJnAM5eL244jP1QC4JaDndeYv7rTzPv67Hqyezz0Av7X85cw5kF9DDqZbD1ZI5B7J3dawz80CuCGhBh6lP0o3j/txR0oFc2p/tTTqQ9ep8gU/mfwn2ri5XyjyQKwJa0v7XNecZxvOVeenPwl94TfyOnK+a3CQfyM7VBfOZB3JFQEvab+WvKa9xu7nhQ96BXMn8jlwlJ/VAOtedTDyQawJa0n6zyPkpiwcBzTmQK/uZTtKBXJ51ybxp7Vw/38QDuSagY11OE/YHqnJ+zvdBQHMO5OZTLok/Qn64hmAn86a1c/18Ew/kmoCOdbGVH/+Y/E4z5+NTOQdy8Y4c53A5B3J+2gvYtG5OFeUdyDUBHau72OR0+c8ibtp4DmjOgVy9I/tfzpwDOT3t0ycCsg7k/khn2oHcENDRLu4afnEeO/Hdti/OkOYcyOU7ctg7zDmQy+Mqme9Ivzl/SuMk60BuCOh452/gufigxV7KnZOXq88pZxzIuaDJB3L+cpLLC0IzDuT+VFHSgdwQ0BL2v7CXG0jmbxy8+kKMpAN5vf4HbZN2IPvOXE7Scg7k/t6mSQdyQ0ABggQUIEhAAYIEFCBIQAGCBBQgSEABggQUIEhAAYIEFCBIQAGCBBQgSEABggQUIEhAAYIEFCBIQAGCBBQgSEABggQUIEhAAYIEFCBIQAGCBBQgSEABggQUIEhAAYIEFCBIQAGCBBQgSEABggQUIEhAAYIEFCBIQAGCBBQgSEABggQUIEhAAYIEFCBIQAGCBBQgSEABggQUIEhAAYIEFCBIQAGCBBQgSEABggQUIEhAAYIEFCBIQAGCBBQgSEBJ5nW1+uOfuZ8E7Agoufz752q1+j73s4AdASWXn1+2Af0897OAHQEll5fVp/9Yffpr7qcBHQEllV/fVn/892r1de7nAR0BJZXXbTy3Ef3t77mfCGwElGTW3e77+uo00ranq21Ru7np4ez8trLONFGDgJLJzy9dJV8vTyO9rPb+1zGgu9NMq11UZ3uePAkBJZOX3eHPf/88n0Z6XZ3tAnrqp4IyOQElkWM5X06nkbr9966Tr8eAdteJ7v7Oq8udmJyAksh2dvn58P+H2eXL6mLHvfvT+YNK27a63IlpCSiJHM8eddPM71ePnFK6vtq7d7kTkxJQ8jhfv3ScZ15c0bQ/v3RxLv40X4WpCCh5XJ4w2k80L3q5/+Pumqab00owFQEljd19RM663fP9tHNnH9CLc/ACyuQElDRu4tjtu9/NQC+KCpMTUNJ4uTgpdDiN9OgYqKs/qUZAyeL6sqT9Sffz6fj7B2ByAkoW17ei3044u5yergPtzh51f3o5fwDJZUxMTUDJYn3Vw26q+fn8SaTd8dEuoLvT8N8Pf9+F9ExLQEni/OmjvdeLz3BennO/fMAElGkJKEm83Hy0vZtqft+8dzcm/WRqAkoO92eH1udPI3WT0YvPHe2b6lNITE5AWQgf3KQ+ASW1881E1nbZqU5ASe10qt05d2YgoKR2+fFOE1BqE1Bye9VP5iOgZLd2zp25CChAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShAkIACBAkoQJCAAgQJKECQgAIECShA0P8Azk1CigWaNBkAAAAASUVORK5CYII=)
main effects model
arth.mod1 <- glm(Better ~ Age + Sex + Treatment , data=Arthritis, family='binomial')
Anova(arth.mod1)
## Analysis of Deviance Table (Type II tests)
##
## Response: Better
## LR Chisq Df Pr(>Chisq)
## Age 6.1587 1 0.0130764 *
## Sex 6.9829 1 0.0082293 **
## Treatment 12.1965 1 0.0004788 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
same, using update()
arth.mod1 <- update(arth.mod0, . ~ . + Sex + Treatment)
Anova(arth.mod1)
## Analysis of Deviance Table (Type II tests)
##
## Response: Better
## LR Chisq Df Pr(>Chisq)
## Age 6.1587 1 0.0130764 *
## Sex 6.9829 1 0.0082293 **
## Treatment 12.1965 1 0.0004788 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plots, using effects package
library(effects)
arth.eff <- allEffects(arth.mod1, xlevels=list(Age=seq(25,75,5)))
plot(arth.eff, ylab="Prob(Better)")
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAPACAMAAADDuCPrAAAAmVBMVEUAAAAAADoAAGYAEyYAOjoAOmYAOpAAZmYAZpAAZrYAgP86AAA6OgA6Ojo6ZmY6kJA6kLY6kNtmAABmOgBmOpBmZgBmZjpmZmZmtrZmtv+QOgCQZgCQkDqQkNuQtpCQ29uQ2/+2ZgC2Zma2tma225C2///Z7P/bkDrbtmbb25Db/7bb/9vb////AP//tmb/25D//7b//9v///8d1HzwAAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nO2da4PjNnZg1U7S2XLPblLO7Eyqk2ysSWzN7thsm///xy3BJwgSIHgJEAB5zge7WqIgPi6O8MajBgAAEY/UJwAAUCoIFABACAIFABCCQAEAhCBQAAAhCBQAQAgCBQAQgkABAIQgUAAAIQgUAEAIAgUAEIJAAQCEIFAAACEIFABACAIFABCCQAEAhFxSoM9Hx0fE73jpX/ESfl/17nvkbz806X/6MVyCkI72YSqiPS2i8yyuKNAxPj//Eusrvn3RvmL2j32JhA3RPQlCKqqHRpQQJTrP44oCHSN0+zdRyKjox5vxD39+/7qnCOIRovsShEQM1aN4MUp0nsgFBdo+qrhVpEr/hkr2dc9dn/AI0X0JQhpeD4Pvfgr9FUTniVxQoG2d5dMfolWQ6j4XDAEz+4c/hOgd6QuEH9rfu0qGPhCdJ3JBgbYh8/mvkepH2lf8svIPfwjRO1LNCp3PKEVQovNErifQrgb/3v28R3pihCjImD+kroMn9GARovNErifQrgb/Y/fE5pHTxet7/8dYdeqrUtana7yv96K+zf7hSu41O0b/2MfyuI/h+PE9M0T7ht6x9GJPEHLiqQXB+GM//NOInO6f/UN/2WKU6EzK9QQ6/ORWj/lDnfojP/2oC3TqplxvjVq87w7R9eRexlFbITq+P8TgPETHoSnjaxcO0UvR9cGv/lYvI6d7qG0hoHviy5Ik0ZmYywl0/FHvYmUKEy12Pv2fSaDa016t6izfd4boanLauID+1Y0Q/fP0bh+CsxCd9+S2ufHCIXophue0/K1ei5xRt10ELSvJRGdqLifQoQZvttDPw2SMYU2r42s6K++7QnQ9OWPo3/tmiOp0V6CHaGUc8V5fOkQvhR4gH7Y3xsjpgraJgGp80NbUiM4kXE6gU6N5NXte1fi8hzBq4+c5HvRcfbzr71ub6VcPn2piXQmgCzpLq3ofoioa9eYqLUS1l6vxWHuCkBVzv0w/2OuB1gXMW/fIl/UjojM5VxOo1iw/q8OPv+Xj3+07+k/nax7RdW1/3xai64dr3VljN9ZGiHZHayetpfzUwlK/xquG6MUwCnF6S/xKIGpHLyvwRGd6riZQvetIr8PPBoz0v+vD4XoMG4PyLO/bQnT18FkD0XP8BmeIzk70o9YTmY/P0q73qiF6NYyKdBcPtkB0TqsjOtNzNYHqg5f0OvxsAHMXlW/1/LG2rxo/85b3bSG6evh87PRq0hqv5Ymqg6YQnSenlbivGqLXY67Q9llaA9G1FgjRmZ6LCXT2A6jXIKpZDA6j8ZY9S/NGUNv7lhBdP3z+1SOuEJ2f6DxEjZHR08DCq4boNdG6bt6dgag1OxoQnRlwMYHOB39qdfj5gx1agFZiavaMbe/7h+i7dS6IK0TfZgfNG2uNplqjMeuCIXpZhoJoExuOQOzfWnmwRGcGXEygz0WMrAUVAoUsGId3OgKxr8P7lUCJzrO5lkCN9vmONjpW6h5vszaaVWzvO0PUPJxKEkw0D1Ib6TGMWrYH4qi9ZQQRnRlwLYGag3hblo3b651Ia/jEkaWZfn5Sw1dXY+NWyGb6D0eCkBHm8kvjY7U+vGkc0/JdojM91xLoWg2+e2yWYUxba9VY3reF6OrhXbFYD8z2CO+BItPwkHsOFLkS5oyi0Te2QFzOK9cgOtNzKYGa09/raQaHZSC9NnRYPe3FA7a8bwvR9cO1oVVafDlDVD/R9pM3Hqp8JWYLLOnxaQm0Pmz/e30pEaIzPZcS6HzypmJ6nM6pnNMkYjNI19/fmCxnHG5+tRZpluJDd4ze/6qFaF8oWZ8sF28fPQhB/3i1ke16FXc90JoDLIvZEZ3JuZJA9VJmz1RlsiwmMluf5mEMA7W+bw3R9cOXyzXUWuPWPFsslmuwTMNbnrMlQciJla7wlVXBhoc6tjVZVmMiOpNzJYFOTZsj2ioMWg/9d/97OnDe72SZLme+bw3R9cOXC4bVWjSvhOg//Mt4tOeCYfYEISsWA0UckaOVCCzrgRKdqbmSQGct3D3asjNj6H73k74uuHWBsdr+vj1ELclpQTUfRfcwjN+n9rchqH2XrLUnCJmxLpi1yNFXX7ZU4onOxFxIoCs1+OE3t39oY7fhfGOFPqqse7os33eEqC25l5lfhkBbO65JberinBJ1bJpgTxCyY9SYUaI0ImfWKt8/8OVPPNGZlAsJdAcZdwluDawCSAfRaXIXgTa/iWZ1AoEC7IPoNLmRQB/rbd6ZQYhCvhCdJncRaN/Ko/XHL9dAzAJCFPKF6DS5jUDNMXCZ1uAJUcgYotPkNgI1B/jmGgWEKOQL0WlyH4HORxFnOxqNEIV8ITpNbiTQMmZDEKKQL0Snya0ECgAQEgQKACAEgQIACEGgAABCECgAgBAECgAgBIECAAhBoAAAQhAoAIAQBAoAIASBAgAIQaAAAEIQKACAEAQKACAEgQIACEGgAABCECgAgBAECgAgBIECAAhBoAAAQhAoAIAQBAoAIASBAgAIQaAAAEIQKACAEAQKACAEgQIACEGgAABCECgAgBAECgAgBIECAAhBoAAAQhAoAIAQBAoAIASBAgAIQaAAAEIQKACAEAQKACAEgQIACEGgAABCECgAgBAECgAgBIECAAhBoAAAQhAoAIAQBAoAIASBAgAIQaAAAEIQKACAEAQKACAEgQIACEGgAABCECgAgBAECgAg5JICfYS/qvBJlpBijCTvAzcvHafd+0s+4yJMUkKKOOAI3Lx0INAjFGGSElLEAUfg5qUDgR6hCJOUkCIOOAI3Lx0I9AhFmKSEFHHAEbh56UCgRyjCJCWkiAOOwM1LBwI9QhEmKSFFHHAEbl46EOgRijBJCSnigCNw89KBQI9QhElKSBEHHIGblw4EeoQiTFJCijjgCNy8dCDQIxRhkhJSxAFH4OalA4EeoQiTlJAiDjgCNy8dCPQIRZikhBRxwBG4eelAoEcowiQlpIgDjsDNSwcC1XhAUaSOl7NJfb9hH2EfftDUYpD6dsNeUkfMuaS+27CXsI8/aGoxuFuGLJ27Pa+7XW/pIFDImrs9r7tdb+kgUMiauz2vu11v6SBQyJq7PS/jen+1kej0wACBQtbc7Xn5ChSD5gEChfz4+efxz7s9L/v14swcQaCQGT8rxn8V9Lx++0GNank3X37NR70s3p+DQMsCgUJW/PxzqQJ99ob89OP8dQR6ZRAo5MPPI+NLxTyvyZPf/WR5A4FeDwQKmfCzzvhqKc/r25fH4/MvdV01jnyzHfRyvNeDQMsCgUIW/Pxz2QJ9Dm5sTGpW4geqzrFOEGhZIFBIz88LxrcKeV6//TDW3F+2anpzjE2tEwi0LBAopGapz/IE2pQ736Y/18uZz80G0BqBlkZCgf7+tW9W36zWhKSQDHkb1uyZh0B3xadW7Gw+Z3QjdfhU4BFoaaQSaDdmbuQ8hyLQjLDYMwOB7o3PRqAf/Z+NQNdq6paXTRBoWaQRqBGeZyoUgWaDXZ+pBbo/Pp+TQJu/10xp7YE3v8n2FQg0RxaBcjA5r6Oe85Ds60pbAzzCgEDzwGXP1AIVxKcuzVWB2nuQEGjJJBCoGjE3/Vz3qNHGPjWcwyDQHNjQZ1KBiuJzLtDFx9sWUK8SAgIti/Or8JUlElXcLuMuOAg0OZv2TCpQWXxulUA9W0ARaGmcLtCmKmMLw2q99zIsCDQtPvZMKVBhfG4J1Dq2yQSBlsX5Av2jI47+gkCvjac+UwpUFp9bvfDW0fUmCLQsShpIb1kuTNG2W3k1MiHQdHjrM3Uv/H4q9zhQ7xo8Ai2MVAJ97q+t25YL04Y8ezSiFpIhL8gOfaYX6N743JiJ5F2DR6CFkUigjfL2Dvy0Lhc2qdXHoAg0Dbv0mVygu+NzYy68dw0egRZGIoE28bZz2KdjubDhJVXF3yw4INAU7NRncoHuj0/3akzrY+vXQKBlUUwJ1BGg4yINyqBbRVAEejq77ZleoPvj07keqG16/AoItCxStYFWOwd9uqpI08+7R00JgZ6MRJ/JBbo7PleamLS5R82fvj5GoGWRrBfev1l9ONzaSD8TKCXQrJDpM71A98Znvezk1AS6IzEEWhbp2kDnbNVwXMuFje/5DBZBoOchtWd6ge6Oz+lD79o/NYH6Nqki0LIoSKDWgcoqsTft/+4zRKAncUCfZQo0CAi0LEoRqHO5sCm1NX8a34RAz+CQPecCTfH4ECj4UcpMpI25xn3702oDKAI9naP6TC7QdCDQsihToIYpp4H0RzbtgjAct+fPyavw6UCgZVGmQOeeVP5UTfdqRiedSIkJok8EugICzZGUAlUNTY3wXj4jPBwC1UbWv7a3BrlbhjyVQPbMRKB74nM43jYQubI3Mc2xXe/3HX5nAqeRTqBduKkA9QksRy/8fITTVhEUgcYinD2zEOi++Kxdi930i4V59UWtX+/3Ez6nAqeRTKB9P2cTbU+fn2bHcmF6gXQ72BFoFILaMweB7oxP52I3oz899k5cvd7vv8egmZJKoKq98vMv374o9z09fpkdM5EQaGpC2zMDge6NT9dceOXiNmJf1hr+BAIti4Rz4d9V1LXu85jC7pgLP6/CI9CTiWDPDAS6Nz5di91MLfOVsI2+9abqREKguZFsQeU2kvoA9Vn7xh6g2i5gr+2yAgINSBx55iDQvfHp+IHXGuaFbfTfzwSKQXMi3XJ2Ksj6AG1izqcOb6kitevRv+t/OM8QgYYinj6TC3R3fDqamHZMhK/tAtXq8f6JQXTSTeVUVe0hQH3247QvF6a10Qsb6WE3Me2ZXqC749Ox2E3lvRq9wkOgGDQjyhGoY7mwaeby9pA9BBqCyPosUqCOYXYf/bZdwnGgpkCRaD7kUYV/ei2XaF8urC+E+qz4gEAPE1ueM0NkUYXfjk/HYjfqn0P1aS1CHwYrqbe349fvTQQXBoExn97BcN3RiaRiqQ/Q/TvQyEGgxzjDnekFujs+HTPlmn/+yT5G1FegWicSEs2IVAKt2q6gLkA9Bh+FA4Ee4SR5phfo3vi0L3bTVd7bEuzqfkkGHuNAkWhGJJuJ1I5ObgP05dV2GQoEKuZEe05SSPW8dsanvQSqjw1Z3bFzjudAeiSaCamnctqbhiKBQIWcKM+fk3ci7Y5PZxV+8u9iJcYFO+bCI9EMSLeYSFe1OdufCFTEafYcXh6/Odnz2hefjl74p9bxuT2kaedqTEg0MSmXs+tD9Ex9IlABZ9lTf2f88oTPa098Oha7eQURqGM9UCSakFIWVA4GAt3HSfL83nh3/P5CnpdjJlKl9RxFEagCiSYCgYKdc9xpyrNEgTrmwjdCHUuk0jZQxeaK9AuJYtH4JJuJZDQTndYNX0iGzIBz5Llmz/QC3R+fjtWYxrdmLrVwQKAKJHoyuQg0g21jYeIkeVrsmaFADyx2M77Vvidf7MZ7TyQkeiJZCFSr/0QHgW5yjjvt8sxOoF7xaV/sRnvryGI3uzaVQ6Incb5AtWh67AisUCBQN6fIc/sz4/mc/rzE8Wlf7KbfUs4rmUACbUGi8TlfoLMRyiM7Fvw6BgJ1kIc80wpUHp+OxW46ux5b7Ea2rTESjUuCKnwljM8wIFAL+cgzrUCzjU/5vvBINB5ZtIGeCQJdI748d35+PLMc2kDPJIZAFUg0DggUcpNnqQI1qvAT2qzQzWWdYglUgUTDw0D6mxPZnRJ5KsbzK+d5mZ1IE3qzakqBKpBoWPIogYpWpJ9ReUVnXVKGjE9Qd4aTp2I8xyxKoD7xuRjGNKG3qqYWqAKJhiMXgQr2RNIYt5XbTgaBdgTzZktQeSrG88xEoEcG0utLNW1yhkAVSDQMWQhUNlB5QtuWk105PQjizIGlO4/KUzGeaw4C9YlPx1TOXRPtzhKoAokep5iB9I5feFW1bz//8hhwcneBBpDbRBR3toznW8hAesdiIqoPyX+eyJkCVSDRYxQzkN7xC/8a47vajvQ7CzSU3VriyVMxnnMhA+kdy9nt2zPxbIEqkKicUgbSO37htRXAzcXA187wpgINabc4tfYZ43kXMpD+ZV9QWSX40WrZZ2hUCoEqkKiMLNpAPXD8wmtveXBDgQY1W3x3townn0MbqAeOLT2a9z79wbelKplAFUh0P6UI1PELv73Kt87NBBrUaifJUzFeQCEC1VdKNjaVG4ePWAxqlnZtXxFfoAokuo9FZeVgcmHOaonjF759q5vswThQnZBCO6HWPmO8iEKel2NXThWa3Qvr64HmJVAFEvUnpUBVu1ATWS+fLkrHL7z659B1ujZcJOwVlkFQmZ3szpbxSgIH6B72xeds5SX9l1xblSn+ivTBQKJ+pJvK2fV2qgD1KTk6993+9Kcxe63E590EGlRkKeSpGK8mmUCDxef8sIh7IgUHiW6TTKD9aJEm1J4+dW/7L3xXeR+3TNjqT7q2QEM67Oxa+4zxilI9r0PxaRXoYozogpwEqkCiblIJVGnv8y/fvqhQe3pMwbQHaCvQPipXZoEszvCqAg3qr5TubBkvK9Hz2hufrl54jWjbGkflnhL91cbsqFQC7SKpC1CP32V3FX7q2zxSRSqW0PJKLU/FeHGJntfe+Kwc40DXD7OQo0AV95OoVaCz55BKoN36Nn2Aesx1c/zCP7WgPBKg5RFBXDnIUzFeY6LntTc+XTORNLbbU3MVqGJZL0l9Rmdhv/eJBNqEpBJdH6BNXG3VkRy/8K8bCjSOtfJwZ8t4pWme1+74dMyUmw9hljcxpReo4p4SzU6gTbypX+IhQKvNAHX8wldaz9HlBRpPWfnIUzFecKqB9Dvj07FWgzZ2qTrSyZmHQBX3k2j5AnX8wuuD6y7cBhpTVxkVPXvGyy5FoPbVwlR/VPfxl8d0ePN6/boyzudeEs1OoEYVyWPFb8dqTONbxwYq50pcU2XS5mkyXn0WVXjRivTjAHptudrtScfG9fr1ZKTiNhLNTqD9GrN9gPqs9+VYD3R4yzJVzjjDIgR6jqUW8X/O1/ow3olknUg743O5Y8I0A2ky6PaI/DLiU+MWEs1PoF1rUBegqo6zHVn2X3h9DdziV6Q/y1DLKthZ3+zFeD/SDWPaGZ+LPbu0KZz9Enk+i4blHp+rXF6i+Qm0G53cBujLx3u16xd+WsLxyHJhiTnRTpnLUzHellTPa398hiHb+Nzi0hLNUKCzhb/9toxx/MJ3dvVJJqcATWGm/N3ZMt6jxFM598RnEHKKz91cVqIZCrSfw352fKYO0KRWKkSeivGGJXte94zP41xSojkKdAzRM8MzRYCmdlFLAbX2GQmf18je+DRqSAu+fbliJ9Ial5NongJNwikBmlo/cwqRZ8LnFQSzjd5E+fUmAlVcSqIIdCRWgKbWj4W85ZnweQVnMUrExGtZvHKu14fLSDRbgbYj5E6twwe84NT62WDpzhzkmfB5CfCPT8c45Y7qouNAt7iERDMT6Guo6Ay1nh27ah7l4AWn9o8nWbkz5fMSIYlPx0y5lq6B9IYCVRQv0awEOgwQeddqPecZdP8FJ1SPiCzkmfB5HUUUn461GlrUEmL/fluBKoqWaE4CnYaH/Gvfqv70WGMhGLYLTiCZ8CSttZ/7vKIhi8+t9UDVQqBemytdVqCKYiWak0Crriaj70PsseJ3MLQLPs8tZ5DQnSc9r3OQxefLvSJ9I9V3v93pLi1QRZESzUmgw4pz3a4z7UtN/ee0uXKXFGgqeZ77vM5BFp/uPZG6Je0R6EBxEs1IoNP2CNOPtseWCYrQA5XPkUxkzq61bz+msJwtFGF86gvRLnfl7F6xCPRhcOTsC6IoiboEGvTxbX98+jWvpnh6+gwVCT5QOapozuBcd27e2iicLRRhfDq3Ne73SUCgJsVINC+B9q3tOwUafqByTNtE50R5bt7TmCQQqCQ+5wKdx+HgZKrwaxQh0Yyq8FKBRhioHM04kTmv1r55N+NTpEDnJdDntKMHAl0le4leQKARBipHsk5UTnLn5m08jfIF+tI68xGolWVkpz4jjfIFGmOgchT1ROQUeW7ewJMpRKD2XvhphCgC3SJbiZYv0BgDlSPoJxYn1No371waChFoZR0H+pr3MVx82+3jZCnR8gUaY6ByaAVFIro7N+9ZSgoRqP0HHoHuJzuJ5iXQNeRVpP4lwUDlwB6KQlR5bt6sDEggUEl82puYEKiQnCRavkBjDFQO6aIo3F2eikWkRP4+YXxudnLWtIEKyEWiVxBo+IHKIX0Unoi19s2HlBGlCHRzmB0CFZKDRDMSqJAYA5WDSiksyHOgGKEsJnrMdo3tj0CgMlJL9GoCDTNQOaiYwkG1XaccoZhTjRFoWFJK9NICFQ9UDummMERs9Az3KM6lIKEYi90g0PCkkmj5Ao0xUDmgngKwiI1gKQd8DOdzN6Hc7Xr3k0KiGQn0tz86Fgb7i7WpPsZA5WCGOswiJrDnwOnDmGTxGQwE6sPZEs1JoD9Yi4mVo68zxkDlYJI6wtKdASvum08jfxKMA5XEZzAQqC9nSjQjgaqy5Oo4OTUOxC6/GAOVw4lKSEx3XsKedZotPQTxGQwEuoezJJqTQLu9ts1f+XEnWRsRBiqHtNVukKcf5wtFFp+1c8eENk0v/yLQvZwh0awEOgz4GOvh/T6I7q1jIwxUDmmsPUSttV9JnooUQpHEp2vHhGmjT3rh4xBbopkJdG26x+amSOEHKoe0li9x3XkxeSqSCCVIfI5M/vQIUAQqJaZEcxPoIkR99pQLPlA5qLo8oOApIJFQdseno4b06mvvKs3NhBDoEWJJND+B1toPs++WxqEHKofU1waRa+1XtWedUij74tPeRj+NW15E7AoI9CgxJJqlQNNwvkAju/PK9qyLEYpjxwRtBJ7HT3wh15s5oSWKQEfOFWh0eV7bnnUxQtnaMaHDXAZnhUKutwBCShSBjpwm0Ni19p+vL09FIULZ2DGhQyumWinkegshlEQR6MgpAo3vzp/voc9ihOLeMWE8hl748wkh0TwF2o0uPmN+nEZ0gZ4iz5vYs04qlD3x6d4xoR7GkOxa8BuCcVSiLoEGfXw7Pl5N37nZNRmQmAI9odbecd7tSk4yoeyLT+eOCd2LCDQty8zp/9kMBTqfwr49/CgUsQR6ljtvZc86nUB3xqdrxwTFMCaKcaBJkUo0vyq8qh71dSPtzxOIIdDz5Hkze9bJhLI3PjdLoAo1kHlrQigCjY1EovkJdPYr/dqOq2AEFuhptXbFWfcoJxIJZW98egnUZyQ9Aj2DvRLNTqBNjUYPyaf3bKTDBBTome68pz3rVELZHZ8+vfD1cpD9EgR6Gjskmp1AjWVrT1mqtiOQQM+V513tWacSyu74tO+YYDvMAgI9FU+JItCRAAJFnidSiED9ZiIh0BzZlKjLrenaQN+s/4rKQYGe686f767PhG2g++LTMRde//f2XE4EmgSHRN3F04S98FOUVSeOBD0g0NPliT5T9sLvi0/7akzNC4NbtT9tINBkrEt0o4KfbCbStPWMzyJf4ZAJ9ORae8dp9yRnUgllb3za1wNVQ0A7bQ4Lg7pAoElZSjQ3ga4s9q3IuA00iTzRZ0+CXTll8WnfMaGfEtqy2VSFQJOzzPBtJxICbdkp0DTyxJ4jxQjUsWPClOZ2Uz8CzYKV6vx6EbQkgW7ueujTErBDoInkiT11yhGoa8eELj59Bjsj0GxYM+jioIKWs7PvejgF/Xac+wo0jTuxp8ndhHK3682cCwnUvuuhXmjYs2Ctrz1Pkyf2XHI3odztenPnMgL12PWwLaPuWKzBR54UPRNzN6Hc7Xqzp7VmOxMpj04kjb7k6Nd/ZB9np5LpRye/tlNzCTRRmyf6dJBQKLviMxQINDPacmdGvfAjWsXbI0QdMz2qqdiphtz5z/RwyhN75kEyoeyLz2Ag0MwwepJMkgl01tu53X3umGssnipnl+eZ9kSfTlIJZWd8BgOBZkamAm1X6O5cV/kM8PDa9dC1FON4hnOBLt15qjyx5xaJhLI3PoOBQHPD6c9kAq20oPSoeHuut9gUG7ZCXbvgxO7Enj4kEsre+AwGAs0Puz5TrsaklSIb722v+L2x62HL6nq15pjo4fW08kSffiyGtJ/ztXvjMxgINEeyWw+0+VHXTffaLDh6bZmwvhKjh0BPlyf29CWNQHfHZzAQaI5kJ9DdC9Zu7Xqo8FgsTL/gVO5En3soZEHlYCDQHLmaQFdLoJXXeJNY2xpjzzgUJNDQazVANmQn0AhVeD9/phbo9gnCjGSbyu2uwgdfqwGyITuB7m6k3+yFf3qONUkqUI/zgznJtvTY24kUfq0GyIb8BLp3mMjGrocqCb+O0oQC9To/mFPKMKYIazVANuQn0L0Dld27Hqro3dorYTjDVAL1Oz0wKGUgfYS1GiAb8hPofKrcdtOQYy78Ssy6zjCNQH1vCxhkMZXzUHyK12qAbMhQoHsXa7D/wu/blS6JQH1PDhYUsphIjLUaIBtyFOjO5cLcux76z7Q7X6DepwYrFLKcXYy1GiAb8hToPqy7HlbzmSrC5ezQZ54UIpQYazVANuQn0Of+qR2WXQ/b5v5MBbr3EmFBqmFMO+MzwloNkA8ugQZ9fDsG0u+fXby+66G5jWI+At17fbBCsoH0++IzwloNkA/ZCfTM9W3mnCXQNFd3PZJN5dwXnxHWaoB8yK4KLyqBBuEUgSa5smtynRLo7rUaIBuyE6iKptPWqJ1xgkCTXNdVSTcTaVd8xlirAbIhP4Fa2oPiE1ugCS7p0qQSys74jLFWA2RDdgI1e36SrLeIPgsgWRvovviMsVYDZAMCHYko0JOu4F4UItAYazVANiDQkVgCPen0b0chAo2xVgNkQ3YCTUccgSa8oItTilAirNUA2YBARyIINOHVXJ9ShBJhrQbIBgQ6ElygCa/lDhQjlPBrNUA2ZCbQbostz1b1wIQVaJJLuBUphCKLz+BrNUA2ZCXQMaL8GzVQaXoAACAASURBVIYCElCgCc7+fpwvFHF8hl6rAbIhK4E+x1hKYdBgAj3/1G/J+ULJJj4hG3IS6NDKrn6ZE8xFCiTQ08/7rpwulHziE7IhJ4GOa9U4Fp6NSBCBnn7W9+V0oeQTn5ANGQlUW+fmlWJBkQACPf2c78zZQskoPiEbMhKottLity8JeuIPC/T0M743Zwslo/iEbMhToElWVT4o0NPP9+4kFGjq+IRsQKAjhwR6+tkCAoX0INARuUBPP1WoESjkAAIdkQr09BOFFgQK6UGgIyKBnn6WMIBAIT0IdGS/QE8/RdBAoJAeBDqyV6CnnyDMQKCQnrwEukYOK9L/3d+ZLzy6T4yfaf94DC9s3rvH1hEP2xHmVywOXJxD/8d44PS/x3gVj3p5NVpK86vT3je/0njDdl+0c5ml6Lwh26/EJaP4hGxAoCM7BGoYpraJwvFdCNRIwn0/ECgCzRIEOuIr0OlYBGp+JQKNBwLNkYwEmhovgc6ORaDmV15ZoKm52/WWAQId8RCocSwCNb8SgcbjbtdbBgh0ZFOgi2MRqPmVCDQed7veMkCgIxsCXTkWgZpfiUDjcbfrLQMEOuIU6OqxCNT8SgQaj7tdbxkg0BGHQBf3AoHWM/sh0Pjc7XrLAIGO2ARar9wLBFrP7IdA43O36y0DBDqyLlDzLf1YBGp+JQKNx92utwwQ6MiKQFfe0l9AoOZXItB43O16ywCBjiwEuvqW/gICNb8SgcbjbtdbBgh0ZC5Q21v6CwjU/EoEGo+7XW/G/GpDP+jOAt18C4HWM/sh0Pjc7XrzxepPBOr7FgKtZ/ZDoPG52/WWDgJ1vIVA65n9EGh87na9pYNAHW8h0HpmPwQan7tdb+ncUKBQFqkj5lxS323YS9jHHzS1GKS+3bCX1BFzLqnvNuwl7OMPmlomRMjD4ZMsIUXqp0fg5qXjtHt/yWdchElKSBEHHIGblw4EeoQiTFJCijjgCNy8dCDQIxRhkhJSxAFH4OalA4EeoQiTlJAiDjgCNy8dCPQIRZikhBRxwBG4eelAoEcowiQlpIgDjsDNSwcCPUIRJikhRRxwBG5eOhDoEYowSQkp4oAjcPPSgUCPUIRJSkgRBxyBm5cOBHqEIkxSQoo44AjcvHQg0CMUYZISUsQBR+DmpQOBHqEIk5SQIg44AjcvHQj0CEWYpIQUccARuHnpQKBHKMIkJaSIA47AzUsHAgUAyB0ECgAgBIECAAhBoAAAQhAoAIAQBAoAIASBAgAIQaAAAEIQKACAEAQKACAEgQIACEGgAABCECgAgBAECgAgBIECAAhBoAAAQi4j0G9fHg3v2iu//WC+Ikz3I1CSv399jAxpHj7Japbc0RRfjxnvgU7yClSP1XsjpomG734Kcma3REXqpx/1V57NK29rh0a90xcR6OSmUSXP/oX5Xd6LckeoJDsPzU7z6El2PxsNY4QcS3FVoGHuZOkg0Kx4rZaXEKiMlbLdpIJDN++pOfloknoW/AiS4ujPx+PzLyFSXBNooDtZOgg0K15a0Le0zweBynj1Aa1+hbqbqtSi/qpsd9WPSlPy4SRfs6p2gBTHyx2uP9Rl92f7FjjJoqkCN2Ig0EO8jDpRV4ZCoCKaO9TfykYp3V/P4WY2+V9e9ewq3VN1+1iST/M5Hk3xNf4GV/1fYS5bTzFgkmWDQLOiif0/60+kic4/I1Ahzc0b7lxfymvMN9yz16Fen+/+fRDo4SSb1PQqx/EUp9+N4c8wl92fW5d2uCQLB4FmRROM//qDlqFej+/+C4Ee59n5TlNq8+fcW/4oG4/17sNJNiqaP9+jKWqfD5TixHPQRbgkC8ci0NkoiN/aLD11EL+09+qhN67/0dOy9WIkBWzS5c2xStTczbdqEuh5d/piAh3KS1phSf770wjjfWq4PJxk8/A+2jaB4akfTXGZpYNcdp/01CsVJsnSWRWoMQpCCfRvX4dOpmHURZ+rp8649taOd3M5kgK2UfmyzaAdarThKNAz7/S1BPoafl60/hqtoruPrsqtC/RYks3v5R/053o4xfbzXeP5h/bKkXNcfjhUksWzJlBtFESbJ5VA/61/4dN/zofW6QMcVEJDtjbTAC9UXGq/6E0N/qdBoKfe6QsJ9KkVz5+z0ZuybN997hUsyaf2XIcen0Mpqs+8ZhER4rIVr1llKEiSxbMiUPXj1d6RYYRCW+ZUj6IrfKq/VK4d3mo/Pwyd6LP1Ig3wos2XY+ZUNfi6SnGnLyvQKasLs32fYV6hkhyfX/sA3wOk2HzmT/Pf1ACXrRh7kMIlWT7zcaBv/Ut9Q0d/x6ZhdNVD+6sfBdZn2kapfX5W/1ukAV60+XJsoG/nC/a3+Nw7fR2BDoPp+8Kdnu0lrca//TAOsVwT6P4ktafWP9iDKXYX3J7k8Jt6/LJbtNb4UEmWz4pAtXvT/dxO89amp60NY6j1F/psvUgDvBjar7p7+1L5oDILlmfc6esItEVF8NIkkp+b5/howhVqtcRVogdTbAXax0E/RjPMOc7aOimB9iwF+ps2jKYrC02yXPurpX1qU7ZepgFedPmyz53NvXyvTYGec6cvJtDhl/9wtl/2HYU0Sde1fbwKP4VEECX3zIYrIdCeZalFX9ugqwu4BTopeJatjTTAiy5f9ib89kUF5iTQE+/01QTau+lo3/H0ExWuF16jCnKSTy1Lh0mxYzZgnl74nqVAtV5dD4HqR0/ZepkGeNHF5Vg715uZT73TlxNoF+fVwdGLxqIaIZKMcpKGQIOc49yTIS+7aFYFamRDh0CHAtB3P81a5u49OeEA/Q97+7/mjqpHo/XCn3enLyrQo/NnVgQackrOrP4hTlFv8wlz2SufZCZSz2oV3vhBcQj09TBeGCuWN/5ROsBLm3JYPYbhSW/12Xf6EgLVi2JdY+DRGdwrAg2QpFaUCzFzve/Mbwlz2SufZC58z1KgqpdiPirBLlCtWF/pFctlGuDF1H303U/PcSzKW332nb6EQDWTDH8GW0NoagM8mKR2ktU4DObYST7NEW9BLtvoKmI1po6VoS9TWWcaxrQpUFXD1AbXLNIAL4Z82S0q0t43U6Cn3OlLCFT9uIyDjgIvjDkJ9GCSs5PsHvHRkxw+Pw7ND3HZZksn64F2rGS6trWtjY7nw6hTLP56PsZVGrpj+/u8SAO8GPJl2zc0TlDphjCeeacvIdBZD9ti6YZjLR9aL/TBJPWTfA9zklpLQ5gV6ev56MZASV6CtVKLPja0X9DbJtDZMFItWy/SAC+GfNmO9uzy/DQT6cQ7fQ2BaoO8tCk00y08gL6M/MEkJ4MG27hpjAl9QOixy152FbEnksK9GtM4+9o6jGlcCuGfv+ojcBZpgBf68ML+L61l7Lw7fRGBDjdHz/ph9pKc7cNxNMlq7vgAKXbRohcNj6a4MkuDXTlra7vZS3+kzoH0476xY1ud1jv3uHf7yH7GfDmbqDC21p92py8jUACAs0GgAABCECgAgBAECgAgBIECAAhBoAAAQhAoAIAQBAoAIASBAgAIQaAAAEIQKACAEAQKACAEgQIACEGgAABCECgAgBAECgAgBIECAAhBoAAAQhAoAIAQBAoAIASBAgAIQaAAAEIQKACAEAQKACAEgQIACEGgAABCECgAgBAECgAgBIECAAhBoAAAQhAoAIAQBAoAIASBnkL1eHz+JfVJAFj49uWhIEZ3g0DP4PevTXR+pD4LgFWqx8h3P6U+mcJAoGfQ/sC/pT4LgDU0fz4en35MfTplgUDP4PX49AdCE7Lktx9Gbb6oxu8FgZ5AE6Kf//p4vKc+D4AlL61ypKpKNDXtAYGeQFNHem8kSvsSZMhTrxu9aGraBwI9gTZEn7PfdlVvaoyqyqZ9lalrieLnH05mJtCmCDoIVA/I5uXu5//3rzRFzUCg8Wmir7Fkpf+2v/om+38aBNqPI6EbFM5mvdBpBOSrb4F60RI1B4HGpws6/bdb7/dsBTqGKwaFk1GxtyhVmgHZR69WY4IWBBqdwZzTj7eqv6uwrAaBqnGi7TEVw53gbJ7LAUzLgOwqUM+lam8OAo3O0Kw0tiMplU4Vd/XXNFGpcSshCufyWlR/VgJSubPi590EgUZn6D1Sv+ofs1dGlWo/7BWNTHA6z3mL0lpANib9H19pYDJBoLGZxi8NP+vaiKauf0lvWdK6QQHOo9IKoasB+WKUyAoINDbLiXJaeHZ/tmOajG4lgNNpHflWrwekqkHx226CQCPTriMyoWpDXbGzpROo1uWJQCElr/ZHfj0gnwh0CQKNjBGLRgVpFCjShER8+6LXy9v2+dWArKjCr4BAI6OPPO67kdbaQGmchzQYQ+NfKkTXArIJ3n/4F37oTRBoXObDkrpO96k7fvkCwLlU87kbbf/7WkAqszJEZAECjct8KfqmwKl0Oo4DVY316q/XbAQeMQrnMcRgR6/TZUC2dSVmwi9AoHF5znzYd2QOM5Ha9lEVvG2v50d/PCEKZ1I9xulvbRy+12sB2Y0MZZCdCQKNyjT7qKPS5nDqXZz6CxRA4VReD51OkGZADnOQnjQ2zUGgUTFXulG/7B+1azUm/Alns/bzPQ/I34c5SKwmYoBAY7JsjH9Os5FUYVSrEr2033+AUxlGzuvBqgfk1FXPenZzEGhSaFMCKBkEmoCpIenJ7zlAwSDQBIxd7fS5AxQNAk2APr2TAihAuSDQFFT4E+AKINA0POlzBygfBAoAIASBAgAIQaAAAEIQKACAEAQKACAEgQIACEGgAABCECgAgBAECgAgBIECAAhBoAAAQhAoAIAQBAoAIASBAgAIQaAAAEIQKACAEAQKACAEgQIACEGgAABCECgAgBAECgAgBIECAAhBoAAAQhAoAIAQBAoAIASBAgAIQaAAAEIQKACAEAQKACAEgQIACEGgAABCECgAgBAECgAgBIECAAhBoAAAQhAoAIAQBAoAIASBAgAIQaAAAEIQKACAEAQKACAEgQIACEGgAABCECgAgBAECgAgBIECAAhBoAAAQhAoAIAQBAoAIASBAgAIQaAAAEIQKACAEAQKACAEgQIACEGgAABCECgAgBAECgAgBIECAAhBoAAAQnIUaPVY4fMvR1N9D3Jyh9N+ddfzsfxHlC/87Ycm/U8/hksQAEbuItBvXx7RFLEn7ebY6Xpm/4j0hV4CjXl3AC7MPQT6+9cmhUiK2JV2q7OWN+Mfkb7QR6Ax7w7ApbmHQJ+PeIrYlfZ0ae/GPyJ9oY9AY94dgEuTo0AHXkHaPhXZCLS9pEFns39E+kIEChARBHpq2rNLEl4fAgXIBQR6atoIFOBKlCTQV5fRX1p+7/th3pefm17Vm1Q/Ol0oobyGF+qhO3yWyiLlqv9n90Z/WkbaBkYa+sFvs384Lma4mrfVizGP+5hfV70UaNtj9Hh899PKSe0eTQVwc8oT6HPK7FM3ttaP/dKM0LpoVaDjUU36YzKTZ1ZS7gQ6vbGW9oxFGm6Brl6MfjVv7i/sBDq+PxhyLtBx4NT4GgIFkFOcQP/n5AfNBka9eERZbk2gf9A++H+nZAbrrKXcJvO/tDdW0tZZpuEU6OrF9MVF7dUNgf55ercX5Eygy5uDQAEOUJxAp8yvFdkGCxke6oy4IlArXfV5NeXF4KqVtDVW0nAJdPUrzXN93xSoeX5zgZqXsPETAAAbFCpQZYTnmOmnv7o/lTo6I/Xm0LtJnpOihgQnFXbftppyNX1zb+mPRdoaq2nYO5EcXznNWeqUaPnC13R+emOqJlDt5Wo81p4gAGxRoED1LpdeAa/hde3FzjlLyT0nufR/d1/xGhW1mvLMsNWmQNfTsAp0/fDndIDWzeUUaHd0V/U3L+apSbOT6ZvjCgBgk/IEOrxQmTLtymeNO96m12wC7Rs79R7ranx9PWVdml3a74u0JyxnZxPo6uGz5svn2EDrFGhfC59+PaZE9JPuv1ETKwIF2E95Ah0a6vRs35a4jMGOToH2qU6lzt45bRrrKWvG6Ut4LoFazs4m0NXDK+3sLEmbd2s4ejrBSaDz5LRLQKAAQooT6KDJeQf1TK0tT/21hUD1UZWmQC0p68duCtR2dhaBrh9eGdevX5lFoOPR40GTQI3bOd0HBAogpDiBzstYM97142ZSdQq0/waXQMd+l5VqtK9A3xeX5BTou3Wmkkugb7OD5q3C8wPMplYECrCf4gS6PkRyUpQxVCewQIdvR6AAULxA19se28OcbaDbAl3vFton0I16tilQ83Cq8ACZU6xA17O91tMjFqirX91XoF6Ws3Qizb9xaDSoxjbekJ1IH44EAWCLcgW6WsHVXCAX6HrVeZ9ALdVvm0BXD9evQBs84D2MaRq8xDAmgCiUK1B9BaWq/0OvCutC2SfQtZT3CnQ9DatA1w/XRlxp9nMKdBwMO3ySgfQA8ShXoH1z5zTJvHmn74yZZkTqAtUnTToFupbyhkAtLZVmGltTOde+cjEx1fKF2lTO/jaYUzn7GajrUzmDrLsKcDMKFqixbkjrysWKGnpptPunh0DXUrYKVE9bYzUNu0DXD18uJmL/wsWlWyaJLr7EliAAbFGwQI0RS1N5q+fvlZOMNZr8BLqWslWgeto6a2k4VqRfPXy5nJ39C9vU/uFfxqM9l7NzXAEAbFCyQGe6nE0Cb1/QpqCPRbk3P4GupWwTqJ72jJU0XFt6rB2+XFDZ/oVdan8blOu7oLLrCgDATdECHa2jT4OvxsKUbrnOHOMqeFsCXUnZKlAtbYPl2TkEunox9ahQvXS4+oVDamvX4djSw30FAOAiZ4HCHiwDpwAgHgj0KiBQgNNBoFcBgQKcDgK9CggU4HQQ6FVAoACng0CvAgIFOB0EehUQKMDpINCrgEABTgeBAgAIQaAAAEIQKACAEAQKACAEgQIACEGgAABCECgAgBAECgAgBIECAAhBoAAAQhAoAIAQBAoAIASBAgAIQaAAAEIQKACAEAQKACAEgQIACEGgAABCECgAgBAECgAgBIECAAhBoAAAQhAoAIAQBAoAIASBAgAIQaAAAEIQKACAEAQKACAEgQIACEGgAABCECgAgBAECgAgBIECAAhBoAAAQhAoAIAQBAoAIASBAgAIQaAAAEIQKACAEAQKACAEgQIACEGgAABCECgAgBAECgAgBIECAAhBoAAAQhAoAIAQBAoAIASBAgAIQaAAAEIQKACAEAQKACAEgQIACEGgAABCShboo+STLxzuPUCNQEEG9x6gRqAgg3sPUCNQkMG9B6gRKMjg3gPUCBRkcO8BagQKMrj3ADUCBRnce4AagYIM7j1AjUBBBvceoEagIIN7D1AjUJDBvQeoESjI4N4D1AgUZHDvAeoyBPqAokgdLwCnkX+0p9YB7CV1xACcRv7RToYsC54X3Ij8o50MWRY8L7gR+Uc7GbIseF5wI/KPdjJkLvxqY3YUzwtuRP7RTobMBatAZwblecGNyD/ayZA5YhQ7NXhecCPyj3YyZI4gUIAagYIMBApQI1CQgUABagQKMhAoQB1ZoL/9oGb2va+99e2LeuvNIxEyZI4gUIA6rkCf/dzoTz+a7/z+dZg3/bGZChkyRxAoQB1VoK9xdYnvfjLeek4rT2walAyZIwgUoN4n0LHc+PkXj6NVJV0dWC2r6sNLqoq/kOviDMmQGYJAAeodAu3aM0e2HfocvNmY1KjEP4eGUZXoVhGUDJkjCBSg9haooU8PhTafGAqXL7Mj6TkadfHWyhmSITMEgQLUvgJ9zpXZ1+WdfehNufNt+nNu25lAKYGWCAIFqP0E2g45Mj33Wu1en70/lC0b385bOsf3mndcaXRnSIbMEAQKUHsJtLKYUnnVXnzUypYLTaoGgTft/+4zJENmCAIFqH0E2ljOpsnK0Yf+1Oz6NA08Namu+ZM9dvLHJVAeH9wGD4H+0dFb9BeHQCdpLgQ6jgRdVTM5MH8QKEAdcSD9XKCGKaeB9JtNoFQJs4QqPEC9Q6DPzRHvxvGOEqjyp+pFUr35dCIVCQIFqP0F2rjOa/7RiEOg2sj61/aQfDJkjiBQgNpfoL/94LV00oSjF34+wmmrCEqGzBEEClBHLIFW9nGgeoF0eyQ9GTJHEChAvaMNtPJZek7DMRMJgZYPAgWo9/TCLyZkunHMhZ9X4RFoiSBQgHpXG+iczU55+2pM2tSm13ZCZMgcQaAAdUyB2tcDbdciedf/cJ4hGTJDEChAHVOgyxXpmzS6kme3IVLHZrMAGTJHEChAfe6eSKNANR1vN6uSIXMEgQLU5+7KOQl0KIT6zG4iQ+YIAgWo2RceZCBQgHqnQFWJsilDvvYNqT8IGTJHEChAvUugXYVcCXTnmPpjkCFzBIEC1HsE2nf8NAJVnUPnGZQMmSMIFKDeIVA1ZvPzL9++qG6gp1/3TxjIkDmCQAHqfXPh31XneduP7rEbcTDIkDmCQAHqPQsqt0M2e4HuXpvpAGTIHEGgAPWu5exUmbMXaFMEPa0OT4bMEQQKUO+ayqn6jQaBuvbjDAwZMj++71h9j+cFNyKqQI2ZSAPTJPnH6vvGGZIhM+P7iZV3eV5wI4RV+KdPG6g5F34AgRbN9987DcrzghuxoxNJlTl7gXrtkLRYjWn5BgItEAQKMLBnGNPbIFCPdeRd64FqvBzvDWdIhsyL1puqEwmBwu3xj/Z29Hwr0JfXMnT2FeknKtYDLY7vZwJdGpTnBTdi91RO3+WUHXsi6cdsbWpMhswNrfaOQOHu7Ij2dgMOX3+6duUcefpMaCJD5gUCBRjZFe29Qr2GgL7s+8IP+FTgyZA5YfQfIVC4O9GiXVvzrhHoWk3d8rIJGTITvv/e8GdNJxLcnWjR/tQ66p+rprT2wD8MYp0iePO9aU9XLzyPD26D/0ykmQO3B9Lr0lwVqL0HiRyYFaY8N8eB8vjgNogFutUQOhfoyrDRansIaAs5MCUrvmQgPcCATKDaGCUbWyVQzxZQMmQ6bKVNpz95XnAnPKL9taiUPTxG0m8J1Dq2aXGGZMgEuKvqdn3yvOBWeET7bAT9yNYAzq1eeO9F7cmQp7PZzsl6oAAtPtFeCfzZbwHSsjYO1LsGT4Y8l+1eohYEClCLO5G22ZiJ5F2DJ0OeiJ88FQgUoI4o0I258P7b0pEhz8FfngoEClBHHEi/sRrT+tj6NciQ8fGst2sgUIA64kB693qgtunxa2dIhozLbnkqEChAHXEg/cqK9Nrco+ZP332RyZAR2V/07EGgAHXEgfT1ck8kTaD+fUhkyFiI5alAoAB1xIH0CmNXzrlAveZx1mTIOByRpwKBAtQRB9IHgwwZmkNFzx4EClBHHEgfDDJkUALIU4FAAeqI40CDQYYMRiB5KhAoQB1ZoEYb6IyuWLu5OTIZMhAh6u0aCBSgjjmQftkLr6EGieojnByQIY8TVp4KBApQ7xSoKlGqfeH9RiAtxoFOjP5kX/joBC569iBQgHqXQLsKuRKoV9XbMRNJpdSK8+XRHUWGPEAUeSoQKEC9R6D9aKZGoE+vxkvHXPjXWPD02NmYDCkkmjwVCBSg3iFQtSf851++fVEufHq0XTpWY9LWAvVYFpQMKSBOvV0DgQLUOwTaLZDcCdRnMTrHeqA7piHVZMj9xJanAoEC1DsE2q2/1Au0KThu1bxf9hXpq13j8MmQe4he9OxBoAC1v0AbCSrp9QJt9LhVh3fsidS+pZoEGAcalLPkqUCgAPWegfSt7AaBVh7bGk92NFZPVv982UY4qXMy8DzFe3OiPBUugfL44DZEFKh1W+Pmn3+yjxFFoLs5s+jZg0ABanEVfntF+rlA9bp6V3lvP7+6Wr15huRAJ+fLU0EVHqDe1Ymkyoq9QJvy6Jb37CXQVqB9L9LqfknGGZIhraSRpwKBAtT7hjG9DQJVCtzq/nFW4afy63MzJTLkOgnq7RoIFKDeMxOpHT3fCvTlM4Xd0Qv/1IYxbQ9pIkOukFSeCgQKUAumcvouolTZx4G+EOgR0hY9exAoQL1rMZGu88fTn66ZSJXWc4RAd5GFPBUIFKDeuZxdr1CvHd0dc+EboY5J0AbqTy7yVCBQgDrmgsqO1ZjGt2YutUCGVGRT9OxBoAB1TIE61gMd3mrfYz3QTTKTpwKBAtT7BdquJe9Vh1+uSD/tC69tNc+K9G4ylKcCgQLUvgJVwmvdN2xz5LUcnbkn0iTQaafk7d1Bbpwhc6u3ayBQgNpPoMMApnet5OhlUGNXTk2gvV19irJ3zZD5ylOBQAFqL4FOw5f+9Yeuz/y5utNmJO6YITMuevYgUIDaS6Cquv1Rzxbw9FiRXmHdF14bUro5J/R2GTJfef5qQz/ods8L7oxHtA9DNbtdkdqXGjN6bG1s3xden9WEQDXylWft8CcChbuyHe3T9h1TudNjSw/nvvDVA4EuyL/e7sVtnheAj0Cn0mY12e653f/jGAfqubN8f4a3yJDXkKfiHs8LoMVLoL3/9gnUMRPJ5+PTGV4+Q16k6Nlz/ecFMBJNoI658H4tAOMZXjpDXkueims/L4AZ0QTqWI3JZ0F77QyvmyEvJ0/FhZ8XgEk0gTr2hW8TajvifUaTXjRDXq/o2XPR5wWwRkyB2lakb9779IdbT+W8qjwVV3xeABaiCdSxL/w4QNRi0IfB5ikWxZXlqTCf3sUeH4COl0DXOLAvvBqRP2wvvzZR6bo58LL1dg0ECjfiJIHOhn1qi4rcaUHlG8hTcZnnBbBNihLo/LBbbOlxh6JnzyWeF4Af0aLdU6Dby5IUnyFvJE9F8c8LwJ9o0e7qhde4+q6c95KnouznBbCLaNHu2BfecpiFcjPkzYqePeU+L4DdRIt210wkje1lRQrNkLeUp6LQ5wUgwaMT6Y+Ose5/sXYlOebCzycpbc1GKjBD3laeigKfF4AUr154WxmxcvXF21dj0sYuVdu7KxWWIe9Zb9co7HkBHMFvS4/VUqJa8NPRfmlfD1QNpO8M+vKYg//kNwAAD/xJREFUDm9kSK9V0VNxd3kqECjcCJ9ob/eCN0uh407HVuz7wrcJ9mzureQr0NQGvX3RsweBwo3wi/Zu8vrYE9TvCbdV+bbvCz8ZdHthenuGTO7MCeQ5gkDhRnhG+8p0JI8lkR37wldeCm7PMHeBIs8ZCBRuhHe0Gwr1X1H+KFkLlHr7AgQKN2JPtI+7uZ9nzzpngSLPNRAo3Iio0W5U4Rd8+1JsGyhFTxsIFG5EzGg3O5FMlF9LFCjydIFA4UZEjPbFMCaTZ4m98MhzAwQKNyJetNsH0vdUxQ1joujpAQKFG7Er2vvhm1trKXfYp3J2dA2k5QgUefqBQOFG7Ij2ahrE5LEdsWMxkRa1xt2/lyJQ5OkPAoUb4R/tU5OmV8lxazk7tY7d9lp2dXqBUm/fBwKFG+Ed7ar63hcptT/tvNwLKjdpvHssBlonFijy3A0ChRvhHe2z3d9e27Mw3Vt6NC81hdK8BUrRUwQChRvhG+2N8XRlPjdnI+nCXW4q171iEag56972FREFijzFLBZNSH1CAPHwDW9jWWXnUsotzl05+52QMhUo8jwCAoUbcZJA56JsEmsLsBlW4Sl6HgVjwo3Y0Qb6Zv3X+vH2EuhzWpA+K4EizwAgULgRe3rhp8Gc1fZIUIdAR2/mJFDkGQgECjfCP9qnrZH0hZGt2HvhpxGiuQiUentAECjcCK9dOdfYagOtrONAX0ZKG7sixRYo8gwLAoUbEU2g9plIGQmUomd4ECjciGgCtc+Fz0WgyDMKCBRuRLxo31qNqU7ZBoo8o4FA4UYkXA80lUCpt0cFgcKNOHNF+kXvfQKBIs/YIFC4EbuivW8O9VtQebknUmqBUvQ8AwQKN2JHtGu9SZ4KNXblTClQ5HkWCBRuhH+0z3rjPZakD0UAgSLPE0GgcCO8o/33r4+hvKi6hbZWswvHQYFS9DwZBAo3wjvaK02aSqbbde9AHBEo8jwfBAo3YsdqTFrDZ1Od31qNqT/MNlC+2+BzYwx9i1SgyDMNCBRuxI4V6eeziTzq8GYv/Cy1njidSNTb04FA4UZEW1B5ZRzoyORPD4PuFijyTAoChRsRT6COmUivvvauqvibJVlbhlz1I0XP5CBQuBHxqvD2ufDT+qA+K4uuZ8gVSSLPLECgcCOidSLZV2PSV7rzGEq/miEXpkSeuYBA4UZEG8ZkXw9Ux9xubu0MNwVK0TMnECjciGgD6V/WFek1tGKq/QxtAm07kZBnbiBQuBHCqZxeffCWPZFmx8h64TtX/oo8MwSBwo2ItpiIXjk3tzUeDrD401ix3iLQlYZQyADz6eFTuDDRlrNz7QvfvxhMoDsuAWKDQOFGRAvvuUCXohwG04vGgSLQfMGYcCP8hzH5LqM8HL9VAlWoIu3WeChbJ1I/Ewl/ZgYChRuxYyD9vhXsvATqM5J+uxd+z2lBbBAo3IgdUzl91l+a8OmFr5eD7Jf4DaSHXECgcCOilUArn3Ggs8MsINCyQKBwI/bMRNq1hrLfTCSpQNfmwkMWIFC4Ef7R7tLgCo658Pq/t+dy7lqNCZKDQOFG7GgDnbPZKW9fjal5Yfi09qf1DIPuCw+xQaBwI+IJ1L4eqBoC2n18WBjUeYYItCgQKNyIeAJdrkg/jlnqNkTq2OzbR6BlgUDhRsSMdnNPpGnQ56Tj7bFRCLQsECjciKjRbuzKqY+a7wqhPr1SCLQsECjciPyjHYGWBQKFG+EX7f67uIcHgZYFAoUb4RPt4y7EmxvAxQCBlgUChRvhE+1DZ1AagyLQskCgcCM8on0YyOm1i3t4EGhZIFC4ER7RPs62dK2qFA8EWhYIFG7EdrRr6zB57OIeHgRaFggUbsR2tGsrgX77kqAnHoGWBQKFG7FLoLtXVQ4BAi0LBAo3IqpAjZlIOt3IUp8mVQRaFggUbkRMgZpz4Wdpeq9JgkDLAoHCjYgo0MVqTHqS/qs6IdCyQKBwI+IJ1L4e6LQM6NNjPSYEWhYIFG5EPIHaV6RXBdB+ONRruwiKQMsCgcKNiCZQx55I1VTsVNPshXsiIdA8QaBwI7wEusZWwdGxK2eYTeUQaJ4gULgR0QT68tsX/rk5lAmBlgUChRsRU6BD0dIxh75JfGt9EgRaFggUbkS0aNfr5vZiptk82p2Tge0rEGiOLH5qU58QQDwiCnSSplWgZutof04ItGAQKNyIkwS63lPU+JOZSFcDY8KNSFkCrXxmciLQwkCgcCMSCtTPnwi0MBAo3Iho0b7ZC//03CAEgZYFAoUbES3aK/c4UDUFyW9WEwItCwQKNyJatDtmInUv+S5uj0DLAoHCjYgW7Y658CvrizhAoGWBQOFGxIt2+2pMSq7+23si0LJAoHAj4kW7fT1QjyWYNMwM+auNECcNh0GgcCMiRvtiRfqh4FnNZ6rsW43J6k8EmgcIFG5EzGg390TqBaoKoHKBQubwvOBGRI12Y1fOXqDm8k4I9FLwvOBG5B/tZMiy4HnBjcg/2her+0DmpI4YgNPIP9pT6wD2kjpiAE6j5Ggnq6aDew9QI1CQwb0HqBEoyODeA9QIFGRw7wFqBAoyuPcANQIFGdx7gBqBggzuPUCNQEEG9x6gRqAgg3sPUCNQkMG9B6gRKMjg3gPUCBRkcO8BagQKMrj3ADUCBRnce4AagYIM7j1AXbZAAQCSgkABAIQgUAAAIQgUAEAIAgUAEIJAAQCEIFAAACEIFABACAIFABCCQAEAhCBQAAAhCBQAQAgCBQAQgkABAIQgUAAAIQgUAEBIJgJ9PTTeu9d+//r47idJYt++PN5CntzFmd17/QHs4Lf/2Drgh8fnX2TnB5AvOQq0z8EI9BwCCPS5+REECpckT4E+PmoEehbHBVptfwSBwiXJR6Afw99NdmzNiUBPpVGc7HYjULgvGQpUCVD9C4GeCgIF2E2OAq2fD2VABHoqCBRgN1kK9GUK9Nk2zX36cTxCVfP1HFmNLad1L9Amxz50j86OgCVzgT6bu1uN93xx8/QHUmnt1stD++eAQOGSlCDQqZejz4Oqjm8Ys6M7Xgm0f2lwbpuNpyNgyUKgfx1uoHF7zQeiC9R26Of/h0DhimQp0GdbJRwEqvcStzXF0Ya9H8dc2+fb5t//+MPsiOkTGNSGKdC/b+/q2/L2mg9EE6j90H9EoHBFchTovBNJyW/0ZpsJn11uVf9WdfTmuM6TVZfhu2w8lI3aWvyzd+/r8aB51IIp0KG8v7i9ywdSTSN3l4eOhVQECtcjQ4EOebcXaDU6rxFjb9SxXKn+qMa82b+gBPo2fKJ/oa/LT3+BwUKg/U1d3N7FAxkFujj0NbygHgkCheuRj0B1WskteuH7LD5l4E67z8mJXU7WLNk1BrymcudLMlHxFiwE+j78Zdze5UeG181Dmyc4vPBCoHBFshRol+sMgar6YfvCvL101r/bDWBq/ju80sn2OX2CIU42FgL9GF42bu/A+EAGgS4O1ZLUngnAdchQoEOhZRJoNb6nXtDKOQqtg6hvadPyefunVhAiI1sxBdrfsuXtrc0Hogl0fqhmVIYxwSXJR6CLIZqDQLWu3TWB6m+vCfTzL3pRloxswyLQ5e1dPJBBoItDtV8r7jtckvwFOpRrvvupz+JLgRpZkxKoBLtAjRu2eCCaQOeHUgKFq5O/QF/j2M0+vy7bQI2xna420IpxTBbsVXjj9i4eiFaFX+31U/DDBZcke4FqxcfqsRzX1JYvjc8Oo2vqQZ1aL/yTXngLFoEubu/ygQwCXTt0eIFeeLgkBQlUVR1Vfp1GKY2DlIacPw5j6oVZmZ9gHKgVi0AXt3f5QMZhTIsnMWpzHHMPcCmyF6iy5DSjs826/Uyk4Z22Ta799FObu/1WaxOP9JlI5ON1bAJd3N7lAxkG0C8O1X7zuPFwRfIX6DRkZsiv2nCZYQLM7AV9LnznBObCb2MT6OL2Lh9I1/3+sTx0euGf6ESCK5K/QPul0xr+eWhSG33YN2dO42fe+38OqzENSmA1pk2sAjVv7/KBtEPqjZFM/aPpDcpqTHBNChBonyvf9e6jypBhNxBf71tqlan1F7Ee6AZ2gRq3d+WBtAZ9Wzt0eIthTHBJMhEoAEB5IFAAACEIFABACAIFABCCQAEAhCBQAAAhCBQAQAgCBQAQgkABAIQgUAAAIQgUAEAIAgUAEIJAAQCEIFAAACEIFABACAIFABCCQAEAhCBQAAAhCBQAQAgCBQAQgkABAIQgUAAAIQgUAEAIAgUAEIJAAQCEIFAAACEIFABACAIFABCCQAEAhCBQAAAhCBQAQAgCBQAQgkB383qYvO9O47f/CHIqgZIBABkIdDcBBPoUODdeMgAgBIHu5rhAK0mhNVoyACAFgUr57YfHdz/JPopAAa4BApWCQAFuDwKVgkABbg8ClTIX6PPx+ZdGaI9PP6p/VW3T6Eetvf0w3lRvN0l8/qVtU+3eec0/NUvl96/q69ojms/MkgGARCBQKQuB/nVw5LcvvduG96duJ+W+uUD/9nXoiGr+1fLWfchIRQn0v7+MH0WgABmAQKWYAv37L739RvMN7tO77d9Ngf5b/69P//n1Mb5RL1P5/auWitI0AgVIDgKVYgp0qFor07U18qovTaqS5Xv/ie6YSnuhTaQrfKq/lDff1lLpBKps+Xr0H6cNFCAxCFTKQqCdP5XW+r+aI/qi4lQrbz9SmUZtNTn+1dfz56kogfbf9+oTRKAAiUGgUhYCfR/+6nqE6qXhho/oAv0Y3uk/1R+zSEUJtE+s8fCsIAsAiUCgUhYCHVXYFx1b071NH2gr4aZA+yTMv5apNJ8elIpAATIBgUoxBTqVIHXmQ472CNRIBYEC5AcClWIRqNZ7PghUf8lToMtUEChAfiBQKXaBjpXv8cDenSttoHaBGqkgUID8QKBS7FV4Y4bn62Fq0qsKb6SCQAHyA4FKsQhUdRXNxrZr5qu8q/CLVBAoQIYgUCkWgWoFzs5wk/lUXd5ToItUEChAhiBQKTaBtk2eH91r7YvP3oWvYRLmOEreIdBFKusCNRpKAeBUEKgUm0C1MUtdCVH/d3dU18f+4RLoIpUVgQ7JAEAiEKgUq0C1MUjv/Xs9/9y3bHbz2t+dAjVTWRHokAwAJAKBSrELdFh/aZyF1LnwfZoW36rvzS1QI5UVgQ7JAEAiECgAgBAECgAgBIECAAhBoAAAQhAoAIAQBAoAIASBAgAIQaAAAEIQKACAEAQKACAEgQIACEGgAABCECgAgBAECgAgBIECAAhBoAAAQhAoAIAQBAoAIASBAgAIQaAAAEIQKACAEAQKACAEgQIACEGgAABCECgAgBAECgAgBIECAAhBoAAAQhAoAIAQBAoAIASBAgAIQaAAAEIQKACAEAQKACAEgQIACEGgAABCECgAgBAECgAgBIECAAhBoAAAQhAoAIAQBAoAIASBAgAIQaAAAEIQKACAEAQKACAEgQIACEGgAABC/j8nKUrEUUY/RQAAAABJRU5ErkJggg==)
forward selection from the main effects model
step(arth.mod1, direction="forward", scope=.~ (Age+Sex+Treatment)^2 + Age^2)
## Start: AIC=100.06
## Better ~ Age + Sex + Treatment
##
## Df Deviance AIC
## + Age:Sex 1 88.640 98.64
## <none> 92.063 100.06
## + Age:Treatment 1 91.452 101.45
## + Sex:Treatment 1 91.636 101.64
##
## Step: AIC=98.64
## Better ~ Age + Sex + Treatment + Age:Sex
##
## Df Deviance AIC
## <none> 88.640 98.64
## + Sex:Treatment 1 88.369 100.37
## + Age:Treatment 1 88.631 100.63
##
## Call: glm(formula = Better ~ Age + Sex + Treatment + Age:Sex, family = "binomial",
## data = Arthritis)
##
## Coefficients:
## (Intercept) Age SexMale TreatmentTreated Age:SexMale
## -4.55477 0.07734 2.75066 1.79705 -0.07945
##
## Degrees of Freedom: 83 Total (i.e. Null); 79 Residual
## Null Deviance: 116.4
## Residual Deviance: 88.64 AIC: 98.64
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