
{"id":5796,"date":"2019-11-04T12:10:03","date_gmt":"2019-11-04T12:10:03","guid":{"rendered":"https:\/\/www.editage.com\/insights\/events\/know-thy-data-episode-2-looking-at-univariate-and-multivariable-regression-analyses\/"},"modified":"2025-04-05T06:06:10","modified_gmt":"2025-04-05T06:06:10","slug":"know-thy-data-episode-2-looking-univariate-and-multivariable-regression-analyses","status":"publish","type":"events","link":"https:\/\/www.editage.com\/insights\/know-thy-data-episode-2-looking-univariate-and-multivariable-regression-analyses","title":{"rendered":"Know thy data (Episode 2) \u2013 Looking at univariate and multivariable regression analyses"},"content":{"rendered":"<p style=\"margin-bottom: .0001pt; margin: 0in 0in 10pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: normal;\"><span style=\"font-family: Calibri,sans-serif;\"><span lang=\"EN-US\" style=\"font-family: 'Segoe UI','sans-serif';\" xml:lang=\"EN-US\">As a researcher, you need to capture and analyze a lot of data. Often, researchers get confused between different methods of data analysis and how they can go about using them. To help you better, we have invited an experienced biostatistician to simplify statistical analysis for you. In a two-part series, Prof. Jo R\u00f8islien, professor of medical statistics at the Faculty of Health Sciences at the University of Stavanger, will take you through some common methods of statistical analysis. <\/span><\/span><\/span><\/span><\/p>\n<p style=\"margin-bottom: .0001pt; margin: 0in 0in 10pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: normal;\"><span style=\"font-family: Calibri,sans-serif;\"><span lang=\"EN-US\" style=\"font-family: 'Segoe UI','sans-serif';\" xml:lang=\"EN-US\">In <b>Episode Two<\/b> of this series, Prof. R\u00f8islien will talk about two types of regression analysis \u2013 univariate regression analysis and multivariable regression analysis. He will explain how you can incorporate a powerful framework for studying your data effectively. Join this webinar to understand how and when you can use both methods of statistical analysis. <u>Topic: Know thy data (Episode 2) \u2013 Looking at univariate and multivariable regression analyses.<\/u><\/span><\/span><\/span><\/span><\/p>\n<p style=\"margin-bottom: .0001pt; margin: 0in 0in 10pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: normal;\"><span style=\"font-family: Calibri,sans-serif;\"><b><span lang=\"EN-US\" style=\"font-family: 'Segoe UI','sans-serif';\" xml:lang=\"EN-US\">Date:<\/span><\/b><span lang=\"EN-US\" style=\"font-family: 'Segoe UI','sans-serif';\" xml:lang=\"EN-US\"> November 21, 2019<\/span><\/span><\/span><\/span><\/p>\n<p style=\"margin-bottom: .0001pt; margin: 0in 0in 10pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: normal;\"><span style=\"font-family: Calibri,sans-serif;\"><b><span lang=\"EN-US\" style=\"font-family: 'Segoe UI','sans-serif';\" xml:lang=\"EN-US\">Time:<\/span><\/b><span lang=\"EN-US\" style=\"font-family: 'Segoe UI','sans-serif';\" xml:lang=\"EN-US\"> 8:00 AM EST<\/span><\/span><\/span><\/span><\/p>\n<p style=\"margin-bottom: .0001pt; margin: 0in 0in 10pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: normal;\"><span style=\"font-family: Calibri,sans-serif;\">\u00a0<\/span><\/span><\/span><\/p>\n<p style=\"margin-bottom: .0001pt; margin: 0in 0in 10pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: normal;\"><span style=\"font-family: Calibri,sans-serif;\"><b><span lang=\"EN-US\" style=\"font-family: 'Segoe UI','sans-serif';\" xml:lang=\"EN-US\">Speaker<\/span><\/b><\/span><\/span><\/span><\/p>\n<p style=\"margin-bottom: .0001pt; margin: 0in 0in 10pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: normal;\"><span style=\"font-family: Calibri,sans-serif;\"><img decoding=\"async\" style=\"width: 189px; height: 189px;\" 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LTShVIYc7jwOOuOlPeUPHtR3GRjBB\/M4zVPzGcIQWUDpnjI\/qMUk0vm4RpFXGDtC7t38\/z\/AExTEL5i4BkVj82AqSAZPt19ugq\/ayyQCQW48lsYwx6Dtz3\/AKVku7BlVVRweOxJ9+gwe1WoXkiIimXgEEc\/d6f5NJoIskQCK4jBYtJJ80jn0789RnAH9asXWqzXtxK0rNtaLygX\/uhgwP5j+frWfKsjSMTJgMeQ3U\/59qZHFczDy0zjOxsDpn1\/z2ot1HckEwVtzoW6hAOD06n2+lAdlhLHG8EYx\/T\/AD\/9aofLkkeE8j7qgHrg8cCnQugiKjKZHHz5HpQFy+rBxtibaeoYjsOP8\/pTlkSONlH+rJO5ycbj9fz\/AE6dBVSdTAuCEwD2zk5zz7D+lRiVynmyAspO0DqPr9KYjTjkWSRUQ4QkD0yM\/wD16gjljjLYUsxyW3evX\/P1pEQhOEJaT5gAB6YyAPpUEi+TCPNcMzEkqrfLn6\/iPzoGTafcypPH\/qyD1VwQCPzrRvHSKdQEcx43Ek8kf5PFYnlyfePzMFB9QR349qsxANxvYZAyDzigDe+0I1lbRAguULMh65Yk4wfYCmEQDSzbyMDMCdrDA5zwf8+vvWf9nbBmV+nJYjIH1\/xpqLKhzOuIlBcvjj8McZzQnZCcbs111CP7JFuKhgo3jk9P8\/pWWVn1e+8qNSQuGkYjhVHr2\/CqjebdyBISoXuyn\/P+FbMEENqoNtI\/mEgsgIyW+vqfT3p36ia7G5ZSrZ2CofKCx5OzOCcZP41f+0RtGGRxsPyg5z7VhQzTFBFswDgMxOD1yeaDLHbXHzMFjYfMScgn8Pr1pqZDgbsyrIqshJKnIYfyqyMHpXOWrPPq0LxK8aklmOeXGOhHp3roFGxc\/wAJ6ZP+NXHuRLTQkxS4qvLfQQOqzN5e4gBmHHP8qsjBGQcj1FUQJilxS0UAJijFHNOxQIbiinYooGY2KWlApcUwG0uKWlApDG4pR3p3SgxtgksFz2NADHB2HbwTwKpvIxKIo+bPU81NK\/7tjuKhTg81nO7fbWEALMoPU9DnP8qylI0jE0Cqg4LNx1wevv8A5\/pWXqm6zvIivKOCpIPO7qCPw\/lTmubiJsSxqmR8wBJyfamSsb5o7YqgDsBuI6H1z7c1FzRKxH9oKxh3csGIIAOPxoMiXNvIqkFy2Ac5qnM6vO3llliGVXd1A\/Gq7PIsglTaGHo2AfX+VA2iS6kdCjElihOQB1Bx+oqrcCLyg8ZJyOMen+c1PNcfadwdAC3O4NjB\/wA+lUQV8razMXBJwEx\/P6UwEjlbGHyFAwCfpViPUjpwSWFs3P8AC55Kc9R79cH8qpNuQhgVORlsc49\/5DNQPB86u2Sp55HT2osFyxNqM0vmBmySc8jNLbyYbJ2q5Iz\/ALOeM4+vrUVs0Cv50nzqgPykYyegxjtT1AAPllVGATu\/iY9h29adhXJ0cljI5G5hwT1wTyc44+vfmmwyEySReWqZBDFVwc9wPXmokmkYMGVNrDJ4DEk9wM\/jT55UVcSKqseeepHqenFAhBlGSRCu1RkE88\/SrEq7VEjNvYrjcQeMdPw+tV1uNyIkIBkJ2sSeRnpwatX08X22drb\/AFe7ZHgHJXGBxj0GaTKRDEs7MzRwu3A4jJB6ccDmo43mE\/7yJ946AqQAKekjuAsEg3t03ckfTsamEigtGwyNuWYjGPw4\/wA+lAFJV3Ss0pJ46g8k9jSPAAcq5weACpH19sVZDLMN0eNqnDEYyv64qRwH2suEX+EKvA\/E\/wA6AK1taGV1UuSx4CD0\/pxWhshtRlGUkZO4jAH\/ANfp7+lMeaOCJlhZRK+cleff\/Iql8iKDI2SejMM8ew\/z7UAW3uri4+YuxB5HAO45PXt2NNLn5TLEWgHTccMPcEjvyec\/pUcs4PLeY4bnYwyB6Drxx\/SrkYiuJRkNGMDHy8Dj0+tAWuTRpZsI2jkkcDna0YXb9CGPOD\/9aq93HGpT5sZ+6T3x6\/8A1s0v2NrOUvG6sDwR82OfqM9PWmSFZIykj74WAKMOCvHvyMUDGxX0aFVnY5XA8wZyP8evX\/GmXdxvGzcoi+8F7fX\/AD6VnXJiRzHJuY9mAxj6j\/P9KWC4X7M0Y5wCVLdQfb2p2FzdC7DyCP3hdDkbDgfTNTpO\/mbn8xSegaTg\/pWfb3c8Dlo1jHyjkqcj8qvxymVhLNGSA3zbFxz7ZosFzUttQkjUK+8r1yEBHH6cVf8A9HKK5jDcgrvGz8OnP4VRtPs3VBtfHAPH+e\/tWvBZs4aQnK4+bcgHH+f50rBzFMZMp8pTG5H8I7e1XoNJnmRXubudYuiqJApP4\/T39aWS1RZ42Cs8RwDt65OOD\/nr+mrEEjH7sOd2MHBAYfU9KuKM5vsV4NMsERwlqjueN79T7571Pp7NFEYHYHyn8sn06EfmCKr6pqC2ShjzO3yhB94j6\/1xVKxvrgAXNzFGba4faWTI24wobnqDjH\/1uKq+pFtLnSDB6GlxSISQSRg9DT8VRA3FFPxRigQzFFPxRQMxsUUtFMAApcUUtIYDikIGeFy3WnYpCQFz1+lAFC7l+zxyOynYR8xHP+RzWW0i286uZEAyNx3YG2t2ZsqVZVKMMNz2965OWy3F5X52ybEzzgjpx9MVlJG0GaUl3Z3IJa4jOeQfMB5piTQxRTyJIhOwxD5sfe4J\/wC+SaynQRsqYUDcfbI47\/nUvmQCEReXjHJOSfw\/z61Botxk0SFg0fzA9hz\/AJ9P61D5qq4ASNs9Q\/3hUsdxGWZNvyqM7cdx39adZiGUNHdY+YEAjt759PalcasR3cLFQUOVzkgjnPcfyqoispmChC6gNk+nrntXQNbxtp5AcGWJjnJ5IHv+J\/Osm5mhS487G0lSCD3BGOfrnP50Jg4maTKkxby1Dqd2Dxz+HHP60k1s7ysUfA3EED+LBwCOMfn61bjeNXjkcERI44\/vAAZ\/DpVKTJmYxr+7Pzfnz6ds4q0QyOKFnZkDBiM5AHJ\/ycdKmYfKTuIZcZOQNpGe1IqPuYqx3YPzMcZ\/w9qPOaP5gTyOdxPT86LgPLpJEsUgwSSQ+eAcfy9fQ\/iDTn8yN2DDbjHynnA9v8ferCvJklmIB+8c4\/QYpQkcvy5Gc8BiF5ouDRCi4XzRGwYcqR2IqRcFQ+8bwuM4z7Z\/T+XrTxYytbsyNGQMZQN0\/PHODmq8ZKNtwfkySTx2GaYrF63+zAhpGdmI5XjBH+fX8qJZY5CdoSNW4UnLc8VH5KhA6xqFD\/6wcDd1x+XbiphCqREEpt4eIFgTj6jr\/wDr9qRQjSMsyKWIA7cMM\/07cVGx2uWViMHt6e3P+NIYQ4U+UQXHBB647AfketSXYBcuwwOMDuDjOPTuaQER2MD+8zu4ztAwPrmmmEK+ZAHQHOe7evFRysqSZQBVzwB1B9KkjDOVyEz6Z9\/r7CgC\/CVlbeEz0IBPbv8AWrqxPIQ0aszKBzknOOmfyx+FUrYkoRGhHOfmNXI3kQqpjYl\/Qnp6ikykKZpU\/wBcrKo5K7eD3\/D61WLRSEbgxDn14B9vz\/r71YmuRHMcTMgwoBYZxjH4fXHrUaGN5AXwA3GTyCfTHb2\/p1oQMxbq3IfzC25GGWY+ox29eR+dR2cywEsX9goXJP09K1ruEpakORg8Bgec9Q3v3GPUj2rKEMaRmbcNwfaykjI9cD06fnVrYzkrMtQsZJVHlnA7k8A\/y9K24TCBsG1yBwVw2Of1P+fSqFvZb23FldAMgbwuB68VfawtSzNkQqDzsbd9eop2JuWRa2tyjFysYHIYMQwNKBe2yEK5uIh0ycNx2x0P9ar+Xaw\/6q7uSR\/D8uPx46VG96y8rcTtJ0zlcD8cUmNGiL9CCJxcRMB0dc4PXPHFR3+qpNujtp1SHGSVf5h6gd\/0rNNxJIuJZpJFznaWPP4VUDC4uVCBYkQZAIzk0rlcvUvpYXMxR8\/NKOBnnaOpPcV11qy3tm1ps2jytq+x6Y\/QD8D0rH04Tht6pDOQc+ar4b9eppbLUFXWpyu4Ju3c8dVx\/MmmnZktNnXodyIx\/iUVJioLdt8SBclMA5qz0GK0MGNxS4paKYCYopaKAMTFLilxS4oGIBS4pcUuKAExTCzH7qHAOcn0qTB\/Cm9Fxk56CgCpfSLFEZJAxVVJyP8AGsFXWWz+0S4CPImSMDGTgH+X1\/WugmUSxbXYBSuOeOoxXFyTSW1sbOaIE42qdvXkYI\/IVnJGkG+hdu7VQ5KSbl\/vMMAfXtWWjeVLI5wQBt3dRnPYdz7kioZLqRpcmUO5+9k8fQdq0rK1kmKIAHjk4BAHX8fb+XtxkbozjcBlJbcxJ7\/NSx3skUnyDv8AMpHX6c\/49T+L760m025lgmjKYUfeH3h\/kisv92zKepxgDOfpzTSE3Y2P7ROC4YKshGQ2eD\/nj8ao3E7SZyoz3zxVZnZkYvjIxnPoRx\/n0pgnZj0PyjtT5Rc1yxLIpACZAySD6A9h+Q\/KnqV3q7OcEkqqj\/OKjDIWlaNdo2lsDpyeR+WRUSyNG25gD3Ofx\/z+VOwrlx5XdgVZM5I2spIz6D\/6\/NKYvLU8qxI5UcYH17jpTIrljIDnDE45Pb+VTbBtB2jYTwCRz68e1IZHHFhg7oAuec\/\/AKqWOLL8Feh6jBpSke7KlACOWOcH\/OetIHh80qpYDJxgj8s\/pSGS5jVCR95Tx8xP+P8An8KgZMx+aA2Seqkk\/wCef0pZJUMIQgBtxI+bmpIishRn4yTkpzkDA\/Dqe1AWQIzRgqrBQ+Cd3G4Y6dfQ1bto0MO55I3QHcxdMjHHY49ccd6glhRCrI6Zx1kUcfh3+uaillLRqNhKqQSR1cjofb+mfejcdrEst5bRQtFbWzIjDlgevr9PSqZnc58wDaONoxwPf\/OaRpRIsjFSCSBkY45z+HQUq2y3A2qx84LhVPfjj+n+cU0iWytIxK9QUY9do61IrmOMgcqeCR7Hp7UKhVj95B3xyD\/hTDtDfxdMkZA4\/HjFUI1LWbDAJHksvzYx1\/PPT\/Jq48ziPaXZexH3SPw6ViqSgEiqsQzx3GcdetTNfzsqbZM5GB8vJqbFXLshPl5dC2Bw+ev+Pr\/nlIFV87mYg\/LuT7wHbjuM447\/AIVXSSeVpG2TTHkMVjLY+v61agt\/4iHQKfmUqQdvfg0h7l5R5kTrlWZT1PIz\/eH44\/nWDdJFCzK0YBBz6HPI4rbtrkRXZWQKcE5yO47foag1e0STUVYuAfL3bu2c5Ofbk0ovWwTjdXMy0lmjUBS2zOQdwH8x\/KtK0tpJMSO4x\/CSKpsqwy4Cg9DuYAD8O2P54q9EX5IyR1J7n39cf56VdyEl1LMkESrtBZ8dflGB\/nmqklv8w+ePd\/dVgf5E1O0yFSvlt8vVmYjHPbAqhLdSpFsaGMu38Kt2HqM\/ypDuI9whk8tVJb03jGfy\/rWhHbyW0HnvbxMnU5fBA9ie\/sKq2do0u64njMaRjcdoPTsP5cfSrsKS3spa8kZLdBkL1x1wSPXr1x\/SqSJbIkuLueQraQFU6EFskGrWnWE1xeXULlpCFAdgT1I46dO\/5U+fULXToViswj3BXI2tlIh7noW+nH5YrW0XTtTt9JEsUipJcZkMjrlgOx59ufxosLmaNDw2gSO4UB1w\/lsrE5DrnPX2x+VbmKoaRpi6datGZGlkeQySyN\/E1aIHFaLYxbuxKMU7FLtpiGYop200UAYuKXFLiigYYpaMUuKAG8DqRQxCoSTgU+o2xyMZXjIoAozhFhf5AuCV3Ec9fX9M+9cPeM91qM8hYBVfyxzge5P1xXbXgzC6qc455PTmuOvAYJrlDkq0hOe2c55x7Fh+FZzNYEE9vCYtq\/eAPIOcgUWWrS2rg8SgjMiydSfXnof\/ANfBqTUFVo1kjUKwJ6HBx9O3NUbaEzZ65UbsevP\/ANcVD0NI3NrVNWF5EonVT\/d3Dj1\/DqKw5MCQsq7dwz6j8DWlJYSm3DRqSp4I4wD7jt9cY9ayZd8UnlkCPOD3H\/6xRFdhyfcFVJEYzS7Fz1A3H9P61AwVGCpKWAO4EZ4PbqBVryFcn5izDnC5BP5+9RESbyWJIyRktn8j1qzMarPGARjIPOzng\/yqQpmWV2bc2\/p656\/zFQcyN1A9vSr0UTTQ7zjeqgAc9PX+VS2UkLaqWAKoF7DjkmrTZCBMcKe4xknA5\/HmoliZZWwkjKpwFHQ8\/wCf8irCu0jPGgVUVcAdsAg\/0qS0io\/muNkSELk4Zgc9uf0FV5YZIWwCST0CHPy885FbjOAnlxlVHQ8c9O\/4k1Vls5J22MDjP3dgXP5daE0DizMD5GFkD46ZOcfX\/OKswAS5BcKxXAx\/n\/PbmtSz8PSzyBFjIHJOR1rf\/wCEVaI+XsBYH69s0nJDjCRyAtpFOGPPcc8H8aURPnLbScYyccAV07eH7gMVRA31B49asjw7K5Xch2qcNxyeKXMkXyNnIra7ucZ74HBFBQxfNsy4GOvJxxxXYT6BLBbM6x4I5J9icHimQ+HXuukRC43EY5Ao50L2bOPLGRCY5FMrcPCec\/h3\/Cq00AEi71dDglBnP4eoHtzXU3\/hmeBmLxv8w49sfhXPyxSxXDxsGAPHBIyccVSkS4NblIrhwQAVPIA6L7AU7c0SHcC4OMHbwcd\/yq9JAywM4jG9TnAGMj14qOJjJH028DBwPwHP407k2H29y4YYdo8cc\/MCPUj\/AOtVx4vKus9CxBUKcDtyD+fSqBlJYIyKr42gjgEc\/r2rSh+YtyxJXaV3ZH+R\/hUsaIJij3sZwFJyWJHAJzUga3v4FjkOHADRk+38\/p71Q1FvLmYJjIyBzkc4zn68\/hVeGWORlQZO07drevTg9u35U0uoSl0Lpt5LcmIMjxnny3GVx6g9RVm3tmaIBDImcH5juBH+f60KRNF5NyA6Ho5zlfQg9eP5fjWcJLyxumhUZHoyhgfx64qrE3N63gZSFdojx\/zxBwPpUV4I0b\/Uk5H90YP4iqiahNIg32sYI7g4I\/z60r3lxcsYVVMdkyTj8zilYd0VJ71wML8oB7t\/Lmoh9omAj8wKCc7dpY\/lU8igEb8CTPGzt+dSW11LZMrgeYCwJGBn\/voUIl+Rv+GvDsTyrcXeDGpBCMSWc+reg9v8K7s5YbQMbvX0\/wAK85ttezs\/dkRgjJ3Kf0IHP4102m6rJLFvtHW6UHmLJEmf5fjk1omjKUWdCF2sPQ\/zqQCqlneJqFv50ayRqGwfMGOR\/T3q6OlWZiYoxTsUUAJRS0UAYmKMU7FFAxKKXFFACHgVGFx8znOefaqV5qKxriJix7HjB+nrVIeISjYmiIyMAN6+3+RU8yK5GaFyplhYxqzHs3BA\/DNczeERO+6NWRh8y4OCnr7c810cV7Z3aiVJJFYYBKA59gc+\/wBRWNqcRS3eRh+7Reo7ccgn35pS1Ki7HM3CRQkeXPweNuQCO+KLC6WNtpxlGyvrjvisolnlbCliOw7Vo2sTSsmYxJER36j1H4Vm0ap3NaXU9oVoW2Ff4fT6en0qhd33mkM0ac9+Bz36cVa1DSoooxLa71AHQsGX8AeR+IFYe+RJSGV0JHLL0PuaEuxTfcstMVAZFzuGDv5GOn5Yxz7e9VJGc\/fJCpgbQMY9M0qypu2Fjhjwdo4NNO9twIYoTzleapIzbJViHyHaQ\/VgfWtC1UtMq5AVRyCaoxvl5AWzvB4Pc5zj9KdbjbNkcMOg7H\/CpZcS9JmRgyYZi3TPzD06\/n+VTWVm6upCtuIHHt36VLZaf9vnHl5G8g4znFekaT4YSJFcjJxtAP51nKaRvCFzl7LQWu2DCNiTjGOAR\/j9K6zS\/BguSPMQoSQeh\/Kuv03RkjQhAFB6Y4K+30rpbS2CgEYOT1xWfM2W7R2Of03wfa2p3NGHIHG4ZzV6Tw7DPIS8efXiumSMDGOvapAgHTirUEYuozk\/+Eat1kLquG9qcmhKn8AyT2rrRADjjn1oMIxT5A9qzjLjw9HKSSoI9NvNRroMMUW0IQRwBnHH+f8APSuweEdx0qBoe4FQ4lKozitR0aOSInawIU4IH8xXl3iLQfI8x1j6NuGB1+n5GveLiDjp9K4zXdME6EHIVSVJx2J4\/LNRflZtH31ZnlFnpFxczyInIgTe5HHQdP1rFu0SCXH3MlkI6AHP\/wCo\/pXqGk6ckZuC5PLEEADlduB+qn881wuvaaVLsVOdz8evHX9K0jO7InCyOblhYSBmKLJnkkjgCpobmIS8fPhhkjOMVFJE0uABh0HB7kdP8P0qp5U0ZMhT5EPzY+uK05bmHMkTmcRuzgEszZfI6Yxxg\/54qq8ZhbzIwAOny8hh6j29j7Vr3Nv5+n7gvI2kOO\/U4P8An171nW6c7HB+TnaDzj0\/OtFoZSVy9Y3EdyAowpzyh6qfb1H8un10oVjlfY4HB2gld30\/z9K5kB0uVZPlYfMmD+n1FdHpx+2wsq53YK8fz\/QfhihocX0ZZks0SN2JWRDwCp3jj1Xt9Rx0qobVdhG5jnnnAA+n6U+S4nhOZQAvQOBjn07DPf1qkbvcQsgcDOehGf8AP5fzqblaDzZyKVwQ45C5IGf1p8dtIofeQCAc7TnZ7cVYeZTCBGwVWAAwMsfx\/wA9PSlitczJGGJkDYLscBSe3sAOfzoFsVYopZ2UQxMobAc7cjOev5VauIrzw\/qCTxzK6nBI2EA5HKkdec\/Wuh0u3UX0tqpVl8kOxwODkgH2yO1aWpWcMmkyIU8yQsrfMvc9MewGRx6VSVtTNyu7FjSL2G7tori33CKUlXRudrAZPP8An8K0\/MKSKMDYxwOeh\/wrn\/C9rJZrPG+CpKshYZPf8uma252UwP8ANhtp68Y64P17\/wCeLTuiJKzsXByAfWilByoJGMgcelFWQJiilooAxaSnYoxSGNxVS+lZUCL1fgDPWr2KpX8RLRydQGwRn14\/z9amWxUNyAW8cEfmsfmf7xI7CnXEEU8JNzE7q38LDqPTHX9KZe3kKWxEkqxEDgMQMn+lVk1OG8QBbiJMLk4b9M8Z\/Op0K1Od1Wzl0u4MkDYXOUUnlR3Hvg\/p71TuLi+mhYNcYV0\/1Y4BU+nY1u6zGZLMcBsMCCTkDPp7f4Vg3MUsCFVBdfVcYPHUgjH5Uhoo2sHljewB3EYB6jP9M1etboWMjqYklQ8kPyeO4\/T8j9akvhFFaW2z5fOQHnjPUDn8KoRx+eShfD5BUk8H2471NzS3Y0xNayuBHPNFHnHlngDPp6fqKoXFujBtwVF7jbsP49T\/AJ\/Kzb297BHvjid0zk+VIOP+A856+lPujJkE2zktwN2O\/wBMfrQhu7MIoN2FizjoxzwP896UOVRi7k9hgcD2\/wDrVPdySBdo+RAfuhduf8\/1qmgDSoWJHI6D3qlqQ1YsBNpzggg45GKuQKrsc5yfzpYbXEAyD0yRWhpduzXY4YjsPSokawVzpPDem3cbmW2SNwx6M+Mf5\/x\/D1PSLGWGMGXkgYVQeB6\/n+FcloFlJGgkTGTzzwB26V3VhvKDORxzmuSUrs7LWRqW0W3IAzitWD5VAx2qlbgcc\/j61eiOMjnp1rSJhIsqelSI2RjFQqcECpF471ojFosA8cg00tkU0PxjvSZ61dybA2CKryegNWCRUEnGT3zUMqJVkAAxmsLVrfdbzEdGU5\/LrW7KRzjr6VRm+ZSoHY1jI3g7M4a3hGIyvXewP1\/ya5\/xBpomtjyCfLJ+rYzn\/PpXWSwfZLx4WGInXgDswwcj0yB+hqhqkO3T3UgBkyD75B\/qazTszoep43cLJazh9uQr7hnnr1B+o\/lUkscag7B+7kXj1HGCD+B\/StjxBZxw3qop+Rwp7dMn\/AfyrFwY45owQdjME75XPb8Bn867Iu6OKcbMqQTvCWt3JCqNrEDORjr+GKnktFI83IKHgMvQcHA\/X\/PQV71cJFdJkM6YK+ox\/wDr\/n2q1ayEH7M3JUbQeuc9j7f56GrMylKm+BVdVDbXCk8cjrz9P1PvUukXLwXCju6gD1yORUlyg8slUDIxLDI5UkdM\/gaow\/u5AwZjsOVJP6cf5PNFxbO5uapG1s1vcwsNsi4ZWAIbsAfTjFZrvbXJ2ogwc\/IB\/I\/5NXTdJcWBt5Bu2biuepVuT9ef0NYqQF5tsfXdkA8evU\/lQDZo2EbR3YiVuuTGcbhn\/PFdLaaXcTTb570rztURqo69ceg6jjvWNaRCeNRJ\/rAcFhn8f8fwNb9vZyxhmtr54mVtpRirIMHjkjP+c5px7Ez2ua9nYwadIyxoQ7EAyuxdmJ7k9zjnHFXZ7aRmV3ZvlBChgBx\/SqVvBqCsWSS3kwfvbCp9em7p3qw0uoNJsc2ygdy5wBz2BJq9zNaENmZP7UnU7vLChT2HPH9BV+5fcogUZLdcHt0\/U5x\/9aqcUUk4cNc5BbbiEYU47knJ\/X0rWtraOPGwHrnOc546+\/ahK2gSd9SVQQME5NLT8UAVZA3FFPxRQBiYoFOxRikAYpksCSxlZRuQ9qlAoI5x60DRh21vGEa4uIUllYnMj4Y49OfSqd7pojnaW3gRo2ALIR+o9\/rx9K6Hy1DeVtG3OSOx4pR8pKSHKjpuqbFXOHvYFVAAjxs2MDAGT\/Kq32WQoWmu3Qg\/dZd\/5n8feunvrMm\/jc4EYOVGePf6CuT1a+j3NFCp3g9zkDp+tTaxadypdM7S7GmEzLnllxkZz0qKOOOR1LO8XONydz9f\/r1CHhiIMjsz4znng9vrUi6gkDnagZccmNtv5gipaKTNkNexgLBeJMnQl0PNV53uzlpplUZ5ITH+H8qzjq6qMpvU\/wB0Y5+pxUa3V1dkysBsU9B1Y+gPX\/Clyl862C4UKMRhmHJ3H+fHT61CFMOMtl25GO3uf8\/0rTitUhKGU75Cd7nOQvoB\/nAHrVO5OZRJ1c5G0nOO386paEvXU27ODesacc8fjXWaNo+64Uqh6dcVz2nrlFYDkYr0rw4FdVOMHHNc9RnTSRt6fZlI0QKMDt9K3LeFlQBSOelRQbRjgn1wDVxWw4OCB7qawRtJk8akDBbH0q\/CcryTVNW3YPFXIyM\/\/XrWJkywq5x8x609VPY\/nTEHoR7ipQeBWiMWHzY60DPc0bs0hf16Z\/OgQfiRUUoyvB6VIWA64qGVlHQjPtSZSKjDA6k1XdO+TVpyTnCn8qhYMV5UfiayZqjJvLUSENjpz6HNczq8MkUcpfDK8eUB45Hr+v612koIBDAH8a5TxI\/l2kgIzgfKfT1\/kKzZrFt6Hl+ryeZd26suPLVVPfJ\/+vWBdqI9QwB95SMdsnI49s1r6nL\/AMTJXA+XIOCOhA3dPwFUri2DSR8ZVVH1Jxk\/qxFdUNjCpuU54w1pECMMFY\/yP86r2spkvWBUgkllKjA7\/hxx+VXbri6tozjaysWGP73\/AOqsiGJo9RjAG4hsEZ5Oc\/8A1q0WphLRmocZKMcZyCfTPI\/I8f8A66rlAfMV12uDsPHQ\/wCfWrUgG1WTldxxxzj39jUcg3Es3U569W7\/ANM0DaKIaSEgMo44HPr+P+frRC\/2d5JIyA\/AwMAD6GrSgtG0ZIIHKMcZ\/XvVafH3sMsn948KR6dPX1ouKxctbtkBYkoVUHg+23\/2aun0qeG8soWwXmKtuXOMYzz+Q\/GuLsh5atG5HmSkDb1IA\/pWjpF9NpuqxtKD5bHDZ7g8fTimnrcmXw2PQUheN2RJ2DLhtrpnOfp05FWk09iQZnUtnsCfyzTxI3kAsMyyfewcg\/X0xjmrtuQ8KsGVh6r3961RixIoFTHGcdMnOKlAHpTgtLimSNxS4pcUuKAExRS0UAYlKBS4pwFACYpCM06lxQMh25Y+571BdT2tmjSzyCNcj+Ln6Dv2q03B6daxUAv9Qa4dl8qNikKnHy44ZgPUkdfSkNGRrF\/dOjBUMNu\/VW4JHqQOnWucl012j80DC9eecj19q6i+iW71yK03eVFHH5hB9e2c9T\/hVDxEBDYTSYAyCqIM5A45z\/np71JdzjrmVC2IU+XjDHrVXnd9auWqLJNvkBYDJ+Y9QOahkUpcYdcNkFh\/SgBqRKcc8Hkf1P04rX0pBNdHC\/uol4Xpnvn68fr6VnrgsqE45PXoM8flxW9oWFYsEAJI4J745\/ULUtlxRJeDYJG24dMjA7YrDt182fHUsvGOxHOBWxdk7LksSx3EcnPPbn6Z\/Ks22iaO5iBHPA+hzU3Ltc62yh2JGMcYruvDIwrYzwMmuPDKIUbGSFz\/AJ\/z2rqtO1W00\/SiWb96w3NgZwPesJ6nTDQ9BsHRwADkjrWoigDHUV5Taa7dzSTNbXOxRxgAjPpz9PyrqNL8WwsVjmlViB8zK2dv19qhKw3rsdoqLnrUqH8vrWfb6hDcIHSRGXHZgf5VMt7ExwHTJ7ZqlYizZpBgFpvm+pqoLkMMAg+1BcEj9c1VyOUt7sjjr70oIbIP5VWD+9Ak+bk4NFx2LgI9KY3zcY7VW88Z6n8KT7QoB5PTrmi6EkyRwMcfzqB8YwBTJLqOMbmcD3JrL1HXrezjZgwYr6H+fpUuxaTZcmYDO7j8a5HxG4Fm7MB97kcfX+lZ8\/i+5lleV4wkCf3WBP8Ahjp3Htmq+q65De6W7rjeIi49yO361DRrFWPPr9imoOMgquCSemcVVaREhWV23ZUNknjn+fAH5\/WmaxdIssy5JJJQAj6Z49eB+VYYupLidAezcL2H+f6VvFaGM3qXbxnmlWUthjJjr04z+hFTIgIe+ZQNqBjuH3WHt3\/+tUUjeZaJlwGDlif609LkACFQBbKd0rNzuP8AdHv7fnwM1oY7kENy8UqrIwDSgyYZsjljge4IA\/T61oeUk0eFViTlSF++p\/rWdPZ+afMiHmRn7kXOV9Bx1x09eBx3p9rJPaEB8gnueuPQ+31FDBeZP9kEg++COucdP8+1QPazBwq+nK7v5etapvoJEAnBR+m5Oh+vP9KGKOu6G6Lp02gkdPXP\/wBelcbVjGktiEzM\/lSYAAA3Fvf2pguPtARpmcbTgOV646D9P0rafT1itDMdsjAcIOR7dPfnmq0VoftUIZDGi42qTjJwMn265\/OmtCHqen6XAWtInklMjbAAAvAP41orGEzjJOeSTVS1UvaoIjtJ746DH\/16ux528ggnnnvWxzsXFLilxS0xDcUlOxS4oENxRTsUUAYlLS4pMUDAUuKUClxQMgkyR8pweg6dazNOKwxIoOWG8Ng9Tn\/P51tFMkH07VgX4\/szU0uCoazuDiTPSN\/U+gPAqWUihJhNcunAz+5AXK9MHtnr1rN1XybnTZPJlDqNoOCCcg1a1efybzeI9rKpjkwc\/I3HX1\/wrk44orDUfIRy+Dj0xUNlpXEtIoYGlkXgoB97sCwzx9OPxqC\/KzTIyj5yCWxz15HP6VtXqWjRDMRTccL5fykgdPb8cVmXlvGkbOh54A75+n4fzoTKcbFaGIXEZ2sA6Dbgnrjv+tbFrKY4sgfMx3EYxjpn+v61gxfu58RD5vTPHStSCWRSRIGTCbsE5wM\/5FJlRZZnkPkQKVxnOWxznHP8\/wDOaVIt99Ey9yvyn1JNVJblZkjU8sD+Wf8AP4VoWa7r2FtvCt19+R\/jUstLU6LUIPI09CuRx+VZtnfSNIiliFQYPvmurvrBrrTERAWYZyOw+npXGyWrWkzK6bWHrWUGmtTaaaeh1iSwsqJ5TJvOXcPgt7fSqOoefaTLwtxbq25SHAkj+h9PbkfQ81zSvrGqXgtbTzEJBOEPOK0tY8C3lvpkF47yy7uJCSePTP61SgRKpZbGzbeK7qLEcUSxoBjlWIP1Ix\/In+da9n4pU3KksApI3DPGeR+HBrzGz8M3d\/cxW0SKz52J5anLc9W9+1bur+H9U8OYBaR0J6D5h+GaJwSHSquR7VpmtxTxrtcNgEcnnitg3qFck9K+ftM8R3VtIscqNGexGcH\/AAPtXb6P4ikv3EJc7jwAKxaaNrJnpa3yk8Hp0pz3igg7q5hLPUVTenINZOq6nd2AIfqffFLmYciZ1N\/rS26krgnpmuNvfiJ9nndUQkqMFTwPzrlNS1yaVySzkHgBeprChj1DVLz7PbRqjlsDjJH1P+FXGN9xSslodHqfjm9vhtjidEK44JPNRWt9qOpIIEhlKZAaSRiRj0xyfwB\/TiuX1bQdT0zUAlw11IEKl0SXazr3wxBAJ7cH6HpUui6VruoagIbK6uYw+4qry7yg5IBPGfyH4Vqqel0c\/ttbHbNbra2vkSu8sZPzIsZUKPxP17Vi6xPFZxFecbOT13dh1\/8Ard6x3u9Z027e2vJZHdGwWYHk+maW4mk1TDyj5wMc8EVPLbc15tNDAufMmk+fJ3DHXIHpUUKMkqqy4O4AhuMH39q1J7N2k284VQfbOaq3MIjlOT1+6Qcfn+dWmjFpsf5wDfKFJPBznrUE0ThgVclcfKAPu\/TH58Dvnuas3Kq0SyLwGQFmHY\/5wfxqFHOTG\/XPzKR+oH\/6x3qiSSJg53RZ3\/8ALRAMfl\/nPp7adoTs3SWu5eo2tj8SM\/zqgFgOWMYIHYtt2n2z\/U59Ktq7yIjSQh2AO0mQ7uf+BDP60h7BePFuO0HHT5iSfp1rIaeZHJLP5YIzg\/d9M+lXcb5hlQScjby3IH1qaW1+yTJIgyAMMhGQwPb6HNFhN3LummG5ESu4Ee7dJK5zgdh6Dv71WnlmjAkmiZAHC\/MOBxkfif5VXtrRoZVKyvHC5IXAyeuNu7sT2OOenvXqWn6Tp0+lxR\/Zllt2AkUMc\/N3J77s9c96tK5lJ8poWcUkdrGrffIBcg9DjoPYf0qzinY9cZoxWpiJS4paWgBtFLRQISilooAxcUYp1FAxMU4UClxQMKgmhSVXR0Vwy4wwyD14+lWKaRk96AOZ1m1isNPlFrEir8u7auMfN0P4dvavPtUikgvFucDY6gqVHGcD9en5ivVNctjPpV2m3fmFiB7gccfrXAXUJaACaMOjqGGfzyP8QfY4zWckaRehkw3Hmb0cN0AIycLj6\/8A6qtssZSSFXJCAFc8gLjtjqaqSRNEBh3GOPm7+3HWl8zcjIQzLnJwKg1KYYR3KlME7unSr0tyjwALgM33hn07H0qCRE271jUA9++f5GmOpDljkrnI78U3sJaMt2cCyFixAjBDFvQ+n+fetWzcC+gAGAGxjPuDXPxyOzDawBXkIOMe\/wBa2LOf\/SIdxBx0yBwKhmkWj2jw9bx3FuMjIbBwfSrt74Ws7hvMaEfhVTwg2LaNmxyoGPbsK7qOETR4xXItzqkzzO+8OjSrqC\/tEZTG3zFR0FdHa6nJNAoaGGUOTlXU8j0\/pXRTWhIEbxBkJIJPas8eHIFbcrGJT2Qn+pq7sSlG2pFbwR2ojaHS7W238M6AA4x6gA+tUr\/RdN1Ob9\/5mezISMfjXQRaPboBl55c9A0hx+VW4tNBPCBQR27U\/eYlKK2OHl8CaSYJTukk3ncBI25lOOufyqPQ\/DEVjexSKMnnGfbpXfXFvDChVQATVCNUE\/A6DAqZOzsCd9TdtoIzbgFc4rmtf0OG9kClB0OOO9dVbY8jPtWfd\/60ZxxVtaGcW7s4uDwNYm1d84lZSoZlzjPf61BH4XTTr2OYXKOwXBxEqE4+n8672OOOZepGfSqs+jxsMSIJFPP1pWfQ0jOzszntQ0rSNWiA1KCQtEMJLHw35jtx6VDpVnpOkw\/6FZTB5IyxeYjd97GM\/rwK1ZtDdCfs908PPRvnBqr\/AGHqWSBdxFemEjKn+fFPmkHLTepzWr6Uuv3lrAsCRso8yQKOh75P+e9Rp4It\/OK4BQHnPY13VjYDT4isUIDt1YDk\/jnNWo7LC7j94\/pUNsfMrnkWoeHEtL64XbuVu4HQf5Jrg9XgSO+eNiEbJ2k\/y\/Wvetes1H7wAZxyf05rxLxja+TdMT8pUhgBzxke9OlJuVmFS3LdGLG4jGD86HjHoT\/k1XaFAfk3AYyqsAR+B\/8A1VC0jRS+WwHOBgHOeO9PLnO04IJ7d8f1roObRk8TFZeYyGAHT5T+Xf8AKtCG6tQCD9q3dQflx\/TNZavKF2hnj9Dg7fy6f570PM+zMk4cdPl3ZH04x\/ntTJbL107r++TLAEZb\/P0qeG7F3EQFO5jkknkf1NYouX27Tkg8YxnJqzpt5FaXaytE8wyA3lttJGe2QfTrQI7bRtHTUfD968qbVLlV3dB8oyR+J\/StrwNcyzaPLBJnNvKUyeuckHP8\/wAawdP8RWlmI3trdnWZGX7KgO4y\/wAPHrngn+grrfDOlyaXpCrcY+1Tu00+OgZjnA9h0raJhK5sUUtJVEC4oxRRQAYpMU7FFAhuKKdiigDFoxS4pcUDEFLS0UDEoxmnYoxQBGy7hgruH1rkb\/T\/ALI5hmXNmWKpKV3eWM\/db6dj+o5rs8cVC6nBym5DxjGTzik0NM89vPD0CRmS0uXkU8Avzu\/Dj29axp4BA5hd0znGN2SDXoOpW8VnZT3EFiFWNCxJUDBH\/wBfj\/Irz7Bl0\/zs\/MzgsxwMZOc+34VDRcWQtAuNy7SexbnHbGOg\/wD11SuGABPyknsCT2rWWGSWTEUiGPjgc7frn\/61Vb2OC3fAQ4xyB1+v41NmXcyNz9+\/YgVo6dLIbpQzEZIHPeqZxIcIoUe4\/rV3T4\/LljbH3iBg+\/8AkUMcdD23wtdBY4VJ7DnNek2EoeNT7ZxXjXh+5KJE2cDOM16hpVwRGFY5PSuJ6SO16xudJwRyOTShEHUDr1FQRElASee1S7lPfHarTM2h+xQMjGKcWwueMU0cdOKbIwGQTgU7isZl87u4UDr1INUkDCZB0xxUeq3xhuFSPJJPNOtrpbgKwGCfasrXZvZpHT25K24+nJrNuuZGbrj9avWxzATyMjoaz7w4DEGtZbGUVqFs7KSCepzWoj5UA965+C8DXPlMCD24rdi+7n2qYhNDpIVfqtMNuijgfrU2e4NIGGK0MyHygCD6dqZKQARnBqRtqgnn6VTuHCk9xUMtGDrbKbaVW9DzXjHjZhMVbgjIUgg9ia9Z1m5JRwOeD0715D4qLmaFVcrhiwz6\/wCc0qS965VT4bHFzIJJAuCDgYz\/AC\/lTwHDbJNuMAgt6fXr+tWZdscy+YkWGP3uAFB9MHpThC8k5tYyFZCT1BGMdOce9dSOV6Gc8cqnaFAPYrkg\/jViwk8yZklDF0PGGwSKfCgVyyuWwcE46f54pthZXF7fMYPkP98HH0xx3\/pTRMti67bNQh2nafR1HA568YPGKup9miuUmO2SNx+8UYDLwOn8qprod7JqcQDPMrNtZwSwRRjqewxXpWleGrBbMrcW6SuT1wMsfUHr0A\/Oq5SXJI5DwbHJfeL3uIY3Fsm47C2Qinpk45\/z616tVTT9MtdPi2W8KoASeKu4q0rIzk7sbilxRS4pkiYpcUuKMUAJilxS4oxQIbRTsUUAYuKXFLRQMTFFLiloGIBS4paKAEIBGDSMpxwehzzTsUuKAKN0q3lnLbjH7yNkIb1x0x\/n8a8wgl\/se8ltrxGK44z3H8uhwfoPx9YeFS27blvXHvXNatoFvqU00ZCxsRvG4YBxwcehAx+fNJopM5eWTTWt\/OiKIoBG1CQW9ic+1c9c3kYZ9oySeqt+nXmtiHwzHJczRhizoSpTBJJ47fj2rIvbSOJhBCnz56g5B9OahopMqFhvBKnI\/hPf2rRjYIkMisNoOwDuT1J\/Wq5ha2GSvVipZh\/nnNJHOUUITwq7eecZ\/wA\/zqWi0z0\/w66vYpnGF716BpNxnAPp615h4SnDWwUkEHr35rudMkMDqCeCeueDXHUWp3U3eJ30E2Yweep71bRhkKPrWHbzZQcnHBHrWpFIMZz+lJMGi+XVR1rKvr8RqyISTinXd0yphVDMeAKI7JI7cyz4aQjJwentTbvogSS1ZgWUpulmuJCCBJhCRyAKv6fHHJK2zGAeAK4KbxtYaVe3djdsYiJGwccYPT+dW\/CfjK1m1J4BOjq5yrZ7+lCRpI9bgjIiwcc96zr1Mbs4xT49Wj8ofOCB6muY8TeK7OwXMk6Kn8TFuBWkmraGEYu+pekUYWTA3LhvrWtaXmQA2eenNedxePdJug0VtN5zt8o2+td\/DaF7GI42ybASc98VnqjWSVtTUEgYcdKjLcjHBqlBOVYxuDuHWrDODVXuZctgkk4yCfSs26lyhOccVYlfHTj1rJ1KUJG7HsDUMaOd1a4VkIU+\/SvKNfLzXk7RnLRqc\/Ngdf0\/+tXourTlLdm\/iI\/HH1ry7VJFkkkKOMPLyCCCpyR9PTv+Va0lqTWehiTyzxzeXMZB\/Ertzu9ev9PerVvJ5Mu5LtFLR+WAY+QM59PrVyWzvbSzFtd2AnhcfupcYYc\/w9ORjp3zWOuINW8i2QTqzhFWWPG4njkHoa6banHzdy9MFhVUbaIn+8VOeM98Zx0rbsru0tbT7Gk1pJDI2cgHKk9M9zjnsK9AstA06BhssoASxLbV4HYjB6Y\/w+taVvpVhbMGhsreNv7yRAH86pRsS53MHQdNkKmTyTDCVwFkyWfnlsHp\/nqa6ZEIHOMnk4p6oF+6MClxVEMTHpRTqKYhMUuKMUtACUoooxQAUlKBS4oENop1FAGNijFOooGJRRRQMKUClxS4oASilxS4oATHrUU9pBcoUliVgfap8UuKAOF1vwzcQSS3+nzu7IcvGx5deh56k\/riuYljBAcWB8xh94vv3D1wT\/SvXzGp7A98Hpms+40O1lQhIVTJ3bVYqCfUhcZ\/GpsNM8lntJ7gAsnkRDg+34etU9nAWNPlk+QH+Js9Sa9PvfDqHKYCAjl4lJz7EnJH8q4m506TTdQbZD5y7flC87eOTkcGk0WpFzwvdeVtXd8pH8uld5bXGWwjdsj2rzDTJfLuZFUlSG3Y\/pXbaZd+YQrNhgMVz1I9Tqoy0sekaZOWhXncV7Ct2CToGXA7GuU0eXMeV55BwK2bi\/W1tz03uSAP8\/jXM3Y6bXNMtEreYwGewNRS6gpULFyCD3x2\/wDr\/wCcVg3WpxNn5uf4+M44PAFVzMguYCZuCvIJztGM5P4c\/rU3ZVjn\/FWj6deTzKYgJVAJPoOvNee6lotzpE\/2iyZkdeSuf516NrMBZjPJtRWiBVi2S54xjj1J\/L2FUI7FbuSbcdwUErnqeccg\/wCefbFaQmKUbnEp4z19VCF3OOOtatjpN34hmWTUpixxuVM4GPUe9b03h+CCyWVlwGJwyjIOeAfz7\/4VdS2ENpEQqY+XDZ69RgDvx19MfSqc0tiVF9SDwx4e0hNcdlRCYBlXPrnv6V63DfAoqgjAI59s15jpCuiXcrEtyCO27\/DJyfxrrLO5IswZD+8ckHB\/MVDmW46HRXEse\/ehGR1Ge1AnyuQfeucn1LypXIfcqHIJ\/uk4\/wAirOm3y3MktuWHmx4\/\/VUqWpLjoaM04AyTx1NYWqT7s456GtS5fZzjr1Nc3qDs8xBJwBk8+9VuSjmfEVyI7eQk\/Mw6+npXm2prEIDggB8HH65\/+tXbeKrgPaynPA7Dkn\/Oaq+FtHj1USSXMSyIw8vy2PG3nJJ7dhn\/ABropIwqySOYfxi\/9mvaSW4lk27VcnjJ\/i9c\/wA+vBpvgzRLrX\/EEc8m9oon82aQnv259c16C\/w10mSYMGZYx\/CVy355x+ldTp2l2ul2qW1pEscS9hXSjjbRNDbpCiquThQMnv8A0\/KpcUtFMkSilxRigQlFOxSgUwG4op+KTFADQKdilooATFFLRigQmKKdiigDFopaKChuKWloAoABTgKQCnUAJiinUmOaAFApaKXFAxKWilFAhAo596guLVJkKFcg9cjNWaR13oVHcYNIDybxBYppWrKYlCxMfl425Hbj9M1asrjbjbwf1roPHVj5mnIyoXcEvkDJABGfw5\/P8K4ewnYKY3GGTqMcispI2ps9S0fWBGmBy+wn8R2qTVdQkRrWPJUurSjB5Bx0P0yT68VwFlqEkM4KkkcA4\/Wui1KW4vLm0u\/KwlufmxzyT\/ke1ckoWZ3wndDLq\/kjHykptAZmzzkHI\/z\/APWrNn8VJbbwJGMr8OAMj7uOf0\/Oun0zRhdo0lyOZWONo7kkYHp7H\/I0R4StIINogRt3cjIB69fTFJcq3K1b3ODvPFsdx5ZLOuz7qlGOFPA\/nn8abF4hRmdZJWcOpVlJKk55\/HGe+f513aeFdNZMPAsbMSo47\/5Nath4a0iGJg9tFIf7zYPB\/wAiqXIbRp9bnPN4t02400CRo\/MKEMOuD2x+efbn8eWbxLH\/AGism4CBXZwC2CcnqSPXgE\/5Prr+FtDmTnTLcsOQ3ljjFVLjwjo7gBbaH5hjhAM+9Foj5EzhIfE+nJgebtMcvmfNgfNjoce\/b27datnxnb3BHkzBo9zEouAfbP1BP6fh0EfhLSYWy0SHJOAF61KPCekE+atnEgVc+1Q+UmULdTkIdWnu5wyuHJz04POP05PTvmtTwzqMtvrvzMzbsI4PfJ44P41q6lpNhpxkKxBSwymBkKeOo\/HPHpWDprmHUUu2RHaNw7AH7x9M59wPwpadDPU9Bv51VMg8HpXFapfktJID0BA9Ov8An8qtalratbhUYFk4YN37GuQutQ8xVKEbRyc9fr+dVFGbdjL1qY3DrHwB3J6D\/Jru\/CNp5Ohw74wCCSGHf8R9K89hjkudRILFow4B5z3x\/P8AnXrllCYLdY9+9eq+wPOK7KascdaVycCiilxWpziUUuKWgBKMU7FFMQmKWjFLQAmKKXFBoASjFLilAoATFLilooEJiilooAxKMUoFFBQ2looxQAopaQU6gApcUYpaAClxQBS0AJRS4oAoAUClopQKQGbq9n9qt8Y+6GPueO3PXnI9SO1eVX9hLp96WKOqtyODyMflnsSPSvZygcYYZGa43xbpkbafLeBgsrAEg8DYOefc+vr+FTJFxZxdrdBZRPnOMHavUn6fnXQWupStamOTbvkOQMdD36\/jXGRny5GGzGWLNk4x7D9fpVmG7mUqfutu2vvP3V4\/lwD7VlKFzeM7Ho+i6q0yQRKw2qCJHZuQT\/jx+ddel+zuiwuWhIGH\/vEZx168889P5+U6RdIZ9sYZ2YbEIOMZI5P07dP5V32kDznaPzsEnkDOQPz69fpiuapGzOqlLmRrXN7BaK9svMhDF3UZ9TgH34HuahupoXjRrc7QVUkknjnqfr\/9bqapPp3lQSznf5bHA7knt\/8Aqrl44ruSeVtrFUODhgMD7o+nfp71K1NdY7HaS3EcBdYLiXOFwPMPAzhjnP4dO31FMhumxseQ78ZVc9cDvz1xn8fyOEVu2+QKw8xNzf7Rwefpnt+HQVrQWEqWyyOwaVssG6EYP\/1vx\/kOw05dzZhmBEbI+H64JweP\/wBQ\/WrLTLiRRgO4LfMOT7\/5\/rVexsXVkJjVfLwSW5zj69Kg1t4rdEk+fPzL8o4yRkHrxjr\/AJzU7i9TM8SvILUtu2nrlhlT22t3xjv06jvxx0GovbvcKjB1O04HOdpJx+h57Z\/K5qeovIwtOG8xhtZ25RizKR9MD261xct8TO6ISu3IyxBPHX69fxreFPQ551bM6OSUEyrI29d2AxPQd\/04rJub\/wAx08tS3HORuDdiPeqFveFywBHl5OHbkAirNkxnm81lUGRgwVl+6c5GPQZ\/lWijYzc2zrfCGlpIqT3GHVhhVYZJ5I5z2OWPvj6V3qrhQPSsTSL+0UCIr5EjHlSMJ9Fxx36cVu\/nW8WraHJNO+oAUUtLiqJEFGKdiimITFGKdiigBMUYp1JQAUlLRigBKWjFLigAooxRQISilooAxRS4oooKExRiloxQAAUtFLQADrSijFAoGLS0lLSEFFFKBQAopaBTsUAIKxfF2F8K3z9CqDHtkgf1rSvb+106DzbqUIvYdS30HevOvEvia71cG1iQ29lnOz+KTHTcf6D9altbFRi2cZPEXR2OSoB55\/z70W7t5T7SSyqcf3gOn+R7VLMjDjOMc9KrSSNGNyHHYgfXNSaGzpN35DoEby9mMtnGDxz7gf8A6q7PRNQ+zRhlUA7W4X1HH8+n09BXncGWQBC2WBLHHJPHA\/E4\/wAK6DTLgrGs8oPk27I3lIAd2T098ke\/APpUThc0hPlPT4tTZlW3kYBIUBbGCF46fXPf9PWCOCOeUwiLYd7BmOAQgxwSPf8Alj3PP2d35r\/Z8rLJ5mSX4XeeX69hlPY4z0NX4dR23BWRzgy7WwMEqpDMe3UDjp0HvXO6djpjUuaUl9bx6sYlQIrryV7KpPH6Y\/DPatP7fDHKsiEHZtcjtx3\/ABPH41wl\/eyKVErZVMszqOinBP1HzDHSlGpyTQuqEGQYjk2sDh8g4\/l+P0pcjL9oj0FNVjhiiMzErICSccAYPINcXrmrSu+2NQMEeVJ3x1Ht6df5c1XvtbklU26EFUhcN35HTHpgEfyrk7vVpHtlifaUXcijH8Ocjkf7xz9KuFPuZTq9hby4eSSVUIV48MAT83B4A+gwfzrE3rJMkpLcPlic4YHv\/LirM8fkxqrNiQEgjH8OeAff26dKrxKzMzbsliCT\/n\/P6V0LQ5ndsWFJGUQjBXtk5xx3H45rprCzIKnq3cgYqDSbAswZgfc5rq7axAK4HPSs5yNqcTc0m1j1CwCSAFwMHPetGOyvLPAhfcg\/gfkD6elV9Hja3mUjucV10EayJnH4Vim09C5JdTAW5xxNG0Z9eo\/OrCsGUFSCD3FbElgjD7o5qm+jIGLRkox7rW0azW5hKin8JUpae9ncxdQJB6jg1C8iRDMrCMdMucVvGpGRhKnJD6UVGk0UoBjlRge6sDUlWZhRS4ooATFFLRQAlLRRQAUUUUCEopaKAMWloxS4oKEoFLRQAUUUtAwpQKSnCgAxS0UUAGKXFMllSGMySOFUdSawr3xTHHvjtIWaQHaHfhR7460hHQkhVJYgAdSapT30rAraR9uZWHA+g71b0zRTNbx3VxLJNJIobLnIGeeB0FaEliqocCuadV7I6qdFbyOCu9Okmdpp3aSQ9WY5rlr23\/0pgBwK9M1SDZFhetcdPY5ZnweazjLXU3lBW0OSuLYnPH096z5ImUkgZPcHvXUz2pA5FZVza98Vumc7RkQt5DF0JAzlh\/T24zzVmG42zJuDYEilz6gE9vpUUsBVs9G+nWgbZGCuAr4wAeh+hpk2Omsr\/ZCYgVjaRpQHzjdwuDnPGWXFT3l+j3zIsjGZZCGI+YEMTkA556sM9D+tcvvmycszcYwe309KsrqMiSO5jj3u2S3IAHORj8c\/hStcabRq3N28l7KJlOdwxhN2BnOR7Y5+hp1jNHbW8jyEu74AiMmGHuec45Pv7dKx\/wC1ly4KHc24biMlQwwcenf86iE+F2jscAjqMf16flSsO5pzarb+Y7w2oxIrK6knABPQc8cjH59sVUdonuGlkQ5ziMD+ADGOO\/OM++aqsBK544Oc496tQ27ScIMDpTbElcrbGmK5GWAA+talhpTyyqdp59BWhp+jF2Ulevauw0\/SFRcY57ms5Tsaxh3Kem6Z5SoMdDmtiK2PnxjHBNaEVosSgAduOKnht8uGxjFZN3NtkWrO1CMDjqfyrbthjI9BVe1jyhPTHSr0abaEiWyxGARipFiB7d6ZHxVmMVaRmyAwKeo5qvNYRTKVeMEHqCK1AmaPLAp8ouY8s8W6bDoc9pNZIIhIWBReBx6enXtWZa+IZrSQGZ5JYiBlWOSPxPNa3xBuvP8AEMVqpOy2iG7\/AHm5\/liuVnUAAEE8Yrrpr3dTlqfEdzZapZ365hlXd\/dJwf8A69XTXk75WbcjFT1BBwa1LLxNqNlw7iaMfwv\/AI1TRB6HS1z9h4usLk7Lg\/Z39W+6a3opI54xJFIroejKcikA7FGKKWgBMUlOpKBCUU6igDFoopRQUJRS4ooAKKKWgYYpaO2e1U7nVLe2yM73HYdPzoAu1Ru9Vt7ZSFPmP6A8ViXeqzXQI3kJ\/cXiqAcuwB9qaQrl27vpbwqzsAAeg4ArJmj5YjuatISc9WP16U94+oIxx6UxHovg29F\/4eiHBlgPlP6+36fyrVuYhtI7964DwZqh03WhbudttdfI2ezfwn+n416ROvWuOrCzOylO6OXvrfzDn8axLqx+U\/LyK624gyTwfQ1QuLc4JI61jax0XOIuLLcGwOQKx57QHIxiu5ktASTt61lXdhySF49atSIcThLuxwThaypICOGXIruLizIBBHFZFxppJJAyPatFIzcDBjJ24cbh6jqKl8gSD5SGFWmsHU8ClS1Oc4yfUU7iSKY09cZ\/SnrZdgCfoK1IbSQgcnFadtp7NgHPvxUuVi1BGJbaa7tyK6PTtGxgkYH0rWsdLA5K1v2tkoxwOPaoc7lqNinZaWF42gfSt2G0CL0wOualhhC8Yqx5TOcYwPQVmO5WEfmNgdKu21tkgY+uKsQWe0AkY9BV6GEKOOBVJEuRHHCEwAOBVgL7U4Lg5xUgXNUkTcaq89OlTxjGKaqc1KBgAVSRDZIpoklSGJ5ZWCxopZm9AOaQe1cr491f7FpC6fE+J7vg46hB1\/Pp+dVFXdjNs85vLp9T1W5vWBBmkLY9Bngf59KrS4ZTgCplASLPcDjA61EQNvUcjiutKxg3cobPnbqce1RtH1+tWWUbzj1xTMDI+8KYio8R4x+VS209zaSboJXiI7qxFTOgA6dOmKaE5xgYH+FIDXtfF1\/Bjztk699wwfzFbdt4x06bCziSBu+RuH6f4Vx32YhD29unaont+T0IHfNFgPULe9tbxc29xHKP9hsn8qn6V5JtaNgyMVYdMGtOx8UalY4Vp\/PQdUmG79etKwj0iiuYh8b2BiBnhlSTuFwR+uKKVgLtFFFBQtIOtFFADh0psjFI2YdQKKKAObvb+4mZ1L4VRkBeKz2\/1jLk4Gfx+tFFUhMTJCH60oHysPeiigAhGXOOD6j61ekQbGzzhTyaKKAKL8IHHBHTH1r1jRLqW90CzuZzuldPmPrgkf0oorGtsjajuWXUbse1U7lAUXjqaKK5WdSMuRFOTiqrRK24Eds0UUijGvrePaXAweentVFIUkQlhzRRTQyo8Ee48VGttEHI20UUxFyK1iHatS0hj2qdo5ooqGWjXt40wOK0IUUHAGKKKQGjFEo5A5q9BGoXdjmiiqRmyxgVMgHI9KKKogeRjFOjGc0UVSJHj71SLzRRTJew7tXkHiW6lvPE1+Zmz5UpiTHZVOB\/n3oorWnuZz2M6VQqHHqf51VuAAAAOpIooroMSvxuPHQA5pFA5GB900UUgGjhjwO3UU4AAke3WiigRIo6fXH60MAEfjpxRRQMrsgIHp\/+v\/CqrgfKcDJoopiI8Z5yaKKKBn\/\/2Q==\" align=\"left\" hspace=\"12\" \/><b><span lang=\"EN-US\" style=\"font-family: 'Segoe UI','sans-serif';\" xml:lang=\"EN-US\">Professor of medical statistics at the Faculty of Health Sciences at the University of Stavanger (UiS)<\/span><\/b><\/span><\/span><\/span><\/p>\n<p style=\"margin-bottom: .0001pt; margin: 0in 0in 10pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: normal;\"><span style=\"font-family: Calibri,sans-serif;\"><span lang=\"EN-US\" style=\"font-family: 'Segoe UI','sans-serif';\" xml:lang=\"EN-US\">Our trainer for this series is <\/span><a style=\"color: blue; text-decoration: underline;\" href=\"https:\/\/www.linkedin.com\/in\/joroislien\/\"><span style=\"font-family: 'Segoe UI','sans-serif';\">Prof. <\/span><span style=\"font-family: 'Segoe UI','sans-serif';\">Jo R\u00f8islien<\/span><\/a><span lang=\"EN-US\" style=\"font-family: 'Segoe UI','sans-serif';\" xml:lang=\"EN-US\">, who is a professor of medical statistics at the Faculty of Health Sciences at the University of Stavanger (UiS), a senior scientist at the Norwegian Air Ambulance Foundation (SNLA), and associate professor II at the Norwegian University of Science and Technology (NTNU). Prof. R\u00f8islien is a renowned research and science communicator. He developed and hosted the popular science show \u201cSiffer\u201d (\u201cDigits\u201d) on NRK, and was the first ever Norwegian to be a host on Discovery Channel. Prof. R\u00f8islien is also one of the experts behind the advanced modules in the\u00a0<\/span><a style=\"color: blue; text-decoration: underline;\" href=\"https:\/\/www.editage.com\/insights\/become-a-great-peer-reviewer-basic-and-advanced\"><span style=\"font-family: 'Segoe UI','sans-serif';\">Editage training course for peer reviewers<\/span><\/a><span lang=\"EN-US\" style=\"font-family: 'Segoe UI','sans-serif';\" xml:lang=\"EN-US\">, where he shares some great tips on reviewing statistics.<\/span><\/span><\/span><\/span><\/p>\n","protected":false},"author":695,"featured_media":33313,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"inline_featured_image":false},"categories":[2420,2415],"event_type":[2358],"new_categories":[],"new_tags":[],"series":[],"class_list":["post-5796","events","type-events","status-publish","has-post-thumbnail","hentry","category-data-analysis","category-data-storage-management","event_type-webinars"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.0 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Know thy data (Episode 2) \u2013 Looking at univariate and multivariable regression analyses | Editage Insights<\/title>\n<meta name=\"description\" content=\"As a researcher, you need to capture and analyze a lot of data. 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