The goal of an experiment is to investigate the factors that affect visitor travel time in a complex, multilevel building on campus. Specifically, we want to determine whether the effect of directional aid (wall signs or map) on travel time depends on starting room location (interior or exterior). Three visitors were assigned to each of the combinations of directional aid and starting room location, and the travel times of each (in seconds) to reach the goal destination room were recorded.
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/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoMDAsKCwsNDhIQDQ4RDgsLEBYQERMUFRUVDA8XGBYUGBIUFRT/2wBDAQMEBAUEBQkFBQkUDQsNFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBT/wAARCACZAO8DASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD9D/jx4m8UeD/AP9q+ErzSLPU01Czt2bWtOlvYWjmuEgOEjuICGBlVgdxGFIx82V5rTPG3xH8MfEux8EeLrvwzqp8Q6bd3Oh+INF0u4s0t7m3CF4rmzkupS6kSKwdJ1ztZSBkMOx+MvgPVviT4Em0LRdbtfD1+95Z3aX15p7X0a+Rcxz7TEJYiQxiCkhwQCcc4IxdE+E/iKTxWnjHxV4p0/XfF1jp1xp2jvY6RJZaZp6zFTJJ9mNzLJI7mOIMWn+7GAuzLEro/n+X4a+a+7R6NrlXf/gr/AIPfT5Hm2qfFf4u2PwP8MeKF1vwNH4g1PxFBpNy0/h+7FjFDNeLaBgPt+4Mh3yFi2GGF2pgscDxZ+0F8U/CelC/vfFHw2s/DZ8QWGmw+ObrRruPSbu1uY2Z5VVtQUKYWTBYTSRyeYoDIVYDuZP2fvH7/AAr8NeEx8QfDrX+ja7DrX9pyeE5yk/k3K3UUZhGojafNDbmD4KkAKhG4r4i/Zu8Tx3FnB4M8baR4b0Cy1yHxFaaLf+HJL6C1uwsnnJEUvISkEkknm+XyUYvtbawVYjzqKvvdX9Pcv+UlpZ6+Zlrr/hdv8VpW/G29191jQPxj8RWXhTwPZ2l94a8a+LfGd9Lb6PqelRy2WlS2qI8z3bI0sz7UgQnakjeYxUBlDbl6yz0z4sQWWpQXXiPwZfXkqD7DfQ+H7u2jtX3rnzbc3shnGzeflli+YKOhJGD4g+Bes+NPCekR674ttrfxpoeqNq+jeIPD2jLZQWEu0psW1llm3xsjurq8hLb2wyHbt7XwRo/jawkuJfGHifR9ccrtgi0PQ5NMhXuWcS3Vy7txgbXQAE5UnBFJXu31/Ky7db3/AM7WQXej/rd/ha3XbomeQeGPGXxr1rw7451N/EngGRvD97qOmxRL4VvYhJJbLlZWP9pPlWPBQYI3Z3nbta34O+KXxI+I91D4a0W88MWOuabo1hqmueIbzR7iWz828V5Iba3sVvFk4jUFpGuCM4wpyQu74b+DvjTQvBPxD0Z/Gmhzaj4nvrq+tL+Lw5LHHYNccSh4jesZsDG3Dx4Iyd3SsvRv2fPF3hC/0zxB4b8eaZpvi3+yLfRdZe48PST6TqsVvuFtKbQXiyRTRo5XetwQQTlSNoWKfOkvaK+kPvtLm287eXqhzXvPl2vP7uaPL/5Lf8b66nMa58c/i7b22taFpWi+GL3x/wCFdXtLXUbBopxaa7aXEJmiezkMwNpKyxyrslEyq4UFip3D0ey+Kd7481H4e6p4N1bSx4X1m4ubfU7XUdLle+SSKN2eEMLhBbyo8TxujxyENnpjmp/wo/X7K+h1ew8Wac/iS91iDVte1PU9Eef7asMflRW9vHHcxC3jRCwXcZTlizbmLFr178CxZ/Fl/HXhrWV0Oa6iml1DSpbP7RaXl/5BhgvCA6MjqjFXCkeaojyVKBqpOVvnfp2jfy118k9rJ3FNXSUXb/8Aaf5K3y8yXVfi/daX8Y9G8NSWFsvhfUHl0r+2JJisg1dYRcpbBcYKtAJDuyPnXaORgy+DPEnjPUvjN460TVrzRJvCuk29pJp6WemSw3hefe2JZmuXVwix44iTcZAeNpB811/9jiDXfAiZuPBtr8Wv7Qj1F/iVD4NUXxmS4EyyL/pXmrJlVUnzyu3K7Np2jrpvhj8VrLxlruv6L8Q/CVm2sWtnDPb3vg25uBHJBGymSMrqceAzOx2tuwAoycFmFzJLuv8Ah9Pnor9PvG1rpt/X9f1Y3P2m9WvtA/Zt+K+p6Ze3GnalZeEtWubW8tJWimglSzlZJEdSCrKwBDAgggEVW+PvxQ1P4Z6b4f8A7Pn07R4tV1A2dz4j1uzmutP0lBDJIJJ44njJ3sixruljUM4y3RWq/tPwXFr+yR8Wobq5N7dR+B9XSW5KBPOcWEoZ9o4XJycDgZruPHOleLNTtbQ+EfEWm+H76KQmU6vpDalbzoRjaY0ngcEHkFZB7g05aba/18gT0ueM+N/in8Rrb4Rr448JeNPhvrmn2qxxSXtjpVzqNrqMj3Ii8yJo79BAoV1JjLTHduG/gE+pf2v4r8G+B7ufxLd6N4g8RmcW9g2kWMun29zJKVSBGikmnZP3jYZvMYbRu45A4/xR+z9qeq/CnXvC+k+JrDTdb8Q6qur6trF1ozTwzT742by7ZLiLywfJiUZkbhSW3Mxatrx98Ib/AOJ+r+FovFF34Z17whp8Zk1bw5qfhw3MWpXRQqJlL3JSIISSqPHLjJ+YnayqSk27aJ2+V9ZW9Nlp8kh9del/8l9616+tzkvFPxw8Vzfssav8QtATQ9L8Y6Ja3B1PTNSt5b21hvLVnjurXCSwuv7xGAck4GDtYEVJ4L8Y/FrxB4i1zQ11rwN4gto9OSW18V6Ro13HY2d8JxHNazRG8k89lQO2I50YFcOE3KTgaj+yTrtnofxO8M+EPFXhXwf4L8ZhRFoFl4PkEOlnylikeIR30aF5ERdxCKCyhtv3g3qXgz4Yap4P8Y3OuRa5YpBrFuJdf0qy0toba81IBV+3W4M7m3ZlAV0Jk3hIzkMGZ73d7Wve/wA1+Fn2e9ulyXdaLpa33/jpvfpfrY858MeMvjXrXh3xzqb+JPAMjeH73UdNiiXwrexCSS2XKysf7SfKseCgwRuzvO3a1T/hefxF8C6l4R1jxk3hrVvh/faJDqGv6jo+l3Nlc6M03+ruCHup1e2U4WQ8MgO/lQ23s/Dfwd8aaF4J+IejP400ObUfE99dX1pfxeHJY47BrjiUPEb1jNgY24ePBGTu6Vd+H3ws8X+GtU00+IPFPh/XtHtdBTRJbK08OTWkkwQ/K5ke9mUAjgoUOfUdKxp+0VnLe0fv5Zc34tfg1sVK15cu15W9OaPL/wCS3/XVmN46+MviP4ear4ruZjpXiLTt2lWfhvSrGxmguJru+lMMaz3XnSq0e4biyQqQpOAxADdl4bsPijaazbz6/rnhHVNKkIE1jpujXVlPbgqclbh7uZZsNgYMUe4EnK4weCtf2RtGh8JeKPCkmvX/APwjt7cWk+gRQoq3Hh37M5lgWGVtwkEczM0YdTtTbGdyrVn/AIZ71rxf428G+I/iL4g8NeKbvwhd/bdKu9O8KCwvjJ5bpiaeS5nBQlxIUiSIeZGjDG0AbQSv73f/AMl9P5k73+VnuRZ8rT8/vvp6K1vxume5V4n8f/iB428D+KPAtr4c8ReEPD2ja3dXNrqF74q0qe5W0WK1muTMHS8gUDEO3a2OWzvGMH2yvMPip8K9e+IPjPwVqtlr2i2Oj6BcTT3OlanoUl8b8TW8ttKhkF1EqKYZnABjf5sE7hlSv6/4cvocPpHxv+IQ0nQdA1DRtGn8ceIddvdL0nUooLi10uext4TM2pGGRzLs2jAiDne2Csmw76q+Pv2hfF37OmoG3+JVvo/iux1DTbu80jUvC1lNpzPdW6oxs5reaecLvD5WYS4GCCg+9W9d/s03Z0PSbHTvGU+mXXhTVGv/AAZqEdl5kuj27RGNrGfdIRd2+xmjwfLbywg3FkDm1r37OknxWtrofFfXLTxXM2nXOmWkWiaa2lWtitwFEs8aPPPJ9owiASGUhQDtVdzZHJ726ff7vXote1tf7ugktddr/q/na3fW3965l+N/iJ8V/g/4fh8beKx4T1fwtFNAuraJo9jcwX2mwyyJGZI7p7h0ujEXyV8iHeASCvQ6Xwt+M2uTeLfHPhb4hSaPBf6IZdR0/UtJt5be3vNLRzG7tHJJIVlidP3gDkbZoSMbsVJH8E/GHiPR7Tw348+IFr4s8JW0kDvbQ6D9jvtSELh40vbj7RIkilkQuIoYd+0g/KzKYZP2d7/xHc6beeLPEdjf39hr1xqkc2i6VJYCWznTE1hKHuZiyOyxMxBXPlKNveiz5tNFr+lvPTV63e6vqhq3Jrvp+Tv066Lor2e1zoPgh418V+O4fFN94mh06whttWks7DTbW0kjuLWFVVgLmR5WEkpEi52pGFKsPm4Nem15/wDCXwF4l8Ct4obxD4j0zxCdX1WTVITp+jyaf9n8wANG265m8wAKgBG08HOc8egURvyR5t7K/rZX/ElXvK/d29Lu34WCiiimMKKKKACuCvPi3aWXxc03wO2nXLC+t5iusBl+zrdxosv2MgnJlMDGbA/hX8u1v3uY7G4eyhiuLxY2MENxKYo3fHyqzhWKgnALBWIHOD0r5f8AFH7M/jHXPBdr4jgS0g+NEOtx64ksnjjV30KC5SXmVLcxGI5gzDs+zD5HYb8/MZu1Jduvz0/4O+6V9GO11pv/AJa/jtt1dtTvPEfxp8c6Fe/E9Y/A2gXFl4OsVv7eaTxPNG+oRshlAZBYMIT5aSfxP86qvRt4l0n41+L9I8S+E7Dx54G03QtK8UyC107WPD+vvqkUV20bSRw3KSWls8e9UcK6CQbgAduc1j6l4C+KviKH4ureaP4O05/FehCw0swa/d3AiuFgeICcGxjwhMrtuXJ+VRt5LDT074dePPHOr+CZfHNvoHh3SfCN0mowWHh7VJ9SfULpIHhiaWWW1t/KjQSO2xVcs2w7lC4JDm2fl913d6dbWt52urDk06d1v735Ll+V7362v5HtlFFFUSFFFFAHlX7WP/JrHxk/7EzWf/SGavVa8q/ax/5NY+Mn/Ymaz/6QzV6rQBXv3uY7G4eyhiuLxY2MENxKYo3fHyqzhWKgnALBWIHOD0ryf4eftE6Vqng+HVPiDfeFPh7q8sVxejSZPE0c7R2cUvlNcO8sUBCiQMpIUqPl+ck4HrF+9zHY3D2UMVxeLGxghuJTFG74+VWcKxUE4BYKxA5welfPHw6+F3xT0nxD8NZvEOieC0sfDf8AagvJ9P1+6nmJu2yHhjewQZX5lKlxkN97scpOal7quPRavb+v6/z2PRfEP7Rvw58M6n4Js7zxdo2zxjI6aRdpqdt5E6hCwkDmQbkYhY1ZN2XdF/irXf41fD2Oy1e8bx54ZWz0e5Sz1K4OsW4jsZ3bakUzb8RuzcBWwSeAK8csP2fvGtl4R8N6RGnh+2k0bW9eeN7bUrhNthqH2rZNGRbDZPELrHk4KHZxIMjbzOj/ALLPjHSPgp4s8MroPh258Wah4fTwzb6nqHjnWtQs57X7rEwXUEv2NQPnEMW9c/LuA5rVO7emi/H/AIbRfP8AutGetvP/AIb89fus3qmfRMXxe8K3viPSdG03WtN1e51D7WQ1lqlo/ki2/wBcWQzCRgrEIfLR9jH59o5qO1+OPw4vdAv9dt/iB4WuNEsLhbO71KLWrZra2nYgLFJIH2o5JACkgkkcV5h8a/g546+JmmeGrHRdM8JeHFe3uxrcn9oTSCCWa2EI8lBaKLpVwufMMO9V24XOVyPHnwH8f+P9G8d6rcaN4K07xVr3hdPCUGmW2p3LWDQea7NcTzm0DMVDkJD5JC4YeZhyREea/vbf1+tl8+bZWHrZ/h/Xpd/K27R9GaH4j0nxPZvd6Nqlnq1rHM9u89jcJMiyIdroWUkBlIII6g9a0ayfDFlLZ6Nbtd6Xp+j6jMqy3lrpcpmgWbaFO2QxxmQAKAGKKSFHA6VrVdraMaCvE/8AhcfxCu/ih4n8M2HgLw3PpWg3llBJqNx4tlguLiO5G9WigNgVMipkmNpQCcAOeo9sr5v1b4K+MF+P/iPx7a+A/h1rc13PZf2R4h1fVbiPVNLiigEUhVEsGJ3bpD5azorDAJGTiG2pRtt19Bt2i2e3f8LH8JH+0f8AiqNF/wCJbex6be/8TCH/AEW7kKrHby/N8krF0ARsMSwwORXH6J+078L9bfxeB440Cxj8K332DVJr3VrWOOFsJhyfMO1CzGMFtuXjdcZWuf174ReLrv4zWnxN09fD8Wq2c40z+ypZ5RDe6VtcCeSYQ7kvEM0xQBHQIzx7v3hkHF/EP9m/xzr3iu61PTP7Jms4PE9xr1tbp4q1XRJrxLizS3kSSezj328kfl/KymVXVmVkUnNNN9e343jb8G3+G6uEtHaPf8LP9Ul577M95uvi94Esb6GyufGvh23vJraG8it5dVgWSSCZgkMqqXyUkYhVYcMSACTWaPjv4FtdBvtc1fxRonh/RLXU5dJXU9T1qyS2nmjOGCSLMyg53DY5WQFTuQcZ4Dw78FNa0D4h+B5YPCHhWPwTo/hq70W5gudeur+8ge4mhmk8sz2hM6AwAbpJEZ/NYkDaA2PpHwo+LOh/EzXfF6aR4G1CO6ur+K20u71q62m1uTAfNaX7CTHKv2fDRqjLIsuC6+WC4+ZTS6Wl96lZL5rrsr3eiIu+W/mvus7/AHP59D2zUPix4I0jVF0y+8Y+H7LUnghuls7jVII5jDK4SKQIWB2u5Cq2MMSAMmuqr5y+H/7PfiD4f+LPh9ZnSvC2u+D9A8MXehXV3c3UsV0HuZ4p38i1NvIghUwKioZh8r9tgDfRtV0T9fzf6WfzGr318jz/AMU/DTxH4g126v7D4seMPDNpNt2aXpdpoz28GFCnYbjT5ZTuILHdI3LHGBgDK/4U34u/6Lt8QP8AwB8Pf/KqvVaKCj51+MGl+J/g54FvPFV38Wfirr9jZuguINHsfCvnRIzbfNbz9PiTYpI3HdkA5xgEhnk6pF8QtH8DXXxw+Jln4q1PRZNcj06XTvDZMcSMqNG8i6YyeZuYjCsVOxjuxgn2z4ieDbT4ieAfEfha/RXs9Z064sJQw42yxsmfwznPtXgM/wAJPiNN8HvCPiZbK1/4XPo17bakLU3CJEAYVs57UzbmXZ9nLMcFlLrnBzkzd6/L8brT00b08uo2k0rb6/hZq/rqvLdnQfDXwv4v+I/ht9Y/4Wv8VPDqC7uLRbXV7Pwq0rmGRo2dTb2EqFC6sAd2SFzjBBOBf3niPTPGU3h24+KHxdWWLVbTSnvhY+Ezbg3MbNDP/wAeO/yiVKfc3huqbfmrE/aF+Bvi/wASWVvoPh3wr/aVp4c0G3fw3q9hDpSXKX8bN5sb3F4WmhZwsRQwLHks5adDjGz42T4hXHxUv72w+EniO/0qbV9Cvkvo9S0dF8u23/aMq98HBUSDaNvzFWHAwxJXcoqH8yT/AMN171/NX9PyxlJqMrLZNr1s7K3rb177npf/AApvxd/0Xb4gf+APh7/5VVRtfhl4nvNTvrCP45fEpZ7MRmV5dG0OOJt4JXy5W0cJJjHOxm2nAbBIrA/af+G+ofELU9DSDwReeNreG0uYo7eYabPpUNxIU2SXUN1LFMpXbkTWr+ao3gAlhXnvxK8F+JNP074madB4Pfw6+utoKWOo2v2OSx1/UI2SOW2aAXHnPHKMIfP8oMisHljADVPP5df1t/SWrurX6avRpf0v6/DW+x7f/wAKb8Xf9F2+IH/gD4e/+VVH/Cm/F3/RdviB/wCAPh7/AOVVfNPgn4NaRYjQdN0z4GSJ4r0zxhFqniUwaToumCztZoZOIVS+lBtmVwohSeU7UkDDkKes1T4Hahoel+KPCln8LdVvPBMXjBtX0vR9FOiSabcWktjGrRmyvZljMSziZjGfKZXdHj3YOGpXvpt+Pw/k5P8A8Bd7dE3Zpf1vL8fdX/gSPav+FN+Lv+i7fED/AMAfD3/yqo/4U34u/wCi7fED/wAAfD3/AMqq6f4SeHr7wp8NfDukajY6fpd5Z2ixSWWl7/s0GOkab3dsAYH3m5BwSMV11aO19ATurny/+038J/FGnfs2/Fe7uPjP441S3g8JatLJY3dnoSw3KrZykxuY9MRwrAYJR1bBOGBwR6X/AMKb8Xf9F2+IH/gD4e/+VVH7WP8Ayax8ZP8AsTNZ/wDSGavVaQzyr/hTfi7/AKLt8QP/AAB8Pf8Ayqo/4U34u/6Lt8QP/AHw9/8AKqvVaKAPn74i6Jrfwy0u2v8AVPjb8T7iCe6t7QGy0jw9IEaa4it0LsdKCqPMnj4JyRuIB2nFLxlonxB8Na8ulaV41+MvjKQW6XM0+iQeC4o4A7Oqq32u2gYk+Wx+UMMDr2rr/wBqLTvEeu/DWDS/DPhPUvFmoS6zpd08GnXFnD5MVtfQXMjMbmeEHKwsqhSTuK5AGWHO/Gr4eJ8WJZ7O/wDg82p6vqOlJFpniu7u7IjQZ9kjI0jeeJ7eWKSRjvs1l3fKd/AC0kuVSWrvt5WXp59d9Lo0tG2r1/4b+uvocx4e8Qax4h1nTYF+Kvxfs9E1W8bTtN8SXOleF/sF7dKHJiTZp7TL/qpAJHiWNivyu25c+p/8Kb8Xf9F2+IH/AIA+Hv8A5VV5/wCFf2dJn+KWm6imk3vhfwt4e1J9T8u81eS7bWtQCuguYbQTSW9lAxkeU+WElkYruVACG+laXT+v63v8rX1uY63t/V/+Gt1et9Tyr/hTfi7/AKLt8QP/AAB8Pf8Ayqo/4U34u/6Lt8QP/AHw9/8AKqvVaKRR5V/wpvxd/wBF2+IH/gD4e/8AlVR/wpvxd/0Xb4gf+APh7/5VV6rRQB4/q3wq8XaVptzdj42/Ei9aFC4trTTvDryynsqj+ygMk8ckD1IGTXI/Duy8SePptdsrn4tfFfwlrehyRpqGka9p3hdZoUkTfFKHg0+WF43AbDJI3KMDggivePFeq6hofhvUb/StEuPEepW8LPb6TazQwyXUn8KB5nVFye7MABnr0PjOk6d448W+CfFajwvqvgvxfqEkN3dX/ia10++g1AJgG0ghstUBWMInlgSTRj94zEszuaWtm/6/4PoXa6X9f1/XnbkB4g1/UvCNz4i8O/Er4z+KrO2vL21nj0zSPC0cka2srRSy/wCkWESldyEqgYysORHwceh+Gfh54h8XeG9J13Tfjz8QpNO1S0ivbZ5NO8PozRSIHQlTpOQcMODyK8g+GvhT4mXnw88R+EviV8MfE95a614h1DUUm8MaxaaG8a3VxLKTOYNZdzEfNJKK74+4Ul2iRvp74W+HdY8I/Djw1oviDUxrOtWFhDbXd8ORLIqgE5wC2MY3EAtjJAJIrWaSlJLbSz/P9Px11solbm93bX89Pw/T58n/AMKb8Xf9F2+IH/gD4e/+VVavhb4aeI/D+u2t/f8AxY8YeJrSHdv0vVLTRkt58qVG82+nxSjaSGG2ReVGcjIPoFFZgFFFFABRRRQAUUUUAFYHjnwJofxI8OzaH4hsje6dK6S7Y55IJY5EYMkkcsbLJG6sAQ6MGB6Gt+igDm/A3w70D4c2Fza6FaSw/a5vtF1dXl3NeXV1LtC75rid3llYKqqC7EgKAOABXSUUU229wCiiikB5V+1j/wAmsfGT/sTNZ/8ASGavVa8q/ax/5NY+Mn/Ymaz/AOkM1eq0AFFFFAHP+L/H/h/wFb2s2vanFp63M0dvCrBneR5JY4lwqgnHmSxKWxgF1yRmjXPH/h/w5r2kaLqOpxW+q6tOLaztMM7ySFJHUEKDtBWGUgtgHy2wcivNf2t9Re1+FMFrBpet6vdT67o8yQaJo13qUgSDUra4mZltopCirFE7ZbAJG0ZYgGh8ffFcI8Q/Be7h0fxNfwReJE1edtO8M6jd/ZrY6fdxbphFAxhbfcRDZJtcbiSuFYjWEFK2u7t+Cf3vU0tHlbb6N/cv89DvdL+OXgrWfFg8O2mrSvftcSWcM72FzHZXNwgYyQQ3bRiCaVQj7o45GYbGyBtOO8r5e0D4TapefFXSNL8OXniu1+HfhjXZdYmi8RRJBZx3AEwFrpq+THcSxF5WcyyvJEFUCItuyv1DWS1im9/68vXvda31M2mpOP8AV9dPy++3QKKKKACiiigCtqWpWuj2FxfXs6W1pboZJZZDhVUdSa4nRfjt4N1/T9burW91COTRXjjvtPvNGvbW/jaTPlAWksKzuZMHZsQ78ELmuv8AEGsL4f0a71F7S8vktk8xrfT4DPOw77I15cgc7VyTjgE4B+e/At9q/hnWvHfi6bUfHWveE9atLSKPVtR8KGLXrW5Vp0Ihsks4pWgiUxsA9s3zSFh5gMhGcpWv/X369dl5jtpf+v6/Q9EX9pf4eyaMmpRarf3AM8ts9hBod/JqEEkW0yiayWA3EIQOhZpI1Ch1JIDDPoOg69pvinRbHWNHvrfU9KvoVuLW8tZBJFNGwyrKw4IINfJvwxTxJ8Jfirq3i3X9O8W+LPBeswXKaXrsvht5NbeaX7JIwvLO1t0kiV2jZEZoI9ogVXCjyyfoT4D+F77wb8HvCej6lCba/t7FTNbHGYGclzEccZTdt44+Xv1rRO8U7evk9rW89+lla61TI15munTzXR/p6o72iiigoKKKKACiiigAooooAKKKKACiiigAooooA8q/ax/5NY+Mn/Ymaz/6QzV6rXlX7WP/ACax8ZP+xM1n/wBIZq9VoAKKKKAOd8X/ABG8J/D1bFvFPijRvDQv5fItDrGoRWn2iT+5H5jDe3I4GTT/ABH8QfC3g7UNMsNf8S6Rod9qknk2FtqV/Fby3b/3YldgXbkcLk815v8AtF/FDwB4R0mTwv4n17wpo+teJ7GaygHie9t7aFbYjbJLL5rLvjUtxGDl24GPmZfKPGHiL4faDpnxA8PXurWepWOveBbDTPCk15cJcjXoEguUSGzfJFzJ5jKzLHlsyI2MEGpbcY81r69Ou+nrppve7001vlvKMdr7t9NrP0+7bfXT6dsviL4U1LxbeeFrTxPo114ns08y50WDUInvYF4+Z4Q29RyOSO4roa+VNE1v/hIvi18LE0DXvCXi9PDsslrqfh/S7Rk1XQpprKVLm8uphOwQGRXXy5IY95m+87hTX1XVJpr+vvXlb1/RYxbaTel0n6eXrcKKKKCwooooAOlUtJ1rT9etnuNMv7bUbdJHhaW0mWVBIjFXQlSRuUggjqCMGn6pFZzabdx6ikEmntE63K3QUxGPB3hw3G3Gc54xXy/8Evivpk3wR+Il94FurTxhPZ+L9bllh8O6nYM9razanO63G6a4iiVfI3SIWkUMACMinbSUr7L9bfcXy3in1vb8G/0PoHxB8VPBXhPTl1DXPGGg6NYNdtYLdahqcEERuVYq0IZ2A8wEEFOoIIxxXTRSpPEkkbrJG4DK6HIYHoQe4r4O+Cnxt8FeKvhB4u0HRfF3g/4ea3eeItbtbKXxU1jqqz2tzeTOLeBba/w7kNHgeYwYqGVJUZWP138DIr63+DPgiDUtF/4Ry9g0a1gk0rLH7LsiVQnz/MOAPlb5h0bkGrnFRlKPa1v6/rfbS7iWkrJ9/wAHZff/AFvp3NFFFZgFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAeVftY/wDJrHxk/wCxM1n/ANIZq9Vryr9rH/k1j4yf9iZrP/pDNXqtABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUVy/xR0pdb+HPiSza7v7DfYTMt1pd7LZ3MTKhZWjmiZXQggdDz0OQSK8E8OfEXxC3wk1X4cX+uXg+IGlRSWJ1mWU/bJ9M+zG4h1LeBzI0G2IyDpcBvSsZ1FTjKT6K/wCf9f8ADGkYczj5tr8v8/03aPqOivmnVPj4vwL+GXg2G3s5PF5g0W01DWEudV1C91W3hmA/fsRbXO4M3mYe5mhT5CA4C8dV4r+NvjbQNT8dwWXw+07UrTwkIrya4PiMwtdWDwtIXiQ2pxcDYw8liqHH+u7VtUcad23om1f03/r/ACMKUvaqNlq0nb12/I9soqlouqw69o1hqdusiQXtvHcxrKu1wrqGAYdjg8irtNqzsxxkpJSWzCiiikUeVftY/wDJrHxk/wCxM1n/ANIZq9Vryr9rH/k1j4yf9iZrP/pDNXqtABRRRQAUV5J+01fanovw7ttV0vWb/Sp4Nb0eArZSiMSrNqtpE4cgbiPLaRcBgCJGyDxjnvir8Yb/AEX4g69pr6B4k1Xwp4W0i21XVD4ZaOO5dpmnO93eaJjDEluWKQs0jl8bSqEM2mo872u/lazbf3/5lqDdrdfv+R75RXzjqOsS+H/j94C1O1k8bp4d8RSywy63d6qt1ouqPNavLbW6WX2gm2ZduRKtvF/q9rM+8tX0dUp3Xn18n2M07/PVej6/n9wUUUUxhRRRQAUVU1bT/wC1tNubM3NxaLOhjM1pJ5cqg9drdVOO45HUEHBrwvwt4l0/4KeG/HPiC98XXEnw9+3W8Og3XjDXHnCzMqxylb27kLtbNKyhS8hxslZcoUyr2v8A18h2utD3+ivj34f634f+Lfw31fXLv4h63401Ww8T6toumxeG/Gd3YRXcs1+4sw/2C4jypjEbLklUh3Mo2gmvqbwP4YbwZ4R0nQ31O+1l7G3WFtQ1K4kuLi4YdXeSRmdiTn7zE4wMmtJx5JOL6f1/XqJ6Sa7X/DT/AD+43KKKKgAooooAKKKKAMnxV4ZsfGfh6+0TUzdiwvY/Km+w3s1nMV7hZoXSRM9DtYZGR0JrmL34GeDdQ0m50+fT7x1udGj8PzXY1W7W9ksI2Z1ha6EomIy7ZbfubcQSQa72ik0mmn1/r9X95SlJbP8ArR/mk/kjym8/Ze+HOoaV/Zt1pep3NkdLXRpIpvEGov51srO0YkJuMyPGZH2SuTJGDhGUYFcz4d+FLeI/jj441HxN4S1210NILC00u8utc3WOqwwxOjrcW0V4xuPmkYj7VEeDn71e+UUPWXO3rv8APuZqEVHlSCiiimUFFFFAHlX7WP8Ayax8ZP8AsTNZ/wDSGavVa8q/ax/5NY+Mn/Ymaz/6QzV6rQAUUUUAcf8AEz4UeHPi9otvpPiaPUp9PguI7tItO1i807MqMGjZmtpYy+1lVlDEgMqsMEAjP8RfAjwX4rvbO81PT72e5t7SOwkkTV7yI3tumdsV5slH2xBuY7bjzBl3P8TZoftC+KfEHgnwPbazoOpRWDJq+l2kytarK8qXGo2tuwDMSFHlyyg/KTkqQV281PjinjLSILPXPC3jKfT7+O6trax8MGytZLTV5HlUNDMzxtOCV3fPFJGI1UuwYKxqknZNbN/jZa/j6lNPlu/P/hv1Oj8LfBjwl4N1galplleCaNi1tDeapd3dtYkqyn7JbzSvFa5VmX9yqfKxXpxXb15Le/EXWp/2gfCvh60lSLwteabrBlQwjzLi5tZLNd4YjIRTPIgC4yytnIC161TlBxjGT2a09Ltfp9wmrBRRRUCCiiigDJ8V+F9P8a+G9R0LVVuH03UIWt7hLW7mtZGjbhgJYWV1yOMqwOCR3rA8HfCHQfAllf2ml3fiSSC9hEEi6p4p1PUDGgBA8k3FxIYTgn5oyp4HPAx1mpRXU+n3EdlcJaXboViuJIvNEbY4bZkbsema8B+FPivXvHPiH4h+Do/iL4iJsPsctjqGveG4tN1u3jkEgmeOKWzhhkh3Iqxym3YZEgO75WKvvHuvv/rcq707J/d/Vj0DwP8As+eCPh5eWt3o9lqb3FreXOoQPqmu3+o+Vc3ClZ5lFzPIA7hny3U73PV2z6PXzhqXxO8ceFP2ePG2of21Fr/iDT4NZuND8Rz2caG7sbWNnW7mijVYt24eWpVVST924Xa9fQGgXMt7oWnXEzb5praOR2wBlioJOB71q4ScfaPr9+1/w/DboS1y/Nv8Hr99/mX6KKKzAKKKKACiiigAooooAKKKKACiiigAooooA8q/ax/5NY+Mn/Ymaz/6QzV6rXlX7WP/ACax8ZP+xM1n/wBIZq9VoAKKKKAOE+MXwoT4xeGbfQ5/E+t+GbWO8t755NEW0MkzwSpNCGNzbzAKssaP8oBJXBJUkHJ8RfBTVdd8UWevQfFTxjo95a2SWSJZ2+jyR4GPMlAn0+TZJIQC5QqDgAAKoA0fjJqfjfQvC97rHhDUfDWnJpdpNfXQ8R2k86XCxqW8sNHNF5AwDmU+Zj/nmcVleJfij4i0i9+Fbf2Ja6daeLdRgsL21vndru0eSxurl49oCqrIbdF3Etnc3yrgE3Tcm7R7/np16aa+mq1V71t8n+Fm/wBP6Ttz0f7G/gKz+NWkfE3TYk0bWdNMjraabomkQwXEkgHmyyuLLz2kcjO/zQyln2FQ7A+7V4RoHxh1q9+MEWg6l4u0Wx0261G8tLPSp/BGp273XkvKvkw6tJdfZJZgIi5VIy2FcbBtJHu9Ta0V2/L+vLQmXxu+4UUUUhBRRRQBn6/pLa7o13YJf3mlvOm1b3T5Ak8LdQyFgwyCOjAg9CCCRXnGp/s/prGgana3fjrxRJr+pRQ2t34oxp4v5bWOR3W12C0+zLETI+4LAC4OGLV6jeNcJZztaRxTXQRjFHNIY0d8fKGYKxUE4yQpx6HpXnnwv+KN14l8E+INc8QpaWj6Trmq6U32JWCOlreS26EBmJLt5Y47lsAdqFHeXb+v16FrmS5l3t+v6fecj8QP2QvDnxY8DyeHvGuvah4qu8NFa+INT0XQ5NRsIGUK8Fs404RxKcA7lj8wEAq4wMerfD7wVZ/DnwTo3hjT5XnsdKtltYZJILeBii8DKW8ccS/REUe1eD3v7QnjG98OR3q3Gm+FrqLVtcs7lE8Ian4oKxWV4tun7qynjdeGy8pymccLmvfPAmuJ4m8GaJq8esaf4gS+s4rhdV0mEw2t0GUESRI0khVTnIUuxHqa1lGUOZPo/wAbf1fq99dyZrlklLdX/Oz/AC/Q3aKKKyEFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAeVftY/wDJrHxk/wCxM1n/ANIZq9Vryr9rH/k1j4yf9iZrP/pDNXqtABRRRQB5v8VvAfjTxrqGjt4e8U6Bo+lWbGa40zXPD02pR3c4IMbsY723+VMZCEMN2GOSq4zPiN8LfHPjq5+Ht3b+MfD2m3nhjUF1a6aXw1PPHe3IglgOxRfoYY9k8p2lpDnYd+AQ3rdFVGTja3R3/r+vyQ22/ut8jzm0+HHiTVPEVhc+LfFVnr+kaPff2hpNra6QbO5EoSREN3N57pNtWUkeXFANwBII4r0aiipBu7uwooooEFFFFAEdwsrQSCB0jmKkI8iF1VscEqCMjPbI+orxXwb+z7rNj8PvE3hfxZ4rttbXVdYudctrrw/aX+hPa3U1y90254dQaV0WZwVVZIztXBLE7h7dRR0a7/8AD/0yuZrT5/p+p85fBv8AZw+I3wW06/stL+Kem39tqWp3GoXcep6Ff3m3zpDI/ktcarI6SZZhvZnDcM6O+939z8GeE7HwJ4U0rw/pvmmx063W3jadg0jgDlnIABZjknAAyTwK2qKptyd2SFFFFSB//9k=)
Explain how to properly analyze these data.
◦ Chi-square test for a 2 x 2 factorial design
◦ ANOVA
F-test for a completely randomized design with four treatments
◦ ANOVA
F-test for interaction in a 2 x 2 factorial design with 3 replications
◦ ANOVA
F-test for a randomized block design with two treatments