[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$f1VGBFpZsy55qEnoblUc5vHASaCrF6DlSRAxfU-Botog":3},{"id":4,"source":5,"question":6,"options":7,"answer":10,"related":11,"type":80,"origin":99,"createTime":24},954893406,"v2","OLS 通过使得样本观测数据的残差平方和最小来选择参数,而 MLE 通过最大化对数似然值 来估计参数.( )",[8,9],"对","错",[],[12,25,35,45,56,66,76,81,87,93],{"id":13,"source":5,"question":14,"options":15,"answer":20,"related":21,"type":22,"origin":23,"createTime":24},954893400,"Logistic 回归属于()回归.( )",[16,17,18,19],"概率型非线性回归","概率型线性回归","非概率型非线性回归","非概率型线性回归",[],[],0,null,"2023-05-07T22:46:25+08:00",{"id":26,"source":5,"question":27,"options":28,"answer":33,"related":34,"type":22,"origin":23,"createTime":24},954893401,"Logistic 回归于多重线性回归比较( )",[29,30,31,32],"Logit 回归的因变量为二分类变量","多重线性回归的因变量为二分类变量","Logit 回归和多重线性回归的因变量都可为二分类变量","Logit 回归的自变量必须是二分类变量",[],[],{"id":36,"source":5,"question":37,"options":38,"answer":43,"related":44,"type":22,"origin":23,"createTime":24},954893402,"Logistic 回归中自变量如分为多分类变量,宜将其按哑变量处理,与其他变量进行变量筛选时可用( )",[39,40,41,42],"软件自动筛选的前进法","软件自动筛选的后退法","软件自动筛选的逐步法","应将几个哑变量作为一个因素,整体进出回归方程",[],[],{"id":46,"source":5,"question":47,"options":48,"answer":53,"related":54,"type":55,"origin":23,"createTime":24},954893403,"Logistic 回归适用于因变量为( )",[49,50,51,52],"二分类变量","多分类有序变量","连续型定量变量","多分类无序变量",[],[],1,{"id":57,"source":5,"question":58,"options":59,"answer":64,"related":65,"type":55,"origin":23,"createTime":24},954893404,"Logistic 回归系数与几率比 OR 的关系为( )",[60,61,62,63],"\u003Cimg src=\"https:\u002F\u002Fimage.zhihuishu.com\u002Fzhs\u002Fdoctrans\u002Fdocx2html\u002F202102\u002F765301be26f4445da3a962ba466f3822.png\">","\u003Cimg src=\"https:\u002F\u002Fimage.zhihuishu.com\u002Fzhs\u002Fdoctrans\u002Fdocx2html\u002F202102\u002F587dce4f53c3484c977eff9837ec2cf2.png\">","\u003Cimg src=\"https:\u002F\u002Fimage.zhihuishu.com\u002Fzhs\u002Fdoctrans\u002Fdocx2html\u002F202102\u002F7fc6176546654e10ab952a39283423c2.png\">","\u003Cimg src=\"https:\u002F\u002Fimage.zhihuishu.com\u002Fzhs\u002Fdoctrans\u002Fdocx2html\u002F202102\u002F2e3bed32dd924a2584a870ac0d5b5e4a.png\">",[],[],{"id":67,"source":5,"question":68,"options":69,"answer":74,"related":75,"type":55,"origin":23,"createTime":24},954893405,"关于最大似然估计,下列说法正确的是:( )",[70,71,72,73],"最大似然估计是似然函数最大所对应的参数作为估计","最大似然估计一定是最小方差无偏估计","在最大似然函数古籍中,要估计的参数是一个不确定的量","在最大似然估计中,可以使用对数形式的似然函数来进行估计",[],[],{"id":4,"source":5,"question":6,"options":77,"answer":78,"related":79,"type":80,"origin":23,"createTime":24},[8,9],[],[],3,{"id":82,"source":5,"question":83,"options":84,"answer":85,"related":86,"type":80,"origin":23,"createTime":24},954893407,"研究应变量 y 不同取值的概率与自变量 x 之间关系应建立 logistic 回归.( )",[8,9],[],[],{"id":88,"source":5,"question":89,"options":90,"answer":91,"related":92,"type":80,"origin":23,"createTime":24},954893408,"由于模型存在非线性,所以最大似然估计通常没有解析解,而只能寻找\"数值解\",在实 践中,一般使用\"迭代法\"进行数值求解.( )",[8,9],[],[],{"id":94,"source":5,"question":95,"options":96,"answer":97,"related":98,"type":80,"origin":23,"createTime":24},954893409,"参数的最大似然估计一定是无偏估计.( )",[8,9],[],[],{"courseName":100,"courseImg":101,"workName":102,"workId":103,"count":22,"courseId":104},"初级社会统计学","https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F6961f77a8814767d4869fc8fd3c80426.jpg","第十章单元测试","49743775","1000001072"]