数据集loan.dta包括了个人抵押贷款的许可，我们关心是否有种族歧视，用whit虚拟变量衡量， （1)请写

某数据集是关于美国60个著名的商学院的数据，包括的变量有GMAT分数（GMAT）、学费（Tuition）、进入MBA前后的工资（SalaPreMBA，SalaPostMBA）等等，其中有7个定量变量。利用R对这7个变量进行因子分析，运行结果如下。试根据结果回答下列问题： > data<-read.csv(file="bschool.csv", header="T,fileEncoding" "gb18030")> x = scale(data[,-c(1,2,10)]);mycor=cor(x);round(mycor,2) FiveYearGain FiveYearGp YearsToPayback SalaPreMBA SalaPostMBA Tuition GMAT FiveYearGain 1.00 0.69 -0.71 0.53 0.70 0.45 0.67 FiveYearGp 0.69 1.00 -0.95 -0.15 0.03 -0.24 0.13 YearsToPayback -0.71 -0.95 1.00 0.12 -0.02 0.16 -0.20 SalaPreMBA 0.53 -0.15 0.12 1.00 0.92 0.78 0.83 SalaPostMBA 0.70 0.03 -0.02 0.92 1.00 0.78 0.78 Tuition 0.45 -0.24 0.16 0.78 0.78 1.00 0.6 6GMAT 0.67 0.13 -0.20 0.83 0.78 0.66 1.00 > fa.parallel(x,fa="fa",main="碎石图")> fa.fit=fa(x,nfactors=4,rotate="varimax",fm="pa");fa.fit Factor Analysis using method = pa Call: fa(r = x, nfactors = 4, rotate = "varimax", fm = "pa")Standardized loadings (pattern matrix) based upon correlation matrix PA1 PA2 PA4 PA3 h2 u2 com FiveYearGain 0.62 0.76 0.16 0.13 1.00 -0.00058 2.1 FiveYearGp -0.07 0.98 -0.11 0.08 0.99 0.00984 1.1 YearsToPayback 0.04 -0.98 -0.02 0.13 0.98 0.02121 1.0 SalaPreMBA 0.98 -0.09 -0.07 -0.01 0.98 0.02018 1.0 SalaPostMBA 0.95 0.09 0.05 0.28 0.99 0.00803 1.2 Tuition 0.81 -0.14 0.39 0.01 0.82 0.17555 1.5 GMAT 0.86 0.21 0.01 -0.20 0.82 0.18004 1.2 PA1 PA2 PA4 PA3 SS loadings 3.65 2.58 0.19 0.16 Proportion Var 0.52 0.37 0.03 0.02 Cumulative Var 0.52 0.89 0.92 0.94 Proportion Explained 0.55 0.39 0.03 0.02 Cumulative Proportion 0.55 0.95 0.98 1.00 Mean item complexity = 1.3 Test of the hypothesis that 4 factors are sufficient. The degrees of freedom for the null model are 21 and the objective function was 11.65 with Chi Square of 650.43 The degrees of freedom for the model are -1 and the objective function was 0.01 The root mean square of the residuals (RMSR) is 0 The df corrected root mean square of the residuals is NA The harmonic number of observations is 60 with the empirical chi square 0 with prob < NA The total number of observations was 60 with Likelihood Chi Square = 0.3 with prob < NA Tucker Lewis Index of factoring reliability = 1.046 Fit based upon off diagonal values = 1 Measures of factor score adequacy PA1 PA2 PA4 PA3 Correlation of (regression) scores with factors 0.99 1.00 0.94 0.92 Multiple R square of scores with factors 0.99 1.00 0.89 0.84 Minimum correlation of possible factor scores 0.98 0.99 0.78 0.69 1) 该数据是否适合进行因子分析？为什么？ 2) 选取几个公共因子比较合适？请给出原因。 3) 请给出学费的因子模型表达。

某数据集是关于美国60个著名的商学院的数据，包括的变量有GMAT分数（GMAT）、学费（Tuition）、进入MBA前后的工资（SalaPreMBA，SalaPostMBA）等等，其中有7个定量变量。利用R对这7个变量进行因子分析，运行结果如下。试根据结果回答下列问题： > data<-read.csv(file="bschool.csv", header="T,fileEncoding" "gb18030")> x = scale(data[,-c(1,2,10)]);mycor=cor(x);round(mycor,2) FiveYearGain FiveYearGp YearsToPayback SalaPreMBA SalaPostMBA Tuition GMAT FiveYearGain 1.00 0.69 -0.71 0.53 0.70 0.45 0.67 FiveYearGp 0.69 1.00 -0.95 -0.15 0.03 -0.24 0.13 YearsToPayback -0.71 -0.95 1.00 0.12 -0.02 0.16 -0.20 SalaPreMBA 0.53 -0.15 0.12 1.00 0.92 0.78 0.83 SalaPostMBA 0.70 0.03 -0.02 0.92 1.00 0.78 0.78 Tuition 0.45 -0.24 0.16 0.78 0.78 1.00 0.6 6GMAT 0.67 0.13 -0.20 0.83 0.78 0.66 1.00 > fa.parallel(x,fa="fa",main="碎石图")> fa.fit=fa(x,nfactors=4,rotate="varimax",fm="pa");fa.fit Factor Analysis using method = pa Call: fa(r = x, nfactors = 4, rotate = "varimax", fm = "pa")Standardized loadings (pattern matrix) based upon correlation matrix PA1 PA2 PA4 PA3 h2 u2 com FiveYearGain 0.62 0.76 0.16 0.13 1.00 -0.00058 2.1 FiveYearGp -0.07 0.98 -0.11 0.08 0.99 0.00984 1.1 YearsToPayback 0.04 -0.98 -0.02 0.13 0.98 0.02121 1.0 SalaPreMBA 0.98 -0.09 -0.07 -0.01 0.98 0.02018 1.0 SalaPostMBA 0.95 0.09 0.05 0.28 0.99 0.00803 1.2 Tuition 0.81 -0.14 0.39 0.01 0.82 0.17555 1.5 GMAT 0.86 0.21 0.01 -0.20 0.82 0.18004 1.2 PA1 PA2 PA4 PA3 SS loadings 3.65 2.58 0.19 0.16 Proportion Var 0.52 0.37 0.03 0.02 Cumulative Var 0.52 0.89 0.92 0.94 Proportion Explained 0.55 0.39 0.03 0.02 Cumulative Proportion 0.55 0.95 0.98 1.00 Mean item complexity = 1.3 Test of the hypothesis that 4 factors are sufficient. The degrees of freedom for the null model are 21 and the objective function was 11.65 with Chi Square of 650.43 The degrees of freedom for the model are -1 and the objective function was 0.01 The root mean square of the residuals (RMSR) is 0 The df corrected root mean square of the residuals is NA The harmonic number of observations is 60 with the empirical chi square 0 with prob < NA The total number of observations was 60 with Likelihood Chi Square = 0.3 with prob < NA Tucker Lewis Index of factoring reliability = 1.046 Fit based upon off diagonal values = 1 Measures of factor score adequacy PA1 PA2 PA4 PA3 Correlation of (regression) scores with factors 0.99 1.00 0.94 0.92 Multiple R square of scores with factors 0.99 1.00 0.89 0.84 Minimum correlation of possible factor scores 0.98 0.99 0.78 0.69 1) 该数据是否适合进行因子分析？为什么？ 2) 选取几个公共因子比较合适？请给出原因。 3) 请给出学费的因子模型表达。

A、需要准备好用于构建网络数据的点数据集、线数据集和网络数据集，缺一不可。

B、需要准备好用于构建网络数据的点数据集或线数据集。

C、需要准备好用于构建网络数据的数据集 ，包括点数据集、线数据集和网络数据集。其中点数据和网络数据集是可选的。

D、准备好网络数据集字段信息，确保用于构网的线数据中包含了表示网络阻力的字段，如表示时间和距离信息的字段。

A、MNIST数据集一共包括7万张图片，其中6万张用于训练，1万张用于测试。

B、每张图片包括1024(32×32)个像素点。

C、MNIST数据集是为0到9的手写数字数据集。

D、Fashion_mnist数据集中的图片被分为20类。

A.抽样方差和插补方差

B.组内方差和组间方差

C.总体方差和样本方差

D.内部方差和外部方差

B.连续变数类别变数互换

C.极端值与标准化

D.衍生变数与筛选有效变数