教材P151 案例7.6
library(readr)
library(tidyverse)
case7_1 <- read_csv("case7.1.csv") %>%
rename(存货周转率 = x1,
总资产周转率 = x2,
流动资产周转率 = x3,
营业利润率 = x4,
毛利率 = x5,
成本费用利润率 = x6,
总资产报酬率 = x7,
净资产收益率 = x8,
每股收益率 = x9,
扣除非经常性损益的每股收益 = x10,
每股未分配利润 = x11,
每股净资产 = x12
)
Attaching package: 'psych'
The following objects are masked from 'package:ggplot2':
%+%, alpha
Kaiser-Meyer-Olkin factor adequacy
Call: KMO(r = case7_1[-1])
Overall MSA = 0.79
MSA for each item =
存货周转率 总资产周转率
0.82 0.65
流动资产周转率 营业利润率
0.61 0.80
毛利率 成本费用利润率
0.89 0.77
总资产报酬率 净资产收益率
0.77 0.78
每股收益率 扣除非经常性损益的每股收益
0.79 0.79
每股未分配利润 每股净资产
0.89 0.88
Attaching package: 'EFAtools'
The following object is masked from 'package:psych':
KMO
ℹ 'x' was not a correlation matrix. Correlations are found from entered raw data.
✔ The Bartlett's test of sphericity was significant at an alpha level of .05.
These data are probably suitable for factor analysis.
𝜒²(66) = 1010.1, p < .001
# step 2
#计算特征值和特征向量
library(tidyverse)
case7_1.ev <- case7_1 %>%
select(-1) %>% cor() %>%
eigen()
case7_1.ev
eigen() decomposition
$values
[1] 6.499533804 2.654940434 1.498015224 0.457112219 0.274204566 0.251019040
[7] 0.107527371 0.097961610 0.067989899 0.055824591 0.026693650 0.009177591
$vectors
[,1] [,2] [,3] [,4] [,5]
[1,] -0.09244934 -0.477942255 0.05813051 0.841178011 0.03145094
[2,] -0.14156255 -0.510840674 -0.24058789 -0.292348983 -0.01653565
[3,] -0.11396951 -0.524423948 -0.24305043 -0.326001981 -0.23004977
[4,] -0.31943729 0.092092805 -0.32649716 0.186801205 -0.46156889
[5,] -0.26320084 0.359901186 -0.20646040 0.001269517 -0.12764452
[6,] -0.33013418 0.251211794 -0.18956461 0.184085750 -0.15605637
[7,] -0.34205073 0.132406992 -0.28087340 -0.013460429 0.30932073
[8,] -0.34921919 -0.077629433 -0.27080622 -0.045567816 0.31120190
[9,] -0.35203886 -0.016220410 0.33198939 -0.047641358 0.22911864
[10,] -0.35587756 -0.005797506 0.29858780 -0.035372439 0.21626107
[11,] -0.33113545 -0.098353749 0.32947303 -0.119334215 0.17254721
[12,] -0.28095737 -0.030775036 0.48410100 -0.108955477 -0.60873581
[,6] [,7] [,8] [,9] [,10] [,11]
[1,] -0.1768332629 0.04573485 -0.03270303 0.09875414 0.07971144 -0.01808396
[2,] -0.2808558579 -0.14250773 0.07575002 -0.57019837 0.29654994 0.23670175
[3,] -0.0250653760 0.33986884 -0.12436745 0.36834886 -0.39212881 -0.26817548
[4,] 0.4907354140 -0.29243188 -0.17916257 -0.25284266 0.04959833 -0.33165214
[5,] -0.6623872468 -0.02297448 -0.49285071 0.16465110 0.16926101 -0.05967790
[6,] 0.0001332728 0.48738638 0.18625489 -0.24484958 -0.39198754 0.49024948
[7,] -0.0177809801 0.26740169 0.53232291 0.12200012 0.37530228 -0.37541743
[8,] 0.1836614909 -0.48270097 0.02303125 0.46759327 -0.07678284 0.43511797
[9,] -0.0676631801 -0.03966850 0.03201316 -0.16744682 -0.18867682 -0.34464400
[10,] -0.1410113860 -0.24951050 -0.03736233 -0.21446362 -0.43964946 -0.12369914
[11,] 0.3727798520 0.39358992 -0.48947667 0.01017771 0.38644169 0.18250240
[12,] -0.1103111084 -0.12350439 0.37699725 0.26605576 0.20542765 0.14203535
[,12]
[1,] -0.01452807
[2,] 0.03681929
[3,] -0.02732101
[4,] -0.01088018
[5,] 0.02772321
[6,] 0.08684279
[7,] -0.19712367
[8,] 0.13649776
[9,] 0.72196255
[10,] -0.63088779
[11,] -0.10907353
[12,] -0.02494923
Loading required package: lattice
Attaching package: 'nFactors'
The following object is masked from 'package:lattice':
parallel
case7_1.ev$values %>% nScree() %>%
plotnScree(legend = F)
#未旋转
fa.pc.none <- principal(case7_1[-1], nfactors = 3,
rotate = "none")
fa.pc.none
Principal Components Analysis
Call: principal(r = case7_1[-1], nfactors = 3, rotate = "none")
Standardized loadings (pattern matrix) based upon correlation matrix
PC1 PC2 PC3 h2 u2 com
存货周转率 0.24 0.78 -0.07 0.67 0.333 1.2
总资产周转率 0.36 0.83 0.29 0.91 0.090 1.6
流动资产周转率 0.29 0.85 0.30 0.90 0.097 1.5
营业利润率 0.81 -0.15 0.40 0.85 0.155 1.5
毛利率 0.67 -0.59 0.25 0.86 0.142 2.3
成本费用利润率 0.84 -0.41 0.23 0.93 0.070 1.6
总资产报酬率 0.87 -0.22 0.34 0.93 0.075 1.4
净资产收益率 0.89 0.13 0.33 0.92 0.081 1.3
每股收益率 0.90 0.03 -0.41 0.97 0.029 1.4
扣除非经常性损益的每股收益 0.91 0.01 -0.37 0.96 0.043 1.3
每股未分配利润 0.84 0.16 -0.40 0.90 0.099 1.5
每股净资产 0.72 0.05 -0.59 0.87 0.133 1.9
PC1 PC2 PC3
SS loadings 6.50 2.65 1.50
Proportion Var 0.54 0.22 0.12
Cumulative Var 0.54 0.76 0.89
Proportion Explained 0.61 0.25 0.14
Cumulative Proportion 0.61 0.86 1.00
Mean item complexity = 1.6
Test of the hypothesis that 3 components are sufficient.
The root mean square of the residuals (RMSR) is 0.03
with the empirical chi square 9.42 with prob < 1
Fit based upon off diagonal values = 1
fa.pc.varimax <- principal(case7_1[-1], nfactors = 3,
rotate = "varimax")
fa.pc.varimax
Principal Components Analysis
Call: principal(r = case7_1[-1], nfactors = 3, rotate = "varimax")
Standardized loadings (pattern matrix) based upon correlation matrix
RC1 RC3 RC2 h2 u2 com
存货周转率 -0.15 0.26 0.76 0.67 0.333 1.3
总资产周转率 0.13 0.07 0.94 0.91 0.090 1.1
流动资产周转率 0.08 0.02 0.95 0.90 0.097 1.0
营业利润率 0.88 0.22 0.16 0.85 0.155 1.2
毛利率 0.85 0.21 -0.32 0.86 0.142 1.4
成本费用利润率 0.89 0.35 -0.12 0.93 0.070 1.3
总资产报酬率 0.91 0.30 0.10 0.93 0.075 1.2
净资产收益率 0.79 0.34 0.42 0.92 0.081 1.9
每股收益率 0.40 0.89 0.12 0.97 0.029 1.4
扣除非经常性损益的每股收益 0.43 0.87 0.12 0.96 0.043 1.5
每股未分配利润 0.31 0.87 0.23 0.90 0.099 1.4
每股净资产 0.15 0.92 0.04 0.87 0.133 1.1
RC1 RC3 RC2
SS loadings 4.25 3.64 2.77
Proportion Var 0.35 0.30 0.23
Cumulative Var 0.35 0.66 0.89
Proportion Explained 0.40 0.34 0.26
Cumulative Proportion 0.40 0.74 1.00
Mean item complexity = 1.3
Test of the hypothesis that 3 components are sufficient.
The root mean square of the residuals (RMSR) is 0.03
with the empirical chi square 9.42 with prob < 1
Fit based upon off diagonal values = 1
#按因子载荷系数降序排列
print(fa.pc.varimax$loadings, digits = 3, cutoff = 0.5,sort = T)
Loadings:
RC1 RC3 RC2
营业利润率 0.877
毛利率 0.845
成本费用利润率 0.892
总资产报酬率 0.910
净资产收益率 0.792
每股收益率 0.894
扣除非经常性损益的每股收益 0.869
每股未分配利润 0.866
每股净资产 0.918
存货周转率 0.758
总资产周转率 0.942
流动资产周转率 0.947
RC1 RC3 RC2
SS loadings 4.246 3.638 2.769
Proportion Var 0.354 0.303 0.231
Cumulative Var 0.354 0.657 0.888
#绘制因子载荷系数图
fa.diagram(fa.pc.varimax$loadings, digits = 3)
plot(fa.pc.varimax$loadings, type = "n")
library(psych)
fa.ml.varimax <- fa(case7_1[-1],
nfactors = 3,
fm = "ml",
rotate = "varimax")
fa.ml.varimax
Factor Analysis using method = ml
Call: fa(r = case7_1[-1], nfactors = 3, rotate = "varimax", fm = "ml")
Standardized loadings (pattern matrix) based upon correlation matrix
ML3 ML1 ML2 h2 u2 com
存货周转率 -0.11 0.23 0.63 0.46 0.5381 1.3
总资产周转率 0.11 0.09 0.95 0.92 0.0785 1.0
流动资产周转率 0.06 0.02 0.96 0.92 0.0803 1.0
营业利润率 0.86 0.20 0.15 0.80 0.1957 1.2
毛利率 0.81 0.22 -0.28 0.79 0.2126 1.4
成本费用利润率 0.89 0.34 -0.12 0.92 0.0768 1.3
总资产报酬率 0.90 0.32 0.10 0.92 0.0769 1.3
净资产收益率 0.77 0.36 0.41 0.90 0.1029 2.0
每股收益率 0.39 0.91 0.12 1.00 0.0048 1.4
扣除非经常性损益的每股收益 0.42 0.88 0.12 0.98 0.0246 1.5
每股未分配利润 0.31 0.85 0.23 0.87 0.1311 1.4
每股净资产 0.16 0.85 0.07 0.76 0.2448 1.1
ML3 ML1 ML2
SS loadings 4.09 3.55 2.59
Proportion Var 0.34 0.30 0.22
Cumulative Var 0.34 0.64 0.85
Proportion Explained 0.40 0.35 0.25
Cumulative Proportion 0.40 0.75 1.00
Mean item complexity = 1.3
Test of the hypothesis that 3 factors are sufficient.
The degrees of freedom for the null model are 66 and the objective function was 18.65 with Chi Square of 1010.1
The degrees of freedom for the model are 33 and the objective function was 1.75
The root mean square of the residuals (RMSR) is 0.02
The df corrected root mean square of the residuals is 0.03
The harmonic number of observations is 60 with the empirical chi square 4.1 with prob < 1
The total number of observations was 60 with Likelihood Chi Square = 91.42 with prob < 2.2e-07
Tucker Lewis Index of factoring reliability = 0.871
RMSEA index = 0.171 and the 90 % confidence intervals are 0.132 0.216
BIC = -43.7
Fit based upon off diagonal values = 1
Measures of factor score adequacy
ML3 ML1 ML2
Correlation of (regression) scores with factors 0.98 0.99 0.98
Multiple R square of scores with factors 0.96 0.99 0.96
Minimum correlation of possible factor scores 0.92 0.97 0.92
#按因子载荷系数降序排列
print(fa.ml.varimax$loadings, digits = 3, cutoff = 0.5,sort = T)
Loadings:
ML3 ML1 ML2
营业利润率 0.862
毛利率 0.812
成本费用利润率 0.892
总资产报酬率 0.901
净资产收益率 0.771
每股收益率 0.911
扣除非经常性损益的每股收益 0.884
每股未分配利润 0.847
每股净资产 0.851
存货周转率 0.628
总资产周转率 0.949
流动资产周转率 0.957
ML3 ML1 ML2
SS loadings 4.090 3.549 2.594
Proportion Var 0.341 0.296 0.216
Cumulative Var 0.341 0.637 0.853
#函数psych::fa，varimax旋转
library(psych)
fa.pa.varimax <- fa(case7_1[-1],
nfactors = 3,
fm = "pa",
rotate = "varimax")
Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
The estimated weights for the factor scores are probably incorrect. Try a
different factor score estimation method.
print(fa.pa.varimax$loadings, digits = 3, cutoff = 0.5,sort = T)
Loadings:
PA1 PA3 PA2
营业利润率 0.839
毛利率 0.816
成本费用利润率 0.896
总资产报酬率 0.906
净资产收益率 0.780
每股收益率 0.913
扣除非经常性损益的每股收益 0.878
每股未分配利润 0.848
每股净资产 0.851
存货周转率 0.640
总资产周转率 0.943
流动资产周转率 0.944
PA1 PA3 PA2
SS loadings 4.094 3.533 2.594
Proportion Var 0.341 0.294 0.216
Cumulative Var 0.341 0.636 0.852
plot(fa.pc.varimax$loadings[,1],
fa.pc.varimax$loadings[,2])
text(fa.pc.varimax$loadings[,1],
fa.pc.varimax$loadings[,2],
rownames(fa.pc.varimax$loadings),
cex = 0.3)
biplot(fa.pc.varimax$scores[,1:2],
fa.pc.varimax$loadings[,1:2])
library(scatterplot3d)
scatterplot3d(fa.pc.varimax$loadings,
main="3D factor loadings",
color=c(rep(1,3), rep(2,5), rep(3,4)),
pch=20)
scatterplot3d(fa.pc.varimax$scores,
main="3D factor scores",
pch=20)
principal(r = case7_1[-1], nfactors = 3, rotate = "varimax")
fa.pc.varimax <- principal(case7_1[-1], nfactors = 3,
rotate = "varimax",
method = "wls")
fa.pc.varimax$scores %>%
data.frame() %>%
cbind(case7_1$证券名称,.) %>%
select(1:2) %>%
arrange(desc(RC1))%>%
head()
case7_1$证券名称 RC1
1 新坐标 3.200275
2 浙江仙通 1.880036
3 岱美股份 1.622674
4 继峰股份 1.438485
5 兆丰股份 1.240185
6 中原内配 1.169983
fa.pc.varimax$scores %>%
data.frame() %>%
cbind(case7_1$证券名称,.) %>%
select(1,3) %>%
arrange(desc(RC3))%>%
head()
case7_1$证券名称 RC3
1 兆丰股份 4.239737
2 苏威孚Ｂ 3.316791
3 华域汽车 2.615909
4 德尔股份 1.690839
5 越博动力 1.594501
6 宁波华翔 1.556167
fa.pc.varimax$scores %>%
data.frame() %>%
cbind(case7_1$证券名称,.) %>%
select(1,4) %>%
arrange(desc(RC2)) %>%
head()
case7_1$证券名称 RC2
1 东风科技 2.567504
2 华域汽车 2.314485
3 亚普股份 2.308875
4 众泰汽车 1.693966
5 交运股份 1.556585
6 凌云股份 1.353060
教材P160 习题7.7
library(readxl)
library(tidyverse)
ex7_7 <- read_excel("ex6.6.xls") %>%
rename(工业废水排放量 = x1,
工业化学需氧量 = x2,
工业氨氮排放量 = x3,
城镇生活污水排放量 = x4,
生活化学需氧量排放量 = x5,
生活氨氮排放量 = x6
)
rownames(ex7_7) <- ex7_7$城市
fa.pc.varimax <- principal(ex7_7[-1], nfactors = 2,
rotate = "varimax")
fa.pc.varimax
Principal Components Analysis
Call: principal(r = ex7_7[-1], nfactors = 2, rotate = "varimax")
Standardized loadings (pattern matrix) based upon correlation matrix
RC1 RC2 h2 u2 com
工业废水排放量 0.77 0.38 0.74 0.257 1.5
工业化学需氧量 0.53 0.81 0.93 0.068 1.7
工业氨氮排放量 0.10 0.96 0.93 0.069 1.0
城镇生活污水排放量 0.94 0.01 0.89 0.114 1.0
生活化学需氧量排放量 0.85 0.30 0.81 0.185 1.2
生活氨氮排放量 0.88 0.35 0.91 0.092 1.3
RC1 RC2
SS loadings 3.28 1.93
Proportion Var 0.55 0.32
Cumulative Var 0.55 0.87
Proportion Explained 0.63 0.37
Cumulative Proportion 0.63 1.00
Mean item complexity = 1.3
Test of the hypothesis that 2 components are sufficient.
The root mean square of the residuals (RMSR) is 0.06
with the empirical chi square 1.82 with prob < 0.77
Fit based upon off diagonal values = 0.99
print(fa.pc.varimax$loadings, digits = 3, cutoff = 0.5,sort = T)
Loadings:
RC1 RC2
工业废水排放量 0.774
城镇生活污水排放量 0.941
生活化学需氧量排放量 0.851
生活氨氮排放量 0.884
工业化学需氧量 0.531 0.806
工业氨氮排放量 0.960
RC1 RC2
SS loadings 3.283 1.931
Proportion Var 0.547 0.322
Cumulative Var 0.547 0.869
fa.pc.promax <- principal(ex7_7[-1], nfactors = 2,
rotate = "promax")
fa.pc.promax
Principal Components Analysis
Call: principal(r = ex7_7[-1], nfactors = 2, rotate = "promax")
Standardized loadings (pattern matrix) based upon correlation matrix
RC1 RC2 h2 u2 com
工业废水排放量 0.75 0.18 0.74 0.257 1.1
工业化学需氧量 0.33 0.75 0.93 0.068 1.4
工业氨氮排放量 -0.22 1.06 0.93 0.069 1.1
城镇生活污水排放量 1.07 -0.29 0.89 0.114 1.2
生活化学需氧量排放量 0.87 0.06 0.81 0.185 1.0
生活氨氮排放量 0.89 0.11 0.91 0.092 1.0
RC1 RC2
SS loadings 3.40 1.82
Proportion Var 0.57 0.30
Cumulative Var 0.57 0.87
Proportion Explained 0.65 0.35
Cumulative Proportion 0.65 1.00
With component correlations of
RC1 RC2
RC1 1.00 0.54
RC2 0.54 1.00
Mean item complexity = 1.1
Test of the hypothesis that 2 components are sufficient.
The root mean square of the residuals (RMSR) is 0.06
with the empirical chi square 1.82 with prob < 0.77
Fit based upon off diagonal values = 0.99
print(fa.pc.promax$loadings, digits = 3, cutoff = 0.5,sort = T)
Loadings:
RC1 RC2
工业废水排放量 0.753
城镇生活污水排放量 1.068
生活化学需氧量排放量 0.867
生活氨氮排放量 0.887
工业化学需氧量 0.745
工业氨氮排放量 1.064
RC1 RC2
SS loadings 3.401 1.823
Proportion Var 0.567 0.304
Cumulative Var 0.567 0.871
biplot(fa.pc.promax$scores[,1:2],
fa.pc.promax$loadings[,1:2],
xlim = c(-3,3),
ylim = c(-3,3),
xlabs = ex7_7$城市)
abline(v = 0, h = 0, lty = 2, col = "grey25")
fa.pc.promax$scores %>%
round(3) %>%
cbind(ex7_7$城市,.) %>%
data.frame() %>%
arrange(desc(RC1)) %>%
head()
V1 RC1 RC2
1 重庆 2.169 1.578
2 上海 1.996 1.167
3 广州 1.018 0.168
4 成都 0.265 -0.65
5 天津 0.195 0.515
6 杭州 0.124 0.718
fa.pc.promax$scores %>%
round(3) %>%
cbind(ex7_7$城市,.) %>%
data.frame() %>%
arrange(desc(RC2)) %>%
head()
V1 RC1 RC2
1 石家庄 -0.622 2.264
2 重庆 2.169 1.578
3 上海 1.996 1.167
4 杭州 0.124 0.718
5 天津 0.195 0.515
6 广州 1.018 0.168
应用举例
library(readxl)
car_sales <- read_excel("car_sales.xlsx")
library(tidyverse)
library(psych)
fa.pc.varimax <- car_sales %>% select(engine_s:mpg) %>%
principal(nfactors = 2,
rotate = "varimax")
fa.pc.varimax
Principal Components Analysis
Call: principal(r = ., nfactors = 2, rotate = "varimax")
Standardized loadings (pattern matrix) based upon correlation matrix
RC1 RC2 h2 u2 com
engine_s 0.88 0.32 0.87 0.13 1.3
horsepow 0.89 0.09 0.80 0.20 1.0
wheelbas 0.19 0.94 0.91 0.09 1.1
width 0.52 0.69 0.75 0.25 1.9
length 0.23 0.89 0.84 0.16 1.1
curb_wgt 0.72 0.58 0.85 0.15 1.9
fuel_cap 0.64 0.59 0.76 0.24 2.0
mpg -0.80 -0.37 0.78 0.22 1.4
RC1 RC2
SS loadings 3.49 3.06
Proportion Var 0.44 0.38
Cumulative Var 0.44 0.82
Proportion Explained 0.53 0.47
Cumulative Proportion 0.53 1.00
Mean item complexity = 1.5
Test of the hypothesis that 2 components are sufficient.
The root mean square of the residuals (RMSR) is 0.07
with the empirical chi square 38.95 with prob < 2e-04
Fit based upon off diagonal values = 0.99
print(fa.pc.varimax$loadings, digits = 3, cutoff = 0.5,sort = T)
Loadings:
RC1 RC2
engine_s 0.876
horsepow 0.888
curb_wgt 0.722 0.577
fuel_cap 0.644 0.586
mpg -0.803
wheelbas 0.935
width 0.517 0.693
length 0.888
RC1 RC2
SS loadings 3.493 3.063
Proportion Var 0.437 0.383
Cumulative Var 0.437 0.819
fa.pc.varimax <- car_sales %>% select(engine_s:mpg) %>%
principal(nfactors = 3,
rotate = "varimax")
fa.pc.varimax
Principal Components Analysis
Call: principal(r = ., nfactors = 3, rotate = "varimax")
Standardized loadings (pattern matrix) based upon correlation matrix
RC2 RC1 RC3 h2 u2 com
engine_s 0.31 0.43 0.80 0.92 0.084 1.9
horsepow 0.13 0.26 0.92 0.93 0.066 1.2
wheelbas 0.88 0.37 0.04 0.91 0.090 1.3
width 0.68 0.35 0.45 0.78 0.216 2.3
length 0.91 0.18 0.24 0.91 0.085 1.2
curb_wgt 0.43 0.75 0.39 0.90 0.099 2.2
fuel_cap 0.39 0.84 0.22 0.91 0.085 1.6
mpg -0.19 -0.83 -0.41 0.89 0.107 1.6
RC2 RC1 RC3
SS loadings 2.55 2.50 2.12
Proportion Var 0.32 0.31 0.26
Cumulative Var 0.32 0.63 0.90
Proportion Explained 0.36 0.35 0.30
Cumulative Proportion 0.36 0.70 1.00
Mean item complexity = 1.7
Test of the hypothesis that 3 components are sufficient.
The root mean square of the residuals (RMSR) is 0.03
with the empirical chi square 8.66 with prob < 0.28
Fit based upon off diagonal values = 1
print(fa.pc.varimax$loadings, digits = 3, cutoff = 0.5,sort = T)
Loadings:
RC2 RC1 RC3
wheelbas 0.879
width 0.680
length 0.908
curb_wgt 0.748
fuel_cap 0.842
mpg -0.829
engine_s 0.797
horsepow 0.922
RC2 RC1 RC3
SS loadings 2.550 2.500 2.119
Proportion Var 0.319 0.312 0.265
Cumulative Var 0.319 0.631 0.896
library(tidyverse)
data <- cbind(car_sales, fa.pc.varimax$scores)
data <- data %>% mutate(suv = if_else(type == 1, 1,0))
# RC1，其值越大，代表车重大、油箱容积大、耗油越高（SUV）
data %>% ggplot(aes(RC1, fill = as.factor(suv)))+
geom_histogram(col = 1)+
facet_wrap(~ suv,ncol = 1)
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# RC2，其值越大，代表车子轮距、车宽、车长大
data %>% ggplot(aes(RC2, fill = as.factor(suv)))+
geom_histogram(col = 1)+
facet_wrap(~ suv,ncol = 1)
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# RC3，动力性能
data %>% ggplot(aes(RC3, fill = as.factor(suv)))+
geom_histogram(col = 1)+
facet_wrap(~ suv,ncol = 1)
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
eq1 <- lm(price ~ RC1 +RC2 +RC3, data)
summary(eq1)
Call:
lm(formula = price ~ RC1 + RC2 + RC3, data = data)
Residuals:
Min 1Q Median 3Q Max
-21.468 -5.049 -0.936 2.972 36.978
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 27.4646 0.7014 39.156 < 2e-16 ***
RC1 4.3130 0.6999 6.162 6.47e-09 ***
RC2 -1.0709 0.7001 -1.530 0.128
RC3 10.6574 0.7036 15.148 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.643 on 148 degrees of freedom
(5 observations deleted due to missingness)
Multiple R-squared: 0.6479, Adjusted R-squared: 0.6407
F-statistic: 90.76 on 3 and 148 DF, p-value: < 2.2e-16
eq2 <- lm(resale ~ RC1 +RC2 +RC3, data)
summary(eq2)
Call:
lm(formula = resale ~ RC1 + RC2 + RC3, data = data)
Residuals:
Min 1Q Median 3Q Max
-14.680 -4.848 -1.442 2.978 30.519
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.5900 0.7066 26.310 < 2e-16 ***
RC1 3.0571 0.7242 4.222 4.9e-05 ***
RC2 -2.1020 0.6729 -3.124 0.00227 **
RC3 7.5295 0.6661 11.303 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.639 on 114 degrees of freedom
(39 observations deleted due to missingness)
Multiple R-squared: 0.5742, Adjusted R-squared: 0.563
F-statistic: 51.25 on 3 and 114 DF, p-value: < 2.2e-16
eq3 <- lm(sales ~ RC1 +RC2 +RC3, data)
summary(eq3)
Call:
lm(formula = sales ~ RC1 + RC2 + RC3, data = data)
Residuals:
Min 1Q Median 3Q Max
-88.46 -35.92 -18.67 20.10 397.87
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 53.30585 5.08555 10.482 < 2e-16 ***
RC1 0.05693 5.08494 0.011 0.99108
RC2 25.16954 5.09264 4.942 2.05e-06 ***
RC3 -14.56111 5.11090 -2.849 0.00501 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 62.88 on 149 degrees of freedom
(4 observations deleted due to missingness)
Multiple R-squared: 0.1808, Adjusted R-squared: 0.1643
F-statistic: 10.96 on 3 and 149 DF, p-value: 1.508e-06
