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statInference.log
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[1mindexing[0m [34mwillhaben_woom_bikes_sample_no_outlier.csv[0m [====================] [32m139.60GB/s[0m, eta: [36m 0s[0m cols(
title = col_character(),
location = col_character(),
last_modified = col_character(),
price = col_character(),
seller_info = col_character(),
details = col_character(),
description = col_character(),
item_url = col_character(),
obs_id = col_double(),
price_parsed = col_double(),
zip_code = col_double(),
city_name = col_character(),
WoomCategory_i = col_double(),
Color_i = col_character(),
last_modified_dt = col_datetime(format = ""),
last_modified_unix = col_double(),
Last_48_hours_i = col_double(),
Cond_i = col_character(),
Uebergabeart_i = col_double(),
Dealer_i = col_double(),
zip_code_result = col_double(),
latitude = col_double(),
longitude = col_double(),
AnzahlSameSizeRadius0To10_i = col_double(),
AnzahlSameSizeRadius10To30_i = col_double(),
AnzahlSameSizeRadius30To60_i = col_double(),
hasPsychologicalPricing_i = col_double(),
log_price = col_double(),
logistic_costs = col_double(),
color = col_character(),
condition = col_character()
)
GVIF Df GVIF^(1/(2*Df))
size 1.447198 6 1.031282
condition 1.102412 2 1.024675
color 1.416164 6 1.029420
Dealer_i 1.443448 1 1.201436
Last_48_hours_i 1.029247 1 1.014518
hasPsychologicalPricing_i 1.058637 1 1.028901
Uebergabeart_i 1.081381 1 1.039895
logistic_costs 1.903907 1 1.379821
AnzahlSameSizeRadius0To10_i 1.967902 1 1.402819
AnzahlSameSizeRadius10To30_i 1.920339 1 1.385763
AnzahlSameSizeRadius30To60_i 1.746294 1 1.321474
t test of coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.4017e+00 3.1532e-02 171.3083 < 2.2e-16 ***
size2 4.7670e-01 1.8922e-02 25.1929 < 2.2e-16 ***
size3 5.4739e-01 1.6066e-02 34.0724 < 2.2e-16 ***
size4 7.4971e-01 1.7212e-02 43.5565 < 2.2e-16 ***
size5 8.5608e-01 2.7881e-02 30.7045 < 2.2e-16 ***
size6 8.4325e-01 2.0910e-02 40.3270 < 2.2e-16 ***
size7 -5.4843e-02 3.1256e-02 -1.7546 0.079702 .
conditiongood 1.3808e-01 3.0954e-02 4.4608 9.331e-06 ***
conditionused -9.6515e-02 9.1701e-03 -10.5250 < 2.2e-16 ***
colorBlau -8.0066e-02 2.5794e-02 -3.1041 0.001976 **
colorGelb -7.2175e-02 2.6621e-02 -2.7112 0.006847 **
colorGrün -6.5398e-02 2.5974e-02 -2.5178 0.012002 *
colorOrange -1.5353e-01 5.0710e-02 -3.0276 0.002544 **
colorRot -7.3203e-02 2.6092e-02 -2.8055 0.005145 **
colorViolett -3.2557e-02 2.5980e-02 -1.2532 0.210508
Dealer_i 1.3968e-02 9.5113e-03 1.4686 0.142346
Last_48_hours_i -6.9551e-03 1.5416e-02 -0.4512 0.651991
hasPsychologicalPricing_i 4.6038e-03 1.2701e-02 0.3625 0.717088
Uebergabeart_i 8.9638e-03 9.8534e-03 0.9097 0.363245
logistic_costs -7.2504e-03 6.7763e-03 -1.0700 0.284956
AnzahlSameSizeRadius0To10_i 1.1086e-04 1.2567e-04 0.8821 0.377980
AnzahlSameSizeRadius10To30_i -9.9106e-05 7.5961e-05 -1.3047 0.192371
AnzahlSameSizeRadius30To60_i -1.5120e-05 4.7852e-05 -0.3160 0.752108
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Call:
lm(formula = log_price ~ size + condition + color + Dealer_i +
Last_48_hours_i + hasPsychologicalPricing_i + Uebergabeart_i +
logistic_costs + AnzahlSameSizeRadius0To10_i + AnzahlSameSizeRadius10To30_i +
AnzahlSameSizeRadius30To60_i, data = data)
Residuals:
Min 1Q Median 3Q Max
-0.44593 -0.06122 0.00197 0.07054 0.40498
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.402e+00 3.034e-02 178.051 < 2e-16 ***
size2 4.767e-01 2.257e-02 21.121 < 2e-16 ***
size3 5.474e-01 2.064e-02 26.520 < 2e-16 ***
size4 7.497e-01 2.158e-02 34.741 < 2e-16 ***
size5 8.561e-01 2.819e-02 30.374 < 2e-16 ***
size6 8.433e-01 3.063e-02 27.529 < 2e-16 ***
size7 -5.484e-02 1.104e-01 -0.497 0.619438
conditiongood 1.381e-01 3.525e-02 3.918 9.70e-05 ***
conditionused -9.652e-02 9.327e-03 -10.348 < 2e-16 ***
colorBlau -8.007e-02 1.988e-02 -4.028 6.17e-05 ***
colorGelb -7.218e-02 2.142e-02 -3.370 0.000787 ***
colorGrün -6.540e-02 2.019e-02 -3.240 0.001245 **
colorOrange -1.535e-01 7.834e-02 -1.960 0.050368 .
colorRot -7.320e-02 1.973e-02 -3.710 0.000222 ***
colorViolett -3.256e-02 2.020e-02 -1.611 0.107471
Dealer_i 1.397e-02 8.939e-03 1.563 0.118557
Last_48_hours_i -6.955e-03 1.367e-02 -0.509 0.610961
hasPsychologicalPricing_i 4.604e-03 1.074e-02 0.429 0.668220
Uebergabeart_i 8.964e-03 9.911e-03 0.904 0.366062
logistic_costs -7.250e-03 7.854e-03 -0.923 0.356178
AnzahlSameSizeRadius0To10_i 1.109e-04 1.189e-04 0.933 0.351257
AnzahlSameSizeRadius10To30_i -9.911e-05 6.641e-05 -1.492 0.135977
AnzahlSameSizeRadius30To60_i -1.512e-05 5.105e-05 -0.296 0.767166
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1064 on 803 degrees of freedom
Multiple R-squared: 0.7198, Adjusted R-squared: 0.7121
F-statistic: 93.76 on 22 and 803 DF, p-value: < 2.2e-16
Start: AIC=-3678.45
log_price ~ size + condition + color + Dealer_i + Last_48_hours_i +
hasPsychologicalPricing_i + Uebergabeart_i + logistic_costs +
AnzahlSameSizeRadius0To10_i + AnzahlSameSizeRadius10To30_i +
AnzahlSameSizeRadius30To60_i
Df Sum of Sq RSS AIC
- AnzahlSameSizeRadius30To60_i 1 0.0010 9.0947 -3680.4
- hasPsychologicalPricing_i 1 0.0021 9.0957 -3680.3
- Last_48_hours_i 1 0.0029 9.0966 -3680.2
- Uebergabeart_i 1 0.0093 9.1029 -3679.6
- logistic_costs 1 0.0097 9.1033 -3679.6
- AnzahlSameSizeRadius0To10_i 1 0.0099 9.1035 -3679.6
<none> 9.0937 -3678.4
- AnzahlSameSizeRadius10To30_i 1 0.0252 9.1189 -3678.2
- Dealer_i 1 0.0276 9.1213 -3677.9
- color 6 0.3954 9.4891 -3655.3
- condition 2 1.6216 10.7153 -3546.9
- size 6 19.9324 29.0261 -2731.8
Step: AIC=-3680.36
log_price ~ size + condition + color + Dealer_i + Last_48_hours_i +
hasPsychologicalPricing_i + Uebergabeart_i + logistic_costs +
AnzahlSameSizeRadius0To10_i + AnzahlSameSizeRadius10To30_i
Df Sum of Sq RSS AIC
- hasPsychologicalPricing_i 1 0.0020 9.0967 -3682.2
- Last_48_hours_i 1 0.0028 9.0975 -3682.1
- Uebergabeart_i 1 0.0089 9.1035 -3681.6
- logistic_costs 1 0.0091 9.1038 -3681.5
- AnzahlSameSizeRadius0To10_i 1 0.0094 9.1040 -3681.5
<none> 9.0947 -3680.4
- AnzahlSameSizeRadius10To30_i 1 0.0265 9.1211 -3680.0
- Dealer_i 1 0.0271 9.1218 -3679.9
+ AnzahlSameSizeRadius30To60_i 1 0.0010 9.0937 -3678.4
- color 6 0.3953 9.4899 -3657.2
- condition 2 1.6257 10.7204 -3548.5
- size 6 19.9406 29.0352 -2733.5
Step: AIC=-3682.18
log_price ~ size + condition + color + Dealer_i + Last_48_hours_i +
Uebergabeart_i + logistic_costs + AnzahlSameSizeRadius0To10_i +
AnzahlSameSizeRadius10To30_i
Df Sum of Sq RSS AIC
- Last_48_hours_i 1 0.0031 9.0998 -3683.9
- logistic_costs 1 0.0092 9.1059 -3683.3
- Uebergabeart_i 1 0.0092 9.1059 -3683.3
- AnzahlSameSizeRadius0To10_i 1 0.0097 9.1063 -3683.3
<none> 9.0967 -3682.2
- AnzahlSameSizeRadius10To30_i 1 0.0267 9.1233 -3681.8
- Dealer_i 1 0.0278 9.1245 -3681.7
+ hasPsychologicalPricing_i 1 0.0020 9.0947 -3680.4
+ AnzahlSameSizeRadius30To60_i 1 0.0009 9.0957 -3680.3
- color 6 0.3940 9.4907 -3659.2
- condition 2 1.6275 10.7241 -3550.2
- size 6 19.9773 29.0740 -2734.4
Step: AIC=-3683.89
log_price ~ size + condition + color + Dealer_i + Uebergabeart_i +
logistic_costs + AnzahlSameSizeRadius0To10_i + AnzahlSameSizeRadius10To30_i
Df Sum of Sq RSS AIC
- Uebergabeart_i 1 0.0092 9.1089 -3685.1
- AnzahlSameSizeRadius0To10_i 1 0.0096 9.1094 -3685.0
- logistic_costs 1 0.0099 9.1096 -3685.0
<none> 9.0998 -3683.9
- Dealer_i 1 0.0276 9.1273 -3683.4
- AnzahlSameSizeRadius10To30_i 1 0.0279 9.1277 -3683.4
+ Last_48_hours_i 1 0.0031 9.0967 -3682.2
+ hasPsychologicalPricing_i 1 0.0022 9.0975 -3682.1
+ AnzahlSameSizeRadius30To60_i 1 0.0008 9.0989 -3682.0
- color 6 0.3923 9.4920 -3661.0
- condition 2 1.6440 10.7437 -3550.7
- size 6 20.0113 29.1111 -2735.4
Step: AIC=-3685.06
log_price ~ size + condition + color + Dealer_i + logistic_costs +
AnzahlSameSizeRadius0To10_i + AnzahlSameSizeRadius10To30_i
Df Sum of Sq RSS AIC
- logistic_costs 1 0.0096 9.1186 -3686.2
- AnzahlSameSizeRadius0To10_i 1 0.0103 9.1192 -3686.1
<none> 9.1089 -3685.1
- Dealer_i 1 0.0258 9.1347 -3684.7
- AnzahlSameSizeRadius10To30_i 1 0.0293 9.1382 -3684.4
+ Uebergabeart_i 1 0.0092 9.0998 -3683.9
+ Last_48_hours_i 1 0.0030 9.1059 -3683.3
+ hasPsychologicalPricing_i 1 0.0026 9.1063 -3683.3
+ AnzahlSameSizeRadius30To60_i 1 0.0005 9.1085 -3683.1
- color 6 0.3881 9.4971 -3662.6
- condition 2 1.7455 10.8544 -3544.2
- size 6 20.0319 29.1408 -2736.5
Step: AIC=-3686.19
log_price ~ size + condition + color + Dealer_i + AnzahlSameSizeRadius0To10_i +
AnzahlSameSizeRadius10To30_i
Df Sum of Sq RSS AIC
- AnzahlSameSizeRadius0To10_i 1 0.0137 9.1323 -3686.9
- AnzahlSameSizeRadius10To30_i 1 0.0212 9.1398 -3686.3
<none> 9.1186 -3686.2
- Dealer_i 1 0.0286 9.1471 -3685.6
+ logistic_costs 1 0.0096 9.1089 -3685.1
+ Uebergabeart_i 1 0.0090 9.1096 -3685.0
+ Last_48_hours_i 1 0.0036 9.1149 -3684.5
+ hasPsychologicalPricing_i 1 0.0028 9.1158 -3684.4
+ AnzahlSameSizeRadius30To60_i 1 0.0010 9.1176 -3684.3
- color 6 0.3850 9.5036 -3664.0
- condition 2 1.7396 10.8581 -3546.0
- size 6 20.0617 29.1803 -2737.4
Step: AIC=-3686.95
log_price ~ size + condition + color + Dealer_i + AnzahlSameSizeRadius10To30_i
Df Sum of Sq RSS AIC
- AnzahlSameSizeRadius10To30_i 1 0.0094 9.1417 -3688.1
- Dealer_i 1 0.0182 9.1505 -3687.3
<none> 9.1323 -3686.9
+ AnzahlSameSizeRadius0To10_i 1 0.0137 9.1186 -3686.2
+ logistic_costs 1 0.0130 9.1192 -3686.1
+ Uebergabeart_i 1 0.0098 9.1225 -3685.8
+ Last_48_hours_i 1 0.0036 9.1286 -3685.3
+ hasPsychologicalPricing_i 1 0.0033 9.1290 -3685.2
+ AnzahlSameSizeRadius30To60_i 1 0.0025 9.1298 -3685.2
- color 6 0.3848 9.5171 -3664.9
- condition 2 1.7300 10.8623 -3547.7
- size 6 20.1337 29.2659 -2737.0
Step: AIC=-3688.1
log_price ~ size + condition + color + Dealer_i
Df Sum of Sq RSS AIC
<none> 9.1417 -3688.1
- Dealer_i 1 0.0287 9.1704 -3687.5
+ Uebergabeart_i 1 0.0104 9.1312 -3687.0
+ AnzahlSameSizeRadius10To30_i 1 0.0094 9.1323 -3686.9
+ Last_48_hours_i 1 0.0043 9.1374 -3686.5
+ hasPsychologicalPricing_i 1 0.0032 9.1385 -3686.4
+ logistic_costs 1 0.0028 9.1388 -3686.4
+ AnzahlSameSizeRadius0To10_i 1 0.0019 9.1398 -3686.3
+ AnzahlSameSizeRadius30To60_i 1 0.0000 9.1416 -3686.1
- color 6 0.3974 9.5391 -3664.9
- condition 2 1.7374 10.8791 -3548.4
- size 6 20.1299 29.2715 -2738.8
Call:
lm(formula = log_price ~ size + condition + color + Dealer_i,
data = data)
Residuals:
Min 1Q Median 3Q Max
-0.46537 -0.06425 0.00282 0.06568 0.39686
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.400128 0.028036 192.612 < 2e-16 ***
size2 0.471597 0.022265 21.181 < 2e-16 ***
size3 0.543118 0.020408 26.614 < 2e-16 ***
size4 0.745396 0.021384 34.858 < 2e-16 ***
size5 0.850394 0.027811 30.577 < 2e-16 ***
size6 0.836619 0.030310 27.602 < 2e-16 ***
size7 -0.055604 0.109794 -0.506 0.612688
conditiongood 0.142356 0.034768 4.094 4.66e-05 ***
conditionused -0.097501 0.009216 -10.579 < 2e-16 ***
colorBlau -0.079909 0.019796 -4.037 5.94e-05 ***
colorGelb -0.071821 0.021326 -3.368 0.000793 ***
colorGrün -0.065430 0.020066 -3.261 0.001157 **
colorOrange -0.160815 0.077486 -2.075 0.038265 *
colorRot -0.073768 0.019641 -3.756 0.000185 ***
colorViolett -0.033155 0.020047 -1.654 0.098546 .
Dealer_i 0.011933 0.007477 1.596 0.110924
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1062 on 810 degrees of freedom
Multiple R-squared: 0.7183, Adjusted R-squared: 0.7131
F-statistic: 137.7 on 15 and 810 DF, p-value: < 2.2e-16
Call:
lm(formula = formula, data = data)
Residuals:
Min 1Q Median 3Q Max
-0.49986 -0.06035 -0.01031 0.06620 0.44004
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.27824 0.02189 241.16 <2e-16 ***
size2 0.46864 0.02458 19.07 <2e-16 ***
size3 0.53555 0.02259 23.71 <2e-16 ***
size4 0.73091 0.02359 30.98 <2e-16 ***
size5 0.88140 0.02923 30.15 <2e-16 ***
size6 0.82252 0.03332 24.68 <2e-16 ***
size7 -0.03122 0.11988 -0.26 0.795
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1179 on 819 degrees of freedom
Multiple R-squared: 0.6494, Adjusted R-squared: 0.6468
F-statistic: 252.8 on 6 and 819 DF, p-value: < 2.2e-16
Call:
lm(formula = formula, data = data)
Residuals:
Min 1Q Median 3Q Max
-0.66554 -0.12737 -0.00221 0.13499 0.44172
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.91256 0.01472 401.561 < 2e-16 ***
conditiongood 0.11519 0.06333 1.819 0.0693 .
conditionused -0.08141 0.01661 -4.901 1.15e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1948 on 823 degrees of freedom
Multiple R-squared: 0.03784, Adjusted R-squared: 0.03551
F-statistic: 16.19 on 2 and 823 DF, p-value: 1.274e-07
Call:
lm(formula = formula, data = data)
Residuals:
Min 1Q Median 3Q Max
-0.65919 -0.11255 0.00578 0.13505 0.42940
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.84912 0.01027 569.247 <2e-16 ***
Dealer_i 0.00303 0.01388 0.218 0.827
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1984 on 824 degrees of freedom
Multiple R-squared: 5.789e-05, Adjusted R-squared: -0.001156
F-statistic: 0.04771 on 1 and 824 DF, p-value: 0.8272
Call:
lm(formula = formula, data = data)
Residuals:
Min 1Q Median 3Q Max
-0.66015 -0.11253 0.00483 0.13586 0.45364
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.853105 0.007203 812.637 <2e-16 ***
Last_48_hours_i -0.028222 0.025103 -1.124 0.261
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1983 on 824 degrees of freedom
Multiple R-squared: 0.001532, Adjusted R-squared: 0.0003198
F-statistic: 1.264 on 1 and 824 DF, p-value: 0.2612
Call:
lm(formula = formula, data = data)
Residuals:
Min 1Q Median 3Q Max
-0.66103 -0.11742 0.00394 0.13658 0.42453
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.853988 0.007474 783.266 <2e-16 ***
hasPsychologicalPricing_i -0.021713 0.019447 -1.117 0.265
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1983 on 824 degrees of freedom
Multiple R-squared: 0.001511, Adjusted R-squared: 0.0002988
F-statistic: 1.247 on 1 and 824 DF, p-value: 0.2645
Call:
lm(formula = formula, data = data)
Residuals:
Min 1Q Median 3Q Max
-0.80858 -0.09829 0.00031 0.12685 0.45150
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.05561 0.02934 206.366 < 2e-16 ***
colorBlau -0.23423 0.03279 -7.144 2.01e-12 ***
colorGelb -0.24012 0.03509 -6.843 1.52e-11 ***
colorGrün -0.20370 0.03344 -6.092 1.72e-09 ***
colorOrange -0.25058 0.13919 -1.800 0.0722 .
colorRot -0.21630 0.03253 -6.650 5.35e-11 ***
colorViolett -0.19404 0.03273 -5.928 4.51e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1924 on 819 degrees of freedom
Multiple R-squared: 0.06557, Adjusted R-squared: 0.05872
F-statistic: 9.578 on 6 and 819 DF, p-value: 3.386e-10
Call:
lm(formula = formula, data = data)
Residuals:
Min 1Q Median 3Q Max
-0.66325 -0.11389 0.00172 0.13353 0.43921
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.8562085 0.0081609 717.590 <2e-16 ***
AnzahlSameSizeRadius0To10_i -0.0001965 0.0001579 -1.245 0.214
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1983 on 824 degrees of freedom
Multiple R-squared: 0.001877, Adjusted R-squared: 0.0006654
F-statistic: 1.549 on 1 and 824 DF, p-value: 0.2136
Call:
lm(formula = formula, data = data)
Residuals:
Min 1Q Median 3Q Max
-0.65946 -0.11256 0.00495 0.13224 0.42554
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.849e+00 9.517e-03 614.554 <2e-16 ***
AnzahlSameSizeRadius10To30_i 2.455e-05 8.936e-05 0.275 0.784
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1984 on 824 degrees of freedom
Multiple R-squared: 9.16e-05, Adjusted R-squared: -0.001122
F-statistic: 0.07548 on 1 and 824 DF, p-value: 0.7836
Call:
lm(formula = formula, data = data)
Residuals:
Min 1Q Median 3Q Max
-0.66510 -0.11015 0.00444 0.13224 0.42681
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.846e+00 1.175e-02 497.638 <2e-16 ***
AnzahlSameSizeRadius30To60_i 3.868e-05 7.203e-05 0.537 0.591
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1984 on 824 degrees of freedom
Multiple R-squared: 0.0003499, Adjusted R-squared: -0.0008633
F-statistic: 0.2884 on 1 and 824 DF, p-value: 0.5914
Call:
lm(formula = formula, data = data)
Residuals:
Min 1Q Median 3Q Max
-0.65940 -0.11385 0.00558 0.13457 0.42616
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.850423 0.007650 764.788 <2e-16 ***
Uebergabeart_i 0.001934 0.017774 0.109 0.913
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1985 on 824 degrees of freedom
Multiple R-squared: 1.437e-05, Adjusted R-squared: -0.001199
F-statistic: 0.01184 on 1 and 824 DF, p-value: 0.9134
Call:
lm(formula = formula, data = data)
Residuals:
Min 1Q Median 3Q Max
-0.66104 -0.10868 0.00409 0.13413 0.42466
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.854300 0.008599 680.798 <2e-16 ***
logistic_costs -0.007281 0.010611 -0.686 0.493
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1984 on 824 degrees of freedom
Multiple R-squared: 0.0005711, Adjusted R-squared: -0.0006418
F-statistic: 0.4709 on 1 and 824 DF, p-value: 0.4928
Predictor R_squared Adj_R_squared
size size 6.494023e-01 0.6468338591
condition condition 3.784498e-02 0.0355068113
Dealer_i Dealer_i 5.789129e-05 -0.0011556307
Last_48_hours_i Last_48_hours_i 1.531514e-03 0.0003197803
hasPsychologicalPricing_i hasPsychologicalPricing_i 1.510580e-03 0.0002988211
color color 6.556664e-02 0.0587209708
AnzahlSameSizeRadius0To10_i AnzahlSameSizeRadius0To10_i 1.876764e-03 0.0006654498
AnzahlSameSizeRadius10To30_i AnzahlSameSizeRadius10To30_i 9.159889e-05 -0.0011218822
AnzahlSameSizeRadius30To60_i AnzahlSameSizeRadius30To60_i 3.498910e-04 -0.0008632766
Uebergabeart_i Uebergabeart_i 1.437316e-05 -0.0011992016
logistic_costs logistic_costs 5.711034e-04 -0.0006417958
t test of coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 217.380979 8.047404 27.0126 < 2.2e-16 ***
size2 121.165345 8.150726 14.8656 < 2.2e-16 ***
size3 144.204776 7.468154 19.3093 < 2.2e-16 ***
size4 219.942580 7.794957 28.2160 < 2.2e-16 ***
size5 277.962707 9.695939 28.6680 < 2.2e-16 ***
size6 261.302057 11.021706 23.7080 < 2.2e-16 ***
size7 5.252961 39.442618 0.1332 0.8941
conditiongood 53.937714 12.633375 4.2695 2.191e-05 ***
conditionused -35.406121 3.343215 -10.5904 < 2.2e-16 ***
Dealer_i 5.065824 3.212185 1.5771 0.1152
Last_48_hours_i -0.336623 4.942133 -0.0681 0.9457
hasPsychologicalPricing_i 2.865422 3.850353 0.7442 0.4570
AnzahlSameSizeRadius0To10_i 0.044366 0.042869 1.0349 0.3010
AnzahlSameSizeRadius10To30_i -0.027388 0.023014 -1.1900 0.2344
AnzahlSameSizeRadius30To60_i -0.001416 0.015812 -0.0896 0.9287
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Call:
lm(formula = price_parsed ~ size + condition + Dealer_i + Last_48_hours_i +
hasPsychologicalPricing_i + AnzahlSameSizeRadius0To10_i +
AnzahlSameSizeRadius10To30_i + AnzahlSameSizeRadius30To60_i,
data = data)
Residuals:
Min 1Q Median 3Q Max
-129.305 -23.979 -1.171 21.632 148.678
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 217.380979 8.047404 27.013 < 2e-16 ***
size2 121.165344 8.150726 14.866 < 2e-16 ***
size3 144.204776 7.468154 19.309 < 2e-16 ***
size4 219.942580 7.794957 28.216 < 2e-16 ***
size5 277.962707 9.695939 28.668 < 2e-16 ***
size6 261.302057 11.021706 23.708 < 2e-16 ***
size7 5.252961 39.442618 0.133 0.894
conditiongood 53.937714 12.633375 4.269 2.19e-05 ***
conditionused -35.406121 3.343215 -10.590 < 2e-16 ***
Dealer_i 5.065824 3.212185 1.577 0.115
Last_48_hours_i -0.336623 4.942133 -0.068 0.946
hasPsychologicalPricing_i 2.865422 3.850353 0.744 0.457
AnzahlSameSizeRadius0To10_i 0.044366 0.042869 1.035 0.301
AnzahlSameSizeRadius10To30_i -0.027388 0.023014 -1.190 0.234
AnzahlSameSizeRadius30To60_i -0.001416 0.015812 -0.090 0.929
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 38.6 on 811 degrees of freedom
Multiple R-squared: 0.6801, Adjusted R-squared: 0.6746
F-statistic: 123.1 on 14 and 811 DF, p-value: < 2.2e-16
t test of coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 217.4295 8.0474 27.0186 < 2.2e-16 ***
size2 120.5329 8.1507 14.7880 < 2.2e-16 ***
size3 143.6975 7.4681 19.2414 < 2.2e-16 ***
size4 219.1951 7.7950 28.1201 < 2.2e-16 ***
size5 276.5616 9.6959 28.5234 < 2.2e-16 ***
size6 260.2355 11.0217 23.6112 < 2.2e-16 ***
size7 4.9430 39.4426 0.1253 0.9003
conditiongood 54.7764 12.6334 4.3359 1.634e-05 ***
conditionused -35.2411 3.3432 -10.5411 < 2.2e-16 ***
Dealer_i 4.5383 3.2122 1.4128 0.1581
Last_48_hours_i -0.5053 4.9421 -0.1022 0.9186
hasPsychologicalPricing_i 2.8945 3.8504 0.7518 0.4524
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Call:
lm(formula = price_parsed ~ size + condition + Dealer_i + Last_48_hours_i +
hasPsychologicalPricing_i + logistic_costs, data = data)
Residuals:
Min 1Q Median 3Q Max
-128.49 -25.42 -1.23 21.74 143.02
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 217.4295 7.8313 27.764 < 2e-16 ***
size2 120.5329 8.1271 14.831 < 2e-16 ***
size3 143.6975 7.4547 19.276 < 2e-16 ***
size4 219.1950 7.7700 28.210 < 2e-16 ***
size5 276.5616 9.6503 28.658 < 2e-16 ***
size6 260.2355 10.9881 23.683 < 2e-16 ***
size7 4.9430 39.3950 0.125 0.9002
conditiongood 54.7765 12.6342 4.336 1.64e-05 ***
conditionused -35.2411 3.3394 -10.553 < 2e-16 ***
Dealer_i 4.5383 2.7338 1.660 0.0973 .
Last_48_hours_i -0.5053 4.9287 -0.103 0.9184
hasPsychologicalPricing_i 2.8945 3.8450 0.753 0.4518
logistic_costs -0.5045 2.0984 -0.240 0.8101
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 38.59 on 813 degrees of freedom
Multiple R-squared: 0.6794, Adjusted R-squared: 0.6747
F-statistic: 143.6 on 12 and 813 DF, p-value: < 2.2e-16