## Загрузка требуемого пакета: ggplot2
## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
## Загрузка требуемого пакета: zoo
##
## Присоединяю пакет: 'zoo'
## Следующие объекты скрыты от 'package:base':
##
## as.Date, as.Date.numeric
## Sales Price Advertising ShelveLoc
## Min. : 0.000 Min. : 24.0 Min. : 0.000 Bad : 96
## 1st Qu.: 5.390 1st Qu.:100.0 1st Qu.: 0.000 Good : 85
## Median : 7.490 Median :117.0 Median : 5.000 Medium:219
## Mean : 7.496 Mean :115.8 Mean : 6.635
## 3rd Qu.: 9.320 3rd Qu.:131.0 3rd Qu.:12.000
## Max. :16.270 Max. :191.0 Max. :29.000
## Warning in ggmatrix_gtable(x, ...): Please use the 'progress' parameter in
## your ggmatrix-like function call. See ?ggmatrix_progress for a few examples.
## ggmatrix_gtable 'progress' and 'progress_format' will soon be deprecated.TRUE
##
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
##
##
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
##
##
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning in ggmatrix_gtable(x, ...): Please use the 'progress' parameter in
## your ggmatrix-like function call. See ?ggmatrix_progress for a few examples.
## ggmatrix_gtable 'progress' and 'progress_format' will soon be deprecated.TRUE
##
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
##
## Call:
## lm(formula = Sales ~ . + Price:ShelveLoc + Advertising:ShelveLoc,
## data = df.train)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.5311 -1.0824 -0.0192 1.1388 4.2740
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.3002861 0.9483076 11.916 < 2e-16 ***
## Price -0.0546137 0.0079913 -6.834 3.98e-11 ***
## Advertising 0.0860625 0.0301535 2.854 0.00459 **
## ShelveLocGood 5.9178585 1.3857589 4.270 2.55e-05 ***
## ShelveLocMedium 1.5300896 1.1908552 1.285 0.19974
## Price:ShelveLocGood -0.0098768 0.0114409 -0.863 0.38860
## Price:ShelveLocMedium 0.0004187 0.0100099 0.042 0.96666
## Advertising:ShelveLocGood 0.0012540 0.0424810 0.030 0.97647
## Advertising:ShelveLocMedium 0.0238116 0.0362447 0.657 0.51166
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.754 on 331 degrees of freedom
## Multiple R-squared: 0.6141, Adjusted R-squared: 0.6048
## F-statistic: 65.85 on 8 and 331 DF, p-value: < 2.2e-16
##
## Call:
## lm(formula = Sales ~ . + Advertising:ShelveLoc, data = df.train)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.5477 -1.0465 0.0038 1.1280 4.2140
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.567499 0.537682 21.514 < 2e-16 ***
## Price -0.056952 0.004143 -13.745 < 2e-16 ***
## Advertising 0.086165 0.030113 2.861 0.00448 **
## ShelveLocGood 4.761605 0.391984 12.147 < 2e-16 ***
## ShelveLocMedium 1.581912 0.311196 5.083 6.2e-07 ***
## Advertising:ShelveLocGood 0.001861 0.042422 0.044 0.96503
## Advertising:ShelveLocMedium 0.023871 0.036195 0.660 0.51002
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.751 on 333 degrees of freedom
## Multiple R-squared: 0.6128, Adjusted R-squared: 0.6058
## F-statistic: 87.83 on 6 and 333 DF, p-value: < 2.2e-16
##
## Call:
## lm(formula = Sales ~ Price + Advertising + ShelveLoc, data = df.train)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7098 -1.0930 0.0193 1.1849 4.1478
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.491113 0.516860 22.233 < 2e-16 ***
## Price -0.056915 0.004134 -13.767 < 2e-16 ***
## Advertising 0.099194 0.014552 6.816 4.35e-11 ***
## ShelveLocGood 4.753523 0.282698 16.815 < 2e-16 ***
## ShelveLocMedium 1.721109 0.231175 7.445 8.27e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.748 on 335 degrees of freedom
## Multiple R-squared: 0.6121, Adjusted R-squared: 0.6074
## F-statistic: 132.1 on 4 and 335 DF, p-value: < 2.2e-16
##
## Call:
## lm(formula = Sales ~ Price + ShelveLoc, data = df.train)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4731 -1.3552 -0.0733 1.2653 5.1448
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.023705 0.544391 22.087 < 2e-16 ***
## Price -0.056765 0.004405 -12.887 < 2e-16 ***
## ShelveLocGood 4.915635 0.300150 16.377 < 2e-16 ***
## ShelveLocMedium 1.784519 0.246119 7.251 2.88e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.862 on 336 degrees of freedom
## Multiple R-squared: 0.5583, Adjusted R-squared: 0.5543
## F-statistic: 141.5 on 3 and 336 DF, p-value: < 2.2e-16
##
## Call:
## lm(formula = Sales ~ Price + ShelveLoc + Price:ShelveLoc, data = df.train)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4781 -1.3598 -0.0816 1.2427 4.9877
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.7470469 0.9943558 11.814 < 2e-16 ***
## Price -0.0543499 0.0084953 -6.398 5.33e-10 ***
## ShelveLocGood 6.1701700 1.4415001 4.280 2.44e-05 ***
## ShelveLocMedium 1.6956329 1.2493941 1.357 0.176
## Price:ShelveLocGood -0.0107554 0.0121608 -0.884 0.377
## Price:ShelveLocMedium 0.0007331 0.0106409 0.069 0.945
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.864 on 334 degrees of freedom
## Multiple R-squared: 0.5599, Adjusted R-squared: 0.5533
## F-statistic: 84.98 on 5 and 334 DF, p-value: < 2.2e-16
##
## studentized Breusch-Pagan test
##
## data: model.4
## BP = 0.35539, df = 3, p-value = 0.9493
##
## Durbin-Watson test
##
## data: model.4
## DW = 2.0934, p-value = 0.805
## alternative hypothesis: true autocorrelation is greater than 0
