Pozos de Huatabampo
Javier Barreras
27/2/2020
ggplot(Huatabampo, aes(x = SNM, y = NF)) +
geom_point()
cor.test(SNM, NF, method=c("pearson", "kendall", "spearman"))##
## Pearson's product-moment correlation
##
## data: SNM and NF
## t = 15.526, df = 291, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6052017 0.7312929
## sample estimates:
## cor
## 0.67311nf<-Huatabampo %>%
filter(NF <=2.9 )
ggplot(nf, aes(x = nf$SNM, y = nf$NF)) +
geom_point()
cor.test(nf$SNM, nf$NF, method=c("pearson", "kendall", "spearman"))##
## Pearson's product-moment correlation
##
## data: nf$SNM and nf$NF
## t = 11.338, df = 164, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.5678834 0.7404278
## sample estimates:
## cor
## 0.6628664#SNM sobre nivel del mar
#NF nivel freatico
#CE
#PPM partes por milon de sal
#PH acidez o alcalinidad
#TEMP temperatura
PPM <- Huatabampo$PPM
PH <- Huatabampo$PH
NF <- Huatabampo$NF
SNM <- Huatabampo$SNM
temperatura<-Huatabampo$TEMP
datos <- data.frame(PPM, PH, NF, SNM,temperatura)
pairs(datos)
cor(datos)## PPM PH NF SNM temperatura
## PPM 1.0000000 -0.54435240 -0.5352205 -0.32158319 0.20043300
## PH -0.5443524 1.00000000 0.1593932 0.05119597 -0.02029087
## NF -0.5352205 0.15939323 1.0000000 0.67310998 -0.26663371
## SNM -0.3215832 0.05119597 0.6731100 1.00000000 -0.19907971
## temperatura 0.2004330 -0.02029087 -0.2666337 -0.19907971 1.00000000 datos_1<-data.frame(NF,SNM)
pairs(datos_1)
cor(datos_1)## NF SNM
## NF 1.00000 0.67311
## SNM 0.67311 1.00000datos_2<-datos_1 %>%
filter(NF <=2.9 )
pairs(datos_2)
cor(datos_2)## NF SNM
## NF 1.0000000 0.6628664
## SNM 0.6628664 1.0000000 regresion <- lm(datos_2$SNM ~ datos_2$NF, data = datos_2)
summary(regresion)##
## Call:
## lm(formula = datos_2$SNM ~ datos_2$NF, data = datos_2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6579 -1.2683 -0.1109 0.8843 4.4633
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2492 0.5331 -0.467 0.641
## datos_2$NF 2.8220 0.2489 11.338 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.683 on 164 degrees of freedom
## Multiple R-squared: 0.4394, Adjusted R-squared: 0.436
## F-statistic: 128.5 on 1 and 164 DF, p-value: < 2.2e-16plot(datos_2$NF, datos_2$SNM, xlab='nivel freautico', ylab='nivel sobre el mar')
abline(regresion)
Quiero saber cual va ser el nivel del mar cuando el nivel freatico este entre 3 y 4 y la coniabilidad de este resultado
a<-datos_2$NF
nuevos.nivele <- data.frame(a =seq(3,4))
predict(regresion, nuevos.nivele)## Warning: 'newdata' had 2 rows but variables found have 166 rows## 1 2 3 4 5 6 7 8
## 7.313648 7.116111 3.419353 5.789793 5.422939 5.394720 4.350597 7.708721
## 9 10 11 12 13 14 15 16
## 5.535817 7.595843 5.761574 5.507598 5.253622 3.870865 6.071988 7.228989
## 17 18 19 20 21 22 23 24
## 7.116111 7.031453 5.676915 5.253622 5.874452 4.491695 5.310061 2.488108
## 25 26 27 28 29 30 31 32
## 1.923717 4.068402 5.310061 5.592256 5.451159 5.733354 6.354184 7.087892
## 33 34 35 36 37 38 39 40
## 5.620476 6.156647 6.636379 5.959110 6.241306 4.604573 3.757987 1.443985
## 41 42 43 44 45 46 47 48
## 3.560450 4.802110 5.761574 5.253622 6.354184 7.482965 7.087892 5.959110
## 49 50 51 52 53 54 55 56
## 7.059672 5.592256 5.027866 7.087892 4.548134 4.943207 6.692818 6.523501
## 57 58 59 60 61 62 63 64
## 6.805696 4.548134 5.168963 5.761574 7.482965 6.438842 6.523501 5.761574
## 65 66 67 68 69 70 71 72
## 6.721038 2.826742 7.087892 3.532231 5.874452 5.761574 6.241306 7.087892
## 73 74 75 76 77 78 79 80
## 5.112524 2.234132 4.096621 2.854962 4.181280 4.181280 2.290571 4.237719
## 81 82 83 84 85 86 87 88
## 1.754400 1.274668 2.770303 4.237719 4.830329 4.463475 6.325964 5.027866
## 89 90 91 92 93 94 95 96
## 7.652282 5.592256 6.523501 7.087892 4.040182 3.842646 5.507598 4.802110
## 97 98 99 100 101 102 103 104
## 4.773890 7.003233 6.071988 5.761574 6.636379 5.959110 4.830329 7.257209
## 105 106 107 108 109 110 111 112
## 7.228989 6.805696 6.946794 2.742084 2.854962 6.805696 6.579940 7.257209
## 113 114 115 116 117 118 119 120
## 4.604573 7.059672 6.269525 6.805696 5.281842 7.087892 6.241306 7.370087
## 121 122 123 124 125 126 127 128
## 7.341867 7.454746 6.382403 4.548134 4.886768 7.652282 4.350597 6.664599
## 129 130 131 132 133 134 135 136
## 6.523501 5.535817 2.967840 5.705135 6.410623 6.805696 6.946794 5.959110
## 137 138 139 140 141 142 143 144
## 6.975013 4.661012 6.890355 5.676915 7.228989 4.548134 7.426526 2.036596
## 145 146 147 148 149 150 151 152
## 4.209499 6.043769 5.056085 3.391133 3.701548 7.652282 7.116111 7.228989
## 153 154 155 156 157 158 159 160
## 5.705135 4.378817 4.830329 7.087892 7.228989 6.805696 6.579940 6.213086
## 161 162 163 164 165 166
## 5.930891 7.934478 6.523501 5.676915 7.370087 6.100208confint(regresion)## 2.5 % 97.5 %
## (Intercept) -1.301825 0.8034522
## datos_2$NF 2.330484 3.3134223