R Markdown

Hi here, we are going to create a linear regression model:

data2<-read.csv("C:/Users/InteL/Desktop/aya nazar/karpur.csv",header = TRUE)#calling data
head(data2)
##    depth caliper ind.deep ind.med  gamma phi.N R.deep  R.med      SP
## 1 5667.0   8.685  618.005 569.781 98.823 0.410  1.618  1.755 -56.587
## 2 5667.5   8.686  497.547 419.494 90.640 0.307  2.010  2.384 -61.916
## 3 5668.0   8.686  384.935 300.155 78.087 0.203  2.598  3.332 -55.861
## 4 5668.5   8.686  278.324 205.224 66.232 0.119  3.593  4.873 -41.860
## 5 5669.0   8.686  183.743 131.155 59.807 0.069  5.442  7.625 -34.934
## 6 5669.5   8.686  109.512  75.633 57.109 0.048  9.131 13.222 -39.769
##   density.corr density phi.core   k.core Facies  X phi.core.frac
## 1       -0.033   2.205  33.9000 2442.590     F1 NA      0.339000
## 2       -0.067   2.040  33.4131 3006.989     F1 NA      0.334131
## 3       -0.064   1.888  33.1000 3370.000     F1 NA      0.331000
## 4       -0.053   1.794  34.9000 2270.000     F1 NA      0.349000
## 5       -0.054   1.758  35.0644 2530.758     F1 NA      0.350644
## 6       -0.058   1.759  35.3152 2928.314     F1 NA      0.353152
plot(data2$phi.N,data2$phi.core.frac)

porosity_model <- lm(phi.core.frac~data2$phi.N+Facies-1,data=data2)
summary(porosity_model)
## 
## Call:
## lm(formula = phi.core.frac ~ data2$phi.N + Facies - 1, data = data2)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.103530 -0.011573 -0.000206  0.010463  0.102852 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## data2$phi.N 0.013364   0.018060    0.74     0.46    
## FaciesF1    0.314805   0.002777  113.37   <2e-16 ***
## FaciesF10   0.207680   0.005072   40.95   <2e-16 ***
## FaciesF2    0.175233   0.009390   18.66   <2e-16 ***
## FaciesF3    0.231939   0.004955   46.81   <2e-16 ***
## FaciesF5    0.272953   0.003914   69.74   <2e-16 ***
## FaciesF7    0.225164   0.008730   25.79   <2e-16 ***
## FaciesF8    0.305884   0.005019   60.94   <2e-16 ***
## FaciesF9    0.264448   0.004825   54.81   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.02326 on 810 degrees of freedom
## Multiple R-squared:  0.9928, Adjusted R-squared:  0.9928 
## F-statistic: 1.246e+04 on 9 and 810 DF,  p-value: < 2.2e-16