##A. Dataset Description library MASS

insul->factor-> before dan after temp-> Celcius Gas-> weekly gas compsumption

library(MASS)
data("whiteside")
#plot 
plot(whiteside)

plot(whiteside$Temp, whiteside$Gas,
     xlab="Outside temperatyre",
     ylab="Heating gas compsumption")

jauh lebih mudah di pahami krn 2 variabel aja. tp regression line nya, klo ga d regression line -> ga ada model tp kita bisa bikin asumsu semakin tinggi temperatur semakin rendah weekly gas comsumption

dependent variable = y, dalam hal ini yg mau di prediksi si gas independent = x

###3. Linear Regression in R lm([target variable])~[predictor variable], data=[data source])

linearModelA <- lm(Gas~Temp, data = whiteside)
#                  gaperlu $
names(linearModelA)
##  [1] "coefficients"  "residuals"     "effects"       "rank"         
##  [5] "fitted.values" "assign"        "qr"            "df.residual"  
##  [9] "xlevels"       "call"          "terms"         "model"

apa yg disimpan dalam linearModelA itu itu semua object

coeffients berisi intercept dan slope

linearModelA$coefficients
## (Intercept)        Temp 
##   5.4861933  -0.2902082
summary(linearModelA)
## 
## Call:
## lm(formula = Gas ~ Temp, data = whiteside)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6324 -0.7119 -0.2047  0.8187  1.5327 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   5.4862     0.2357  23.275  < 2e-16 ***
## Temp         -0.2902     0.0422  -6.876 6.55e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8606 on 54 degrees of freedom
## Multiple R-squared:  0.4668, Adjusted R-squared:  0.457 
## F-statistic: 47.28 on 1 and 54 DF,  p-value: 6.545e-09

semakin banyak bintang semakin bagus -0,29 - berarti slope negatif downward arahnya 0.29 berarti artinya setiap kenaikan 1 derajat akan turun 0.29

jadi persamaannya: y = 5.4862 + (-0.2902 x)