simple linear regression

calories_consumed<- read.csv(file.choose())
View(calories_consumed)
attach(calories_consumed)
summary(calories_consumed)
mean(Weight.gained..grams.)
mean(Calories.Consumed)
median(Weight.gained..grams.)
median(Calories.Consumed)

reg<- lm(Calories.Consumed~Weight.gained..grams.)
summary(reg)
confint(reg, level = 0.95)
predict(reg,interval="predict")

# R^2 value 0.8968, adjusted R^2 0.8882


reg_xlog<- lm(Calories.Consumed~log(Weight.gained..grams.))
summary(reg_xlog)
confint(reg_xlog, level = 0.95)
predict(reg_xlog,interval="predict")

# M.R^2 0.877  A R^2 0.8674


reg_ylog<- lm(log(Calories.Consumed)~Weight.gained..grams.)
summary(reg_ylog)
confint(reg_ylog, level = 0.95)
predict(reg_ylog,interval="predict")


#  M.R^2 0.8077   A R^2 0.7917

reg_xylog<- lm(log(Calories.Consumed)~log(Weight.gained..grams.))
summary(reg_xylog)
confint(reg_xylog, level = 0.95)
predict(reg_xylog,interval="predict")


# best model

reg<- lm(Calories.Consumed~Weight.gained..grams.)
summary(reg)
confint(reg, level = 0.95)
predict(reg,interval="predict")

# R^2 value 0.8968, adjusted R^2 0.8882