# Basic Linear Regression in R: Dependent and Independent Variables [duplicate]

This is probably a very basic question but I cannot find the answer after experimenting a lot with it. I am using the Davis dataset from https://vincentarelbundock.github.io/Rdatasets/datasets.html (Self-Reports of Height and Weight) from 200 individuals. The question that I am trying to answer is what weight is to be expected for a specific height. My first assumption here is that weight depends on height, so I am building my model using

model=lm(davis$weight~davis$height)


As far as I understand, the independent variable is on the right whereas the dependent is on the left, right? Now, the summary of the model is

Call:
lm(formula = davis$weight ~ davis$height)

Residuals:
Min      1Q  Median      3Q     Max
-19.658  -5.381  -0.555   4.807  42.894

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)  -130.91040   11.52792  -11.36   <2e-16 ***
davis$height 1.15009 0.06749 17.04 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 8.505 on 198 degrees of freedom Multiple R-squared: 0.5946, Adjusted R-squared: 0.5925 F-statistic: 290.4 on 1 and 198 DF, p-value: < 2.2e-16  and I am assuming that I can calculate weight = (height*1.15009) -130.91040 So, assuming this is correct, I was approached by a colleague who said that height does not predict weight but vice versa. However, changing the model to model=lm(davis$weight~davis$height)  gives me different results when I use the values of > summary(model) Call: lm(formula = davis$height ~ davis$weight) Residuals: Min 1Q Median 3Q Max -18.3492 -3.7151 -0.2466 3.5349 20.8802 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 136.83054 2.02039 67.72 <2e-16 *** davis$weight   0.51696    0.03034   17.04   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 5.702 on 198 degrees of freedom
Multiple R-squared:  0.5946,    Adjusted R-squared:  0.5925
F-statistic: 290.4 on 1 and 198 DF,  p-value: < 2.2e-16


and calculate height = (weight*0.517) + 136.831. For example, according to the first model, a person with 190 centimeters is expected to weigh 87.6067 kilograms where when I enter these kilos into the second model, that person is expected to be about 182 centimeters tall. Any advice or a good book where I can really understand this? Thank you in advance!