• Data science allows us to find useful patterns in day to day information
• In this scenario we will be examining the following: Can we predict protein in a meal based only on the total amount of calories?
• In order to do this, we will use something called Simple Linear Regression and see if we can see the relationship

• Simple Linear Regression allows us to create a model between two continuous variables
• The model we work with is the following: \[y = \beta_0 + \beta_1x + \epsilon\]
• Y is our predicted protein value
• x is the calories
• \(\beta_0\) is the y-intercept
• \(\beta_1x\) is the slope

• Since we only have a sample, we will use our data to calculate an estimated regression line \[\hat{y} = b_0 + b_1x\]
• \(\hat{y}\) is our predicted protein value
• \(b_0\) is our estimated intercept
• \(b_1x\) is our estimated slope

macro_model <- lm(Protein ~ Calories, data = nutrition_data)
coef(macro_model)

## (Intercept)    Calories 
##  7.86258729  0.07629223