Now have all the necessary functions to calculate the slope, intercept, best-fit line, prediction and visualizations. In this lab you will put them all together to run a regression experiment and calculate model loss.

• Run a complete regression analysis through code only.

Use the functions created earlier to implement these formulas to run a regression analysis using X and Y as input variables.

# Combine all the functions created so far to run a complete regression experiment. 
# Produce an output similar to the one shown below. 

X = np.array([1,2,3,4,5,6,7,8,9,10], dtype=np.float64)
Y = np.array([7,7,8,9,9,10,10,11,11,12], dtype=np.float64)

# Basic Regression Diagnostics
# ----------------------------
# Slope: 0.56
# Y-Intercept: 6.33
# R-Squared: 0.97
# ----------------------------
# Model: Y = 0.56 * X + 6.33

Basic Regression Diagnostics
----------------------------
Slope: 0.56
Y-Intercept: 6.33
R-Squared: 0.97
----------------------------
Model: Y = 0.56 * X + 6.33

Predict and plot the value of y using regression line above for a new value of x = 4.5.

# Make prediction using given value and visualize on the scatter plot

Load the "heightWeight.csv" dataset. Use the height as an independant and weight as a dependant variable and draw a regression line to data using your code above. Calculate your R-square for the model and try to predict new values of Y.

In this lab, we ran a complete simple regression analysis experiment using functions created so far. Next We shall see how we can use python's built in modules to perform such analyses with a much higher level of sophistication.