<jupyter_start><jupyter_text>**Linear Regression**Used to predict quanitiative (numerical) response.*   Can we determine if a relationship exists between predictors and response?*   How strong is this relationship?*   Which predictors are related to the response?*   How accurate are predictions once a model is fit?*   Is a linear model appropriate?*   Are there interaction effects?  **Simple Linear Regression**This simply means one predictor is linearly related to the response. $Y = \beta_0 + \beta_0X + \epsilon$There are two unknown contants that we need to estimate, the intercept $\beta_0$ and the slope $\beta_1$. Also called coefficients or parameters. Once they are estimated, the estimated fit becomes$\hat{Y} = \hat{\beta_0} + \hat{\beta_1}X$<jupyter_code>import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns 
%matplotlib inline<jupyter_output><empty_output>
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