1. Correlation tells us how strongly the pair of variables are linearly related and change together. Causation says any change in the value of one variable will cause a change in the value of another variable. An example would be swimsuit sales increasing and boot sales decreasing, the sales of swimsuits are not going up because boot sales are going down, and one has nothing to do with the other. Swimsuit sales are going up because the weather is warming up and same for boot sales decreasing.

2. Plotting the data gives us a lot better insight of data than a regression line. Here, the relationship might have been exponential (which typically is the case with growth), and fitting a linear regression line will underestimate the produce. Further, the model is being used for prediction for upcoming year, i.e., extrapolation, but regression model is only valid for interpolation.

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