Today we are collecting more data than ever but making sense out of the data is very challenging. Machine learning helps us analyze the data and build an analytical model to help us with future prediction. To create a machine learning prediction model we usually develop a hypothesis based on our observation of the collected data and fine tune the model to reduce the cost function by training the model. One of the very simple hypothesis we can develop by assuming a linear relationship between input and output parameters. Suppose we have data on housing price and we assume that housing prices are linearly related to the floor area of the house then we can use linear regression to predict the price of a house with specific floor area. One of the most important steps towards building the hypothesis is being able to visualize the data and understand the trend. So let first draw a scatter plot of our data as shown in the figure below. I am using Grapher package to draw the scatter p...