A linear regression problem is one where you have to predict the value of one continuous variable, given the value of one or more other variables (data points); for example, predicting the selling price of a house, given its floor space. You can plot the known features with their associated labels on a simple linear graph in these examples, as in the familiar x, y scatter plots, and plot a line that best fits the data. This is known as a line of best fit. You can then read off the label corresponding to any value of your feature that lies within the x range of the plot.
However, linear regression problems may involve several features in which the terminology multiple or multivariate linear regression is used. In this case, it is not a line that best fits the data, but a plane (two features) or a hyperplane (more than two features). In the house price example...