Summary

In this chapter, we skimmed through the basic concepts of statistics. Here is a brief summary of the concepts we learned:

• Hypothesis testing is used to test the statistical significance of a hypothesis. The one which already exists or is assumed to be true is a null hypothesis, the one which someone is not sure about or is being proposed as an alternate premise is an alternate hypothesis.
• One needs to calculate a statistic and the associated p-value to conduct the test.
• Hypothesis testing (p-values) is used to test the significance of the estimates of the coefficients calculated by the model.
• The chi-square test is used to test the causal relationship between a predictor and an input variable. It can also be used to check whether the data is fair or fake.
• The correlation coefficient can range from -1 to 1. The closer it is to the extremes, the stronger is the relationship between the two variables.

Linear regression is part of the family of algorithms called supervised algorithms as the dataset on which they are built has an output variable. In a sense, one can say that this output variable governs or supervises the development of the model and hence the name. More on this is covered in the next chapter.