We talked before about probability using simple sample spaces, but in reality, we will map situations to probabilistic models that cater to all possible results. The variables to which we are going to construct probabilistic models are called random variables.
For any two events A and B, with \(\mathbb{P}(B) > 0\), we define the conditional probability of A given B (\(\mathbb{P}(A|B)\)) as
\[\mathbb{P}(A|B) = \frac{\mathbb{P}(A \cap B)}{\mathbb{P}(B)}\]
Example: We throw a dice once, and it lands on an even number.
Probability is at the heart of statistics, as most of the time, we won’t have the entire dataset to work on. Still, instead, we will need to understand how the population works relying on a sample.
Bidimensional Analysis Previously we talked about understanding the distribution of a single variable, i.e., to summarise the data set. Frequently we are interested in understanding how two or more variables behave together.
Statistical Analysis So, you are starting your journey into the statistical world, maybe because you are interested in analysing data, perhaps because you are working with datasets already and find yourself needing to draw meaningful conclusions from them, or perhaps you just want to know more about statistics.
It’s been a while since I created this blog, but I haven’t figured out what to write and what to share here, but I want to make it a habit of putting my mind and thoughts into words.