In the context of this project, I will test a hypothesis related to the musical preferences of two cities. To do this, I will analyze data from an online music streaming service to test the hypothesis presented below and compare the behavior of users in these two cities.
This will involve analyzing data from a real streaming service to compare the behavior of users in Springfield and Shelbyville. The project is divided into three stages, each of which has its own specific objectives.
In Stage 1, I will provide an overview of the data and comment on my observations.
In Stage 2, I will preprocess the data by cleaning it.
Finally, in Stage 3, I will test the hypothesis by taking the necessary programming steps to test each statement and commenting on their results in the appropriate blocks.
For this project, we gathered the requirements and prepared a hypothesis that we need to confirm or reject.
When testing hypotheses, it is important to realize that they can be fully accepted, partially accepted, partially rejected, or completely rejected.
When a hypothesis is fully accepted, it means that the test results confirm the statement made about the population without any doubt.
If it is partially accepted, it means that the results confirm the statement to some extent, but not enough to fully accept it.
On the other hand, if a hypothesis is completely rejected, it means that the test results do not confirm the statement made about the population.
Finally, a hypothesis can also be partially rejected if the data indicates that it is false, but you cannot completely reject it. When interpreting the results of a hypothesis test, it is important to consider all of these different possibilities.
Here is the hypothesis that we need to accept or reject:
User activity is different depending on the day of the week and the city.