So, you think you know linear regression? I have likely run thousands of regression models as a researcher and data analyst. However, a recent job interview made me think about some finer details of linear regression. As a consequence, I wanted to brush up on the topic. The outcome is a series of posts on linear regression. This is the first part and provides the foundations of linear regression.
This project uses time series analysis and forecasting techniques to explore crime data during the COVID-19 pandemic, while also considering the weather.
Did COVID-19 prevent homicides?
Admittedly, that header is a bit sensational, but when I analyzed Boston’s crime data during Covid-19, the drop in reported offenses was quite astonishing. While verbal dispute offenses sky-rocketed, and more people set out to rob a bank, the overall number of crimes dropped by more than half. So indeed, the lock-down prevented harm by reducing the risk of catching the virus and also decreased the probability of getting hit by a car or bullet.
I am writing this at a time when COVID-19 paralyzes the world. The fatalities, geographic patterns, and economic impact of the disease are subject to fantastic visualizations elsewhere.
However, the lockdown in response to the disease has an impact on almost all aspects of our life and, hence, creates striking patterns in otherwise consistent data.
Here, we’ll take a look at other (equally sad) graphs—the crime statistics in Boston. Did the number of reported offenses change during COVID-19? Did the occurrence of specific offenses vary in comparison to other periods?