Description: This calculates the correlation coefficient (r) between the number of victims and time. It measures the strength and direction of the linear relationship. If r is close to 1 or -1, the correlation is strong; if it's near 0, the relationship is weak or nonexistent.
Description: This plots the monthly victim data along with the regression line. It allows visualizing how well the linear model fits the actual data and helps identify the trend over time.
Description: Using the regression model, future values (e.g., first months of 2023) are predicted. This projection helps anticipate trends and can be used for planning or awareness.
Description: This step evaluates how accurate the regression model is by computing the Mean Squared Error (MSE), which indicates the average deviation of predicted values from actual data. A lower MSE implies a better model fit.
Description: It compares the monthly average number of victims in 2021 and 2022 to identify yearly changes. This analysis helps assess whether the situation has improved, worsened, or remained stable.
Description: A histogram is created to show how often different victim counts occurred. This visual tool helps identify common values, patterns, or possible outliers in the data distribution.
Description: This identifies the months with the highest and lowest number of victims. It highlights peak periods, which are useful for focused analysis or preventive actions.