A valuable function not provided by the Microsoft spreadsheet program Excel is calculating the area under a curve. This basic function is needed for many quantitative analysis problems when the data does not fit a defined algebraic function.
Credit: Clipped from a photo by Isaac Smith on Unsplash1
We have previously1 used the CPI data available since 1913 to create annualized inflation data aligned with the federal fiscal years from 1914 to 2022. In this post, we use the timeline-matched data to examine leads or lags that may exist between the two variables – government spending and inflation.
Credit: Photo by Ann H from Pexels
In previous posts on this topic,1,2,3 we have looked at the historical records of inflation and deficit spending by the U.S. federal government. The changing dates for the government’s fiscal year over the country’s history confounded the correlation analysis of that data. Since 1913, U.S. inflation data has been recorded monthly. In this post, we will use the data since 1913 to align the timelines of the two variables.
This post will address how data sampling affects the analysis results for correlations between U.S. deficit spending and inflation. In Part 11of this series, it was seen that there is significant variability in this correlation over time. It is essential to know whether that variability is affected by changing the data sampling structure. If changes are connected to data treatment, care must be taken to ensure conclusions are fundamental to the overall data and not an artifact of how it is organized.
Image credit: Gerd Altmann from Pixabay2
With all the agitation for gun control to reduce violence in the US, a key point is missed. Over a quarter of US homicides occur by means other than firearms. Even if all gun-related murders are eliminated, the US still has a higher homicide rate than many other countries.
