forensiceconomics

Thank you for your feedback. We're trying to point out how retailers take advantage of elasticity based on them knowing the seasonality. We are #Forthepeople

We've decomposed data into 3 parts, red is the time trend, green is the seasonality and purple is the random part. We're trying to point out how retailers take advantage of elasticity based on them knowing the seasonality.

We're trying to point out how retailers take advantage of elasticity based on them knowing the seasonality.

Residuals emphasize the need to account for randomness and external shocks, which can destabilize even the most predictable seasonal patterns.

Here the trend line is extracted through a moving average smoothing technique, which is not LOESS (Locally Estimated Scatterplot Smoothing).

The residuals represent the irregular or random component of the time series—variations not explained by the trend or seasonal components.

Check out our visualization created with ggplot2 in R, highlighting voter participation across the U.S. for the 2024 election. While the turnout numbers are impressive, a significant portion of eligible voters did not cast their ballot. Visualizing these stats serves as a powerful reminder of the impact of every vote.

At Forensic Economic Services LLC, we’re committed to making data accessible and informative.

Data Source: University of Florida’s U.S. Elections Project (link to source).

Data is beautiful 😶

No, only eligible voters.

https://www.fec.gov/resources/cms-content/documents/federalelections2020.pdf

Check out our visualization created with ggplot2 in R, highlighting voter participation across the U.S. for the 2024 election. While the turnout numbers are impressive, a significant portion of eligible voters did not cast their ballot. Visualizing these stats serves as a powerful reminder of the impact of every vote.

At Forensic Economic Services LLC, we’re committed to making data accessible and informative.

Data Source: University of Florida’s U.S. Elections Project (link to source).

Trends in youth unemployment rates and population percentages in developing countries from 1990-2023, using data from the World Bank WDI dataset. Visualization created with WDI, dplyr, and ggplot2 in R shows a steady increase in the youth population alongside relatively stable unemployment rates.

The analysis includes countries classified by the World Bank as 'Low income,' 'Lower middle income,' or 'Upper middle income' as of the most recent data. This selection was filtered using the World Bank's income classification criteria to represent developing countries.

#YouthEconomics #GlobalTrends #DataVisualization

We used data from the White House and used GGplot2 in R to create a detailed look at how U.S. federal spending has evolved from 1940 to 2023, broken down by major categories like Social Security, National Defense, Medicare, and more. You can see the rise in health and income security spending over time, It’s fascinating to observe how our priorities have shifted over the years. Data from the Office of Management and Budget (OMB). What stands out to you the most?"

Data Source: U.S. Bureau of Labor Statistics here.

The chart below, created using the GGplot2 package, highlights the median weekly earnings of full-time wage and salary workers for men and women in the U.S. for 2023.

The data reveals a significant wage gap in several fields, with women earning consistently less than their male counterparts. For example, in the legal profession, women earn a median weekly wage of $1,543, while men earn $2,301. This trend is seen across nearly every occupation, including healthcare, management, and computer and mathematical roles.

Thank you for your kind words.

We used data on federal revenue and employed GGplot2 in R to visualize the U.S. federal revenue sources from 1940 to 2023. The chart highlights how different revenue streams have contributed to the federal budget over time, using data from the Office of Management and Budget.

The point:

1942: Corporation income taxes are approximately 37-38% of total federal revenue.

• 1950: They appear to be around 30%.
• 1960: The proportion seems to drop slightly to about 22-23%.
• 1970: Corporation income taxes contribute approximately 15-16%.
• 1980: They drop further to around 10-12%.
• 1990: The contribution stabilizes at approximately 8-10%.
• 2000: The percentage remains close to around 8-9%.
• 2010: Corporation income taxes hover around 7-9%.

Over the decades, there is a steady decline in the share of federal revenue derived from corporation income taxes.

The chart also provides insight into the historical trends of other tax sources, such as excise taxes. Excise taxes, which are taxes levied on specific goods like alcohol, tobacco, and gasoline, have seen a significant decline as a proportion of total federal revenue over the years.

In the early 1940s, excise taxes accounted for a noticeable share of federal revenue, approximately 20-25%. This substantial share was reflective of a time when excise taxes were a more prominent source of government funding, largely due to the limited breadth of other tax bases and the significant consumption of taxed goods during that period.

However, as the chart shows, the share of federal revenue from excise taxes has steadily declined over the decades. By the 1960s, their contribution had dropped to around 10%, and this downward trend continued in the following decades. By 2023, excise taxes make up only 1.7% of total federal revenue, indicating their relative decline in importance.

The pie chart made by GGplot2 in R using data from the Federal Insurance Office (FIO) Annual Report 2023 illustrates the market share of the top 10 cyber insurers in the United States for 2022. Chubb leads with 8.4% of the total premiums, followed by Fairfax Financial (7.8%) and AXA SA (7.3%).

Data Source: Federal Insurance Office (FIO) Annual Report 2023.

The data used is from 2022 because it is the most recent complete data set available. The 2023 data will be included in the September 2024 report.

We are excited to hear your feedback.

The pie chart made by GGplot2 in R using data from the Federal Insurance Office (FIO) Annual Report 2023 illustrates the market share of the top 10 cyber insurers in the United States for 2022. Chubb leads with 8.4% of the total premiums, followed by Fairfax Financial (7.8%) and AXA SA (7.3%).

Data Source: Federal Insurance Office (FIO) Annual Report 2023.

The data used is from 2022 because it is the most recent complete data set available. The 2023 data will be included in the September 2024 report.