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: The most frequent causes include alcoholic liver disease, non-alcoholic fatty liver disease (NAFLD), viral hepatitis (A, B, and C), and congestive heart failure.

Hépatomégalie (hepatomegaly) is a clinical sign rather than a standalone disease; it indicates that the liver has enlarged beyond its normal size. In adults, a liver is typically considered enlarged when its vertical span exceeds on the midclavicular line or 3 cm on the xiphoid-umbilical line. Understanding Hepatomegaly Download HГ©patomГ©galie pdf

The liver is an essential organ responsible for filtering toxins, processing nutrients, and aiding digestion. When it swells, it is often a response to inflammation, infection, or underlying systemic issues. : The most frequent causes include alcoholic liver

Hepatomegaly (Enlarged Liver) Medically Reviewed.Last updated on 03/26/2025. An enlarged liver is a symptom of underlying disease. Cleveland Clinic Understanding Hepatomegaly The liver is an essential organ

: Hepatomegaly can also signal more severe conditions such as primary liver cancer (hepatocellular carcinoma), leukemia, or rare genetic storage diseases like Gaucher or Niemann-Pick disease. Diagnostic Resources

As with birth rates, we use data for 4 categories of countries from 1990 to 2015 (100 observations total). We have two explanatory variables, AGE and Y, where AGE is defined as the percentage of the population aged over 65 and Y is per capita GDP.

After eyeballing the scattergrams, we test the following functional form:

d = (minY^a)/Y^a * (1/AGE^g)

Where minY is the constant equal to the smallest value of Y in the series.

Logarithmic transformation gives:

ln(d) = ln(minY^a) – a*ln(Y) – g*ln(AGE)

which we test on the data using OLS. Here are the results:

Adjusted R square: 75.191

Intercept coefficient: 7.37384
t-Stat: 20.4011

Y coefficient: -1.01444
t-Stat: -13.1059

AGE coefficient: 2.0097
t-Stat: 11.5208

The estimated intercept is a good, but not perfect, approximation of ln(minY^a)

Here are the fitted against actual values of the scattergram for death rate against per capita GDP:

While the results are not as good as with the birth rates calculations, it is nevertheless a good enough fit and the explanatory variables have a strong enough confidence factor to be usable in our estimations.

We begin by examining the scatter of data for 100 observations of per capita GDP and per capita emissions for 4 categories of countries, over 25 years (1990 – 2015).

The scatter suggests a cubic functional form, so we test:

GHG = a + b*Y + c*Y^2 + d*Y^3

where GHG are per capita emissions of GHG, and Y is per capita GDP.

The results from OLS regression are:

Adjusted R square: 0.980438073

coefficient a: 1090
t-stat a: 3.06

coefficient b: 0.709310153
t-Stat b: 8.241453

coefficient c: -0.0000047025
t-Stat c: -1.01233

coefficient d: -0.000000000105314
t-Stat d: -1.47005

While the t-scores on the squared and cubed terms are low, the number of observations are also limited.

Here is the plot of the fitted against actual values: