Angelina Tobey Apr 2026

Angelina Tobey lived during the mid-1800s to the early 1900s. She is documented in genealogical records such as Find a Grave :

: She was the daughter of Nathaniel Wheeler Tobey (1810–1892). She grew up in a large family with several siblings, including Avery, Samuel Jasper, John Henry, and Ruth Tobey. angelina tobey

: A separate individual born in 1831 and deceased in 1914. Angelina Tobey lived during the mid-1800s to the early 1900s

: Passed away on March 20, 1920, at the age of 75 in Moran, Shackelford County, Texas. : A separate individual born in 1831 and deceased in 1914

: She married into the Dennis family, becoming Angelina Tobey Dennis . Her husband was Hilley Dennis, and they had children including Samuel Dennis (1867–1956). Additional Entities with Similar Names

: Listed in 2026 financial records regarding unclaimed funds in the City of Houston.

Search results also indicate other individuals with the same or similar names who may appear in modern records:

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: