T’(x) = T = 3X^2=64153.55415 K

T’’(x) = P = 6X = 877.4067755 atm

T’’’(x) = V = 6 = 6.000000000 L

n = .999999999 mol

T’(x) = T = 9X^2+5 = 1,732,145.962 K

T’’(x) = P = 18X = 7,896.66098 atm

T’’’(x) = V = 18 = 18.000000000 L

n = 1.000000000 mol

T’(x) = T = 3X^2+2X = 64,446.02308 K

T’’(x) = P = 6X+2 = 877.4067755 atm

T’’’(x) = V = 6 = 6.000000000 L

n = .9954618 mol

T’(x) = T = 3X^2+2 = 63,861.08522 K

T’’(x) = P = 6X = 875.4067755 atm

T’’’(x) = V = 6 = 6.000000000 L

n = 1.002289884 mol

T’(x) = T = (3X^2/3)-2 = 2374.057561 K

T’’(x) = P = 6X/3 = 97.48964173 atm

T’’’(x) = V = 2 = 2.000000000 L

n = 1.00084244 mol

T’(x) = T = (X^2/2)-2X = 248.2623742 K

T’’(x) = P = X-2 = 91,794/4103 atm

T’’’(x) = V = 1 = 1.000000000 L

n = 1.098171987 mol

T’(x) = T = X^2/2 = 297.0071951 K

T’’(x) = P = X = 24.37241043 atm

T’’’(x) = V = 1 = 1.000000000 L

n = 1.000000000 mol

T’(x) = T = (X^2/2)+1 = 298.0071951 K

T’’(x) = P = X = 24.37241043 atm

T’’’(x) = V = 1 = 1.000000000 L

n = .996644376 mol

T’(x) = T = (X^2/3)+8X = 217.9883209 K

T’’(x) = P = ((2X)/3)+8 = 18.83218241 atm

T’’’(x) = V = 2/3 = 2/3 L

n = .701850566 mol

T’(x) = T = (X^2/3)+1 = 88.00213189 K

T’’(x) = P = (2X/3) = 10.83218241 atm

T’’’(x) = V = 2/3 = 2/3 L

n = 1.0000000000 mol

T’(x) = T = (X^2/3) + 2X = 120.4986791 K

T’’(x) = P = 2X/3 + 2 = 10.83218241 atm

T’’’(x) = 2/3 = 2/3 L

n = .730316154 mol

T’(x) = T = (X^2/2)+2X = 345.752016 K

T’’(x) = P = X = 26.37241043 atm

T’’’(x) = 1 = 1.000000000 L

n = .929508985 mol

T’(x) = T = 30X^2+X = 64,155,016.49 K

T’’(x) = P = 60X+1 = 87,740.67755 atm

T’’’(x) = V = 60 = 60.000000000 L

n = .999977082 mol

Over a thirteen member data set with no omissions for error, there is a standard deviation of

0.121197852.

However, when three of the set are discarded because the approximation based on the first term

becomes less valid as the second term increases, the standard deviation is reduced to 0.032788121.

An example might be when X^3/large term + large term * X^2 could be predicted to cause a large

deviation in the approximation used here to solve involving the X^3 term because large term*X^2

becomes more significant in the equation. These became easy to identify in the sample problems

and were omitted for obvious systematic error .

In three cases, the number of moles was 1.000000000 to nine digits past the decimal on the

calculator, and those are the results I am reporting with a standard deviation of 0.000000000.

There was absolutely no error to the limits of the calculator for these three problems.

d2Td3T=nRdT=McdT is confirmed to be the theory of everything because out of a thirteen

member data set using mathematical approximations in most instances, three sets of Pressures,

Temperatures and Volumes did 100% satisfy the ideal gas law equation, by inspection, with a

0.000000000 standard of deviation from a standard true value 1.000000000 to nine digits precision.

d2Td3T=nRT=McdT is confirmed as the theory of everything. Blindly applying this formula over

a thirteen member data set gave a standard deviation of .121197852. Using a little mathematical

savvy, one may learn to identify mathematical sources of error and omit three of the data set giving

a standard deviation of .032788121.

However, any deviation at all is unacceptable as the point is not how good I am as a

mathematician at calculating these values but do they actually hit the mark? The answer is

unequivocally that they do, three members of my data set hit the mark right on with a standard

deviation of 0.0000000000. I am definitely calculating data sets of Temperature, Pressure, and

Volume that satisfy the ideal gas equation using a formula and derivatives for the first time ever.

The theory of everything is that different data sets of Temperatures, Pressures, and Volumes at a

constant mass may be represented using a T(x) equation and the formula d2Td3T=nRdT=McdT.

The data here supports this finding by giving three good examples of Temperature, Pressure, and

Volume that 100% satisfy the Ideal gas law equation with a 0.000000000 standard of deviation, and

further supports this finding over a ten member data set with a low standard deviation of

.032788121 using close mathematical approximations. Temperature, Pressure and Volume are

100% with a 0.000000000 standard of deviation related via an f(x) equation as T=f’(x), P=f’’(x),

and V=f’’’(x) using the formula d2Td3T=nRdT=McdT, the mathematical expression of the theory

of everything.