PolymathPlus Report
📊   Multiple linear regression 2022-04-01 12:48 

# Example 28 - Linear Regression. Pressure Data.
# Verified Solution: a0 = 8.75201, a1 = 2035.33
# Ref.: Comput. Appl. Eng. Educ. 6: 173, 1998

[
TC P
-36.7 1
-19.6 5
-11.5 10
-2.6 20
7.6 40
15.4 60
26.1 100
42.2 200
60.6 400
80.1 760
]
logP=log(P)
TK=TC+273.15
neginvTK = -1/TK

mlinfit neginvTK logP


logP = a0 + a1neginvTK

Variable Value 95% confidence
a0 8.75201 0.54233497
a1 2035.331 153.62831

R^2   R^2adj   Rmsd   Variance  
0.9915016    0.9904393    0.024644    0.0075916   

Regression Plot

 0 1 2 3 -1 0 1 2 3 4 logP y=x Line logP Calc



Residuals Plot

 -0.15 -0.1 -0.05 0 0.05 0.1 0 1 2 3 logP Zero Line logP - logPCalc



Source data points and calculated data points

  neginvTK logP logP calc Delta logP
1 -0.00422922 0 0.14413927 -0.14413927
2 -0.003944 0.69897 0.72467403 -0.02570403
3 -0.0038219 1 0.97317937 0.02682063
4 -0.00369617 1.30103 1.2290714 0.07195858
5 -0.00356189 1.60206 1.5023892 0.09967078
6 -0.0034656 1.7781513 1.6983589 0.07979239
7 -0.00334169 2 1.9505696 0.04943039
8 -0.00317108 2.30103 2.297813 0.00321704
9 -0.00299625 2.60206 2.6536399 -0.05157986
10 -0.00283086 2.8808136 2.9902802 -0.10946665

General

Number of independent variables = 1
Regression including a free parameter
Number of observations = 10