Approximate a log curve as linear to predict a value

Hello there,

I have the following code:

clf
P = [0.01; 0.1; 1; 3; 5; 10; 25; 50; 75; 90; 95; 97; 99; 99.9];
phi_1 = [5.28; 4.10; 2.82; 2.15; 1.82; 1.33; 0.59; -0.12; -0.72; -1.18; -1.42; -1.57; -1.81; -2.14];
Cs_1 = Cv_Q
K_p_1 = Cv_Q.*phi_1 + ones(length(phi_1),1);
Q_p_1 = meanQ*K_p_1;
semilogx(P, Q_p_1)

Which produces this curve

My plan is to approximate a line from P = 0.01 and P = 0.1 and then plot this line to predict the Q_p value at P = 0.01.

What is the best way to go about this?

Thanks,

Brian

[Are Mjaavatten]

You have not given values for Cv_Q and meanQ. And what is this Q_p that you want to approximate?

[Brian Robinson]

Hi Are,

Cv_Q = 0.7129

Cs_Q = 2.0790

And that should read Q_p_1.

[Adam Danz]

What is this?

meanQ*K_p_1

[Brian Robinson]

Hi Adam,

meanQ = 2775

K_p =

4.76406175741670

3.92285098587282

3.01035116589301

2.53271454137233

2.29746068153379

1.94814434419777

1.42060538577194

0.914453141876893

0.486718851261359

0.158789228456116

-0.0123044877900973

-0.119238060443981

-0.290331776690195

-0.525585636528738

Answer 1

[Matt J]

idx=(P<=0.1);

c=polyfit(log10(P(idx)),Q_p_1(idx),1);

semilogx(P, Q_p_1, P,polyval(c,log10(P)),'x-'  )