Regression Model
y = a * x / (b + x)
where a, b are the variables to find based on y = f(x)
| Model Variable | Initial Guess | Value | 95% confidence |
|---|---|---|---|
| a | 2 | 1.3275313 | 0.0269902 |
| b | 1 | 0.026461537 | 0.00285336 |
| R² | R²adj | Rmsd | Variance |
|---|---|---|---|
| 0.998802 | 0.998502 | 0.00440595 | 0.000174712 |
Nonlinear Regression Fit
Nonlinear Regression y Residuals
Data Table
| x | y | Calculated y | Residual % | |
|---|---|---|---|---|
| 1 | 0.5 | 1.255 | 1.2608056 | -0.4626% |
| 2 | 0.387 | 1.25 | 1.2425693 | 0.5945% |
| 3 | 0.24 | 1.189 | 1.1956979 | -0.5633% |
| 4 | 0.136 | 1.124 | 1.1113046 | 1.129% |
| 5 | 0.04 | 0.783 | 0.79897717 | -2.041% |
| 6 | 0.011 | 0.402 | 0.38980899 | 3.033% |
Settings and Hints
| Name/Source | Value | Type | |
|---|---|---|---|
| 1 | Algorithm | LM | Setting |
| 2 | Sample size | 6 | Setting |
| 3 | Model vars | 2 | Setting |
| 4 | Indep vars | 1 | Setting |
| 5 | Iterations | 9 | Setting |
| 6 | Elapsed time | 0.784 ms | Setting |
| 7 | #@NLR_SOLUTION_METHOD_INDEX | 1 | Setting |
| 8 | #@NLR_maxiter | 128 | Setting |
| 9 | #@NLR_miniter | 1 | Setting |
| 10 | Version | 7.0.73 | Setting |
| 11 | #@report_fix_digits = 8 | Default (integer) | |
| 12 | #@chart_size = 428,300 | Default (array) | |
| 13 | #@report_show_charts = true | Default (boolean) | |
| 14 | #@report_show_source = true | Default (boolean) | |
| 15 | #@report_show_header = false | Default (boolean) | |
| 16 | #@report_show_settings = true | Default (boolean) | |
| 17 | #@report_show_data_points = true | Default (boolean) | |
Messages
| Severity | Description | |
|---|---|---|
| 1 | Info | -Infor- 2 Known vectors variables (x, y) |
| 2 | Info | -Infor- 0 Explicit expressions |
| 3 | Info | -Infor- 2 Model Variables (, a, b) |
| 4 | Info | -Infor- Regression command: nlinfit y = a * x / (b + x) |
Source
# Example: Nonlinear Regression
[
x y
0.5 1.255
0.387 1.25
0.24 1.189
0.136 1.124
0.04 0.783
0.011 0.402
]
# Nonlinear regression model
nlinfit y = a * x / (b + x)
# Initial guess of the
# regression model variables
m(a)= 2
m(b)= 1
[
x y
0.5 1.255
0.387 1.25
0.24 1.189
0.136 1.124
0.04 0.783
0.011 0.402
]
# Nonlinear regression model
nlinfit y = a * x / (b + x)
# Initial guess of the
# regression model variables
m(a)= 2
m(b)= 1