AIM Score vs. Gene Expression
Full X range:
Auto X range:
Group Comparisons: Boxplots
CP73
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
F-statistic | p-value | df difference |
1.241 | 0.278 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.690 |
Model: | OLS | Adj. R-squared: | 0.641 |
Method: | Least Squares | F-statistic: | 14.09 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 4.53e-05 |
Time: | 04:30:23 | Log-Likelihood: | -99.641 |
No. Observations: | 23 | AIC: | 207.3 |
Df Residuals: | 19 | BIC: | 211.8 |
Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | -180.9498 | 149.867 | -1.207 | 0.242 | -494.626 132.726 |
C(dose)[T.1] | 265.5043 | 194.076 | 1.368 | 0.187 | -140.702 671.711 |
expression | 27.7497 | 17.672 | 1.570 | 0.133 | -9.237 64.737 |
expression:C(dose)[T.1] | -25.1394 | 22.535 | -1.116 | 0.279 | -72.305 22.026 |
Omnibus: | 0.403 | Durbin-Watson: | 1.503 |
Prob(Omnibus): | 0.817 | Jarque-Bera (JB): | 0.479 |
Skew: | -0.267 | Prob(JB): | 0.787 |
Kurtosis: | 2.535 | Cond. No. | 550. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.670 |
Model: | OLS | Adj. R-squared: | 0.637 |
Method: | Least Squares | F-statistic: | 20.26 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.55e-05 |
Time: | 04:30:23 | Log-Likelihood: | -100.37 |
No. Observations: | 23 | AIC: | 206.7 |
Df Residuals: | 20 | BIC: | 210.1 |
Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | -49.9388 | 93.675 | -0.533 | 0.600 | -245.341 145.464 |
C(dose)[T.1] | 49.2379 | 9.271 | 5.311 | 0.000 | 29.898 68.578 |
expression | 12.2898 | 11.032 | 1.114 | 0.278 | -10.723 35.303 |
Omnibus: | 0.348 | Durbin-Watson: | 1.619 |
Prob(Omnibus): | 0.840 | Jarque-Bera (JB): | 0.335 |
Skew: | -0.246 | Prob(JB): | 0.846 |
Kurtosis: | 2.672 | Cond. No. | 193. |
Model:
AIM ~ C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.649 |
Model: | OLS | Adj. R-squared: | 0.632 |
Method: | Least Squares | F-statistic: | 38.84 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.51e-06 |
Time: | 04:30:23 | Log-Likelihood: | -101.06 |
No. Observations: | 23 | AIC: | 206.1 |
Df Residuals: | 21 | BIC: | 208.4 |
Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 54.2083 | 5.919 | 9.159 | 0.000 | 41.900 66.517 |
C(dose)[T.1] | 53.3371 | 8.558 | 6.232 | 0.000 | 35.539 71.135 |
Omnibus: | 0.322 | Durbin-Watson: | 1.888 |
Prob(Omnibus): | 0.851 | Jarque-Bera (JB): | 0.485 |
Skew: | 0.060 | Prob(JB): | 0.785 |
Kurtosis: | 2.299 | Cond. No. | 2.57 |
Model:
AIM ~ expression
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.204 |
Model: | OLS | Adj. R-squared: | 0.166 |
Method: | Least Squares | F-statistic: | 5.368 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0307 |
Time: | 04:30:23 | Log-Likelihood: | -110.49 |
No. Observations: | 23 | AIC: | 225.0 |
Df Residuals: | 21 | BIC: | 227.2 |
Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | -227.1615 | 132.613 | -1.713 | 0.101 | -502.946 48.623 |
expression | 35.5440 | 15.342 | 2.317 | 0.031 | 3.639 67.449 |
Omnibus: | 2.070 | Durbin-Watson: | 2.164 |
Prob(Omnibus): | 0.355 | Jarque-Bera (JB): | 1.500 |
Skew: | 0.424 | Prob(JB): | 0.472 |
Kurtosis: | 2.080 | Cond. No. | 180. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
F-statistic | p-value | df difference |
0.070 | 0.796 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.469 |
Model: | OLS | Adj. R-squared: | 0.324 |
Method: | Least Squares | F-statistic: | 3.236 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0644 |
Time: | 04:30:23 | Log-Likelihood: | -70.555 |
No. Observations: | 15 | AIC: | 149.1 |
Df Residuals: | 11 | BIC: | 151.9 |
Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | -51.1807 | 307.669 | -0.166 | 0.871 | -728.356 625.994 |
C(dose)[T.1] | 259.4161 | 355.379 | 0.730 | 0.481 | -522.769 1041.601 |
expression | 16.0019 | 41.478 | 0.386 | 0.707 | -75.290 107.294 |
expression:C(dose)[T.1] | -28.1911 | 47.732 | -0.591 | 0.567 | -133.248 76.866 |
Omnibus: | 3.674 | Durbin-Watson: | 1.039 |
Prob(Omnibus): | 0.159 | Jarque-Bera (JB): | 1.964 |
Skew: | -0.882 | Prob(JB): | 0.375 |
Kurtosis: | 3.179 | Cond. No. | 501. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.452 |
Model: | OLS | Adj. R-squared: | 0.361 |
Method: | Least Squares | F-statistic: | 4.948 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0271 |
Time: | 04:30:23 | Log-Likelihood: | -70.789 |
No. Observations: | 15 | AIC: | 147.6 |
Df Residuals: | 12 | BIC: | 149.7 |
Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 106.6100 | 148.395 | 0.718 | 0.486 | -216.714 429.934 |
C(dose)[T.1] | 49.7433 | 15.829 | 3.143 | 0.008 | 15.255 84.232 |
expression | -5.2861 | 19.960 | -0.265 | 0.796 | -48.776 38.204 |
Omnibus: | 2.635 | Durbin-Watson: | 0.847 |
Prob(Omnibus): | 0.268 | Jarque-Bera (JB): | 1.675 |
Skew: | -0.810 | Prob(JB): | 0.433 |
Kurtosis: | 2.757 | Cond. No. | 145. |
Model:
AIM ~ C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.449 |
Model: | OLS | Adj. R-squared: | 0.406 |
Method: | Least Squares | F-statistic: | 10.58 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00629 |
Time: | 04:30:23 | Log-Likelihood: | -70.833 |
No. Observations: | 15 | AIC: | 145.7 |
Df Residuals: | 13 | BIC: | 147.1 |
Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 67.4286 | 11.044 | 6.106 | 0.000 | 43.570 91.287 |
C(dose)[T.1] | 49.1964 | 15.122 | 3.253 | 0.006 | 16.527 81.866 |
Omnibus: | 2.713 | Durbin-Watson: | 0.810 |
Prob(Omnibus): | 0.258 | Jarque-Bera (JB): | 1.868 |
Skew: | -0.843 | Prob(JB): | 0.393 |
Kurtosis: | 2.619 | Cond. No. | 2.70 |
Model:
AIM ~ expression
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.001 |
Model: | OLS | Adj. R-squared: | -0.076 |
Method: | Least Squares | F-statistic: | 0.01273 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.912 |
Time: | 04:30:23 | Log-Likelihood: | -75.293 |
No. Observations: | 15 | AIC: | 154.6 |
Df Residuals: | 13 | BIC: | 156.0 |
Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 72.0381 | 191.968 | 0.375 | 0.714 | -342.683 486.759 |
expression | 2.8964 | 25.671 | 0.113 | 0.912 | -52.563 58.356 |
Omnibus: | 0.620 | Durbin-Watson: | 1.629 |
Prob(Omnibus): | 0.733 | Jarque-Bera (JB): | 0.588 |
Skew: | 0.055 | Prob(JB): | 0.745 |
Kurtosis: | 2.036 | Cond. No. | 144. |