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 |
| 0.815 | 0.377 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.681 |
| Model: | OLS | Adj. R-squared: | 0.631 |
| Method: | Least Squares | F-statistic: | 13.55 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.82e-05 |
| Time: | 04:04:12 | Log-Likelihood: | -99.950 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 19 | BIC: | 212.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -64.9206 | 86.380 | -0.752 | 0.462 | -245.717 115.875 |
| C(dose)[T.1] | 184.0239 | 120.448 | 1.528 | 0.143 | -68.076 436.124 |
| expression | 16.6029 | 12.010 | 1.382 | 0.183 | -8.535 41.741 |
| expression:C(dose)[T.1] | -18.3431 | 17.410 | -1.054 | 0.305 | -54.783 18.096 |
| Omnibus: | 0.062 | Durbin-Watson: | 1.982 |
| Prob(Omnibus): | 0.970 | Jarque-Bera (JB): | 0.266 |
| Skew: | 0.074 | Prob(JB): | 0.876 |
| Kurtosis: | 2.495 | Cond. No. | 256. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.663 |
| Model: | OLS | Adj. R-squared: | 0.629 |
| Method: | Least Squares | F-statistic: | 19.66 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.90e-05 |
| Time: | 04:04:12 | Log-Likelihood: | -100.60 |
| No. Observations: | 23 | AIC: | 207.2 |
| Df Residuals: | 20 | BIC: | 210.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -2.2849 | 62.841 | -0.036 | 0.971 | -133.368 128.798 |
| C(dose)[T.1] | 57.5369 | 9.774 | 5.887 | 0.000 | 37.149 77.925 |
| expression | 7.8734 | 8.719 | 0.903 | 0.377 | -10.314 26.060 |
| Omnibus: | 0.368 | Durbin-Watson: | 2.002 |
| Prob(Omnibus): | 0.832 | Jarque-Bera (JB): | 0.080 |
| Skew: | 0.142 | Prob(JB): | 0.961 |
| Kurtosis: | 2.947 | Cond. No. | 104. |
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: | 04:04:12 | 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: | 0.079 |
| Model: | OLS | Adj. R-squared: | 0.035 |
| Method: | Least Squares | F-statistic: | 1.790 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.195 |
| Time: | 04:04:12 | Log-Likelihood: | -112.16 |
| No. Observations: | 23 | AIC: | 228.3 |
| Df Residuals: | 21 | BIC: | 230.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 194.2403 | 85.890 | 2.261 | 0.034 | 15.622 372.859 |
| expression | -16.5494 | 12.371 | -1.338 | 0.195 | -42.277 9.178 |
| Omnibus: | 1.511 | Durbin-Watson: | 2.106 |
| Prob(Omnibus): | 0.470 | Jarque-Bera (JB): | 0.970 |
| Skew: | 0.124 | Prob(JB): | 0.616 |
| Kurtosis: | 2.025 | Cond. No. | 87.9 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.408 | 0.535 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.559 |
| Model: | OLS | Adj. R-squared: | 0.438 |
| Method: | Least Squares | F-statistic: | 4.641 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0248 |
| Time: | 04:04:12 | Log-Likelihood: | -69.166 |
| No. Observations: | 15 | AIC: | 146.3 |
| Df Residuals: | 11 | BIC: | 149.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -136.2774 | 189.279 | -0.720 | 0.487 | -552.878 280.323 |
| C(dose)[T.1] | 372.4569 | 212.113 | 1.756 | 0.107 | -94.400 839.314 |
| expression | 32.2455 | 29.913 | 1.078 | 0.304 | -33.594 98.085 |
| expression:C(dose)[T.1] | -50.0862 | 33.116 | -1.512 | 0.159 | -122.974 22.802 |
| Omnibus: | 1.733 | Durbin-Watson: | 1.070 |
| Prob(Omnibus): | 0.421 | Jarque-Bera (JB): | 1.276 |
| Skew: | -0.521 | Prob(JB): | 0.528 |
| Kurtosis: | 2.022 | Cond. No. | 298. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.467 |
| Model: | OLS | Adj. R-squared: | 0.378 |
| Method: | Least Squares | F-statistic: | 5.255 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0230 |
| Time: | 04:04:12 | Log-Likelihood: | -70.582 |
| No. Observations: | 15 | AIC: | 147.2 |
| Df Residuals: | 12 | BIC: | 149.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 121.8965 | 86.057 | 1.416 | 0.182 | -65.606 309.400 |
| C(dose)[T.1] | 52.5063 | 16.324 | 3.217 | 0.007 | 16.939 88.073 |
| expression | -8.6220 | 13.504 | -0.638 | 0.535 | -38.045 20.802 |
| Omnibus: | 2.189 | Durbin-Watson: | 0.755 |
| Prob(Omnibus): | 0.335 | Jarque-Bera (JB): | 1.694 |
| Skew: | -0.731 | Prob(JB): | 0.429 |
| Kurtosis: | 2.242 | Cond. No. | 75.0 |
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: | 04:04:12 | 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: | 0.007 |
| Model: | OLS | Adj. R-squared: | -0.069 |
| Method: | Least Squares | F-statistic: | 0.09492 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.763 |
| Time: | 04:04:12 | Log-Likelihood: | -75.246 |
| No. Observations: | 15 | AIC: | 154.5 |
| Df Residuals: | 13 | BIC: | 155.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 59.9309 | 109.964 | 0.545 | 0.595 | -177.633 297.494 |
| expression | 5.1725 | 16.789 | 0.308 | 0.763 | -31.097 41.442 |
| Omnibus: | 0.581 | Durbin-Watson: | 1.575 |
| Prob(Omnibus): | 0.748 | Jarque-Bera (JB): | 0.581 |
| Skew: | 0.106 | Prob(JB): | 0.748 |
| Kurtosis: | 2.059 | Cond. No. | 72.8 |