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 |
| 4.889 | 0.039 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.719 |
| Model: | OLS | Adj. R-squared: | 0.674 |
| Method: | Least Squares | F-statistic: | 16.17 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.84e-05 |
| Time: | 23:01:28 | Log-Likelihood: | -98.527 |
| No. Observations: | 23 | AIC: | 205.1 |
| Df Residuals: | 19 | BIC: | 209.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -21.8143 | 46.389 | -0.470 | 0.644 | -118.908 75.279 |
| C(dose)[T.1] | 68.5171 | 63.809 | 1.074 | 0.296 | -65.036 202.070 |
| expression | 12.8090 | 7.759 | 1.651 | 0.115 | -3.432 29.050 |
| expression:C(dose)[T.1] | -2.0386 | 10.922 | -0.187 | 0.854 | -24.900 20.822 |
| Omnibus: | 0.211 | Durbin-Watson: | 1.576 |
| Prob(Omnibus): | 0.900 | Jarque-Bera (JB): | 0.015 |
| Skew: | -0.040 | Prob(JB): | 0.993 |
| Kurtosis: | 2.905 | Cond. No. | 124. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.718 |
| Model: | OLS | Adj. R-squared: | 0.690 |
| Method: | Least Squares | F-statistic: | 25.46 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 3.18e-06 |
| Time: | 23:01:28 | Log-Likelihood: | -98.548 |
| No. Observations: | 23 | AIC: | 203.1 |
| Df Residuals: | 20 | BIC: | 206.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -15.7079 | 32.084 | -0.490 | 0.630 | -82.633 51.218 |
| C(dose)[T.1] | 56.7063 | 8.008 | 7.081 | 0.000 | 40.002 73.410 |
| expression | 11.7801 | 5.328 | 2.211 | 0.039 | 0.667 22.893 |
| Omnibus: | 0.245 | Durbin-Watson: | 1.596 |
| Prob(Omnibus): | 0.885 | Jarque-Bera (JB): | 0.004 |
| Skew: | 0.006 | Prob(JB): | 0.998 |
| Kurtosis: | 2.939 | Cond. No. | 49.4 |
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, 03 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 23:01:28 | 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: | 0.011 |
| Model: | OLS | Adj. R-squared: | -0.036 |
| Method: | Least Squares | F-statistic: | 0.2317 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.635 |
| Time: | 23:01:28 | Log-Likelihood: | -112.98 |
| No. Observations: | 23 | AIC: | 230.0 |
| Df Residuals: | 21 | BIC: | 232.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 53.0373 | 55.889 | 0.949 | 0.353 | -63.191 169.266 |
| expression | 4.6013 | 9.559 | 0.481 | 0.635 | -15.278 24.480 |
| Omnibus: | 2.962 | Durbin-Watson: | 2.516 |
| Prob(Omnibus): | 0.227 | Jarque-Bera (JB): | 1.521 |
| Skew: | 0.303 | Prob(JB): | 0.467 |
| Kurtosis: | 1.896 | Cond. No. | 46.8 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.322 | 0.153 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.558 |
| Model: | OLS | Adj. R-squared: | 0.437 |
| Method: | Least Squares | F-statistic: | 4.627 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0251 |
| Time: | 23:01:28 | Log-Likelihood: | -69.179 |
| No. Observations: | 15 | AIC: | 146.4 |
| Df Residuals: | 11 | BIC: | 149.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -50.3098 | 128.933 | -0.390 | 0.704 | -334.089 233.469 |
| C(dose)[T.1] | -146.1328 | 262.680 | -0.556 | 0.589 | -724.288 432.023 |
| expression | 17.9871 | 19.629 | 0.916 | 0.379 | -25.215 61.190 |
| expression:C(dose)[T.1] | 26.6687 | 38.064 | 0.701 | 0.498 | -57.110 110.448 |
| Omnibus: | 1.624 | Durbin-Watson: | 1.007 |
| Prob(Omnibus): | 0.444 | Jarque-Bera (JB): | 0.865 |
| Skew: | 0.018 | Prob(JB): | 0.649 |
| Kurtosis: | 1.824 | Cond. No. | 303. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.538 |
| Model: | OLS | Adj. R-squared: | 0.461 |
| Method: | Least Squares | F-statistic: | 6.991 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00971 |
| Time: | 23:01:28 | Log-Likelihood: | -69.506 |
| No. Observations: | 15 | AIC: | 145.0 |
| Df Residuals: | 12 | BIC: | 147.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -96.7297 | 108.235 | -0.894 | 0.389 | -332.554 139.094 |
| C(dose)[T.1] | 37.5353 | 16.313 | 2.301 | 0.040 | 1.992 73.079 |
| expression | 25.0788 | 16.457 | 1.524 | 0.153 | -10.778 60.935 |
| Omnibus: | 0.744 | Durbin-Watson: | 0.871 |
| Prob(Omnibus): | 0.689 | Jarque-Bera (JB): | 0.685 |
| Skew: | -0.235 | Prob(JB): | 0.710 |
| Kurtosis: | 2.065 | Cond. No. | 105. |
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, 03 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 23:01:28 | 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: | 0.334 |
| Model: | OLS | Adj. R-squared: | 0.283 |
| Method: | Least Squares | F-statistic: | 6.531 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0239 |
| Time: | 23:01:28 | Log-Likelihood: | -72.247 |
| No. Observations: | 15 | AIC: | 148.5 |
| Df Residuals: | 13 | BIC: | 149.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -197.3803 | 114.188 | -1.729 | 0.108 | -444.068 49.308 |
| expression | 42.8408 | 16.764 | 2.556 | 0.024 | 6.625 79.056 |
| Omnibus: | 1.373 | Durbin-Watson: | 1.521 |
| Prob(Omnibus): | 0.503 | Jarque-Bera (JB): | 0.918 |
| Skew: | 0.280 | Prob(JB): | 0.632 |
| Kurtosis: | 1.925 | Cond. No. | 95.8 |