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 |
| 0.187 | 0.670 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.680 |
| Model: | OLS | Adj. R-squared: | 0.630 |
| Method: | Least Squares | F-statistic: | 13.49 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.98e-05 |
| Time: | 04:19:04 | Log-Likelihood: | -99.985 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 19 | BIC: | 212.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 85.9024 | 82.179 | 1.045 | 0.309 | -86.101 257.905 |
| C(dose)[T.1] | -121.1728 | 136.651 | -0.887 | 0.386 | -407.187 164.842 |
| expression | -4.3605 | 11.277 | -0.387 | 0.703 | -27.963 19.242 |
| expression:C(dose)[T.1] | 25.2777 | 19.546 | 1.293 | 0.211 | -15.632 66.188 |
| Omnibus: | 0.332 | Durbin-Watson: | 1.832 |
| Prob(Omnibus): | 0.847 | Jarque-Bera (JB): | 0.497 |
| Skew: | -0.163 | Prob(JB): | 0.780 |
| Kurtosis: | 2.358 | Cond. No. | 277. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.652 |
| Model: | OLS | Adj. R-squared: | 0.618 |
| Method: | Least Squares | F-statistic: | 18.76 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.58e-05 |
| Time: | 04:19:04 | Log-Likelihood: | -100.96 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 20 | BIC: | 211.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 24.7474 | 68.331 | 0.362 | 0.721 | -117.789 167.284 |
| C(dose)[T.1] | 55.1239 | 9.656 | 5.709 | 0.000 | 34.982 75.266 |
| expression | 4.0532 | 9.364 | 0.433 | 0.670 | -15.480 23.587 |
| Omnibus: | 0.176 | Durbin-Watson: | 1.801 |
| Prob(Omnibus): | 0.916 | Jarque-Bera (JB): | 0.386 |
| Skew: | 0.069 | Prob(JB): | 0.825 |
| Kurtosis: | 2.381 | Cond. No. | 114. |
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: | 04:19:04 | 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: | 0.086 |
| Model: | OLS | Adj. R-squared: | 0.042 |
| Method: | Least Squares | F-statistic: | 1.970 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.175 |
| Time: | 04:19:04 | Log-Likelihood: | -112.07 |
| No. Observations: | 23 | AIC: | 228.1 |
| Df Residuals: | 21 | BIC: | 230.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 212.4111 | 94.799 | 2.241 | 0.036 | 15.266 409.556 |
| expression | -18.8013 | 13.396 | -1.403 | 0.175 | -46.661 9.058 |
| Omnibus: | 1.056 | Durbin-Watson: | 2.558 |
| Prob(Omnibus): | 0.590 | Jarque-Bera (JB): | 0.993 |
| Skew: | 0.430 | Prob(JB): | 0.609 |
| Kurtosis: | 2.457 | Cond. No. | 99.1 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.128 | 0.727 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.456 |
| Model: | OLS | Adj. R-squared: | 0.308 |
| Method: | Least Squares | F-statistic: | 3.073 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0728 |
| Time: | 04:19:04 | Log-Likelihood: | -70.735 |
| No. Observations: | 15 | AIC: | 149.5 |
| Df Residuals: | 11 | BIC: | 152.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 25.2135 | 112.143 | 0.225 | 0.826 | -221.612 272.039 |
| C(dose)[T.1] | 101.8158 | 326.254 | 0.312 | 0.761 | -616.264 819.895 |
| expression | 6.9421 | 18.337 | 0.379 | 0.712 | -33.417 47.301 |
| expression:C(dose)[T.1] | -8.6078 | 52.335 | -0.164 | 0.872 | -123.797 106.582 |
| Omnibus: | 2.671 | Durbin-Watson: | 0.668 |
| Prob(Omnibus): | 0.263 | Jarque-Bera (JB): | 1.895 |
| Skew: | -0.841 | Prob(JB): | 0.388 |
| Kurtosis: | 2.553 | Cond. No. | 299. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.455 |
| Model: | OLS | Adj. R-squared: | 0.364 |
| Method: | Least Squares | F-statistic: | 5.001 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0263 |
| Time: | 04:19:04 | Log-Likelihood: | -70.754 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 12 | BIC: | 149.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 31.6393 | 100.766 | 0.314 | 0.759 | -187.911 251.190 |
| C(dose)[T.1] | 48.2250 | 15.891 | 3.035 | 0.010 | 13.602 82.848 |
| expression | 5.8854 | 16.463 | 0.357 | 0.727 | -29.985 41.756 |
| Omnibus: | 2.854 | Durbin-Watson: | 0.689 |
| Prob(Omnibus): | 0.240 | Jarque-Bera (JB): | 1.939 |
| Skew: | -0.864 | Prob(JB): | 0.379 |
| Kurtosis: | 2.653 | Cond. No. | 82.2 |
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: | 04:19:04 | 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: | 0.036 |
| Model: | OLS | Adj. R-squared: | -0.038 |
| Method: | Least Squares | F-statistic: | 0.4850 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.498 |
| Time: | 04:19:04 | Log-Likelihood: | -75.025 |
| No. Observations: | 15 | AIC: | 154.1 |
| Df Residuals: | 13 | BIC: | 155.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 4.6469 | 128.208 | 0.036 | 0.972 | -272.330 281.623 |
| expression | 14.4300 | 20.719 | 0.696 | 0.498 | -30.331 59.191 |
| Omnibus: | 0.692 | Durbin-Watson: | 1.593 |
| Prob(Omnibus): | 0.708 | Jarque-Bera (JB): | 0.611 |
| Skew: | 0.027 | Prob(JB): | 0.737 |
| Kurtosis: | 2.013 | Cond. No. | 81.6 |