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 |
| 0.781 | 0.387 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.663 |
| Model: | OLS | Adj. R-squared: | 0.609 |
| Method: | Least Squares | F-statistic: | 12.43 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 9.93e-05 |
| Time: | 03:38:20 | Log-Likelihood: | -100.61 |
| No. Observations: | 23 | AIC: | 209.2 |
| Df Residuals: | 19 | BIC: | 213.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 132.4914 | 106.092 | 1.249 | 0.227 | -89.561 354.544 |
| C(dose)[T.1] | 30.8969 | 161.317 | 0.192 | 0.850 | -306.743 368.537 |
| expression | -11.8641 | 16.052 | -0.739 | 0.469 | -45.461 21.733 |
| expression:C(dose)[T.1] | 3.1055 | 24.899 | 0.125 | 0.902 | -49.008 55.219 |
| Omnibus: | 1.231 | Durbin-Watson: | 1.938 |
| Prob(Omnibus): | 0.540 | Jarque-Bera (JB): | 0.979 |
| Skew: | -0.255 | Prob(JB): | 0.613 |
| Kurtosis: | 2.127 | Cond. No. | 304. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.662 |
| Model: | OLS | Adj. R-squared: | 0.628 |
| Method: | Least Squares | F-statistic: | 19.61 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.93e-05 |
| Time: | 03:38:20 | Log-Likelihood: | -100.62 |
| No. Observations: | 23 | AIC: | 207.2 |
| Df Residuals: | 20 | BIC: | 210.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 123.9747 | 79.173 | 1.566 | 0.133 | -41.177 289.127 |
| C(dose)[T.1] | 50.9845 | 9.006 | 5.661 | 0.000 | 32.198 69.771 |
| expression | -10.5733 | 11.965 | -0.884 | 0.387 | -35.532 14.385 |
| Omnibus: | 1.212 | Durbin-Watson: | 1.940 |
| Prob(Omnibus): | 0.545 | Jarque-Bera (JB): | 0.983 |
| Skew: | -0.266 | Prob(JB): | 0.612 |
| Kurtosis: | 2.138 | Cond. No. | 123. |
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: | 03:38:20 | 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: | 0.121 |
| Model: | OLS | Adj. R-squared: | 0.079 |
| Method: | Least Squares | F-statistic: | 2.891 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.104 |
| Time: | 03:38:20 | Log-Likelihood: | -111.62 |
| No. Observations: | 23 | AIC: | 227.2 |
| Df Residuals: | 21 | BIC: | 229.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 278.3509 | 117.018 | 2.379 | 0.027 | 34.999 521.703 |
| expression | -30.5971 | 17.995 | -1.700 | 0.104 | -68.020 6.826 |
| Omnibus: | 3.534 | Durbin-Watson: | 2.240 |
| Prob(Omnibus): | 0.171 | Jarque-Bera (JB): | 1.432 |
| Skew: | 0.138 | Prob(JB): | 0.489 |
| Kurtosis: | 1.809 | Cond. No. | 115. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 7.650 | 0.017 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.675 |
| Model: | OLS | Adj. R-squared: | 0.586 |
| Method: | Least Squares | F-statistic: | 7.600 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00500 |
| Time: | 03:38:20 | Log-Likelihood: | -66.881 |
| No. Observations: | 15 | AIC: | 141.8 |
| Df Residuals: | 11 | BIC: | 144.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 199.8705 | 188.901 | 1.058 | 0.313 | -215.898 615.639 |
| C(dose)[T.1] | 188.6761 | 214.747 | 0.879 | 0.398 | -283.979 661.331 |
| expression | -22.0801 | 31.455 | -0.702 | 0.497 | -91.312 47.152 |
| expression:C(dose)[T.1] | -21.8118 | 35.487 | -0.615 | 0.551 | -99.917 56.294 |
| Omnibus: | 0.540 | Durbin-Watson: | 0.911 |
| Prob(Omnibus): | 0.763 | Jarque-Bera (JB): | 0.604 |
| Skew: | -0.285 | Prob(JB): | 0.739 |
| Kurtosis: | 2.199 | Cond. No. | 324. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.663 |
| Model: | OLS | Adj. R-squared: | 0.607 |
| Method: | Least Squares | F-statistic: | 11.82 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00146 |
| Time: | 03:38:20 | Log-Likelihood: | -67.134 |
| No. Observations: | 15 | AIC: | 140.3 |
| Df Residuals: | 12 | BIC: | 142.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 302.6644 | 85.523 | 3.539 | 0.004 | 116.326 489.002 |
| C(dose)[T.1] | 56.9230 | 12.613 | 4.513 | 0.001 | 29.441 84.405 |
| expression | -39.2175 | 14.179 | -2.766 | 0.017 | -70.111 -8.324 |
| Omnibus: | 0.560 | Durbin-Watson: | 0.880 |
| Prob(Omnibus): | 0.756 | Jarque-Bera (JB): | 0.564 |
| Skew: | 0.033 | Prob(JB): | 0.754 |
| Kurtosis: | 2.053 | Cond. No. | 87.9 |
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: | 03:38:20 | 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: | 0.092 |
| Model: | OLS | Adj. R-squared: | 0.022 |
| Method: | Least Squares | F-statistic: | 1.318 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.272 |
| Time: | 03:38:20 | Log-Likelihood: | -74.576 |
| No. Observations: | 15 | AIC: | 153.2 |
| Df Residuals: | 13 | BIC: | 154.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 246.5251 | 133.511 | 1.846 | 0.088 | -41.908 534.958 |
| expression | -25.0451 | 21.818 | -1.148 | 0.272 | -72.179 22.089 |
| Omnibus: | 0.910 | Durbin-Watson: | 1.875 |
| Prob(Omnibus): | 0.634 | Jarque-Bera (JB): | 0.683 |
| Skew: | 0.042 | Prob(JB): | 0.711 |
| Kurtosis: | 1.958 | Cond. No. | 86.6 |