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 |
| 0.160 | 0.693 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.668 |
| Model: | OLS | Adj. R-squared: | 0.616 |
| Method: | Least Squares | F-statistic: | 12.75 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 8.49e-05 |
| Time: | 18:58:06 | Log-Likelihood: | -100.42 |
| No. Observations: | 23 | AIC: | 208.8 |
| Df Residuals: | 19 | BIC: | 213.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -129.3849 | 208.809 | -0.620 | 0.543 | -566.428 307.658 |
| C(dose)[T.1] | 407.0961 | 365.879 | 1.113 | 0.280 | -358.697 1172.890 |
| expression | 20.2565 | 23.029 | 0.880 | 0.390 | -27.944 68.457 |
| expression:C(dose)[T.1] | -39.0644 | 40.405 | -0.967 | 0.346 | -123.633 45.504 |
| Omnibus: | 0.683 | Durbin-Watson: | 1.945 |
| Prob(Omnibus): | 0.711 | Jarque-Bera (JB): | 0.699 |
| Skew: | 0.176 | Prob(JB): | 0.705 |
| Kurtosis: | 2.222 | Cond. No. | 925. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.652 |
| Model: | OLS | Adj. R-squared: | 0.617 |
| Method: | Least Squares | F-statistic: | 18.72 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 2.62e-05 |
| Time: | 18:58:06 | Log-Likelihood: | -100.97 |
| 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 | -14.3690 | 171.328 | -0.084 | 0.934 | -371.752 343.014 |
| C(dose)[T.1] | 53.4568 | 8.740 | 6.116 | 0.000 | 35.225 71.688 |
| expression | 7.5664 | 18.891 | 0.401 | 0.693 | -31.841 46.973 |
| Omnibus: | 0.101 | Durbin-Watson: | 1.947 |
| Prob(Omnibus): | 0.951 | Jarque-Bera (JB): | 0.311 |
| Skew: | 0.089 | Prob(JB): | 0.856 |
| Kurtosis: | 2.459 | Cond. No. | 361. |
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: | Tue, 28 Jan 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 18:58:06 | 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: | 0.001 |
| Model: | OLS | Adj. R-squared: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.01343 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.909 |
| Time: | 18:58:06 | Log-Likelihood: | -113.10 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 46.9570 | 282.791 | 0.166 | 0.870 | -541.139 635.053 |
| expression | 3.6176 | 31.217 | 0.116 | 0.909 | -61.302 68.537 |
| Omnibus: | 3.424 | Durbin-Watson: | 2.495 |
| Prob(Omnibus): | 0.180 | Jarque-Bera (JB): | 1.626 |
| Skew: | 0.309 | Prob(JB): | 0.444 |
| Kurtosis: | 1.853 | Cond. No. | 359. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 8.287 | 0.014 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.716 |
| Model: | OLS | Adj. R-squared: | 0.639 |
| Method: | Least Squares | F-statistic: | 9.264 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.00240 |
| Time: | 18:58:06 | Log-Likelihood: | -65.848 |
| No. Observations: | 15 | AIC: | 139.7 |
| Df Residuals: | 11 | BIC: | 142.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -343.9303 | 340.395 | -1.010 | 0.334 | -1093.134 405.274 |
| C(dose)[T.1] | -539.7970 | 477.593 | -1.130 | 0.282 | -1590.972 511.378 |
| expression | 43.4092 | 35.909 | 1.209 | 0.252 | -35.626 122.445 |
| expression:C(dose)[T.1] | 65.7931 | 51.245 | 1.284 | 0.226 | -46.997 178.583 |
| Omnibus: | 1.298 | Durbin-Watson: | 2.044 |
| Prob(Omnibus): | 0.523 | Jarque-Bera (JB): | 0.424 |
| Skew: | 0.409 | Prob(JB): | 0.809 |
| Kurtosis: | 3.088 | Cond. No. | 1.01e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.674 |
| Model: | OLS | Adj. R-squared: | 0.620 |
| Method: | Least Squares | F-statistic: | 12.40 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.00120 |
| Time: | 18:58:06 | Log-Likelihood: | -66.895 |
| No. Observations: | 15 | AIC: | 139.8 |
| Df Residuals: | 12 | BIC: | 141.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -650.0731 | 249.397 | -2.607 | 0.023 | -1193.463 -106.683 |
| C(dose)[T.1] | 73.1049 | 14.680 | 4.980 | 0.000 | 41.119 105.091 |
| expression | 75.7153 | 26.301 | 2.879 | 0.014 | 18.409 133.021 |
| Omnibus: | 0.514 | Durbin-Watson: | 1.525 |
| Prob(Omnibus): | 0.773 | Jarque-Bera (JB): | 0.588 |
| Skew: | 0.269 | Prob(JB): | 0.745 |
| Kurtosis: | 2.193 | Cond. No. | 390. |
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: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 18:58:06 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.077 |
| Method: | Least Squares | F-statistic: | 0.001967 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.965 |
| Time: | 18:58:06 | Log-Likelihood: | -75.299 |
| 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 | 78.6031 | 339.785 | 0.231 | 0.821 | -655.458 812.664 |
| expression | 1.6184 | 36.489 | 0.044 | 0.965 | -77.211 80.447 |
| Omnibus: | 0.588 | Durbin-Watson: | 1.641 |
| Prob(Omnibus): | 0.745 | Jarque-Bera (JB): | 0.576 |
| Skew: | 0.052 | Prob(JB): | 0.750 |
| Kurtosis: | 2.046 | Cond. No. | 315. |