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 |
| 1.247 | 0.277 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.731 |
| Model: | OLS | Adj. R-squared: | 0.689 |
| Method: | Least Squares | F-statistic: | 17.21 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.20e-05 |
| Time: | 22:48:43 | Log-Likelihood: | -98.003 |
| No. Observations: | 23 | AIC: | 204.0 |
| Df Residuals: | 19 | BIC: | 208.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 72.3034 | 186.425 | 0.388 | 0.702 | -317.888 462.494 |
| C(dose)[T.1] | 917.9293 | 411.779 | 2.229 | 0.038 | 56.066 1779.793 |
| expression | -1.8396 | 18.944 | -0.097 | 0.924 | -41.490 37.811 |
| expression:C(dose)[T.1] | -84.6514 | 40.656 | -2.082 | 0.051 | -169.745 0.442 |
| Omnibus: | 0.503 | Durbin-Watson: | 1.485 |
| Prob(Omnibus): | 0.778 | Jarque-Bera (JB): | 0.579 |
| Skew: | 0.043 | Prob(JB): | 0.748 |
| Kurtosis: | 2.227 | Cond. No. | 1.24e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.670 |
| Model: | OLS | Adj. R-squared: | 0.637 |
| Method: | Least Squares | F-statistic: | 20.27 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.55e-05 |
| Time: | 22:48:43 | Log-Likelihood: | -100.37 |
| No. Observations: | 23 | AIC: | 206.7 |
| Df Residuals: | 20 | BIC: | 210.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 253.0970 | 178.194 | 1.420 | 0.171 | -118.609 624.803 |
| C(dose)[T.1] | 60.7992 | 10.819 | 5.620 | 0.000 | 38.232 83.367 |
| expression | -20.2195 | 18.106 | -1.117 | 0.277 | -57.987 17.548 |
| Omnibus: | 3.169 | Durbin-Watson: | 1.952 |
| Prob(Omnibus): | 0.205 | Jarque-Bera (JB): | 1.322 |
| Skew: | -0.060 | Prob(JB): | 0.516 |
| Kurtosis: | 1.832 | Cond. No. | 425. |
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: | 22:48:44 | 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: | 0.148 |
| Model: | OLS | Adj. R-squared: | 0.107 |
| Method: | Least Squares | F-statistic: | 3.648 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0699 |
| Time: | 22:48:44 | Log-Likelihood: | -111.26 |
| No. Observations: | 23 | AIC: | 226.5 |
| Df Residuals: | 21 | BIC: | 228.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -347.0851 | 223.561 | -1.553 | 0.135 | -812.005 117.834 |
| expression | 42.6249 | 22.317 | 1.910 | 0.070 | -3.786 89.036 |
| Omnibus: | 3.655 | Durbin-Watson: | 2.120 |
| Prob(Omnibus): | 0.161 | Jarque-Bera (JB): | 1.955 |
| Skew: | 0.444 | Prob(JB): | 0.376 |
| Kurtosis: | 1.882 | Cond. No. | 340. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.420 | 0.529 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.526 |
| Model: | OLS | Adj. R-squared: | 0.396 |
| Method: | Least Squares | F-statistic: | 4.061 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0361 |
| Time: | 22:48:44 | Log-Likelihood: | -69.709 |
| No. Observations: | 15 | AIC: | 147.4 |
| Df Residuals: | 11 | BIC: | 150.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -544.3265 | 474.257 | -1.148 | 0.275 | -1588.160 499.507 |
| C(dose)[T.1] | 816.5695 | 661.122 | 1.235 | 0.243 | -638.550 2271.689 |
| expression | 70.5410 | 54.671 | 1.290 | 0.223 | -49.789 190.871 |
| expression:C(dose)[T.1] | -88.5210 | 76.287 | -1.160 | 0.270 | -256.427 79.386 |
| Omnibus: | 2.057 | Durbin-Watson: | 1.402 |
| Prob(Omnibus): | 0.358 | Jarque-Bera (JB): | 1.210 |
| Skew: | -0.689 | Prob(JB): | 0.546 |
| Kurtosis: | 2.811 | Cond. No. | 1.02e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.467 |
| Model: | OLS | Adj. R-squared: | 0.379 |
| Method: | Least Squares | F-statistic: | 5.266 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0228 |
| Time: | 22:48:44 | Log-Likelihood: | -70.575 |
| No. Observations: | 15 | AIC: | 147.1 |
| Df Residuals: | 12 | BIC: | 149.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -150.0525 | 335.601 | -0.447 | 0.663 | -881.263 581.158 |
| C(dose)[T.1] | 49.6294 | 15.485 | 3.205 | 0.008 | 15.890 83.369 |
| expression | 25.0776 | 38.676 | 0.648 | 0.529 | -59.190 109.345 |
| Omnibus: | 3.818 | Durbin-Watson: | 0.890 |
| Prob(Omnibus): | 0.148 | Jarque-Bera (JB): | 2.384 |
| Skew: | -0.975 | Prob(JB): | 0.304 |
| Kurtosis: | 2.913 | Cond. No. | 383. |
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: | 22:48:44 | 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: | 0.012 |
| Model: | OLS | Adj. R-squared: | -0.064 |
| Method: | Least Squares | F-statistic: | 0.1522 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.703 |
| Time: | 22:48:44 | Log-Likelihood: | -75.213 |
| No. Observations: | 15 | AIC: | 154.4 |
| Df Residuals: | 13 | BIC: | 155.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -77.2752 | 438.258 | -0.176 | 0.863 | -1024.074 869.523 |
| expression | 19.7321 | 50.575 | 0.390 | 0.703 | -89.529 128.994 |
| Omnibus: | 1.257 | Durbin-Watson: | 1.760 |
| Prob(Omnibus): | 0.533 | Jarque-Bera (JB): | 0.800 |
| Skew: | 0.127 | Prob(JB): | 0.670 |
| Kurtosis: | 1.898 | Cond. No. | 381. |