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 |
| 1.079 | 0.311 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.674 |
| Model: | OLS | Adj. R-squared: | 0.623 |
| Method: | Least Squares | F-statistic: | 13.10 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.20e-05 |
| Time: | 04:56:06 | Log-Likelihood: | -100.21 |
| No. Observations: | 23 | AIC: | 208.4 |
| Df Residuals: | 19 | BIC: | 213.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 146.0406 | 230.308 | 0.634 | 0.534 | -336.000 628.081 |
| C(dose)[T.1] | 303.2859 | 378.436 | 0.801 | 0.433 | -488.790 1095.362 |
| expression | -8.5317 | 21.390 | -0.399 | 0.694 | -53.301 36.237 |
| expression:C(dose)[T.1] | -21.8318 | 34.189 | -0.639 | 0.531 | -93.390 49.726 |
| Omnibus: | 2.862 | Durbin-Watson: | 1.621 |
| Prob(Omnibus): | 0.239 | Jarque-Bera (JB): | 1.317 |
| Skew: | 0.154 | Prob(JB): | 0.518 |
| Kurtosis: | 1.869 | Cond. No. | 1.20e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.667 |
| Model: | OLS | Adj. R-squared: | 0.634 |
| Method: | Least Squares | F-statistic: | 20.03 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.68e-05 |
| Time: | 04:56:06 | Log-Likelihood: | -100.46 |
| No. Observations: | 23 | AIC: | 206.9 |
| Df Residuals: | 20 | BIC: | 210.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 238.0189 | 177.026 | 1.345 | 0.194 | -131.251 607.289 |
| C(dose)[T.1] | 61.7507 | 11.771 | 5.246 | 0.000 | 37.197 86.305 |
| expression | -17.0770 | 16.438 | -1.039 | 0.311 | -51.365 17.211 |
| Omnibus: | 3.189 | Durbin-Watson: | 1.671 |
| Prob(Omnibus): | 0.203 | Jarque-Bera (JB): | 1.316 |
| Skew: | -0.002 | Prob(JB): | 0.518 |
| Kurtosis: | 1.828 | Cond. No. | 461. |
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: | 04:56:06 | 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: | 0.209 |
| Model: | OLS | Adj. R-squared: | 0.171 |
| Method: | Least Squares | F-statistic: | 5.544 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0284 |
| Time: | 04:56:06 | Log-Likelihood: | -110.41 |
| No. Observations: | 23 | AIC: | 224.8 |
| Df Residuals: | 21 | BIC: | 227.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -385.0006 | 197.480 | -1.950 | 0.065 | -795.682 25.681 |
| expression | 42.2499 | 17.944 | 2.354 | 0.028 | 4.932 79.567 |
| Omnibus: | 1.406 | Durbin-Watson: | 2.372 |
| Prob(Omnibus): | 0.495 | Jarque-Bera (JB): | 1.246 |
| Skew: | 0.436 | Prob(JB): | 0.536 |
| Kurtosis: | 2.265 | Cond. No. | 341. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.810 | 0.203 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.617 |
| Model: | OLS | Adj. R-squared: | 0.513 |
| Method: | Least Squares | F-statistic: | 5.908 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0118 |
| Time: | 04:56:06 | Log-Likelihood: | -68.101 |
| No. Observations: | 15 | AIC: | 144.2 |
| Df Residuals: | 11 | BIC: | 147.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 136.5569 | 145.927 | 0.936 | 0.369 | -184.627 457.741 |
| C(dose)[T.1] | -261.4007 | 184.489 | -1.417 | 0.184 | -667.457 144.656 |
| expression | -8.8935 | 18.730 | -0.475 | 0.644 | -50.117 32.330 |
| expression:C(dose)[T.1] | 38.7428 | 23.327 | 1.661 | 0.125 | -12.600 90.086 |
| Omnibus: | 0.245 | Durbin-Watson: | 1.678 |
| Prob(Omnibus): | 0.885 | Jarque-Bera (JB): | 0.304 |
| Skew: | -0.244 | Prob(JB): | 0.859 |
| Kurtosis: | 2.501 | Cond. No. | 310. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.521 |
| Model: | OLS | Adj. R-squared: | 0.441 |
| Method: | Least Squares | F-statistic: | 6.526 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0121 |
| Time: | 04:56:06 | Log-Likelihood: | -69.779 |
| No. Observations: | 15 | AIC: | 145.6 |
| Df Residuals: | 12 | BIC: | 147.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -57.5802 | 93.539 | -0.616 | 0.550 | -261.384 146.223 |
| C(dose)[T.1] | 44.1026 | 15.153 | 2.911 | 0.013 | 11.088 77.118 |
| expression | 16.0827 | 11.955 | 1.345 | 0.203 | -9.965 42.130 |
| Omnibus: | 0.579 | Durbin-Watson: | 0.911 |
| Prob(Omnibus): | 0.749 | Jarque-Bera (JB): | 0.628 |
| Skew: | -0.324 | Prob(JB): | 0.731 |
| Kurtosis: | 2.236 | Cond. No. | 104. |
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: | 04:56:06 | 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: | 0.183 |
| Model: | OLS | Adj. R-squared: | 0.120 |
| Method: | Least Squares | F-statistic: | 2.910 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.112 |
| Time: | 04:56:06 | Log-Likelihood: | -73.785 |
| No. Observations: | 15 | AIC: | 151.6 |
| Df Residuals: | 13 | BIC: | 153.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -103.1096 | 115.726 | -0.891 | 0.389 | -353.121 146.902 |
| expression | 24.7773 | 14.526 | 1.706 | 0.112 | -6.604 56.158 |
| Omnibus: | 0.117 | Durbin-Watson: | 1.735 |
| Prob(Omnibus): | 0.943 | Jarque-Bera (JB): | 0.172 |
| Skew: | -0.151 | Prob(JB): | 0.918 |
| Kurtosis: | 2.572 | Cond. No. | 102. |