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 |
| 5.695 | 0.027 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.727 |
| Model: | OLS | Adj. R-squared: | 0.684 |
| Method: | Least Squares | F-statistic: | 16.86 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.38e-05 |
| Time: | 05:10:30 | Log-Likelihood: | -98.175 |
| No. Observations: | 23 | AIC: | 204.4 |
| Df Residuals: | 19 | BIC: | 208.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 138.5067 | 62.284 | 2.224 | 0.038 | 8.144 268.869 |
| C(dose)[T.1] | 55.8989 | 77.598 | 0.720 | 0.480 | -106.515 218.313 |
| expression | -16.3412 | 12.027 | -1.359 | 0.190 | -41.514 8.831 |
| expression:C(dose)[T.1] | -1.5550 | 15.303 | -0.102 | 0.920 | -33.584 30.474 |
| Omnibus: | 0.269 | Durbin-Watson: | 1.832 |
| Prob(Omnibus): | 0.874 | Jarque-Bera (JB): | 0.451 |
| Skew: | 0.146 | Prob(JB): | 0.798 |
| Kurtosis: | 2.380 | Cond. No. | 140. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.727 |
| Model: | OLS | Adj. R-squared: | 0.700 |
| Method: | Least Squares | F-statistic: | 26.61 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.31e-06 |
| Time: | 05:10:30 | Log-Likelihood: | -98.181 |
| No. Observations: | 23 | AIC: | 202.4 |
| Df Residuals: | 20 | BIC: | 205.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 143.4613 | 37.782 | 3.797 | 0.001 | 64.649 222.274 |
| C(dose)[T.1] | 48.0589 | 8.047 | 5.972 | 0.000 | 31.273 64.845 |
| expression | -17.3017 | 7.250 | -2.386 | 0.027 | -32.425 -2.178 |
| Omnibus: | 0.239 | Durbin-Watson: | 1.836 |
| Prob(Omnibus): | 0.887 | Jarque-Bera (JB): | 0.430 |
| Skew: | 0.120 | Prob(JB): | 0.806 |
| Kurtosis: | 2.374 | Cond. No. | 51.5 |
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: | 05:10:30 | 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: | 0.240 |
| Model: | OLS | Adj. R-squared: | 0.203 |
| Method: | Least Squares | F-statistic: | 6.620 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0177 |
| Time: | 05:10:30 | Log-Likelihood: | -109.95 |
| No. Observations: | 23 | AIC: | 223.9 |
| Df Residuals: | 21 | BIC: | 226.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 226.1041 | 57.240 | 3.950 | 0.001 | 107.068 345.141 |
| expression | -29.2030 | 11.350 | -2.573 | 0.018 | -52.806 -5.600 |
| Omnibus: | 0.783 | Durbin-Watson: | 2.444 |
| Prob(Omnibus): | 0.676 | Jarque-Bera (JB): | 0.797 |
| Skew: | 0.362 | Prob(JB): | 0.671 |
| Kurtosis: | 2.446 | Cond. No. | 47.7 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.090 | 0.769 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.513 |
| Model: | OLS | Adj. R-squared: | 0.381 |
| Method: | Least Squares | F-statistic: | 3.869 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0411 |
| Time: | 05:10:30 | Log-Likelihood: | -69.898 |
| No. Observations: | 15 | AIC: | 147.8 |
| Df Residuals: | 11 | BIC: | 150.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 104.3238 | 86.527 | 1.206 | 0.253 | -86.121 294.769 |
| C(dose)[T.1] | -140.7444 | 161.102 | -0.874 | 0.401 | -495.327 213.838 |
| expression | -7.0012 | 16.279 | -0.430 | 0.675 | -42.832 28.829 |
| expression:C(dose)[T.1] | 32.8469 | 28.080 | 1.170 | 0.267 | -28.957 94.651 |
| Omnibus: | 1.040 | Durbin-Watson: | 0.944 |
| Prob(Omnibus): | 0.594 | Jarque-Bera (JB): | 0.914 |
| Skew: | -0.435 | Prob(JB): | 0.633 |
| Kurtosis: | 2.161 | Cond. No. | 152. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.453 |
| Model: | OLS | Adj. R-squared: | 0.362 |
| Method: | Least Squares | F-statistic: | 4.966 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0268 |
| Time: | 05:10:30 | Log-Likelihood: | -70.777 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 46.1454 | 71.883 | 0.642 | 0.533 | -110.475 202.766 |
| C(dose)[T.1] | 46.5644 | 17.970 | 2.591 | 0.024 | 7.412 85.717 |
| expression | 4.0387 | 13.466 | 0.300 | 0.769 | -25.302 33.379 |
| Omnibus: | 2.283 | Durbin-Watson: | 0.797 |
| Prob(Omnibus): | 0.319 | Jarque-Bera (JB): | 1.650 |
| Skew: | -0.774 | Prob(JB): | 0.438 |
| Kurtosis: | 2.509 | Cond. No. | 54.2 |
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: | 05:10:30 | 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: | 0.147 |
| Model: | OLS | Adj. R-squared: | 0.081 |
| Method: | Least Squares | F-statistic: | 2.235 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.159 |
| Time: | 05:10:30 | Log-Likelihood: | -74.110 |
| No. Observations: | 15 | AIC: | 152.2 |
| Df Residuals: | 13 | BIC: | 153.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -24.7512 | 79.756 | -0.310 | 0.761 | -197.054 147.552 |
| expression | 21.0806 | 14.099 | 1.495 | 0.159 | -9.379 51.540 |
| Omnibus: | 0.160 | Durbin-Watson: | 1.307 |
| Prob(Omnibus): | 0.923 | Jarque-Bera (JB): | 0.303 |
| Skew: | -0.194 | Prob(JB): | 0.859 |
| Kurtosis: | 2.421 | Cond. No. | 49.5 |