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 |
| 1.019 | 0.325 | 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: | 13.11 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.14e-05 |
| Time: | 04:34:51 | Log-Likelihood: | -100.20 |
| 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 | 75.6086 | 63.266 | 1.195 | 0.247 | -56.808 208.025 |
| C(dose)[T.1] | 124.8131 | 101.977 | 1.224 | 0.236 | -88.628 338.254 |
| expression | -3.7384 | 11.002 | -0.340 | 0.738 | -26.766 19.289 |
| expression:C(dose)[T.1] | -12.1294 | 17.510 | -0.693 | 0.497 | -48.779 24.521 |
| Omnibus: | 2.348 | Durbin-Watson: | 1.753 |
| Prob(Omnibus): | 0.309 | Jarque-Bera (JB): | 1.340 |
| Skew: | 0.277 | Prob(JB): | 0.512 |
| Kurtosis: | 1.956 | Cond. No. | 174. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.666 |
| Model: | OLS | Adj. R-squared: | 0.633 |
| Method: | Least Squares | F-statistic: | 19.95 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.72e-05 |
| Time: | 04:34:51 | Log-Likelihood: | -100.49 |
| No. Observations: | 23 | AIC: | 207.0 |
| Df Residuals: | 20 | BIC: | 210.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 103.0196 | 48.716 | 2.115 | 0.047 | 1.400 204.640 |
| C(dose)[T.1] | 54.4342 | 8.623 | 6.312 | 0.000 | 36.446 72.422 |
| expression | -8.5267 | 8.447 | -1.009 | 0.325 | -26.147 9.094 |
| Omnibus: | 1.549 | Durbin-Watson: | 1.545 |
| Prob(Omnibus): | 0.461 | Jarque-Bera (JB): | 0.996 |
| Skew: | 0.149 | Prob(JB): | 0.608 |
| Kurtosis: | 2.025 | Cond. No. | 68.4 |
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: | 04:34:51 | 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: | 0.01631 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.900 |
| Time: | 04:34:51 | 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 | 90.1703 | 82.168 | 1.097 | 0.285 | -80.707 261.047 |
| expression | -1.8066 | 14.146 | -0.128 | 0.900 | -31.225 27.612 |
| Omnibus: | 3.193 | Durbin-Watson: | 2.482 |
| Prob(Omnibus): | 0.203 | Jarque-Bera (JB): | 1.542 |
| Skew: | 0.286 | Prob(JB): | 0.462 |
| Kurtosis: | 1.868 | Cond. No. | 68.1 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 7.692 | 0.017 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.673 |
| Model: | OLS | Adj. R-squared: | 0.584 |
| Method: | Least Squares | F-statistic: | 7.549 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00512 |
| Time: | 04:34:51 | Log-Likelihood: | -66.915 |
| No. Observations: | 15 | AIC: | 141.8 |
| Df Residuals: | 11 | BIC: | 144.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -94.0746 | 138.382 | -0.680 | 0.511 | -398.652 210.503 |
| C(dose)[T.1] | -39.8644 | 171.423 | -0.233 | 0.820 | -417.163 337.434 |
| expression | 25.5110 | 21.810 | 1.170 | 0.267 | -22.492 73.514 |
| expression:C(dose)[T.1] | 14.9601 | 27.217 | 0.550 | 0.594 | -44.944 74.864 |
| Omnibus: | 0.138 | Durbin-Watson: | 0.936 |
| Prob(Omnibus): | 0.933 | Jarque-Bera (JB): | 0.341 |
| Skew: | -0.131 | Prob(JB): | 0.843 |
| Kurtosis: | 2.309 | Cond. No. | 247. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.664 |
| Model: | OLS | Adj. R-squared: | 0.608 |
| Method: | Least Squares | F-statistic: | 11.86 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00144 |
| Time: | 04:34:51 | Log-Likelihood: | -67.118 |
| No. Observations: | 15 | AIC: | 140.2 |
| Df Residuals: | 12 | BIC: | 142.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -154.8901 | 80.660 | -1.920 | 0.079 | -330.634 20.854 |
| C(dose)[T.1] | 54.0966 | 12.413 | 4.358 | 0.001 | 27.050 81.143 |
| expression | 35.1174 | 12.662 | 2.773 | 0.017 | 7.529 62.706 |
| Omnibus: | 0.564 | Durbin-Watson: | 0.929 |
| Prob(Omnibus): | 0.754 | Jarque-Bera (JB): | 0.575 |
| Skew: | -0.111 | Prob(JB): | 0.750 |
| Kurtosis: | 2.066 | Cond. No. | 85.0 |
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: | 04:34:51 | 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: | 0.132 |
| Model: | OLS | Adj. R-squared: | 0.066 |
| Method: | Least Squares | F-statistic: | 1.985 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.182 |
| Time: | 04:34:51 | Log-Likelihood: | -74.234 |
| No. Observations: | 15 | AIC: | 152.5 |
| Df Residuals: | 13 | BIC: | 153.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -76.9010 | 121.437 | -0.633 | 0.538 | -339.251 185.449 |
| expression | 27.2633 | 19.351 | 1.409 | 0.182 | -14.543 69.069 |
| Omnibus: | 1.187 | Durbin-Watson: | 2.106 |
| Prob(Omnibus): | 0.552 | Jarque-Bera (JB): | 0.778 |
| Skew: | 0.119 | Prob(JB): | 0.678 |
| Kurtosis: | 1.910 | Cond. No. | 82.6 |