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 |
| 0.844 | 0.369 | 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: | 13.12 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.11e-05 |
| Time: | 03:57:57 | Log-Likelihood: | -100.20 |
| No. Observations: | 23 | AIC: | 208.4 |
| Df Residuals: | 19 | BIC: | 212.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 183.1593 | 109.385 | 1.674 | 0.110 | -45.787 412.106 |
| C(dose)[T.1] | -52.9715 | 133.336 | -0.397 | 0.696 | -332.047 226.104 |
| expression | -17.7081 | 14.999 | -1.181 | 0.252 | -49.101 13.685 |
| expression:C(dose)[T.1] | 14.6675 | 18.141 | 0.809 | 0.429 | -23.302 52.637 |
| Omnibus: | 0.032 | Durbin-Watson: | 1.854 |
| Prob(Omnibus): | 0.984 | Jarque-Bera (JB): | 0.244 |
| Skew: | 0.025 | Prob(JB): | 0.885 |
| Kurtosis: | 2.498 | Cond. No. | 326. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.663 |
| Model: | OLS | Adj. R-squared: | 0.630 |
| Method: | Least Squares | F-statistic: | 19.70 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.87e-05 |
| Time: | 03:57:57 | Log-Likelihood: | -100.59 |
| No. Observations: | 23 | AIC: | 207.2 |
| Df Residuals: | 20 | BIC: | 210.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 110.1458 | 61.192 | 1.800 | 0.087 | -17.499 237.791 |
| C(dose)[T.1] | 54.6017 | 8.700 | 6.276 | 0.000 | 36.453 72.750 |
| expression | -7.6816 | 8.364 | -0.918 | 0.369 | -25.128 9.764 |
| Omnibus: | 0.336 | Durbin-Watson: | 1.890 |
| Prob(Omnibus): | 0.846 | Jarque-Bera (JB): | 0.498 |
| Skew: | 0.128 | Prob(JB): | 0.779 |
| Kurtosis: | 2.326 | Cond. No. | 107. |
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: | 03:57:57 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.048 |
| Method: | Least Squares | F-statistic: | 0.002027 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.965 |
| Time: | 03:57:57 | 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 | 75.1157 | 102.475 | 0.733 | 0.472 | -137.993 288.225 |
| expression | 0.6252 | 13.887 | 0.045 | 0.965 | -28.255 29.505 |
| Omnibus: | 3.266 | Durbin-Watson: | 2.483 |
| Prob(Omnibus): | 0.195 | Jarque-Bera (JB): | 1.556 |
| Skew: | 0.286 | Prob(JB): | 0.459 |
| Kurtosis: | 1.861 | Cond. No. | 107. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 4.017 | 0.068 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.671 |
| Model: | OLS | Adj. R-squared: | 0.582 |
| Method: | Least Squares | F-statistic: | 7.489 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00527 |
| Time: | 03:57:57 | Log-Likelihood: | -66.955 |
| No. Observations: | 15 | AIC: | 141.9 |
| Df Residuals: | 11 | BIC: | 144.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 22.5737 | 84.612 | 0.267 | 0.795 | -163.655 208.802 |
| C(dose)[T.1] | -157.5279 | 126.770 | -1.243 | 0.240 | -436.547 121.492 |
| expression | 6.5660 | 12.311 | 0.533 | 0.604 | -20.531 33.663 |
| expression:C(dose)[T.1] | 31.6753 | 18.861 | 1.679 | 0.121 | -9.837 73.188 |
| Omnibus: | 5.127 | Durbin-Watson: | 1.566 |
| Prob(Omnibus): | 0.077 | Jarque-Bera (JB): | 3.007 |
| Skew: | -1.089 | Prob(JB): | 0.222 |
| Kurtosis: | 3.252 | Cond. No. | 177. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.587 |
| Model: | OLS | Adj. R-squared: | 0.518 |
| Method: | Least Squares | F-statistic: | 8.529 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00496 |
| Time: | 03:57:57 | Log-Likelihood: | -68.667 |
| No. Observations: | 15 | AIC: | 143.3 |
| Df Residuals: | 12 | BIC: | 145.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -69.6200 | 69.097 | -1.008 | 0.334 | -220.169 80.929 |
| C(dose)[T.1] | 54.2644 | 13.856 | 3.916 | 0.002 | 24.074 84.455 |
| expression | 20.0617 | 10.009 | 2.004 | 0.068 | -1.747 41.870 |
| Omnibus: | 3.933 | Durbin-Watson: | 1.212 |
| Prob(Omnibus): | 0.140 | Jarque-Bera (JB): | 2.264 |
| Skew: | -0.951 | Prob(JB): | 0.322 |
| Kurtosis: | 3.087 | Cond. No. | 70.2 |
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: | 03:57:57 | 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: | 0.059 |
| Model: | OLS | Adj. R-squared: | -0.013 |
| Method: | Least Squares | F-statistic: | 0.8182 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.382 |
| Time: | 03:57:57 | Log-Likelihood: | -74.842 |
| No. Observations: | 15 | AIC: | 153.7 |
| Df Residuals: | 13 | BIC: | 155.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 7.2229 | 96.074 | 0.075 | 0.941 | -200.332 214.777 |
| expression | 12.9086 | 14.271 | 0.905 | 0.382 | -17.922 43.739 |
| Omnibus: | 0.248 | Durbin-Watson: | 2.041 |
| Prob(Omnibus): | 0.883 | Jarque-Bera (JB): | 0.425 |
| Skew: | -0.133 | Prob(JB): | 0.809 |
| Kurtosis: | 2.219 | Cond. No. | 67.1 |