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 |
| 1.186 | 0.289 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.681 |
| Model: | OLS | Adj. R-squared: | 0.630 |
| Method: | Least Squares | F-statistic: | 13.51 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.92e-05 |
| Time: | 04:14:12 | Log-Likelihood: | -99.972 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 19 | BIC: | 212.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 457.8503 | 295.601 | 1.549 | 0.138 | -160.849 1076.549 |
| C(dose)[T.1] | -302.3688 | 424.279 | -0.713 | 0.485 | -1190.394 585.656 |
| expression | -42.7072 | 31.270 | -1.366 | 0.188 | -108.155 22.741 |
| expression:C(dose)[T.1] | 37.7316 | 44.445 | 0.849 | 0.406 | -55.293 130.756 |
| Omnibus: | 0.753 | Durbin-Watson: | 1.901 |
| Prob(Omnibus): | 0.686 | Jarque-Bera (JB): | 0.719 |
| Skew: | -0.149 | Prob(JB): | 0.698 |
| Kurtosis: | 2.187 | Cond. No. | 1.23e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.669 |
| Model: | OLS | Adj. R-squared: | 0.636 |
| Method: | Least Squares | F-statistic: | 20.18 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.59e-05 |
| Time: | 04:14:12 | Log-Likelihood: | -100.40 |
| No. Observations: | 23 | AIC: | 206.8 |
| Df Residuals: | 20 | BIC: | 210.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 281.3273 | 208.634 | 1.348 | 0.193 | -153.876 716.531 |
| C(dose)[T.1] | 57.7333 | 9.429 | 6.123 | 0.000 | 38.065 77.401 |
| expression | -24.0302 | 22.066 | -1.089 | 0.289 | -70.058 21.998 |
| Omnibus: | 0.387 | Durbin-Watson: | 1.819 |
| Prob(Omnibus): | 0.824 | Jarque-Bera (JB): | 0.533 |
| Skew: | -0.146 | Prob(JB): | 0.766 |
| Kurtosis: | 2.314 | Cond. No. | 474. |
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: | 04:14:13 | 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: | 0.048 |
| Model: | OLS | Adj. R-squared: | 0.002 |
| Method: | Least Squares | F-statistic: | 1.050 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.317 |
| Time: | 04:14:13 | Log-Likelihood: | -112.54 |
| No. Observations: | 23 | AIC: | 229.1 |
| Df Residuals: | 21 | BIC: | 231.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -242.8524 | 314.811 | -0.771 | 0.449 | -897.538 411.834 |
| expression | 33.8163 | 32.995 | 1.025 | 0.317 | -34.800 102.433 |
| Omnibus: | 3.059 | Durbin-Watson: | 2.496 |
| Prob(Omnibus): | 0.217 | Jarque-Bera (JB): | 1.395 |
| Skew: | 0.195 | Prob(JB): | 0.498 |
| Kurtosis: | 1.858 | Cond. No. | 431. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 4.477 | 0.056 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.553 |
| Method: | Least Squares | F-statistic: | 6.774 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00749 |
| Time: | 04:14:13 | Log-Likelihood: | -67.452 |
| No. Observations: | 15 | AIC: | 142.9 |
| Df Residuals: | 11 | BIC: | 145.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -290.0446 | 327.446 | -0.886 | 0.395 | -1010.748 430.659 |
| C(dose)[T.1] | -701.0204 | 590.912 | -1.186 | 0.260 | -2001.610 599.569 |
| expression | 37.6619 | 34.484 | 1.092 | 0.298 | -38.236 113.560 |
| expression:C(dose)[T.1] | 77.2787 | 61.591 | 1.255 | 0.236 | -58.283 212.840 |
| Omnibus: | 0.766 | Durbin-Watson: | 0.815 |
| Prob(Omnibus): | 0.682 | Jarque-Bera (JB): | 0.659 |
| Skew: | -0.146 | Prob(JB): | 0.719 |
| Kurtosis: | 2.015 | Cond. No. | 1.08e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.599 |
| Model: | OLS | Adj. R-squared: | 0.532 |
| Method: | Least Squares | F-statistic: | 8.946 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00419 |
| Time: | 04:14:13 | Log-Likelihood: | -68.455 |
| No. Observations: | 15 | AIC: | 142.9 |
| Df Residuals: | 12 | BIC: | 145.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -519.9698 | 277.784 | -1.872 | 0.086 | -1125.210 85.270 |
| C(dose)[T.1] | 40.1970 | 14.089 | 2.853 | 0.015 | 9.499 70.895 |
| expression | 61.8859 | 29.248 | 2.116 | 0.056 | -1.840 125.612 |
| Omnibus: | 3.589 | Durbin-Watson: | 0.859 |
| Prob(Omnibus): | 0.166 | Jarque-Bera (JB): | 1.437 |
| Skew: | -0.346 | Prob(JB): | 0.487 |
| Kurtosis: | 1.651 | Cond. No. | 402. |
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: | 04:14:13 | 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: | 0.326 |
| Model: | OLS | Adj. R-squared: | 0.274 |
| Method: | Least Squares | F-statistic: | 6.295 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0261 |
| Time: | 04:14:13 | Log-Likelihood: | -72.338 |
| No. Observations: | 15 | AIC: | 148.7 |
| Df Residuals: | 13 | BIC: | 150.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -739.5754 | 332.209 | -2.226 | 0.044 | -1457.270 -21.881 |
| expression | 87.0754 | 34.706 | 2.509 | 0.026 | 12.099 162.052 |
| Omnibus: | 2.087 | Durbin-Watson: | 2.176 |
| Prob(Omnibus): | 0.352 | Jarque-Bera (JB): | 0.988 |
| Skew: | 0.126 | Prob(JB): | 0.610 |
| Kurtosis: | 1.768 | Cond. No. | 385. |