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 |
| 1.035 | 0.321 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.745 |
| Model: | OLS | Adj. R-squared: | 0.704 |
| Method: | Least Squares | F-statistic: | 18.47 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.40e-06 |
| Time: | 04:49:12 | Log-Likelihood: | -97.407 |
| No. Observations: | 23 | AIC: | 202.8 |
| Df Residuals: | 19 | BIC: | 207.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 15.1514 | 58.841 | 0.257 | 0.800 | -108.004 138.307 |
| C(dose)[T.1] | 303.1759 | 100.786 | 3.008 | 0.007 | 92.228 514.123 |
| expression | 7.6573 | 11.489 | 0.666 | 0.513 | -16.390 31.704 |
| expression:C(dose)[T.1] | -43.4738 | 18.012 | -2.414 | 0.026 | -81.174 -5.774 |
| Omnibus: | 1.291 | Durbin-Watson: | 1.685 |
| Prob(Omnibus): | 0.524 | Jarque-Bera (JB): | 0.882 |
| Skew: | 0.071 | Prob(JB): | 0.644 |
| Kurtosis: | 2.051 | Cond. No. | 187. |
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: | 19.97 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.71e-05 |
| Time: | 04:49:12 | Log-Likelihood: | -100.48 |
| 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 | 105.3690 | 50.629 | 2.081 | 0.050 | -0.242 210.980 |
| C(dose)[T.1] | 61.2056 | 11.530 | 5.309 | 0.000 | 37.155 85.256 |
| expression | -10.0304 | 9.858 | -1.017 | 0.321 | -30.594 10.534 |
| Omnibus: | 0.829 | Durbin-Watson: | 2.111 |
| Prob(Omnibus): | 0.661 | Jarque-Bera (JB): | 0.780 |
| Skew: | 0.389 | Prob(JB): | 0.677 |
| Kurtosis: | 2.544 | Cond. No. | 68.5 |
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: | 04:49:12 | 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: | 0.196 |
| Model: | OLS | Adj. R-squared: | 0.158 |
| Method: | Least Squares | F-statistic: | 5.125 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0343 |
| Time: | 04:49:12 | Log-Likelihood: | -110.59 |
| No. Observations: | 23 | AIC: | 225.2 |
| Df Residuals: | 21 | BIC: | 227.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -57.5708 | 60.988 | -0.944 | 0.356 | -184.403 69.262 |
| expression | 25.0720 | 11.075 | 2.264 | 0.034 | 2.040 48.104 |
| Omnibus: | 1.136 | Durbin-Watson: | 2.146 |
| Prob(Omnibus): | 0.567 | Jarque-Bera (JB): | 0.930 |
| Skew: | 0.460 | Prob(JB): | 0.628 |
| Kurtosis: | 2.646 | Cond. No. | 53.6 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.509 | 0.243 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.512 |
| Model: | OLS | Adj. R-squared: | 0.379 |
| Method: | Least Squares | F-statistic: | 3.843 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0419 |
| Time: | 04:49:12 | Log-Likelihood: | -69.923 |
| No. Observations: | 15 | AIC: | 147.8 |
| Df Residuals: | 11 | BIC: | 150.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 162.3460 | 121.187 | 1.340 | 0.207 | -104.385 429.077 |
| C(dose)[T.1] | 26.9431 | 146.286 | 0.184 | 0.857 | -295.031 348.917 |
| expression | -17.3251 | 22.024 | -0.787 | 0.448 | -65.799 31.149 |
| expression:C(dose)[T.1] | 4.6698 | 26.178 | 0.178 | 0.862 | -52.948 62.287 |
| Omnibus: | 3.383 | Durbin-Watson: | 0.787 |
| Prob(Omnibus): | 0.184 | Jarque-Bera (JB): | 1.960 |
| Skew: | -0.885 | Prob(JB): | 0.375 |
| Kurtosis: | 2.993 | Cond. No. | 162. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.510 |
| Model: | OLS | Adj. R-squared: | 0.429 |
| Method: | Least Squares | F-statistic: | 6.253 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0138 |
| Time: | 04:49:13 | Log-Likelihood: | -69.945 |
| No. Observations: | 15 | AIC: | 145.9 |
| Df Residuals: | 12 | BIC: | 148.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 144.2380 | 63.469 | 2.273 | 0.042 | 5.952 282.524 |
| C(dose)[T.1] | 52.8860 | 15.136 | 3.494 | 0.004 | 19.908 85.864 |
| expression | -14.0199 | 11.415 | -1.228 | 0.243 | -38.890 10.851 |
| Omnibus: | 3.579 | Durbin-Watson: | 0.768 |
| Prob(Omnibus): | 0.167 | Jarque-Bera (JB): | 2.064 |
| Skew: | -0.908 | Prob(JB): | 0.356 |
| Kurtosis: | 3.030 | Cond. No. | 50.3 |
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: | 04:49:13 | 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: | 0.012 |
| Model: | OLS | Adj. R-squared: | -0.064 |
| Method: | Least Squares | F-statistic: | 0.1599 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.696 |
| Time: | 04:49:13 | Log-Likelihood: | -75.208 |
| No. Observations: | 15 | AIC: | 154.4 |
| Df Residuals: | 13 | BIC: | 155.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 127.9662 | 86.377 | 1.481 | 0.162 | -58.640 314.573 |
| expression | -6.1042 | 15.267 | -0.400 | 0.696 | -39.087 26.878 |
| Omnibus: | 0.651 | Durbin-Watson: | 1.667 |
| Prob(Omnibus): | 0.722 | Jarque-Bera (JB): | 0.599 |
| Skew: | 0.054 | Prob(JB): | 0.741 |
| Kurtosis: | 2.027 | Cond. No. | 49.9 |