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 |
| 0.536 | 0.473 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.724 |
| Model: | OLS | Adj. R-squared: | 0.680 |
| Method: | Least Squares | F-statistic: | 16.61 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.53e-05 |
| Time: | 04:54:19 | Log-Likelihood: | -98.303 |
| No. Observations: | 23 | AIC: | 204.6 |
| Df Residuals: | 19 | BIC: | 209.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 187.8354 | 115.264 | 1.630 | 0.120 | -53.415 429.085 |
| C(dose)[T.1] | -258.0997 | 147.056 | -1.755 | 0.095 | -565.892 49.693 |
| expression | -17.4098 | 15.000 | -1.161 | 0.260 | -48.805 13.986 |
| expression:C(dose)[T.1] | 40.9206 | 19.241 | 2.127 | 0.047 | 0.648 81.193 |
| Omnibus: | 0.153 | Durbin-Watson: | 1.568 |
| Prob(Omnibus): | 0.926 | Jarque-Bera (JB): | 0.358 |
| Skew: | -0.109 | Prob(JB): | 0.836 |
| Kurtosis: | 2.429 | Cond. No. | 390. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.658 |
| Model: | OLS | Adj. R-squared: | 0.624 |
| Method: | Least Squares | F-statistic: | 19.26 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.18e-05 |
| Time: | 04:54:19 | Log-Likelihood: | -100.76 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 20 | BIC: | 210.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -3.0445 | 78.429 | -0.039 | 0.969 | -166.645 160.556 |
| C(dose)[T.1] | 54.1760 | 8.730 | 6.206 | 0.000 | 35.965 72.387 |
| expression | 7.4593 | 10.189 | 0.732 | 0.473 | -13.794 28.712 |
| Omnibus: | 0.618 | Durbin-Watson: | 1.901 |
| Prob(Omnibus): | 0.734 | Jarque-Bera (JB): | 0.674 |
| Skew: | 0.192 | Prob(JB): | 0.714 |
| Kurtosis: | 2.254 | Cond. No. | 141. |
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: | 04:54:19 | 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: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.002474 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.961 |
| Time: | 04:54:19 | 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 | 86.1084 | 128.698 | 0.669 | 0.511 | -181.534 353.751 |
| expression | -0.8385 | 16.859 | -0.050 | 0.961 | -35.900 34.223 |
| Omnibus: | 3.239 | Durbin-Watson: | 2.478 |
| Prob(Omnibus): | 0.198 | Jarque-Bera (JB): | 1.574 |
| Skew: | 0.300 | Prob(JB): | 0.455 |
| Kurtosis: | 1.868 | Cond. No. | 138. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.018 | 0.894 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.646 |
| Model: | OLS | Adj. R-squared: | 0.549 |
| Method: | Least Squares | F-statistic: | 6.689 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00782 |
| Time: | 04:54:19 | Log-Likelihood: | -67.513 |
| No. Observations: | 15 | AIC: | 143.0 |
| Df Residuals: | 11 | BIC: | 145.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -182.5865 | 141.495 | -1.290 | 0.223 | -494.016 128.843 |
| C(dose)[T.1] | 500.7922 | 183.567 | 2.728 | 0.020 | 96.763 904.821 |
| expression | 31.8287 | 17.972 | 1.771 | 0.104 | -7.727 71.384 |
| expression:C(dose)[T.1] | -58.4177 | 23.654 | -2.470 | 0.031 | -110.480 -6.356 |
| Omnibus: | 3.105 | Durbin-Watson: | 1.303 |
| Prob(Omnibus): | 0.212 | Jarque-Bera (JB): | 1.296 |
| Skew: | -0.686 | Prob(JB): | 0.523 |
| Kurtosis: | 3.438 | Cond. No. | 302. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.450 |
| Model: | OLS | Adj. R-squared: | 0.358 |
| Method: | Least Squares | F-statistic: | 4.902 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0278 |
| Time: | 04:54:19 | Log-Likelihood: | -70.821 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 82.3007 | 110.165 | 0.747 | 0.469 | -157.729 322.331 |
| C(dose)[T.1] | 48.6783 | 16.184 | 3.008 | 0.011 | 13.416 83.940 |
| expression | -1.8933 | 13.948 | -0.136 | 0.894 | -32.284 28.498 |
| Omnibus: | 2.968 | Durbin-Watson: | 0.827 |
| Prob(Omnibus): | 0.227 | Jarque-Bera (JB): | 2.016 |
| Skew: | -0.882 | Prob(JB): | 0.365 |
| Kurtosis: | 2.661 | Cond. No. | 111. |
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: | 04:54:19 | 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: | 0.035 |
| Model: | OLS | Adj. R-squared: | -0.040 |
| Method: | Least Squares | F-statistic: | 0.4671 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.506 |
| Time: | 04:54:19 | Log-Likelihood: | -75.035 |
| No. Observations: | 15 | AIC: | 154.1 |
| Df Residuals: | 13 | BIC: | 155.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 184.5387 | 133.335 | 1.384 | 0.190 | -103.513 472.591 |
| expression | -11.7877 | 17.247 | -0.683 | 0.506 | -49.048 25.473 |
| Omnibus: | 0.995 | Durbin-Watson: | 1.781 |
| Prob(Omnibus): | 0.608 | Jarque-Bera (JB): | 0.446 |
| Skew: | -0.417 | Prob(JB): | 0.800 |
| Kurtosis: | 2.870 | Cond. No. | 105. |