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 |
| 0.000 | 0.994 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.655 |
| Model: | OLS | Adj. R-squared: | 0.601 |
| Method: | Least Squares | F-statistic: | 12.05 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000120 |
| Time: | 04:35:31 | Log-Likelihood: | -100.85 |
| No. Observations: | 23 | AIC: | 209.7 |
| Df Residuals: | 19 | BIC: | 214.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 47.6298 | 32.651 | 1.459 | 0.161 | -20.709 115.969 |
| C(dose)[T.1] | 98.8570 | 77.452 | 1.276 | 0.217 | -63.252 260.966 |
| expression | 1.9378 | 9.445 | 0.205 | 0.840 | -17.830 21.706 |
| expression:C(dose)[T.1] | -15.6799 | 26.426 | -0.593 | 0.560 | -70.990 39.630 |
| Omnibus: | 0.352 | Durbin-Watson: | 1.944 |
| Prob(Omnibus): | 0.839 | Jarque-Bera (JB): | 0.504 |
| Skew: | 0.204 | Prob(JB): | 0.777 |
| Kurtosis: | 2.400 | Cond. No. | 67.9 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 18.49 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.83e-05 |
| Time: | 04:35:31 | Log-Likelihood: | -101.06 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 54.4295 | 30.074 | 1.810 | 0.085 | -8.305 117.164 |
| C(dose)[T.1] | 53.3006 | 10.031 | 5.314 | 0.000 | 32.377 74.224 |
| expression | -0.0651 | 8.677 | -0.008 | 0.994 | -18.165 18.035 |
| Omnibus: | 0.321 | Durbin-Watson: | 1.886 |
| Prob(Omnibus): | 0.852 | Jarque-Bera (JB): | 0.484 |
| Skew: | 0.059 | Prob(JB): | 0.785 |
| Kurtosis: | 2.299 | Cond. No. | 24.4 |
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: | 04:35:31 | 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: | 0.154 |
| Model: | OLS | Adj. R-squared: | 0.113 |
| Method: | Least Squares | F-statistic: | 3.811 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0644 |
| Time: | 04:35:31 | Log-Likelihood: | -111.19 |
| No. Observations: | 23 | AIC: | 226.4 |
| Df Residuals: | 21 | BIC: | 228.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 149.8890 | 36.555 | 4.100 | 0.001 | 73.869 225.909 |
| expression | -22.4444 | 11.498 | -1.952 | 0.064 | -46.355 1.466 |
| Omnibus: | 0.199 | Durbin-Watson: | 1.947 |
| Prob(Omnibus): | 0.905 | Jarque-Bera (JB): | 0.405 |
| Skew: | -0.039 | Prob(JB): | 0.817 |
| Kurtosis: | 2.355 | Cond. No. | 19.2 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.366 | 0.265 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.624 |
| Model: | OLS | Adj. R-squared: | 0.521 |
| Method: | Least Squares | F-statistic: | 6.085 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0107 |
| Time: | 04:35:31 | Log-Likelihood: | -67.964 |
| No. Observations: | 15 | AIC: | 143.9 |
| Df Residuals: | 11 | BIC: | 146.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 62.5713 | 33.920 | 1.845 | 0.092 | -12.087 137.229 |
| C(dose)[T.1] | -58.6978 | 61.057 | -0.961 | 0.357 | -193.083 75.687 |
| expression | 1.1087 | 7.404 | 0.150 | 0.884 | -15.187 17.405 |
| expression:C(dose)[T.1] | 27.0086 | 14.483 | 1.865 | 0.089 | -4.867 58.885 |
| Omnibus: | 3.853 | Durbin-Watson: | 1.423 |
| Prob(Omnibus): | 0.146 | Jarque-Bera (JB): | 2.318 |
| Skew: | -0.963 | Prob(JB): | 0.314 |
| Kurtosis: | 2.991 | Cond. No. | 49.0 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.505 |
| Model: | OLS | Adj. R-squared: | 0.423 |
| Method: | Least Squares | F-statistic: | 6.123 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0147 |
| Time: | 04:35:31 | Log-Likelihood: | -70.025 |
| No. Observations: | 15 | AIC: | 146.0 |
| Df Residuals: | 12 | BIC: | 148.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 31.6446 | 32.502 | 0.974 | 0.349 | -39.171 102.460 |
| C(dose)[T.1] | 52.2282 | 15.138 | 3.450 | 0.005 | 19.246 85.211 |
| expression | 8.1675 | 6.989 | 1.169 | 0.265 | -7.061 23.396 |
| Omnibus: | 1.596 | Durbin-Watson: | 1.271 |
| Prob(Omnibus): | 0.450 | Jarque-Bera (JB): | 1.269 |
| Skew: | -0.568 | Prob(JB): | 0.530 |
| Kurtosis: | 2.139 | Cond. No. | 20.2 |
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: | 04:35:31 | 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: | 0.014 |
| Model: | OLS | Adj. R-squared: | -0.062 |
| Method: | Least Squares | F-statistic: | 0.1867 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.673 |
| Time: | 04:35:31 | Log-Likelihood: | -75.193 |
| 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 | 76.7891 | 40.343 | 1.903 | 0.079 | -10.367 163.945 |
| expression | 4.0345 | 9.337 | 0.432 | 0.673 | -16.138 24.207 |
| Omnibus: | 0.695 | Durbin-Watson: | 1.760 |
| Prob(Omnibus): | 0.706 | Jarque-Bera (JB): | 0.613 |
| Skew: | 0.032 | Prob(JB): | 0.736 |
| Kurtosis: | 2.012 | Cond. No. | 18.1 |