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 |
| 1.071 | 0.313 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.693 |
| Model: | OLS | Adj. R-squared: | 0.645 |
| Method: | Least Squares | F-statistic: | 14.32 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 4.08e-05 |
| Time: | 17:39:22 | Log-Likelihood: | -99.511 |
| No. Observations: | 23 | AIC: | 207.0 |
| Df Residuals: | 19 | BIC: | 211.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 62.8767 | 58.037 | 1.083 | 0.292 | -58.596 184.349 |
| C(dose)[T.1] | 189.9451 | 105.644 | 1.798 | 0.088 | -31.171 411.061 |
| expression | -1.3306 | 8.864 | -0.150 | 0.882 | -19.883 17.222 |
| expression:C(dose)[T.1] | -20.1822 | 15.768 | -1.280 | 0.216 | -53.185 12.821 |
| Omnibus: | 0.151 | Durbin-Watson: | 1.803 |
| Prob(Omnibus): | 0.927 | Jarque-Bera (JB): | 0.032 |
| Skew: | 0.049 | Prob(JB): | 0.984 |
| Kurtosis: | 2.846 | Cond. No. | 206. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.667 |
| Model: | OLS | Adj. R-squared: | 0.634 |
| Method: | Least Squares | F-statistic: | 20.02 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 1.68e-05 |
| Time: | 17:39:22 | Log-Likelihood: | -100.46 |
| No. Observations: | 23 | AIC: | 206.9 |
| Df Residuals: | 20 | BIC: | 210.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 104.4244 | 48.872 | 2.137 | 0.045 | 2.480 206.369 |
| C(dose)[T.1] | 55.1755 | 8.727 | 6.323 | 0.000 | 36.972 73.379 |
| expression | -7.7083 | 7.447 | -1.035 | 0.313 | -23.242 7.826 |
| Omnibus: | 0.032 | Durbin-Watson: | 1.823 |
| Prob(Omnibus): | 0.984 | Jarque-Bera (JB): | 0.244 |
| Skew: | 0.023 | Prob(JB): | 0.885 |
| Kurtosis: | 2.497 | Cond. No. | 78.1 |
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: | Tue, 28 Jan 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 17:39:22 | 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: | 0.001 |
| Model: | OLS | Adj. R-squared: | -0.046 |
| Method: | Least Squares | F-statistic: | 0.02314 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.881 |
| Time: | 17:39:22 | Log-Likelihood: | -113.09 |
| 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 | 67.2923 | 81.993 | 0.821 | 0.421 | -103.222 237.807 |
| expression | 1.8745 | 12.322 | 0.152 | 0.881 | -23.750 27.499 |
| Omnibus: | 3.193 | Durbin-Watson: | 2.491 |
| Prob(Omnibus): | 0.203 | Jarque-Bera (JB): | 1.568 |
| Skew: | 0.302 | Prob(JB): | 0.457 |
| Kurtosis: | 1.873 | Cond. No. | 77.3 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.023 | 0.882 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.455 |
| Model: | OLS | Adj. R-squared: | 0.306 |
| Method: | Least Squares | F-statistic: | 3.059 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0735 |
| Time: | 17:39:22 | Log-Likelihood: | -70.750 |
| No. Observations: | 15 | AIC: | 149.5 |
| Df Residuals: | 11 | BIC: | 152.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 86.7201 | 192.818 | 0.450 | 0.662 | -337.669 511.109 |
| C(dose)[T.1] | -35.2557 | 274.141 | -0.129 | 0.900 | -638.635 568.124 |
| expression | -2.5194 | 25.133 | -0.100 | 0.922 | -57.837 52.798 |
| expression:C(dose)[T.1] | 12.0689 | 38.008 | 0.318 | 0.757 | -71.586 95.724 |
| Omnibus: | 4.262 | Durbin-Watson: | 0.801 |
| Prob(Omnibus): | 0.119 | Jarque-Bera (JB): | 2.504 |
| Skew: | -1.000 | Prob(JB): | 0.286 |
| Kurtosis: | 3.099 | Cond. No. | 320. |
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: | 4.906 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0277 |
| Time: | 17:39:22 | Log-Likelihood: | -70.819 |
| 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 | 46.3112 | 139.326 | 0.332 | 0.745 | -257.255 349.877 |
| C(dose)[T.1] | 51.4957 | 21.814 | 2.361 | 0.036 | 3.968 99.023 |
| expression | 2.7579 | 18.134 | 0.152 | 0.882 | -36.752 42.268 |
| Omnibus: | 2.922 | Durbin-Watson: | 0.806 |
| Prob(Omnibus): | 0.232 | Jarque-Bera (JB): | 1.941 |
| Skew: | -0.868 | Prob(JB): | 0.379 |
| Kurtosis: | 2.703 | Cond. No. | 132. |
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: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 17:39:22 | 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: | 0.194 |
| Model: | OLS | Adj. R-squared: | 0.132 |
| Method: | Least Squares | F-statistic: | 3.136 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.100 |
| Time: | 17:39:22 | Log-Likelihood: | -73.679 |
| No. Observations: | 15 | AIC: | 151.4 |
| Df Residuals: | 13 | BIC: | 152.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 287.7692 | 109.995 | 2.616 | 0.021 | 50.140 525.399 |
| expression | -26.9119 | 15.198 | -1.771 | 0.100 | -59.745 5.921 |
| Omnibus: | 0.324 | Durbin-Watson: | 1.203 |
| Prob(Omnibus): | 0.851 | Jarque-Bera (JB): | 0.463 |
| Skew: | -0.026 | Prob(JB): | 0.793 |
| Kurtosis: | 2.141 | Cond. No. | 88.9 |