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 |
| 1.047 | 0.318 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.667 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 12.66 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 8.88e-05 |
| Time: | 06:20:06 | Log-Likelihood: | -100.47 |
| No. Observations: | 23 | AIC: | 208.9 |
| Df Residuals: | 19 | BIC: | 213.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 138.6113 | 103.247 | 1.343 | 0.195 | -77.488 354.710 |
| C(dose)[T.1] | 62.7976 | 194.208 | 0.323 | 0.750 | -343.685 469.280 |
| expression | -10.9131 | 13.327 | -0.819 | 0.423 | -38.806 16.980 |
| expression:C(dose)[T.1] | -0.9850 | 24.733 | -0.040 | 0.969 | -52.751 50.781 |
| Omnibus: | 0.690 | Durbin-Watson: | 1.857 |
| Prob(Omnibus): | 0.708 | Jarque-Bera (JB): | 0.718 |
| Skew: | 0.211 | Prob(JB): | 0.698 |
| Kurtosis: | 2.244 | Cond. No. | 420. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.667 |
| Model: | OLS | Adj. R-squared: | 0.633 |
| Method: | Least Squares | F-statistic: | 19.99 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.70e-05 |
| Time: | 06:20:06 | 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 | 140.8231 | 84.838 | 1.660 | 0.113 | -36.146 317.793 |
| C(dose)[T.1] | 55.0713 | 8.715 | 6.319 | 0.000 | 36.892 73.251 |
| expression | -11.1991 | 10.943 | -1.023 | 0.318 | -34.025 11.627 |
| Omnibus: | 0.630 | Durbin-Watson: | 1.854 |
| Prob(Omnibus): | 0.730 | Jarque-Bera (JB): | 0.686 |
| Skew: | 0.206 | Prob(JB): | 0.710 |
| Kurtosis: | 2.261 | Cond. No. | 158. |
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: | 06:20:06 | 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: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.01533 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.903 |
| Time: | 06:20:06 | 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 | 62.1883 | 141.769 | 0.439 | 0.665 | -232.637 357.013 |
| expression | 2.2450 | 18.133 | 0.124 | 0.903 | -35.465 39.955 |
| Omnibus: | 3.209 | Durbin-Watson: | 2.498 |
| Prob(Omnibus): | 0.201 | Jarque-Bera (JB): | 1.526 |
| Skew: | 0.273 | Prob(JB): | 0.466 |
| Kurtosis: | 1.863 | Cond. No. | 156. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.216 | 0.292 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.535 |
| Model: | OLS | Adj. R-squared: | 0.408 |
| Method: | Least Squares | F-statistic: | 4.218 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0326 |
| Time: | 06:20:06 | Log-Likelihood: | -69.558 |
| No. Observations: | 15 | AIC: | 147.1 |
| Df Residuals: | 11 | BIC: | 149.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 28.2335 | 146.041 | 0.193 | 0.850 | -293.200 349.667 |
| C(dose)[T.1] | 201.7418 | 167.245 | 1.206 | 0.253 | -166.363 569.846 |
| expression | 5.7220 | 21.259 | 0.269 | 0.793 | -41.069 52.513 |
| expression:C(dose)[T.1] | -22.2662 | 24.315 | -0.916 | 0.379 | -75.783 31.250 |
| Omnibus: | 1.953 | Durbin-Watson: | 0.752 |
| Prob(Omnibus): | 0.377 | Jarque-Bera (JB): | 1.409 |
| Skew: | -0.565 | Prob(JB): | 0.494 |
| Kurtosis: | 2.011 | Cond. No. | 233. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.499 |
| Model: | OLS | Adj. R-squared: | 0.416 |
| Method: | Least Squares | F-statistic: | 5.988 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0157 |
| Time: | 06:20:06 | Log-Likelihood: | -70.109 |
| No. Observations: | 15 | AIC: | 146.2 |
| Df Residuals: | 12 | BIC: | 148.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 144.8300 | 71.047 | 2.039 | 0.064 | -9.968 299.628 |
| C(dose)[T.1] | 49.2131 | 14.998 | 3.281 | 0.007 | 16.535 81.891 |
| expression | -11.2997 | 10.248 | -1.103 | 0.292 | -33.628 11.029 |
| Omnibus: | 2.434 | Durbin-Watson: | 0.995 |
| Prob(Omnibus): | 0.296 | Jarque-Bera (JB): | 1.629 |
| Skew: | -0.609 | Prob(JB): | 0.443 |
| Kurtosis: | 1.940 | Cond. No. | 67.0 |
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: | 06:20:06 | 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: | 0.050 |
| Model: | OLS | Adj. R-squared: | -0.023 |
| Method: | Least Squares | F-statistic: | 0.6901 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.421 |
| Time: | 06:20:06 | Log-Likelihood: | -74.912 |
| No. Observations: | 15 | AIC: | 153.8 |
| Df Residuals: | 13 | BIC: | 155.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 170.8450 | 93.434 | 1.829 | 0.091 | -31.006 372.696 |
| expression | -11.2658 | 13.562 | -0.831 | 0.421 | -40.564 18.033 |
| Omnibus: | 0.302 | Durbin-Watson: | 1.833 |
| Prob(Omnibus): | 0.860 | Jarque-Bera (JB): | 0.452 |
| Skew: | -0.018 | Prob(JB): | 0.798 |
| Kurtosis: | 2.151 | Cond. No. | 66.4 |