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 |
| 3.579 | 0.073 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.702 |
| Model: | OLS | Adj. R-squared: | 0.656 |
| Method: | Least Squares | F-statistic: | 14.95 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.08e-05 |
| Time: | 03:41:07 | Log-Likelihood: | -99.163 |
| No. Observations: | 23 | AIC: | 206.3 |
| Df Residuals: | 19 | BIC: | 210.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -29.8345 | 78.402 | -0.381 | 0.708 | -193.932 134.263 |
| C(dose)[T.1] | 62.7817 | 92.992 | 0.675 | 0.508 | -131.853 257.416 |
| expression | 16.9346 | 15.756 | 1.075 | 0.296 | -16.043 49.912 |
| expression:C(dose)[T.1] | -1.9573 | 18.644 | -0.105 | 0.917 | -40.980 37.065 |
| Omnibus: | 0.245 | Durbin-Watson: | 2.340 |
| Prob(Omnibus): | 0.885 | Jarque-Bera (JB): | 0.417 |
| Skew: | 0.175 | Prob(JB): | 0.812 |
| Kurtosis: | 2.440 | Cond. No. | 167. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.702 |
| Model: | OLS | Adj. R-squared: | 0.673 |
| Method: | Least Squares | F-statistic: | 23.59 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.46e-06 |
| Time: | 03:41:07 | Log-Likelihood: | -99.170 |
| No. Observations: | 23 | AIC: | 204.3 |
| Df Residuals: | 20 | BIC: | 207.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -22.8974 | 41.139 | -0.557 | 0.584 | -108.712 62.917 |
| C(dose)[T.1] | 53.0581 | 8.078 | 6.568 | 0.000 | 36.207 69.909 |
| expression | 15.5368 | 8.213 | 1.892 | 0.073 | -1.595 32.668 |
| Omnibus: | 0.244 | Durbin-Watson: | 2.321 |
| Prob(Omnibus): | 0.885 | Jarque-Bera (JB): | 0.400 |
| Skew: | 0.191 | Prob(JB): | 0.819 |
| Kurtosis: | 2.479 | Cond. No. | 53.2 |
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: | 03:41:07 | 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: | 0.060 |
| Model: | OLS | Adj. R-squared: | 0.016 |
| Method: | Least Squares | F-statistic: | 1.346 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.259 |
| Time: | 03:41:07 | Log-Likelihood: | -112.39 |
| No. Observations: | 23 | AIC: | 228.8 |
| Df Residuals: | 21 | BIC: | 231.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -2.4171 | 71.128 | -0.034 | 0.973 | -150.336 145.502 |
| expression | 16.5215 | 14.238 | 1.160 | 0.259 | -13.088 46.131 |
| Omnibus: | 2.057 | Durbin-Watson: | 2.676 |
| Prob(Omnibus): | 0.358 | Jarque-Bera (JB): | 1.274 |
| Skew: | 0.285 | Prob(JB): | 0.529 |
| Kurtosis: | 1.997 | Cond. No. | 52.8 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 4.191 | 0.063 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.592 |
| Model: | OLS | Adj. R-squared: | 0.481 |
| Method: | Least Squares | F-statistic: | 5.323 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0165 |
| Time: | 03:41:07 | Log-Likelihood: | -68.574 |
| No. Observations: | 15 | AIC: | 145.1 |
| Df Residuals: | 11 | BIC: | 148.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -52.6790 | 131.004 | -0.402 | 0.695 | -341.017 235.659 |
| C(dose)[T.1] | 35.8979 | 152.146 | 0.236 | 0.818 | -298.974 370.770 |
| expression | 25.0497 | 27.237 | 0.920 | 0.377 | -34.899 84.998 |
| expression:C(dose)[T.1] | 4.3187 | 32.054 | 0.135 | 0.895 | -66.232 74.869 |
| Omnibus: | 0.236 | Durbin-Watson: | 1.245 |
| Prob(Omnibus): | 0.889 | Jarque-Bera (JB): | 0.029 |
| Skew: | -0.053 | Prob(JB): | 0.986 |
| Kurtosis: | 2.814 | Cond. No. | 155. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.591 |
| Model: | OLS | Adj. R-squared: | 0.523 |
| Method: | Least Squares | F-statistic: | 8.686 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00465 |
| Time: | 03:41:07 | Log-Likelihood: | -68.587 |
| No. Observations: | 15 | AIC: | 143.2 |
| Df Residuals: | 12 | BIC: | 145.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -67.6303 | 66.714 | -1.014 | 0.331 | -212.988 77.728 |
| C(dose)[T.1] | 56.3022 | 13.988 | 4.025 | 0.002 | 25.825 86.779 |
| expression | 28.1679 | 13.760 | 2.047 | 0.063 | -1.813 58.148 |
| Omnibus: | 0.159 | Durbin-Watson: | 1.262 |
| Prob(Omnibus): | 0.923 | Jarque-Bera (JB): | 0.053 |
| Skew: | -0.064 | Prob(JB): | 0.974 |
| Kurtosis: | 2.738 | Cond. No. | 48.7 |
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: | 03:41:07 | 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: | 0.040 |
| Model: | OLS | Adj. R-squared: | -0.034 |
| Method: | Least Squares | F-statistic: | 0.5398 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.476 |
| Time: | 03:41:07 | Log-Likelihood: | -74.995 |
| No. Observations: | 15 | AIC: | 154.0 |
| Df Residuals: | 13 | BIC: | 155.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 26.4468 | 92.033 | 0.287 | 0.778 | -172.378 225.271 |
| expression | 14.4241 | 19.633 | 0.735 | 0.476 | -27.990 56.838 |
| Omnibus: | 0.127 | Durbin-Watson: | 1.746 |
| Prob(Omnibus): | 0.938 | Jarque-Bera (JB): | 0.302 |
| Skew: | 0.164 | Prob(JB): | 0.860 |
| Kurtosis: | 2.387 | Cond. No. | 45.3 |