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 |
| 0.410 | 0.529 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.660 |
| Model: | OLS | Adj. R-squared: | 0.606 |
| Method: | Least Squares | F-statistic: | 12.27 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000107 |
| Time: | 04:51:42 | Log-Likelihood: | -100.71 |
| No. Observations: | 23 | AIC: | 209.4 |
| Df Residuals: | 19 | BIC: | 214.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 40.2418 | 44.735 | 0.900 | 0.380 | -53.389 133.872 |
| C(dose)[T.1] | 17.7786 | 83.870 | 0.212 | 0.834 | -157.763 193.321 |
| expression | 3.3276 | 10.558 | 0.315 | 0.756 | -18.770 25.425 |
| expression:C(dose)[T.1] | 9.2173 | 20.779 | 0.444 | 0.662 | -34.274 52.709 |
| Omnibus: | 0.745 | Durbin-Watson: | 1.882 |
| Prob(Omnibus): | 0.689 | Jarque-Bera (JB): | 0.682 |
| Skew: | -0.001 | Prob(JB): | 0.711 |
| Kurtosis: | 2.156 | Cond. No. | 96.6 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.622 |
| Method: | Least Squares | F-statistic: | 19.08 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.31e-05 |
| Time: | 04:51:42 | Log-Likelihood: | -100.83 |
| No. Observations: | 23 | AIC: | 207.7 |
| Df Residuals: | 20 | BIC: | 211.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 30.2544 | 37.871 | 0.799 | 0.434 | -48.744 109.253 |
| C(dose)[T.1] | 54.7603 | 8.961 | 6.111 | 0.000 | 36.068 73.453 |
| expression | 5.7072 | 8.909 | 0.641 | 0.529 | -12.877 24.291 |
| Omnibus: | 1.015 | Durbin-Watson: | 1.778 |
| Prob(Omnibus): | 0.602 | Jarque-Bera (JB): | 0.784 |
| Skew: | -0.038 | Prob(JB): | 0.676 |
| Kurtosis: | 2.099 | Cond. No. | 38.3 |
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: | 04:51:42 | 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: | 0.014 |
| Model: | OLS | Adj. R-squared: | -0.033 |
| Method: | Least Squares | F-statistic: | 0.2983 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.591 |
| Time: | 04:51:43 | Log-Likelihood: | -112.94 |
| No. Observations: | 23 | AIC: | 229.9 |
| Df Residuals: | 21 | BIC: | 232.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 111.4842 | 58.600 | 1.902 | 0.071 | -10.381 233.349 |
| expression | -7.7900 | 14.262 | -0.546 | 0.591 | -37.450 21.870 |
| Omnibus: | 3.858 | Durbin-Watson: | 2.469 |
| Prob(Omnibus): | 0.145 | Jarque-Bera (JB): | 1.713 |
| Skew: | 0.312 | Prob(JB): | 0.425 |
| Kurtosis: | 1.817 | Cond. No. | 35.6 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.240 | 0.287 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.536 |
| Model: | OLS | Adj. R-squared: | 0.410 |
| Method: | Least Squares | F-statistic: | 4.238 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0321 |
| Time: | 04:51:43 | Log-Likelihood: | -69.539 |
| 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 | 74.4401 | 78.930 | 0.943 | 0.366 | -99.285 248.165 |
| C(dose)[T.1] | 148.1223 | 108.512 | 1.365 | 0.200 | -90.710 386.955 |
| expression | -1.7576 | 19.592 | -0.090 | 0.930 | -44.879 41.364 |
| expression:C(dose)[T.1] | -24.7870 | 26.931 | -0.920 | 0.377 | -84.063 34.489 |
| Omnibus: | 1.238 | Durbin-Watson: | 1.055 |
| Prob(Omnibus): | 0.539 | Jarque-Bera (JB): | 1.037 |
| Skew: | -0.544 | Prob(JB): | 0.595 |
| Kurtosis: | 2.309 | Cond. No. | 83.0 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.500 |
| Model: | OLS | Adj. R-squared: | 0.417 |
| Method: | Least Squares | F-statistic: | 6.010 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0155 |
| Time: | 04:51:43 | Log-Likelihood: | -70.095 |
| 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 | 126.7706 | 54.397 | 2.330 | 0.038 | 8.250 245.291 |
| C(dose)[T.1] | 49.2201 | 14.984 | 3.285 | 0.007 | 16.572 81.868 |
| expression | -14.8752 | 13.357 | -1.114 | 0.287 | -43.977 14.227 |
| Omnibus: | 0.681 | Durbin-Watson: | 0.905 |
| Prob(Omnibus): | 0.712 | Jarque-Bera (JB): | 0.660 |
| Skew: | -0.239 | Prob(JB): | 0.719 |
| Kurtosis: | 2.090 | Cond. No. | 31.3 |
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: | 04:51:43 | 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: | 0.051 |
| Model: | OLS | Adj. R-squared: | -0.022 |
| Method: | Least Squares | F-statistic: | 0.7016 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.417 |
| Time: | 04:51:43 | Log-Likelihood: | -74.906 |
| 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 | 152.7734 | 71.256 | 2.144 | 0.052 | -1.166 306.713 |
| expression | -14.8130 | 17.685 | -0.838 | 0.417 | -53.019 23.393 |
| Omnibus: | 1.792 | Durbin-Watson: | 1.502 |
| Prob(Omnibus): | 0.408 | Jarque-Bera (JB): | 0.935 |
| Skew: | 0.145 | Prob(JB): | 0.626 |
| Kurtosis: | 1.812 | Cond. No. | 30.8 |