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 |
| 1.583 | 0.223 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.681 |
| Model: | OLS | Adj. R-squared: | 0.631 |
| Method: | Least Squares | F-statistic: | 13.55 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.80e-05 |
| Time: | 05:15:10 | Log-Likelihood: | -99.947 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 19 | BIC: | 212.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 136.7249 | 61.201 | 2.234 | 0.038 | 8.629 264.820 |
| C(dose)[T.1] | -7.0984 | 92.976 | -0.076 | 0.940 | -201.700 187.503 |
| expression | -12.8110 | 9.457 | -1.355 | 0.191 | -32.605 6.983 |
| expression:C(dose)[T.1] | 9.2677 | 14.649 | 0.633 | 0.535 | -21.394 39.929 |
| Omnibus: | 0.429 | Durbin-Watson: | 1.475 |
| Prob(Omnibus): | 0.807 | Jarque-Bera (JB): | 0.549 |
| Skew: | -0.097 | Prob(JB): | 0.760 |
| Kurtosis: | 2.268 | Cond. No. | 177. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.675 |
| Model: | OLS | Adj. R-squared: | 0.642 |
| Method: | Least Squares | F-statistic: | 20.75 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.32e-05 |
| Time: | 05:15:10 | Log-Likelihood: | -100.19 |
| No. Observations: | 23 | AIC: | 206.4 |
| Df Residuals: | 20 | BIC: | 209.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 111.8472 | 46.187 | 2.422 | 0.025 | 15.502 208.193 |
| C(dose)[T.1] | 51.4639 | 8.573 | 6.003 | 0.000 | 33.582 69.346 |
| expression | -8.9487 | 7.113 | -1.258 | 0.223 | -23.787 5.889 |
| Omnibus: | 0.090 | Durbin-Watson: | 1.667 |
| Prob(Omnibus): | 0.956 | Jarque-Bera (JB): | 0.307 |
| Skew: | 0.067 | Prob(JB): | 0.858 |
| Kurtosis: | 2.450 | Cond. No. | 71.7 |
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: | 05:15:10 | 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: | 0.089 |
| Model: | OLS | Adj. R-squared: | 0.045 |
| Method: | Least Squares | F-statistic: | 2.045 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.167 |
| Time: | 05:15:10 | Log-Likelihood: | -112.04 |
| No. Observations: | 23 | AIC: | 228.1 |
| Df Residuals: | 21 | BIC: | 230.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 183.4951 | 72.889 | 2.517 | 0.020 | 31.915 335.075 |
| expression | -16.3663 | 11.443 | -1.430 | 0.167 | -40.164 7.432 |
| Omnibus: | 1.259 | Durbin-Watson: | 2.277 |
| Prob(Omnibus): | 0.533 | Jarque-Bera (JB): | 1.160 |
| Skew: | 0.460 | Prob(JB): | 0.560 |
| Kurtosis: | 2.396 | Cond. No. | 69.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.470 | 0.249 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.510 |
| Model: | OLS | Adj. R-squared: | 0.376 |
| Method: | Least Squares | F-statistic: | 3.813 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0427 |
| Time: | 05:15:10 | Log-Likelihood: | -69.953 |
| No. Observations: | 15 | AIC: | 147.9 |
| Df Residuals: | 11 | BIC: | 150.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -119.1262 | 215.634 | -0.552 | 0.592 | -593.733 355.480 |
| C(dose)[T.1] | 79.3982 | 293.408 | 0.271 | 0.792 | -566.388 725.184 |
| expression | 27.0453 | 31.218 | 0.866 | 0.405 | -41.665 95.755 |
| expression:C(dose)[T.1] | -5.8442 | 41.236 | -0.142 | 0.890 | -96.604 84.916 |
| Omnibus: | 1.844 | Durbin-Watson: | 0.908 |
| Prob(Omnibus): | 0.398 | Jarque-Bera (JB): | 1.327 |
| Skew: | -0.531 | Prob(JB): | 0.515 |
| Kurtosis: | 2.002 | Cond. No. | 381. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.509 |
| Model: | OLS | Adj. R-squared: | 0.427 |
| Method: | Least Squares | F-statistic: | 6.218 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0140 |
| Time: | 05:15:10 | Log-Likelihood: | -69.966 |
| No. Observations: | 15 | AIC: | 145.9 |
| Df Residuals: | 12 | BIC: | 148.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -96.0224 | 135.261 | -0.710 | 0.491 | -390.731 198.686 |
| C(dose)[T.1] | 37.8963 | 17.538 | 2.161 | 0.052 | -0.316 76.109 |
| expression | 23.6959 | 19.546 | 1.212 | 0.249 | -18.891 66.283 |
| Omnibus: | 1.888 | Durbin-Watson: | 0.876 |
| Prob(Omnibus): | 0.389 | Jarque-Bera (JB): | 1.331 |
| Skew: | -0.523 | Prob(JB): | 0.514 |
| Kurtosis: | 1.983 | Cond. No. | 134. |
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: | 05:15:10 | 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: | 0.318 |
| Model: | OLS | Adj. R-squared: | 0.265 |
| Method: | Least Squares | F-statistic: | 6.057 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0286 |
| Time: | 05:15:10 | Log-Likelihood: | -72.431 |
| No. Observations: | 15 | AIC: | 148.9 |
| Df Residuals: | 13 | BIC: | 150.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -236.3536 | 134.353 | -1.759 | 0.102 | -526.607 53.899 |
| expression | 46.1424 | 18.748 | 2.461 | 0.029 | 5.639 86.645 |
| Omnibus: | 1.952 | Durbin-Watson: | 1.547 |
| Prob(Omnibus): | 0.377 | Jarque-Bera (JB): | 1.242 |
| Skew: | 0.442 | Prob(JB): | 0.537 |
| Kurtosis: | 1.903 | Cond. No. | 117. |