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 |
| 1.702 | 0.207 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.750 |
| Model: | OLS | Adj. R-squared: | 0.710 |
| Method: | Least Squares | F-statistic: | 18.95 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 6.17e-06 |
| Time: | 04:18:28 | Log-Likelihood: | -97.184 |
| No. Observations: | 23 | AIC: | 202.4 |
| Df Residuals: | 19 | BIC: | 206.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 69.9087 | 42.035 | 1.663 | 0.113 | -18.071 157.888 |
| C(dose)[T.1] | -97.7061 | 65.162 | -1.499 | 0.150 | -234.093 38.680 |
| expression | -2.9250 | 7.770 | -0.376 | 0.711 | -19.187 13.337 |
| expression:C(dose)[T.1] | 28.7972 | 12.242 | 2.352 | 0.030 | 3.175 54.420 |
| Omnibus: | 1.048 | Durbin-Watson: | 1.710 |
| Prob(Omnibus): | 0.592 | Jarque-Bera (JB): | 0.984 |
| Skew: | 0.348 | Prob(JB): | 0.611 |
| Kurtosis: | 2.264 | Cond. No. | 118. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.677 |
| Model: | OLS | Adj. R-squared: | 0.644 |
| Method: | Least Squares | F-statistic: | 20.92 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.25e-05 |
| Time: | 04:18:28 | Log-Likelihood: | -100.12 |
| No. Observations: | 23 | AIC: | 206.2 |
| Df Residuals: | 20 | BIC: | 209.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 7.6439 | 36.166 | 0.211 | 0.835 | -67.798 83.086 |
| C(dose)[T.1] | 54.5209 | 8.468 | 6.439 | 0.000 | 36.857 72.184 |
| expression | 8.6750 | 6.650 | 1.305 | 0.207 | -5.197 22.547 |
| Omnibus: | 2.328 | Durbin-Watson: | 1.714 |
| Prob(Omnibus): | 0.312 | Jarque-Bera (JB): | 1.236 |
| Skew: | 0.193 | Prob(JB): | 0.539 |
| Kurtosis: | 1.932 | Cond. No. | 47.7 |
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: | 04:18:28 | 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: | 0.006 |
| Model: | OLS | Adj. R-squared: | -0.041 |
| Method: | Least Squares | F-statistic: | 0.1305 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.721 |
| Time: | 04:18:28 | Log-Likelihood: | -113.03 |
| No. Observations: | 23 | AIC: | 230.1 |
| Df Residuals: | 21 | BIC: | 232.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 58.0485 | 60.403 | 0.961 | 0.347 | -67.567 183.664 |
| expression | 4.0866 | 11.311 | 0.361 | 0.721 | -19.435 27.608 |
| Omnibus: | 3.780 | Durbin-Watson: | 2.534 |
| Prob(Omnibus): | 0.151 | Jarque-Bera (JB): | 1.552 |
| Skew: | 0.214 | Prob(JB): | 0.460 |
| Kurtosis: | 1.802 | Cond. No. | 46.4 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.411 | 0.146 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.542 |
| Model: | OLS | Adj. R-squared: | 0.417 |
| Method: | Least Squares | F-statistic: | 4.338 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0301 |
| Time: | 04:18:28 | Log-Likelihood: | -69.444 |
| No. Observations: | 15 | AIC: | 146.9 |
| Df Residuals: | 11 | BIC: | 149.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -34.5434 | 139.068 | -0.248 | 0.808 | -340.630 271.543 |
| C(dose)[T.1] | 18.8541 | 172.517 | 0.109 | 0.915 | -360.854 398.562 |
| expression | 18.1584 | 24.687 | 0.736 | 0.477 | -36.178 72.495 |
| expression:C(dose)[T.1] | 4.5628 | 30.228 | 0.151 | 0.883 | -61.968 71.093 |
| Omnibus: | 2.956 | Durbin-Watson: | 0.629 |
| Prob(Omnibus): | 0.228 | Jarque-Bera (JB): | 2.187 |
| Skew: | -0.892 | Prob(JB): | 0.335 |
| Kurtosis: | 2.438 | Cond. No. | 196. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.541 |
| Model: | OLS | Adj. R-squared: | 0.465 |
| Method: | Least Squares | F-statistic: | 7.072 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00935 |
| Time: | 04:18:28 | Log-Likelihood: | -69.460 |
| No. Observations: | 15 | AIC: | 144.9 |
| Df Residuals: | 12 | BIC: | 147.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -51.6348 | 77.387 | -0.667 | 0.517 | -220.246 116.976 |
| C(dose)[T.1] | 44.7930 | 14.640 | 3.060 | 0.010 | 12.896 76.690 |
| expression | 21.2019 | 13.653 | 1.553 | 0.146 | -8.546 50.950 |
| Omnibus: | 2.753 | Durbin-Watson: | 0.621 |
| Prob(Omnibus): | 0.252 | Jarque-Bera (JB): | 2.062 |
| Skew: | -0.856 | Prob(JB): | 0.357 |
| Kurtosis: | 2.391 | Cond. No. | 64.3 |
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: | 04:18:28 | 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: | 0.183 |
| Model: | OLS | Adj. R-squared: | 0.120 |
| Method: | Least Squares | F-statistic: | 2.911 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.112 |
| Time: | 04:18:28 | Log-Likelihood: | -73.785 |
| No. Observations: | 15 | AIC: | 151.6 |
| Df Residuals: | 13 | BIC: | 153.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -74.0806 | 98.753 | -0.750 | 0.467 | -287.424 139.263 |
| expression | 29.2933 | 17.170 | 1.706 | 0.112 | -7.801 66.388 |
| Omnibus: | 4.373 | Durbin-Watson: | 1.519 |
| Prob(Omnibus): | 0.112 | Jarque-Bera (JB): | 1.315 |
| Skew: | 0.004 | Prob(JB): | 0.518 |
| Kurtosis: | 1.550 | Cond. No. | 63.7 |