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 |
| 1.128 | 0.301 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.669 |
| Model: | OLS | Adj. R-squared: | 0.616 |
| Method: | Least Squares | F-statistic: | 12.79 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 8.35e-05 |
| Time: | 22:45:53 | Log-Likelihood: | -100.40 |
| No. Observations: | 23 | AIC: | 208.8 |
| Df Residuals: | 19 | BIC: | 213.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 162.8780 | 105.256 | 1.547 | 0.138 | -57.426 383.182 |
| C(dose)[T.1] | -3.2889 | 233.354 | -0.014 | 0.989 | -491.704 485.126 |
| expression | -17.5275 | 16.949 | -1.034 | 0.314 | -53.002 17.947 |
| expression:C(dose)[T.1] | 9.0381 | 37.952 | 0.238 | 0.814 | -70.396 88.473 |
| Omnibus: | 0.232 | Durbin-Watson: | 1.781 |
| Prob(Omnibus): | 0.890 | Jarque-Bera (JB): | 0.254 |
| Skew: | -0.198 | Prob(JB): | 0.881 |
| Kurtosis: | 2.672 | Cond. No. | 392. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.668 |
| Model: | OLS | Adj. R-squared: | 0.635 |
| Method: | Least Squares | F-statistic: | 20.10 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.64e-05 |
| Time: | 22:45:53 | Log-Likelihood: | -100.43 |
| No. Observations: | 23 | AIC: | 206.9 |
| Df Residuals: | 20 | BIC: | 210.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 151.7022 | 91.967 | 1.650 | 0.115 | -40.138 343.542 |
| C(dose)[T.1] | 52.2435 | 8.594 | 6.079 | 0.000 | 34.316 70.171 |
| expression | -15.7249 | 14.803 | -1.062 | 0.301 | -46.603 15.153 |
| Omnibus: | 0.755 | Durbin-Watson: | 1.750 |
| Prob(Omnibus): | 0.686 | Jarque-Bera (JB): | 0.359 |
| Skew: | -0.305 | Prob(JB): | 0.836 |
| Kurtosis: | 2.940 | Cond. No. | 137. |
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, 03 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 22:45:54 | 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: | 0.054 |
| Model: | OLS | Adj. R-squared: | 0.009 |
| Method: | Least Squares | F-statistic: | 1.199 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.286 |
| Time: | 22:45:54 | Log-Likelihood: | -112.47 |
| No. Observations: | 23 | AIC: | 228.9 |
| Df Residuals: | 21 | BIC: | 231.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 243.1598 | 149.413 | 1.627 | 0.119 | -67.561 553.881 |
| expression | -26.5041 | 24.202 | -1.095 | 0.286 | -76.835 23.827 |
| Omnibus: | 3.403 | Durbin-Watson: | 2.208 |
| Prob(Omnibus): | 0.182 | Jarque-Bera (JB): | 1.467 |
| Skew: | 0.199 | Prob(JB): | 0.480 |
| Kurtosis: | 1.829 | Cond. No. | 135. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.535 | 0.479 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.478 |
| Model: | OLS | Adj. R-squared: | 0.336 |
| Method: | Least Squares | F-statistic: | 3.359 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0589 |
| Time: | 22:45:54 | Log-Likelihood: | -70.423 |
| No. Observations: | 15 | AIC: | 148.8 |
| Df Residuals: | 11 | BIC: | 151.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 27.3852 | 515.444 | 0.053 | 0.959 | -1107.099 1161.869 |
| C(dose)[T.1] | -170.2074 | 613.065 | -0.278 | 0.786 | -1519.554 1179.140 |
| expression | 6.3912 | 82.248 | 0.078 | 0.939 | -174.635 187.417 |
| expression:C(dose)[T.1] | 33.8849 | 97.040 | 0.349 | 0.734 | -179.698 247.468 |
| Omnibus: | 2.454 | Durbin-Watson: | 0.728 |
| Prob(Omnibus): | 0.293 | Jarque-Bera (JB): | 1.857 |
| Skew: | -0.748 | Prob(JB): | 0.395 |
| Kurtosis: | 2.143 | Cond. No. | 735. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.472 |
| Model: | OLS | Adj. R-squared: | 0.384 |
| Method: | Least Squares | F-statistic: | 5.370 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0216 |
| Time: | 22:45:54 | Log-Likelihood: | -70.506 |
| No. Observations: | 15 | AIC: | 147.0 |
| Df Residuals: | 12 | BIC: | 149.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -125.1255 | 263.514 | -0.475 | 0.643 | -699.273 449.022 |
| C(dose)[T.1] | 43.7764 | 17.090 | 2.561 | 0.025 | 6.540 81.013 |
| expression | 30.7331 | 42.021 | 0.731 | 0.479 | -60.822 122.288 |
| Omnibus: | 2.453 | Durbin-Watson: | 0.736 |
| Prob(Omnibus): | 0.293 | Jarque-Bera (JB): | 1.874 |
| Skew: | -0.767 | Prob(JB): | 0.392 |
| Kurtosis: | 2.195 | Cond. No. | 225. |
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, 03 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 22:45:54 | 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: | 0.184 |
| Model: | OLS | Adj. R-squared: | 0.121 |
| Method: | Least Squares | F-statistic: | 2.927 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.111 |
| Time: | 22:45:55 | Log-Likelihood: | -73.777 |
| 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 | -398.5835 | 287.873 | -1.385 | 0.189 | -1020.496 223.329 |
| expression | 77.4050 | 45.244 | 1.711 | 0.111 | -20.339 175.149 |
| Omnibus: | 0.328 | Durbin-Watson: | 1.424 |
| Prob(Omnibus): | 0.849 | Jarque-Bera (JB): | 0.465 |
| Skew: | 0.031 | Prob(JB): | 0.792 |
| Kurtosis: | 2.140 | Cond. No. | 204. |