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 |
| 0.794 | 0.383 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.689 |
| Model: | OLS | Adj. R-squared: | 0.640 |
| Method: | Least Squares | F-statistic: | 14.02 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 4.67e-05 |
| Time: | 04:55:28 | Log-Likelihood: | -99.680 |
| No. Observations: | 23 | AIC: | 207.4 |
| Df Residuals: | 19 | BIC: | 211.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 14.4335 | 125.519 | 0.115 | 0.910 | -248.281 277.148 |
| C(dose)[T.1] | -335.5237 | 307.817 | -1.090 | 0.289 | -979.793 308.745 |
| expression | 4.5045 | 14.200 | 0.317 | 0.755 | -25.216 34.225 |
| expression:C(dose)[T.1] | 44.7322 | 35.263 | 1.269 | 0.220 | -29.074 118.538 |
| Omnibus: | 0.181 | Durbin-Watson: | 2.024 |
| Prob(Omnibus): | 0.913 | Jarque-Bera (JB): | 0.016 |
| Skew: | 0.028 | Prob(JB): | 0.992 |
| Kurtosis: | 2.883 | Cond. No. | 742. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.662 |
| Model: | OLS | Adj. R-squared: | 0.629 |
| Method: | Least Squares | F-statistic: | 19.63 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.92e-05 |
| Time: | 04:55:28 | Log-Likelihood: | -100.61 |
| No. Observations: | 23 | AIC: | 207.2 |
| Df Residuals: | 20 | BIC: | 210.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -49.6131 | 116.654 | -0.425 | 0.675 | -292.949 193.723 |
| C(dose)[T.1] | 54.7995 | 8.756 | 6.259 | 0.000 | 36.535 73.064 |
| expression | 11.7578 | 13.194 | 0.891 | 0.383 | -15.764 39.280 |
| Omnibus: | 0.005 | Durbin-Watson: | 2.053 |
| Prob(Omnibus): | 0.998 | Jarque-Bera (JB): | 0.132 |
| Skew: | -0.019 | Prob(JB): | 0.936 |
| Kurtosis: | 2.630 | Cond. No. | 242. |
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: | 04:55:28 | 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: | 0.001 |
| Model: | OLS | Adj. R-squared: | -0.046 |
| Method: | Least Squares | F-statistic: | 0.02922 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.866 |
| Time: | 04:55:28 | Log-Likelihood: | -113.09 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 112.3307 | 190.935 | 0.588 | 0.563 | -284.740 509.401 |
| expression | -3.7185 | 21.755 | -0.171 | 0.866 | -48.960 41.523 |
| Omnibus: | 3.291 | Durbin-Watson: | 2.476 |
| Prob(Omnibus): | 0.193 | Jarque-Bera (JB): | 1.582 |
| Skew: | 0.298 | Prob(JB): | 0.453 |
| Kurtosis: | 1.862 | Cond. No. | 235. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.372 | 0.553 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.495 |
| Model: | OLS | Adj. R-squared: | 0.357 |
| Method: | Least Squares | F-statistic: | 3.590 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0499 |
| Time: | 04:55:28 | Log-Likelihood: | -70.180 |
| No. Observations: | 15 | AIC: | 148.4 |
| Df Residuals: | 11 | BIC: | 151.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 365.5109 | 300.305 | 1.217 | 0.249 | -295.456 1026.478 |
| C(dose)[T.1] | -282.6622 | 417.839 | -0.676 | 0.513 | -1202.321 636.996 |
| expression | -33.3779 | 33.602 | -0.993 | 0.342 | -107.336 40.580 |
| expression:C(dose)[T.1] | 37.0985 | 46.389 | 0.800 | 0.441 | -65.003 139.200 |
| Omnibus: | 3.389 | Durbin-Watson: | 1.179 |
| Prob(Omnibus): | 0.184 | Jarque-Bera (JB): | 2.077 |
| Skew: | -0.910 | Prob(JB): | 0.354 |
| Kurtosis: | 2.896 | Cond. No. | 653. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.465 |
| Model: | OLS | Adj. R-squared: | 0.376 |
| Method: | Least Squares | F-statistic: | 5.222 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0234 |
| Time: | 04:55:28 | Log-Likelihood: | -70.604 |
| No. Observations: | 15 | AIC: | 147.2 |
| Df Residuals: | 12 | BIC: | 149.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 191.6748 | 204.070 | 0.939 | 0.366 | -252.954 636.304 |
| C(dose)[T.1] | 51.2480 | 15.862 | 3.231 | 0.007 | 16.687 85.809 |
| expression | -13.9125 | 22.816 | -0.610 | 0.553 | -63.623 35.798 |
| Omnibus: | 2.449 | Durbin-Watson: | 0.911 |
| Prob(Omnibus): | 0.294 | Jarque-Bera (JB): | 1.793 |
| Skew: | -0.805 | Prob(JB): | 0.408 |
| Kurtosis: | 2.474 | Cond. No. | 241. |
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: | 04:55:28 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.077 |
| Method: | Least Squares | F-statistic: | 0.003458 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.954 |
| Time: | 04:55:28 | Log-Likelihood: | -75.298 |
| No. Observations: | 15 | AIC: | 154.6 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 78.1473 | 264.098 | 0.296 | 0.772 | -492.401 648.696 |
| expression | 1.7226 | 29.293 | 0.059 | 0.954 | -61.560 65.005 |
| Omnibus: | 0.573 | Durbin-Watson: | 1.612 |
| Prob(Omnibus): | 0.751 | Jarque-Bera (JB): | 0.569 |
| Skew: | 0.029 | Prob(JB): | 0.753 |
| Kurtosis: | 2.048 | Cond. No. | 237. |