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 |
| 0.008 | 0.931 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.594 |
| Method: | Least Squares | F-statistic: | 11.72 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000142 |
| Time: | 04:15:05 | Log-Likelihood: | -101.06 |
| No. Observations: | 23 | AIC: | 210.1 |
| Df Residuals: | 19 | BIC: | 214.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 55.2227 | 112.296 | 0.492 | 0.629 | -179.815 290.261 |
| C(dose)[T.1] | 44.1335 | 135.087 | 0.327 | 0.747 | -238.608 326.875 |
| expression | -0.1640 | 18.123 | -0.009 | 0.993 | -38.095 37.767 |
| expression:C(dose)[T.1] | 1.4916 | 21.806 | 0.068 | 0.946 | -44.149 47.132 |
| Omnibus: | 0.330 | Durbin-Watson: | 1.907 |
| Prob(Omnibus): | 0.848 | Jarque-Bera (JB): | 0.492 |
| Skew: | 0.086 | Prob(JB): | 0.782 |
| Kurtosis: | 2.304 | Cond. No. | 270. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 18.51 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.82e-05 |
| Time: | 04:15:05 | Log-Likelihood: | -101.06 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 48.8488 | 61.088 | 0.800 | 0.433 | -78.579 176.276 |
| C(dose)[T.1] | 53.3532 | 8.770 | 6.084 | 0.000 | 35.059 71.647 |
| expression | 0.8663 | 9.825 | 0.088 | 0.931 | -19.628 21.361 |
| Omnibus: | 0.321 | Durbin-Watson: | 1.903 |
| Prob(Omnibus): | 0.852 | Jarque-Bera (JB): | 0.485 |
| Skew: | 0.068 | Prob(JB): | 0.785 |
| Kurtosis: | 2.302 | Cond. No. | 88.9 |
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: | 04:15:06 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.048 |
| Method: | Least Squares | F-statistic: | 0.0005463 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.982 |
| Time: | 04:15:06 | Log-Likelihood: | -113.10 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 82.0544 | 100.249 | 0.819 | 0.422 | -126.425 290.534 |
| expression | -0.3783 | 16.184 | -0.023 | 0.982 | -34.036 33.279 |
| Omnibus: | 3.384 | Durbin-Watson: | 2.485 |
| Prob(Omnibus): | 0.184 | Jarque-Bera (JB): | 1.585 |
| Skew: | 0.289 | Prob(JB): | 0.453 |
| Kurtosis: | 1.851 | Cond. No. | 88.3 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.176 | 0.683 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.612 |
| Model: | OLS | Adj. R-squared: | 0.506 |
| Method: | Least Squares | F-statistic: | 5.779 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0127 |
| Time: | 04:15:06 | Log-Likelihood: | -68.203 |
| No. Observations: | 15 | AIC: | 144.4 |
| Df Residuals: | 11 | BIC: | 147.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 306.6640 | 129.054 | 2.376 | 0.037 | 22.617 590.711 |
| C(dose)[T.1] | -312.2493 | 172.026 | -1.815 | 0.097 | -690.876 66.377 |
| expression | -39.3649 | 21.170 | -1.859 | 0.090 | -85.961 7.231 |
| expression:C(dose)[T.1] | 60.5448 | 28.881 | 2.096 | 0.060 | -3.022 124.111 |
| Omnibus: | 0.967 | Durbin-Watson: | 1.655 |
| Prob(Omnibus): | 0.617 | Jarque-Bera (JB): | 0.870 |
| Skew: | -0.463 | Prob(JB): | 0.647 |
| Kurtosis: | 2.269 | Cond. No. | 206. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.457 |
| Model: | OLS | Adj. R-squared: | 0.366 |
| Method: | Least Squares | F-statistic: | 5.044 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0257 |
| Time: | 04:15:06 | Log-Likelihood: | -70.724 |
| No. Observations: | 15 | AIC: | 147.4 |
| Df Residuals: | 12 | BIC: | 149.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 108.9552 | 99.779 | 1.092 | 0.296 | -108.445 326.355 |
| C(dose)[T.1] | 47.0968 | 16.410 | 2.870 | 0.014 | 11.343 82.851 |
| expression | -6.8330 | 16.310 | -0.419 | 0.683 | -42.370 28.704 |
| Omnibus: | 3.764 | Durbin-Watson: | 0.881 |
| Prob(Omnibus): | 0.152 | Jarque-Bera (JB): | 2.098 |
| Skew: | -0.914 | Prob(JB): | 0.350 |
| Kurtosis: | 3.120 | Cond. No. | 78.5 |
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: | 04:15:06 | 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: | 0.084 |
| Model: | OLS | Adj. R-squared: | 0.013 |
| Method: | Least Squares | F-statistic: | 1.189 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.295 |
| Time: | 04:15:06 | Log-Likelihood: | -74.644 |
| No. Observations: | 15 | AIC: | 153.3 |
| Df Residuals: | 13 | BIC: | 154.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 218.6194 | 115.002 | 1.901 | 0.080 | -29.827 467.066 |
| expression | -21.1301 | 19.378 | -1.090 | 0.295 | -62.993 20.733 |
| Omnibus: | 1.183 | Durbin-Watson: | 1.562 |
| Prob(Omnibus): | 0.554 | Jarque-Bera (JB): | 0.852 |
| Skew: | 0.265 | Prob(JB): | 0.653 |
| Kurtosis: | 1.960 | Cond. No. | 72.2 |