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 |
| 2.059 | 0.167 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.682 |
| Model: | OLS | Adj. R-squared: | 0.632 |
| Method: | Least Squares | F-statistic: | 13.57 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.74e-05 |
| Time: | 06:20:55 | Log-Likelihood: | -99.934 |
| 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 | 23.8141 | 29.333 | 0.812 | 0.427 | -37.581 85.209 |
| C(dose)[T.1] | 55.1994 | 43.225 | 1.277 | 0.217 | -35.271 145.670 |
| expression | 8.5913 | 8.120 | 1.058 | 0.303 | -8.405 25.588 |
| expression:C(dose)[T.1] | -0.6384 | 11.886 | -0.054 | 0.958 | -25.516 24.239 |
| Omnibus: | 0.830 | Durbin-Watson: | 1.924 |
| Prob(Omnibus): | 0.660 | Jarque-Bera (JB): | 0.725 |
| Skew: | -0.077 | Prob(JB): | 0.696 |
| Kurtosis: | 2.144 | Cond. No. | 50.3 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.682 |
| Model: | OLS | Adj. R-squared: | 0.650 |
| Method: | Least Squares | F-statistic: | 21.43 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.06e-05 |
| Time: | 06:20:55 | Log-Likelihood: | -99.936 |
| No. Observations: | 23 | AIC: | 205.9 |
| Df Residuals: | 20 | BIC: | 209.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 24.8683 | 21.249 | 1.170 | 0.256 | -19.456 69.193 |
| C(dose)[T.1] | 52.9238 | 8.356 | 6.334 | 0.000 | 35.494 70.353 |
| expression | 8.2933 | 5.780 | 1.435 | 0.167 | -3.764 20.351 |
| Omnibus: | 0.822 | Durbin-Watson: | 1.916 |
| Prob(Omnibus): | 0.663 | Jarque-Bera (JB): | 0.721 |
| Skew: | -0.074 | Prob(JB): | 0.697 |
| Kurtosis: | 2.145 | Cond. No. | 20.0 |
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: | 06:20:55 | 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: | 0.044 |
| Model: | OLS | Adj. R-squared: | -0.002 |
| Method: | Least Squares | F-statistic: | 0.9558 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.339 |
| Time: | 06:20:55 | Log-Likelihood: | -112.59 |
| No. Observations: | 23 | AIC: | 229.2 |
| Df Residuals: | 21 | BIC: | 231.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 45.6843 | 35.520 | 1.286 | 0.212 | -28.184 119.552 |
| expression | 9.5555 | 9.774 | 0.978 | 0.339 | -10.771 29.882 |
| Omnibus: | 3.235 | Durbin-Watson: | 2.493 |
| Prob(Omnibus): | 0.198 | Jarque-Bera (JB): | 1.559 |
| Skew: | 0.291 | Prob(JB): | 0.459 |
| Kurtosis: | 1.865 | Cond. No. | 19.6 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.279 | 0.280 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.507 |
| Model: | OLS | Adj. R-squared: | 0.373 |
| Method: | Least Squares | F-statistic: | 3.770 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0440 |
| Time: | 06:20:55 | Log-Likelihood: | -69.996 |
| No. Observations: | 15 | AIC: | 148.0 |
| Df Residuals: | 11 | BIC: | 150.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 117.3154 | 69.076 | 1.698 | 0.118 | -34.719 269.350 |
| C(dose)[T.1] | 31.9548 | 79.261 | 0.403 | 0.695 | -142.497 206.407 |
| expression | -14.9570 | 20.428 | -0.732 | 0.479 | -59.920 30.006 |
| expression:C(dose)[T.1] | 7.4770 | 22.152 | 0.338 | 0.742 | -41.280 56.234 |
| Omnibus: | 3.810 | Durbin-Watson: | 0.919 |
| Prob(Omnibus): | 0.149 | Jarque-Bera (JB): | 2.150 |
| Skew: | -0.926 | Prob(JB): | 0.341 |
| Kurtosis: | 3.105 | Cond. No. | 69.8 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.502 |
| Model: | OLS | Adj. R-squared: | 0.419 |
| Method: | Least Squares | F-statistic: | 6.045 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0153 |
| Time: | 06:20:55 | Log-Likelihood: | -70.073 |
| No. Observations: | 15 | AIC: | 146.1 |
| Df Residuals: | 12 | BIC: | 148.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 96.1071 | 27.614 | 3.480 | 0.005 | 35.942 156.272 |
| C(dose)[T.1] | 58.0438 | 16.884 | 3.438 | 0.005 | 21.256 94.832 |
| expression | -8.5983 | 7.603 | -1.131 | 0.280 | -25.164 7.968 |
| Omnibus: | 4.051 | Durbin-Watson: | 0.918 |
| Prob(Omnibus): | 0.132 | Jarque-Bera (JB): | 2.274 |
| Skew: | -0.951 | Prob(JB): | 0.321 |
| Kurtosis: | 3.156 | Cond. No. | 16.2 |
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: | 06:20:55 | 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: | 0.011 |
| Model: | OLS | Adj. R-squared: | -0.065 |
| Method: | Least Squares | F-statistic: | 0.1484 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.706 |
| Time: | 06:20:55 | Log-Likelihood: | -75.215 |
| No. Observations: | 15 | AIC: | 154.4 |
| Df Residuals: | 13 | BIC: | 155.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 80.0224 | 36.836 | 2.172 | 0.049 | 0.442 159.603 |
| expression | 3.5128 | 9.120 | 0.385 | 0.706 | -16.190 23.216 |
| Omnibus: | 0.622 | Durbin-Watson: | 1.468 |
| Prob(Omnibus): | 0.733 | Jarque-Bera (JB): | 0.589 |
| Skew: | 0.059 | Prob(JB): | 0.745 |
| Kurtosis: | 2.036 | Cond. No. | 15.6 |