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 |
0.079 | 0.781 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.654 |
Model: | OLS | Adj. R-squared: | 0.599 |
Method: | Least Squares | F-statistic: | 11.95 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000127 |
Time: | 04:41:58 | Log-Likelihood: | -100.91 |
No. Observations: | 23 | AIC: | 209.8 |
Df Residuals: | 19 | BIC: | 214.4 |
Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 69.8424 | 82.214 | 0.850 | 0.406 | -102.234 241.919 |
C(dose)[T.1] | 15.3905 | 95.767 | 0.161 | 0.874 | -185.052 215.833 |
expression | -2.5407 | 13.323 | -0.191 | 0.851 | -30.426 25.344 |
expression:C(dose)[T.1] | 6.6018 | 16.001 | 0.413 | 0.685 | -26.888 40.092 |
Omnibus: | 0.537 | Durbin-Watson: | 1.917 |
Prob(Omnibus): | 0.765 | Jarque-Bera (JB): | 0.614 |
Skew: | 0.136 | Prob(JB): | 0.736 |
Kurtosis: | 2.247 | Cond. No. | 183. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.650 |
Model: | OLS | Adj. R-squared: | 0.615 |
Method: | Least Squares | F-statistic: | 18.61 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.72e-05 |
Time: | 04:41:58 | Log-Likelihood: | -101.02 |
No. Observations: | 23 | AIC: | 208.0 |
Df Residuals: | 20 | BIC: | 211.4 |
Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 41.6783 | 44.861 | 0.929 | 0.364 | -51.900 135.257 |
C(dose)[T.1] | 54.6794 | 9.964 | 5.488 | 0.000 | 33.895 75.464 |
expression | 2.0362 | 7.224 | 0.282 | 0.781 | -13.032 17.105 |
Omnibus: | 0.580 | Durbin-Watson: | 1.881 |
Prob(Omnibus): | 0.748 | Jarque-Bera (JB): | 0.622 |
Skew: | 0.087 | Prob(JB): | 0.733 |
Kurtosis: | 2.213 | Cond. No. | 62.6 |
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: | 04:41:58 | 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: | 0.124 |
Model: | OLS | Adj. R-squared: | 0.082 |
Method: | Least Squares | F-statistic: | 2.976 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0992 |
Time: | 04:41:58 | Log-Likelihood: | -111.58 |
No. Observations: | 23 | AIC: | 227.2 |
Df Residuals: | 21 | BIC: | 229.4 |
Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | 178.4345 | 57.625 | 3.096 | 0.005 | 58.597 298.272 |
expression | -16.9088 | 9.802 | -1.725 | 0.099 | -37.294 3.476 |
Omnibus: | 4.159 | Durbin-Watson: | 2.301 |
Prob(Omnibus): | 0.125 | Jarque-Bera (JB): | 1.827 |
Skew: | 0.343 | Prob(JB): | 0.401 |
Kurtosis: | 1.802 | Cond. No. | 51.6 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
F-statistic | p-value | df difference |
12.375 | 0.004 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.732 |
Model: | OLS | Adj. R-squared: | 0.658 |
Method: | Least Squares | F-statistic: | 9.991 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00179 |
Time: | 04:41:58 | Log-Likelihood: | -65.438 |
No. Observations: | 15 | AIC: | 138.9 |
Df Residuals: | 11 | BIC: | 141.7 |
Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | -380.8294 | 271.605 | -1.402 | 0.188 | -978.629 216.970 |
C(dose)[T.1] | -72.1914 | 332.357 | -0.217 | 0.832 | -803.704 659.321 |
expression | 59.2912 | 35.908 | 1.651 | 0.127 | -19.742 138.325 |
expression:C(dose)[T.1] | 15.0703 | 43.745 | 0.345 | 0.737 | -81.211 111.352 |
Omnibus: | 1.636 | Durbin-Watson: | 1.565 |
Prob(Omnibus): | 0.441 | Jarque-Bera (JB): | 1.167 |
Skew: | -0.645 | Prob(JB): | 0.558 |
Kurtosis: | 2.552 | Cond. No. | 647. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
Dep. Variable: | AIM | R-squared: | 0.729 |
Model: | OLS | Adj. R-squared: | 0.683 |
Method: | Least Squares | F-statistic: | 16.11 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000399 |
Time: | 04:41:58 | Log-Likelihood: | -65.518 |
No. Observations: | 15 | AIC: | 137.0 |
Df Residuals: | 12 | BIC: | 139.2 |
Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | -457.6003 | 149.464 | -3.062 | 0.010 | -783.255 -131.945 |
C(dose)[T.1] | 42.2371 | 11.219 | 3.765 | 0.003 | 17.792 66.682 |
expression | 69.4457 | 19.741 | 3.518 | 0.004 | 26.434 112.457 |
Omnibus: | 0.901 | Durbin-Watson: | 1.636 |
Prob(Omnibus): | 0.637 | Jarque-Bera (JB): | 0.777 |
Skew: | -0.472 | Prob(JB): | 0.678 |
Kurtosis: | 2.406 | Cond. No. | 211. |
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: | 04:41:58 | 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: | 0.408 |
Model: | OLS | Adj. R-squared: | 0.363 |
Method: | Least Squares | F-statistic: | 8.964 |
Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0104 |
Time: | 04:41:58 | Log-Likelihood: | -71.367 |
No. Observations: | 15 | AIC: | 146.7 |
Df Residuals: | 13 | BIC: | 148.1 |
Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
Intercept | -534.8461 | 210.067 | -2.546 | 0.024 | -988.669 -81.023 |
expression | 82.5500 | 27.571 | 2.994 | 0.010 | 22.985 142.115 |
Omnibus: | 1.491 | Durbin-Watson: | 2.480 |
Prob(Omnibus): | 0.475 | Jarque-Bera (JB): | 0.849 |
Skew: | 0.099 | Prob(JB): | 0.654 |
Kurtosis: | 1.852 | Cond. No. | 208. |