根据基于自动里程的线性回归模型carbigdata. Then obtain the default 95% confidence intervals for the resulting model coefficients.

Load the data and create a table.

loadcarbigOrigin = nominal(Origin); tbl = table(Horsepower,Weight,MPG,Origin);

Fit a linear regression model. Use horsepower, weight, and origin as predictor variables, and miles per gallon as the response variable.

modelspec ='mpg〜1 +马力 +重量 +起源'; mdl = fitlm(tbl,modelspec);

View the names of the coefficients.

MDL。CoefficientNames

ans = 1×9 cell array Columns 1 through 4 '(Intercept)' 'Horsepower' 'Weight' 'Origin_France' Columns 5 through 8 'Origin_Germany' 'Origin_Italy' 'Origin_Japan' 'Origin_Sweden' Column 9 'Origin_USA'

Find confidence intervals for the coefficients of the model.

ci = coefCI(mdl)

ci = 43.3611 59.9390 -0.0748 -0.0315 -0.0059 -0.0037 -17.3623 -0.3477 -15.7503 0.7434 -17.2091 0.0613 -14.5106 1.8738 -18.5820 -1.5036 -17.3114 -0.9642

根据基于自动里程的线性回归模型carbigdata. Then obtain confidence intervals for the resulting model coefficients at the 99% level.

Load the data and create a table.

loadcarbigOrigin = nominal(Origin); tbl = table(Horsepower,Weight,MPG,Origin);

Fit a linear regression model using horsepower, weight, and origin as the predictor variables, and miles per gallon as the response variable.

modelspec ='mpg〜1 +马力 +重量 +起源'; mdl = fitlm(tbl,modelspec);

找到该系数的99％置信区间。

ci = coefCI(mdl,.01)

ci = 40.7365 62.5635 -0.0816 -0.0246 -0.0062 -0.0034 -20.0560 2.3459 -18.3615 3.3546 -19.9433 2.7955 -17.1045 4.4676 -21.2858 1.2002 -19.8995 1.6238

置信区间比默认的5％置信区间宽。