cov(X , Y) = cov(Y, X )
cov(X , X ) = Var(X )
cov(aX , bY) = ab cov(X , Y)
cov(X , Y) = 0代表X與Y 為統計獨立。
第I 型錯誤又稱為α 風險
對固定樣本而言,若第I 型錯誤發生的機率增加時,第II 型錯誤發生的機率將
降低
若我們只有一種錯誤的風險可以控制時,應該要控制第II 型錯誤
增加樣本數時可以同時降低兩者錯誤的風險
multicollinearity may be present.
autocorrelation may be present.
the regression is good.
a nonlinear model would be a better fit.
none of the above.
independence.
equality of proportions.
equality of medians.
all of the above.
none of the above.
there is no statistical evidence that any population mean is different from any other.
no two population means are equal.
no two variances are equal.
the null hypothesis should be accepted.
there is strong statistical evidence that not all the population means are equal.
is always negative.
applies to any relationship between x and y.
is a ratio of unexplained variation to explained variation.
has the same sign as the slope of the regression line.
ranges from zero to one.
more time periods back is called :
multicollinearity.
a transformation.
autocorrelation.
variance inflation.
interaction.
repeated measures design.
Tukey design.
Latin square design.
one-way ANOVA.
randomized complete block design.
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