Which of the following regarding the power of a significance test is/are correct? select all that apply. as the effect size increases, the power decreases. as the standard deviation increases, the power of the test increases. as the β, the probability of type ii error, increases (e. g., from 0.05 to 0.10), the power of the test increases. as the sample size increases, the power of a test increases. as the α, the significance level, increases (e. g., from 0.05 to 0.10), the power of the test decreases. as the standard deviation increases, the power of the test decreases.

According to the theory on the effect size the following statements are correct:

As the standard deviation increases, the power of the test increases. An increment of the standard deviation means that a larger sample size is required for the test and a larger sample size implies that the power increases, which means that as the standard deviation increases the power also increases.

As the sample size increases, the power of a test increases. When the sample size increases the amount of information increases, this means that the probability of rejecting H0 given that H0 is false increases. In other words, the probability that the significance test performs according to reality is larger.

solution:

→c= a b , that is product of a and b is possible only when when a is m × n matrix and b is n × p matrix.

that is, number of columns of a= number of rows of b.

so, size of matrix , c= a b is , if a = (m × n)matrix

b= (n × p)matrix

then a b = [ (m × n)matrix] ×[   (n × p)matrix ]

→→c= a b= [m × p ] matrix

→→size of product a b will be equal to = number of rows of a × number of columns of b

ok so the answer to your question is simple as long as you look at the different dimensions that contain the exemplary letters that combined in the right order give you words that eventually within the 5th dimension will conform and give you the correct answer to your question. hope i , stop looking at this easily, look at it with a spectral view.

step-by-step explanation:

told you