Answer:
Randomized block design
Step-by-step explanation:
From the question, we can see the following:
- There are 30 plants of each variety. This means that they are divided into variety subgroups which we will call blocks.
- Now, we are told each plant in each block all are potted in the same amount and type of soil, given the same amount of water, and exposed to the same amount of light. This means that each plant in each block is assigned a treatment condition.
- The procedure is repeated by subjecting each plant one after the other in teach Block to different treatments and this will reduce variability.
Looking at all the statements above, it is clear that this is a randomized block design because a randomized block design is when the experimenter/researcher divides members/participants into subgroups called blocks in a manner that the variability within the blocks is less than the variability between the blocks. Thereafter, the participants within each block will now be randomly assigned to treatment conditions.
Answer:
X= 75, Y=30 there you go!
Step-by-step explanation:
Answer:
Its a right isosceles triangle
In 1 hour you'll have 4 cells so multiply that by 3 and you will have 12 cells in 3 hours
Answer:
The probability that the sample mean would differ from the population mean by more than 2.6 mm is 0.0043.
Step-by-step explanation:
According to the Central Limit Theorem if we have a population with mean μ and standard deviation σ and appropriately huge random samples (n > 30) are selected from the population with replacement, then the distribution of the sample means will be approximately normally distributed.
Then, the mean of the distribution of sample mean is given by,
And the standard deviation of the distribution of sample mean is given by,
The information provided is:
<em>μ</em> = 144 mm
<em>σ</em> = 7 mm
<em>n</em> = 50.
Since <em>n</em> = 50 > 30, the Central limit theorem can be applied to approximate the sampling distribution of sample mean.
Compute the probability that the sample mean would differ from the population mean by more than 2.6 mm as follows:
*Use a <em>z</em>-table for the probability.
Thus, the probability that the sample mean would differ from the population mean by more than 2.6 mm is 0.0043.