The answer is the first one,
76%
Yes, there's am equation that shows this pretty easily,
depending on how many exponents there are is how many times you'll move the decimal point left or right( depending if the decimal is positive or negative, if positive the to the right, if negative the left)
The answers are shorter, higher, 2cm, 15cm, 20% and 15%.
Step-by-step explanation:
Step 1; From the given data Hugo measured his plant higher than its original height. Hugo measured his plant as 12 cm while it was actually 10 cm. So Hugo's measurement was off by 2cm. Hugo's percent error is given by dividing the difference in values by the actual value.= multiplied by 100.
% error = (difference in values / actual value) × 100
Hugo's % error = (12 - 10)/ 10 × 100 = 2/10 × 100 = 20%.
Step 2; Koby measured his plant lower than his plant's original height. He measured it to be 85 cm while it was 100 cm tall. So Koby's measurement was off by 15cm. The percent error is calculated in the same way.
% error = (difference in values / actual value) × 100
Koby's % error = (100 - 85)/ 100 × 100 = 15/100 × 100 = 15%.
Step 3; Hugo had a percent error of 20% while Koby has a percent error of 15%. So Hugo had a higher percent error of the two.
Answer:
D. To produce treatment groups with similar characteristics.
Step-by-step explanation:
Random Assignment is the process of classifying the participants into groups. A group such as the one in the question could be classified into the treatment or experimental groups and the placebo groups. The importance of random assignment is to ensure that any differences in the group is as a result of the treatment and not any other factor. Therefore the participants must have similar characteristics so that any observed differences in the experiment, can be attributed to the treatment.
Random selection is used by experimenters in research work for the purpose of ensuring that all members of the group have an equal chance of being selected as participants of the study. In this instance, the researcher is trying to ensure that the number of participants in the study is a true representation of the entire population.