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1/8 of her time was spent on things other than working with clients. 1/8 = 0.125 * 100 = 12.5%
All you have to do is tell what u see about the three triangles and say what the same and different about them.
675 people will have score between 85 and 120
Step-by-step explanation:
Given
Mean = 100
SD = 15
If we have to find percentage of score between two values we have to find the z-score for both values and then area under the curve for both values
z-score is given by:
for a value x:
So,
For 85:
Now we have to find the area under the curve for both values of z-score. z-score tables are used for this purpose.
So,
For z1 : 0.1587
For z2: 0.9082
The area between z11 and z2:
So the probability of score between 85 and 120 is 0.7495
As the sample is of 900 people, the people with scores between 85 and 120 will be:
900*0.7495 = 674.55 people
Rounding off to nearest whole number
675 people will have score between 85 and 120
Keywords: Probability, SD
Learn more about probability at:
#LearnwithBrainly
Answer:
The mean is the better method.
Step-by-step explanation:
The best way to meassure the average height is throught mean. The mean of a sample is the average of that sample's height, and it will be a good estimate for the population's average height.
The mode just finds the most frequent height. Even tough the most frequent height will influence the average height, knowing only what height is the most frequent one doesnt give you enough informtation about how the height is centrally distributed.
As for the median, it is fine to use the median of a sample to estimate the median of the population, but if you use the median to estimate the average height you may have a few issues. For example, if you include babies in your population, the babies will push the average height down a lot and they are far below te median height. This, as a result, will give you a median height of a sample way above the average height of the population, becuase median just weights every person's height the same, while average will weight extreme values more, in the sense that a small proportion of extreme values can push the average far from the median.
