We are asked to find the probability that a data value in a normal distribution is between a z-score of -1.32 and a z-score of -0.34.
The probability of a data score between two z-scores is given by formula .
Using above formula, we will get:
Now we will use normal distribution table to find probability corresponding to both z-scores as:
Now we will convert into percentage as:
Upon rounding to nearest tenth of percent, we will get:
Therefore, our required probability is 27.4% and option C is the correct choice.
Answer:
I think the answer would be 22.15 but I'm not really sure.
Answer:
He is being paid $180 on Monday.
Step-by-step explanation:
Since it asks for function notation, I'll relate the variables accordingly. So, x, the independent variable, is the one that is being adjusted. That would be the amount of miles that he is assigned so x = miles assigned. Next, the dependent variable, f(x), is the amount of cash he is paid, so f(x) = total amount paid.
Here is the function that can be used to represent the situation:
f(x) = 3.50x + 75
Now, plug in 30 to find out how much he earns after completing a 30 mile route:
f(30) = 3.50(30) + 75
f(30) = 105 + 75
f(30) = 180
Also, the $75 is a fixed amount. No variable association.
Answer:
y+3 = -6(x+9)
Step-by-step explanation:
m(x-x1)= (y-y1)
-6[x-(-9)] = [y-(-3)]
-6(x+9) = y+3
.....
Answer:
Step-by-step explanation:
The 90th percentile of a normally distributed curve occurs at 1.282 standard deviations. Similarly, the 10th percentile of a normally distributed curve occurs at -1.282 standard deviations.
To find the percentile for the television weights, use the formula:
, where is the average of the set, is some constant relevant to the percentile you're finding, and is one standard deviation.
As I mentioned previously, 90th percentile occurs at 1.282 standard deviations. The average of the set and one standard deviation is already given. Substitute , , and :
Therefore, the 90th percentile weight is 5.1282 pounds.
Repeat the process for calculating the 10th percentile weight:
The difference between these two weights is .