Μ = 500, population mean
σ = 110, population stadard deviation
The given table is
z 0.00 0.25 0.35 0.45 1.00 1.26 1.35 1.36
P 0.5000 0.5987 0.6368 0.6736 0.8413 0.8961 0.9115 0.9131
Range of random variable is X = [350, 550].
Calculate z-score for x = 350.
z = (350 - 500)/110 = -1.364
From the given tables,
The probability at x = 350 is
1 - 9131 = 0.0869
Calculate the z-score for x = 550.
z = (550 - 500)/110 = 0.454
From the given tables,
The probability at x = 550 is 0.6736
The probability that x =[350,550] is
0.6736 - 0.0869 = 0.5867
Answer: 0.5867 (or 58.7%)
Answer:
8% or 0.08
Step-by-step explanation:
Probability of missing the first pass = 40% = 0.40
Probability of missing the second pass = 20% = 0.20
We have to find the probability that he misses both the passes. Since the two passes are independent of each other, the probability that he misses two passes will be:
Probability of missing 1st pass x Probability of missing 2nd pass
i.e.
Probability of missing two passes in a row = 0.40 x 0.20 = 0.08 = 8%
Thus, there is 8% probability that he misses two passes in a row.
A score of 85 would be 1 standard deviation from the mean, 74. Using the 68-95-99.7 rule, we know that 68% of normally distributed data falls within 1 standard deviation of the mean. This means that 100%-68% = 32% of the data is either higher or lower. 32/2 = 16% of the data will be higher than 1 standard deviation from the mean and 16% of the data will be lower than 1 standard deviation from the mean. This means that 16% of the graduating seniors should have a score above 85%.
Answer:
c) 44000 miles
Step-by-step explanation:
The regression line has the following format:

In which b is the slope(how much each mile costs) and a is the fixed number of miles flown.
In this problem, we have that:
. So

A person who spent $ 2000 is predicted to have flown:
This is y when x = 2000.


So the correct answer is:
c) 44000 miles
Question: The sample data and the scatter plot was not added to your question. See the attached file for the scatter plot.
Answer: Yes
Step-by-step explanation:
From scatter plot, it was discovered that there is a linear relationship between the two variables and both variables are quantitative.
Therefore, it appropriate to use the correlation coefficient to describe the strength of the relationship between "Time" and "Fish Quality"?