The conditional relative frequency table was generated using data that compares the favorite subjects of male and female student
s at a high school. The survey was given to 120 male students and 180 female students.
How many students in the survey said that math was their favorite subject?
A) 42
B) 81
C) 120
D) 123
How many students in the survey said that math was their favorite subject?
A) 42
B) 81
C) 120
D) 123
2 answers:
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Answer:
see the explanation
Step-by-step explanation:
<u><em>Verify each case</em></u>
Part 1) The graph show two identical lines, then the system has infinitely many solutions
Part 2) The graph show two perpendicular lines.
Remember that the solution of the system is the intersection point both graphs
The intersection point is (2,6)
therefore
The system has one solution at (2,6)
Part 3) The graph show two intersecting lines at (-6,-2)
Remember that the solution of the system is the intersection point both graphs
The intersection point is (-6,-2)
therefore
The system has one solution at (-6,-2)
Answer:
a) 0.011 = 1.1% probability that the sample mean hardness for a random sample of 17 pins is at least 51
b) 0.0001 = 0.1% probability that the sample mean hardness for a random sample of 45 pins is at least 51
Step-by-step explanation:
To solve this question, we need to understand the normal probability distribution and the central limit theorem.
Normal probability distribution
Problems of normally distributed samples are solved using the z-score formula.
In a set with mean and standard deviation
, the zscore of a measure X is given by:
The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the pvalue, we get the probability that the value of the measure is greater than X.
Central Limit Theorem
The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean and standard deviation
, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean
and standard deviation
.
For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.
In this problem, we have that:
(a) If the distribution is normal, what is the probability that the sample mean hardness for a random sample of 17 pins is at least 51?
Here
This probability is 1 subtracted by the pvalue of Z when X = 51. So
By the Central Limit Theorem
has a pvalue of 0.9890
1 - 0.989 = 0.011
0.011 = 1.1% probability that the sample mean hardness for a random sample of 17 pins is at least 51
(b) What is the (approximate) probability that the sample mean hardness for a random sample of 45 pins is at least 51?
Here
has a pvalue of 0.9999
1 - 0.9999 = 0.0001
0.0001 = 0.1% probability that the sample mean hardness for a random sample of 45 pins is at least 51