The point (3, 18) is on the parabola y =
1 answer:
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Answer:
See figure attached and explanation below.
Step-by-step explanation:
For this case we have the following dataset given:
0.74, 0.32, 1.66, 3.59, 4.55, 6.47, 9.99, 0.7, 0.37, 0.76, 1.9, 1.77, 2.42, 1.09, 2.03, 2.69,2.41, 0.54, 8.32, 5.70, 0.75, 1.96, 3.36, 4.06, 12.48
And for this case we can use the followinf R code to create the frequency histogram.
> x<-c(0.74, 0.32, 1.66, 3.59, 4.55, 6.47, 9.99,0.7, 0.37, 0.76, 1.9, 1.77, 2.42, 1.09, 2.03, 2.69,2.41, 0.54, 8.32, 5.70, 0.75, 1.96, 3.36, 4.06,12.48)
> length(x)
[1] 25
> hist(x, prob=TRUE)
And for this case we have the histogram on the figure attached. For the number of classes we use the formula of sturges:
And for this case we have approximately 6 classes. And that's what we can see on the figure attached
As we can see most of the values are on the left so then we have a right skewed to the right and the distribution is assymetrical, with most of the values between 0 and 6
Answer:
1)
2)
Other conditions that are important are:
3) n is large
4) p is close to 1/2 or 0.5
Step-by-step explanation:
1) Previous concepts
The binomial distribution is a "DISCRETE probability distribution that summarizes the probability that a value will take one of two independent values under a given set of parameters. The assumptions for the binomial distribution are that there is only one outcome for each trial, each trial has the same probability of success, and each trial is mutually exclusive, or independent of each other".
2) Solution to the problem
Let X the random variable of interest, on this case we now that:
The probability mass function for the Binomial distribution is given as:
Where (nCx) means combinatory and it's given by this formula:
In order to apply the normal apprximation we need to satisfy these two conditions:
1)
2)
Other conditions that are important are:
3) n is large
4) p is close to 1/2 or 0.5