Try making up two of your own conditional statements with a false hypothesis and explain why they must be true.

1 answer:

6 0

Another example you can use is P:trees provide air, Q: 7 is an odd number. Write pq as a sentence. Then construct a truth table for this conditional. Solution: The conditional pq represents " If trees provide air, then 7 is an odd number." Trees provide air is the hypothesis, and 7 is an odd number is the conclusion. Note that the logical meaning of this conditional statement is not the same as its intuitive meaning. In logic, the conditional is defined to be true unless a true hypothesis leads to a false conclusion.

The implication of pq is that: since trees provide air, this makes 7 an odd number. However, intuitively, we know that this is false because the trees and the number 7have nothing to do with one another! Therefore, the logical conditional allows implications to be true even when the hypothesis and the conclusion have no logical connection

You might be interested in

I need help please

aniked [119]

The value of X must be 0. Hope this helps and a Brainliest would be appreciated

3 0

3 years ago

The cost of 5 gallons of ice cream has a variance of 49 with a mean of 37 dollars during the summer. What is the probability tha

VMariaS [17]

Answer:

0.0119 = 1.19% probability that the sample mean would be less than 35.2 dollars if a sample of 77 5-gallon pails is randomly selected

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 $\mu$ and standard deviation(which is the square root of the variance) $\sigma$ , the zscore of a measure X is given by:

$Z = \frac{X - \mu}{\sigma}$

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 $\mu$ and standard deviation $\sigma$ , the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean $\mu$ and standard deviation $s = \frac{\sigma}{\sqrt{n}}$ .