Can someone please help I can’t figure this out :(

Define the set X = {(x,y)|x > 0,y ≤ √x}.

Kitty [74]

The set X is convex.

In geometry, a subset of an affine space over the real numbers, or more broadly a subset of a Euclidean space, is said to be convex if it contains the entire line segment connecting any two points in the subset. A solid cube is an example of a convex set, whereas anything hollow or with an indent, such as a crescent shape, is not. Alternatively, a convex region is a subset that crosses every line into a single line segment.

b)The set X is convex as any two points on the set X is included in the whole set as x>0. So a line joining any two points on the set X is completely inside the set x.

c)set X is not a closed set as the compliment of the set is not an open set.

d)Set X is not bounded. If a set S contains both upper and lower bounds, it is said to be bounded. A set of real numbers is therefore said to be bounded if it fits inside a defined range. hence set x is not bounded.

To learn more about convex sets:

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3 0

2 years ago

Omg plz help it would mean so much

liraira [26]

Answer: 1st blank- 2.4

2nd blank- 3.6

3rd blank- 1.4

Step-by-step explanation: you have to plug in the y and subtract by the last number to get x

7 0

3 years ago

Read 2 more answers

A chess club with 40 members is electing a new president. Hong received 36

Blababa [14]

36/40 = 9/10
and convert into percentage would be (9/10 )*100 which would be 90

6 0

3 years ago

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) Study cited that the calcium levels in men and women ages 70 years or older are summarized that sample size of 55 men with mea

sergey [27]

Answer:

0.5188 = 51.88% probability of obtaining a difference between sample means of 110 mg or more

Step-by-step explanation:

To solve this question, we need to understand the normal probability distribution, the central limit theorem and difference between normal variables.

Normal Probability Distribution:

Problems of normal distributions can be solved using the z-score formula.

In a set with mean $\mu$ and standard deviation $\sigma$ , the z-score 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 p-value, 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 $s = \frac{\sigma}{\sqrt{n}}$ .

For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.

Subtraction of normal sample means:

When we subtract two distribution of the sample means, which are normal, the mean is the subtraction of the means, while the standard deviation is the square root of the sum of standard errors squared.

Sample size of 55 men with mean 785 and standard deviation 489

This means that:

$\mu_A = 785$