MATHEMATICS COLLEGE

The following dot plot shows the number of cavities each of Dr. Vance's 63 patients had last month. Each dot represents a different patient. Which of the following is a typical number of cavities one patient had?

Answers

Answer 1

Answer:

Answer:

$The$ $answer$ $is$ $2$

Step-by-step explanation:

There are lots of ways we can think about the typical number of cavities.

What was the most common number of cavities?

If we split the cavities evenly among all the patients, how many cavities would each patient have?

What would be the balance point of the data?

What is the middlemost number of cavities?

The most patients had 0cavities.

If we split the cavities evenly, each patient would have 2 or 3 cavities.

If we put our dot plot on a balance scale, it would balance when the pivot was between 2 and 3 cavities.

The scale would tip if, for example, we put the pivot at 5 cavities.

There are 8 patients with 2 cavities each. About half of the rest of the patients have fewer than 2 cavities and about half have more than 2 cavities.

Of the choices, it is reasonable to say that a patient typically had about 2 cavities.

$Thank$ $you$ $for$ $reading$ , $stay$ $safe!!!$ -Written in $2/4/2021$

Answer 2

Answer:

Final answer:

The 'typical' number of cavities one patient had can be determined by finding the mode (most common number) in the data set, which should be represented in the dot plot. To do this, one would count the number of dots at each value on the dot plot. The value with the most dots would be the 'typical' number of cavities.

Explanation:

The question is asking for a 'typical' number of cavities one patient had out of Dr. Vance's 63 patients. In statistics, a typical, or 'common', value can be shown by calculating the mode, which is the number that appears most frequently in a data set.

Unfortunately, the dot plot is missing from the information provided. However, to find the mode (or typical value) using a dot plot, you would typically count how many dots are at each value on the plot. The value with the most dots (indicating the most patients with that number of cavities) is the mode. This would be the 'typical' number of cavities a patient of Dr. Vance had last month.

Let's create a hypothetical scenario. If your dot plot looked like this:

0 cavities: 10 patients
1 cavity: 15 patients
2 cavities: 24 patients
3 cavities: 8 patients
4 cavities: 6 patients

The mode would be 2 cavities because 24 patients had this amount, more than any other amount. Therefore, the 'typical' number of cavities one patient had would be 2.

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A survey of 1000 young adults nationwide found that among adults aged 18-29, 35% of women and 30% of men had tattoos. Suppose that these percentages are based on random samples of 500 women and 500 men. Using a 2.5% level of significance, can you conclude that among adults aged 18-29, the proportion of all women who have tattoos exceeds the proportion of all men who have tattoos?

Answers

Answer:

95% confidence interval for adults in age group 18-29 is (-0.007, 0.107)

Step-by-step explanation:

Given

N1 = n2 = 500

% of males = 0.30

% of females = 0.35

95% confidence interval

(p2-p1) + z(0.025) sqrt (p1q1/n1 + p2q2/n2) and (p2-p1) - z(0.025) sqrt (p1q1/n1 + p2q2/n2)

Substituting the given values, we get –

(0.35-0.30) + 1.96 sqrt (0.3*0.7/500 + 0.35*0.65/500) and (0.35-0.30) - 1.96 sqrt (0.3*0.7/500 + 0.35*0.65/500)

0.05 + (1.96 *0.029) and 0.05 - (1.96 *0.029)

0.05 + 0.057 and 0.05 – 0.057

(-0.007, 0.107)

Four friends want to share two largepizzas equally. Each pizza is cut into
ten slices. How many slices will each
person get?

Answers

Answer: 5

Step-by-step explanation:

2 pizzas ten slices eatch, so 20 slices. 20 slices devided by 4 is five. :)

3. Error Analysis Isabel says that the equation x - 2 = -(x - 2) has nosolution because a number can never be equal to its opposite. Explain the
error Isabel made

Answers

Given:

The equation is

$(x-2)=-(x-2)$

Isabel says that the given equation has no solution because a number can never be equal to its opposite.

To find:

Isabel's mistake.

Solution:

Her reason "a number can never be equal to its opposite" is not correct.

Zero is a number that can be equal to its opposite.

So, the given equation has solution for which LHS=RHS=0.

We have,

$(x-2)=-(x-2)$

$x-2=-(x)-(-2)$

$x-2=-x+2$

Add x and 2 on both sides.

$x-2+x+2=-x+2+x+2$

$2x=4$

Divide both sides by 2.

$x=(4)/(2)$

$x=2$

So, the given equation has a solution x=2.

Can someone plz help me on this

Answers

Step-by-step explanation:

I know it's not B because it has the most explanation and C is the only answer that dosnt relate

MATH

ANSWER and I will give you brainiliest

Answers

Answer:

2000

Step-by-step explanation:

If 3 days = 600 we need to know what 1 day is so we do 600 / 3

600 / 3 = 200

Then multiply 200 by 10 to find 10 days

200 * 10 = 2000

10 days = 2000

Suppose that a random sample of size 36 is to be selected from a population with mean 50 and standard deviation 7. What is the approximate probability that will be within 0.5 of the population mean?

Answers

Answer:

The probability that the sample mean will be within 0.5 of the population mean is 0.3328.

Step-by-step explanation:

It is provided that a random variable X has mean, μ = 50 andstandard deviation, σ = 7.

A random sample of size, n = 36 is selected.

According to the Central Limit Theorem if we have a population with mean μ and standard deviation σ and we take appropriately huge random samples (n ≥ 30) from the population with replacement, then the distribution of the sample mean will be approximately normally distributed.

Then, the mean of the distribution of sample mean is given by,

$\mu_(\bar x)=\mu=50$

And the standard deviation of the distribution of sample mean is given by,

$\sigma_(\bar x)=(\sigma)/(√(n))=(7)/(√(36))=1.167$

So, the distribution of the sample mean of X is N (50, 1.167²).

Compute the probability that the sample mean will be within 0.5 of the population mean as follows:

$P(|\bar X-\mu_(\bar x)|\leq 0.50)=P(-0.50<(\bar X-\mu_(\bar x))<0.50)$

$=P((-0.50)/(1167)<(\bar X-\mu_(\bar x))/(\sigma_(\bar x))<(0.50)/(1.167))\n\n=P(-0.43<Z<0.43)\n\n=P(Z<0.43)-P(Z<-0.43)\n\n=0.66640-0.33360\n\n=0.3328$

Thus, the probability that the sample mean will be within 0.5 of the population mean is 0.3328.

Final answer:

To approximate the probability that the sample mean will be within 0.5 of the population mean, we can use the Central Limit Theorem. This theorem states that the sampling distribution of the sample means will be approximately normal, regardless of the shape of the population distribution, as long as the sample size is large enough. To calculate the probability, we need to find the standard error of the mean (SE), calculate the z-score for the upper bound of 0.5 deviations above the mean, and then find the cumulative probability corresponding to that z-score using a z-table or calculator.

Explanation:

To find the approximate probability that the sample mean will be within 0.5 of the population mean, we can use the Central Limit Theorem. According to the Central Limit Theorem, the sampling distribution of the sample means will be approximately normal, regardless of the shape of the population distribution, as long as the sample size is large enough (typically n ≥ 30).

Calculate the standard error of the mean (SE) using the formula SE = σ/√n, where σ is the standard deviation of the population and n is the sample size. In this case, σ = 7 and n = 36, so SE = 7/√36 = 7/6 = 1.1667.
Next, calculate the z-score corresponding to the upper bound of 0.5 deviations from the mean by using the formula z = (X - μ)/SE, where X is the value 0.5 deviations above the mean (50 + 0.5 = 50.5 in this case), μ is the mean of the population, and SE is the standard error of the mean. The z-score for 0.5 deviations above the mean can be calculated as z = (50.5 - 50)/1.1667 ≈ 0.4292.
Finally, use a z-table or a calculator to find the probability corresponding to the z-score found in the previous step. The probability can be determined by subtracting the cumulative probability of the lower bound (z = -0.4292) from the cumulative probability of the upper bound (z = 0.4292). This can be expressed as p = P(Z < 0.4292) - P(Z < -0.4292).

Using a standard normal distribution table or a calculator, the approximate probability that the sample mean will be within 0.5 of the population mean is the difference between the cumulative probabilities of the upper and lower bounds found in step 3.

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