what is 20 plus 2 /6

Final answer:

The probability of being dealt a card greater than 2 and less than 5 from a standard deck of 52 cards is 2/13.

Explanation:

The probability of being dealt a card greater than 2 and less than 5 from a standard 52-card deck means you are looking for 3s and 4s from each suit (hearts, spades, clubs, diamonds).

For each suit, there is one 3 and one 4, making a total of 8 possible cards (2 cards per suit multiplied by 4 suits). To calculate the probability, you divide the number of favorable outcomes by the number of possible outcomes. So, this probability is 8/52, which simplifies to 2/13 when reduced.

Answer:

x=19

Step-by-step explanation:

The consecutive integers will be x, x+1 and x+2 since consecutive integers have a pattern.

So,  the equation is x+x+1+x+2=60

Solve:

x+x+1+x+2=60

3x+3=60

Subtract 3 on both sides:

3x+3-3=60-3

3x=57

Divide by 3

3x/3=57/3

x=19

Answer:

We have 40% antifreeze and 70% antifreeze and we need to make 240 liters of 64% antifreeze.

We set up 2 equations where "f" is the 40 % and "s" is the 70%

A) f + s = 240

B) .40f + .70s = (.64 * 240) (or 153.6)

To solve both equations we multiply A) by -.40

A) -.40 f  - .40 s = -96.00   then we add this to B)

B) .40f + .70s = 153.60

.30s = 57.6

s = 192

f = 48

48 liters of 40% = 19.20 liters of antifreeze

192 liters of  70% = 134.40 liters of antifreeze

That equals 153.60 liters of antifreeze in a TOTAL liquid amount of 240 liters.

Double Check

153.60 / 240 = 64% antifreeze.

Step-by-step explanation:

Answer:

B. 0.132

Step-by-step explanation:

For each time the dice is thrown, there are only two possible outcomes. Either it lands on a five, or it does not. The probability of a throw landing on a five is independent of other throws. So we use the binomial probability distribution to solve this question.

Binomial probability distribution

The binomial probability is the probability of exactly x successes on n repeated trials, and X can only have two outcomes.

[tex]P(X = x) = C_{n,x}.p^{x}.(1-p)^{n-x}[/tex]

In which [tex]C_{n,x}[/tex] is the number of different combinations of x objects from a set of n elements, given by the following formula.

[tex]C_{n,x} = \frac{n!}{x!(n-x)!}[/tex]

And p is the probability of X happening.

Timothy creates a game in which the player rolls 4 dice.

This means that [tex]n = 4[/tex]

The dice can land in 6 numbers, one of which is 5.

This means that [tex]p = \frac{1}{6}[/tex]

What is the probability in this game of having exactly two dice or more land on a five?

[tex]P(X \geq 2) = P(X = 2) + P(X = 3) + P(X = 4)[/tex]

In which

[tex]P(X = x) = C_{n,x}.p^{x}.(1-p)^{n-x}[/tex]

[tex]P(X = 2) = C_{4,2}.(\frac{1}{6})^{2}.(\frac{5}{6})^{2} = 0.116[/tex]

[tex]P(X = 3) = C_{4,2}.(\frac{1}{6})^{3}.(\frac{5}{6})^{1} = 0.015[/tex]

[tex]P(X = 4) = C_{4,4}.(\frac{1}{6})^{4}.(\frac{5}{6})^{0} = 0.001[/tex]

[tex]P(X \geq 2) = P(X = 2) + P(X = 3) + P(X = 4) = 0.116 + 0.015 + 0.001 = 0.132[/tex]

So the correct answer is:

B. 0.132

The probability of having exactly two dice or more land on a five is: 0.132.

To determine the probability of having exactly two dice or more land on a five when rolling four dice, we first need to calculate the probabilities of each possible outcome involving getting at least two fives.

1: Define the basic probabilities
Each die has 6 faces, so the probability ( p ) of rolling a five on a single die is:
[tex]\[ p = \frac{1}{6} \]The probability \( q \) of not rolling a five on a single die is:\[ q = 1 - p = \frac{5}{6} \][/tex]
2: Define the number of dice rolls
We are rolling 4 dice.

3: Calculate probabilities for 0, 1, 2, 3, and 4 fives
We use the binomial distribution formula:
[tex]\[ P(X = k) = \binom{n}{k} p^k (1-p)^{n-k} \]where \( n = 4 \), \( p = \frac{1}{6} \), and \( k \)[/tex] is the number of fives rolled.
Probability of rolling exactly 0 fives (\( k = 0 \)):
[tex]\[ P(X = 0) = \binom{4}{0} \left(\frac{1}{6}\right)^0 \left(\frac{5}{6}\right)^4 = 1 \times 1 \times \left(\frac{5}{6}\right)^4 = \left(\frac{5}{6}\right)^4 \][/tex]
Probability of rolling exactly 1 five (\( k = 1 \)):
[tex]\[ P(X = 1) = \binom{4}{1} \left(\frac{1}{6}\right)^1 \left(\frac{5}{6}\right)^3 = 4 \times \frac{1}{6} \times \left(\frac{5}{6}\right)^3 \]Probability of rolling exactly 2 fives (\( k = 2 \)):\[ P(X = 2) = \binom{4}{2} \left(\frac{1}{6}\right)^2 \left(\frac{5}{6}\right)^2 = 6 \times \left(\frac{1}{6}\right)^2 \times \left(\frac{5}{6}\right)^2 \][/tex]
[tex]Probability of rolling exactly 3 fives (\( k = 3 \)):\[ P(X = 4) = \binom{4}{4} \left(\frac{1}{6}\right)^4 \left(\frac{5}{6}\right)^0 = 1 \times \left(\frac{1}{6}\right)^4 \times 1 \])^1 \]Probability of rolling exactly 4 fives (\( k = 4 \)):[/tex]
[tex]\[ P(X = 4) = \binom{4}{4} \left(\frac{1}{6}\right)^4 \left(\frac{5}{6}\right)^0 = 1 \times \left(\frac{1}{6}\right)^4 \times 1 \][/tex]
4: Sum probabilities for \( k \geq 2 \)
We need to find \( P(X \geq 2) \), which is:
[tex]\[ P(X \geq 2) = P(X = 2) + P(X = 3) + P(X = 4) \][/tex]
5: Perform calculations
[tex]\[P(X = 0) = \left(\frac{5}{6}\right)^4 \approx 0.4823\]\[P(X = 1) = 4 \times \frac{1}{6} \times \left(\frac{5}{6}\right)^3 = 4 \times \frac{1}{6} \times \left(\frac{125}{216}\right) \approx 0.3858\]\[P(X = 2) = 6 \times \left(\frac{1}{6}\right)^2 \times \left(\frac{5}{6}\right)^2 = 6 \times \frac{1}{36} \times \frac{25}{36} = \frac{150}{1296} \approx 0.1157\][/tex]
[tex]\[P(X = 3) = 4 \times \left(\frac{1}{6}\right)^3 \times \frac{5}{6} = 4 \times \frac{1}{216} \times \frac{5}{6} = \frac{20}{1296} \approx 0.0154\]\[P(X = 4) = \left(\frac{1}{6}\right)^4 = \frac{1}{1296} \approx 0.0008\][/tex]

6: Summing up [tex]\( P(X \geq 2) \)[/tex]
[tex]\[P(X \geq 2) = P(X = 2) + P(X = 3) + P(X = 4) \approx 0.1157 + 0.0154 + 0.0008 = 0.1319\][/tex]
Rounding this to three decimal places, we get 0.132.
Thus, the probability of having exactly two dice or more land on a five is:
[tex]\[ \boxed{0.132} \][/tex].

Answer:

90% confidence interval for the proportion of all US adults ages 55 to 64 to use online dating is [0.095 , 0.148].

Step-by-step explanation:

We are given that a survey conducted in July 2015 asked a random sample of American adults whether they had ever used online dating.

The survey included 411 adults between the ages of 55 and 64, and 50 of them said that they had used online dating.

Firstly, the pivotal quantity for 90% confidence interval for the population proportion is given by;

                                P.Q. =  [tex]\frac{\hat p-p}{\sqrt{\frac{\hat p(1-\hat p)}{n} } }[/tex]  ~ N(0,1)

where, [tex]\hat p[/tex] = sample proportion of adults who said that they had used online dating =  [tex]\frac{50}{411}[/tex]  = 0.122

n = sample of adults between the ages of 55 and 64 = 411

p = population proportion of all US adults ages 55 to 64 to use online dating

Here for constructing 90% confidence interval we have used One-sample z proportion statistics.

So, 90% confidence interval for the population proportion, p is ;

P(-1.645 < N(0,1) < 1.645) = 0.90  {As the critical value of z at 5% level

                                                  of significance are -1.645 & 1.645}  

P(-1.645 < [tex]\frac{\hat p-p}{\sqrt{\frac{\hat p(1-\hat p)}{n} } }[/tex] < 1.645) = 0.90

P( [tex]-1.645 \times {\sqrt{\frac{\hat p(1-\hat p)}{n} } }[/tex] < [tex]{\hat p-p}[/tex] < [tex]1.645 \times {\sqrt{\frac{\hat p(1-\hat p)}{n} } }[/tex] ) = 0.90

P( [tex]\hat p-1.645 \times {\sqrt{\frac{\hat p(1-\hat p)}{n} } }[/tex] < p < [tex]\hat p+1.645 \times {\sqrt{\frac{\hat p(1-\hat p)}{n} } }[/tex] ) = 0.90

90% confidence interval for p = [[tex]\hat p-1.645 \times {\sqrt{\frac{\hat p(1-\hat p)}{n} } }[/tex], [tex]\hat p+1.645 \times {\sqrt{\frac{\hat p(1-\hat p)}{n} } }[/tex]]

= [ [tex]0.122-1.645 \times {\sqrt{\frac{0.122(1-0.122)}{411} } }[/tex] , [tex]0.122+1.645 \times {\sqrt{\frac{0.122(1-0.122)}{411} } }[/tex] ]

 = [0.095 , 0.148]

Therefore, 90% confidence interval for the proportion of all US adults ages 55 to 64 to use online dating is [0.095 , 0.148].

Answer:

Check the explanation

Step-by-step explanation:

Key Differences Between Descriptive and Inferential Statistics:

The difference between descriptive and inferential statistics can be drawn clearly on the following grounds:

Descriptive Statistics is a discipline which is concerned with describing the population under study. Inferential Statistics is a type of statistics; that focuses on drawing conclusions about the population, on the basis of sample analysis and observation.

Descriptive Statistics collects, organised, analyzes and presents data in a meaningful way. On the contrary, Inferential Statistics, compares data, test hypothesis and make predictions of the future outcomes.

There is a diagrammatic or tabular representation of final result in descriptive statistics whereas the final result is displayed in the form of probability.

Descriptive statistics describes a situation while inferential statistics explains the likelihood of the occurrence of an event.

Descriptive statistics explains the data, which is already known, to summaries sample. Conversely, inferential statistics attempts to reach the conclusion to learn about the population; that extends beyond the data available.

Ex. Of 350 randomly selected people in the town of Luserna, Italy, 280 people had the last name Nicolussi. An example of descriptive statistics is the following statement :

"80% of these people have the last name Nicolussi."

Ex. Of 350 randomly selected people in the town of Luserna, Italy, 280 people had the last name Nicolussi. An example of inferential statistics is the following statement :

"80% of all people living in Italy have the last name Nicolussi."

We have no information about all people living in Italy, just about the 350 living in Luserna. We have taken that information and generalized it to talk about all people living in Italy. The easiest way to tell that this statement is not descriptive is by trying to verify it based upon the information provided.

Answer:

The question is incomplete, the complementary part is left on the graph and the requested ones are answered

Differences between inferential and descriptive statistics:

1. We know that in descriptive statistics the main objective is only to describe data from the population, in inferential what is sought is to analyze data to draw conclusions from them

2. In descriptive statistics, the process involves collecting information, organizing it, and displaying data that are relevant or significant. In inferential statistics, the data is taken and processed to evaluate hypotheses and draw conclusions regarding future results.

3. the representation in the descriptive statistics is made by means of diagrams or tabulations and the result in the inferential one is given in probability

4. When we have a situation, descriptive statistics allows us to describe it and, in inferential, we obtain the probability that the event will occur.

descriptive statistics is based on known data, inferential it is intended to project future situations, which allows with all the previous answers to generate an important panorama for the development of engineering and its true application in a real field

-. an example is the following

Of 350 people taken at random in Madrid, Spain, 280 had the surname Martinez, we can take this situation to the descriptive statistic as follows:

80% of people carry the surname Martinez

We have, then, the result of a small sample of a city, not of the entire population of Spain, so the result has been generalized and we refer to a country from a small sample, however we must verify it and take it to a statistic inferential

Answer:

65,000

Step-by-step explanation:

because the scale is 1:50,000, so just simply multiply by 50,000.

Answer:i am not going to answer htis question 4 u but i will tell u how 2 do it first u mulitply 50,000 by 13 then you convert the number of centimeters u get into kilometers (this is where most people get wrong cuz there 2 lazy to read the question.

Answer:

Given:

Sample size, n = 20

Significance level = 5% = 0.05

Mean, u = 8

Here, the sample mean was not given.

Here the null and alternative hypothesis will be:

H0 : u = 8

H1 : u < 8

In this case, since there is no evidence to reject the null hypothesis, H0, at 0.05 level of significance, we can say that the p-value is greater than the level of significance, 0.05.

Answer: I think the answer is 11.9% , because I had this on a quiz and this was the same question.

Step-by-step explanation:

Answer: its letter A

Step-by-step explanation:

Answer:

145

Step-by-step explanation:

This is pretty simple, the three angles inside the triangle equal to 180 degrees, so if you add 80 and 65 and subtract that from 180, you get 35. So two angles that are connected to a single line equal to 180, so you just subtract 35 from 180 to get 145. Therefore, <1 = 145.

Answer:

all real numbers

y>0

Step-by-step explanation:

The range of f(x) = 4x is all real numbers.

The range of values that we are permitted to enter into our function is known as the domain of a function. The x values for a function like f make up this set (x). A function's range is the collection of values it can take as input. After we enter an x value, the function outputs this sequence of values.

Therefore, The range of f(x) = 4x is all real numbers.

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Answer:

1 (line B)

2 (line A)

3 (line D)

Step-by-step explanation: We both know I don't have one.

Answer:

b , a , d

Step-by-step explanation:

edg2020

To evaluate f(0) without using a calculator, simply substitute x with 0 in the function f(x) = 3x - 3 to get f(0) = -3.

A mathematical function is a relationship between a set of inputs (domain) and corresponding outputs (range) such that each input is associated with exactly one output. It is a rule or process that assigns a unique value to each input. Functions are fundamental in expressing and analyzing mathematical relationships.

To evaluate f(0) without using a calculator, we can simply replace x with 0 in the given function f(x) = 3x - 3.

Therefore, f(0) = 3(0) - 3 = 0 - 3 = -3.

So, f(0) = -3.

Answer:

The answer is y minus x = negative 4

Step-by-step explanation:

Answer:

160160160 times

Step-by-step explanation:

(444/999)360360360=160160160

Answer:

its d

Step-by-step explanation:

its d

The theoretical probability of drawing a green marble is 1/5, a red marble is 3/10, and a marble that is not yellow is 13/20 from a bag of 40 marbles with varying colors.

The question involves calculating the theoretical probability of drawing certain colored marbles from a bag with a mixed collection of marbles. To find the probability of each event, we can use the formula P(event) = Number of favorable outcomes / Total number of outcomes.

Drawing a green marble: There are 8 green marbles out of a total of 40 marbles. So, P(green) = 8/40 = 1/5.

Drawing a red marble: There are 12 red marbles out of a total of 40 marbles. So, P(red) = 12/40 = 3/10.

Drawing a marble that is not yellow: There are 26 marbles that are not yellow (8 green, 6 blue, and 12 red) out of a total of 40 marbles. So, P(not yellow) = 26/40 = 13/20.

Answer:

b = 3

Step-by-step explanation:

the area of the parallelogram is 7*b, since we know it is 21, b = 3.

Answer:

if a= (1/27)^2  + 3, then  a is about  3.00137

Step-by-step explanation:

I assume you have a typo here.

Your equation should be   a = (1/27)^2  + 3 ...

If that is so,    a =  (1/27)^2 + 3 =  3.00137...

Answer:

8 - 2x

Step-by-step explanation:

Let x be the unknown number

difference is subtraction

8 - 2x

Answer:

NO.

Step-by-step explanation:

From the question, we are given a random sample of 20 automotive​ batteries, a confidence interval =(0.22​,0.33​). Also, the population standard deviation is said to be less than 0.26 hour.

The confidence interval does NOT suggest that the variation in the​ batteries' reserve capacities is at an acceptable​ level.

REASON: If you look at the confidence interval, we can see that it contains the value for the standard deviation that is 0.26 and even MORE value than 0.26.

This does not give us any suggestion that the standard deviation is less than 0.26.

Final answer:

The confidence interval suggests that the variation in the batteries' reserve capacities is not at an acceptable level because the interval (0.22, 0.33) lies above the acceptable standard deviation of less than 0.26 hour.

Explanation:

In the context of the question, a constructed confidence interval for the population standard deviation of automotive batteries' reserve capacity is (0.22, 0.33 hours). To evaluate whether the variation in the batteries' reserve capacities is at an acceptable level, we would compare the interval to the stated acceptable level of a population standard deviation which should be less than 0.26 hour. Since the lower bound of the interval is above 0.22 and the entire confidence interval lies above 0.26, this suggests that the variability may be higher than the acceptable level, hence the variation is not at an acceptable level according to the specified criteria.

As for the example related to the NeverReady batteries, it demonstrates the use of sample data to question a company's claim about product performance by calculating the probability of obtaining a sample with a mean as low as or lower than observed when assuming that the company's claim is true. If the probability is very low, it casts doubt on the claim.

Answer:

The total number of reading schedules is [tex]83.3927415552\cdot 10^{13}[/tex]

Step-by-step explanation:

Recall that if we have n elements, the number of ways in which we can choose k elements without minding the order is [tex]\binom{n}{k}=\frac{n!}{(n-k)! k!}[/tex].

At first, suppose that we have already chosen 9 books. If we want to number the order in which we are reading this books from 1 to 9, for position 1 we have 9 options, for position 2, we have 8 and so on. Using the multiplication principle, we have that the number of ways or arranging 9 books is 9!

Recall that we want the same amount from novels, plays and nonfiction. That is, we are choosing 3 books from each group. We can easy calculate the total number of ways of choosing the 9 books by simply multiplying the number of ways we choose 3 from each cathegory. Hence the total number of ways of choosing the 9 books is

[tex]\binom{9}{3}\cdot \binom{22}{3}\cdot \binom{22}{3}[/tex]

For each selection of 9 books, we have 9! different ways of organizing them, then the total number is

[tex]\binom{9}{3}\cdot \binom{22}{3}\cdot \binom{22}{3}\cdot 9! = 83.3927415552\cdot 10^{13}[/tex]

A P-value of 0.045 suggests that there is a 4.5% chance of observing a sample proportion as extreme as 70% or more, assuming the null hypothesis is true. If the P-value<0.05 (usually considered a threshold), we typically reject the null hypothesis - here, indicating that more than 68% consider health care cost reduction a national priority.

In statistics, a P-value is a probability that provides a measure of the evidence against the null hypothesis (H₀). A P-value of 0.045, as given in this instance, indicates that if the null hypothesis were true (i.e., 68% of U.S. adults still consider reducing health care costs a national priority), there is only a 4.5% probability of observing a sample proportion as extreme as 70% or more.

Commonly, a threshold (α) is set at 0.05 or 5%. If the P-value is less than α (which it is in this case, 0.045 < 0.05), we reject the null hypothesis in favor of the alternative hypothesis. Consequently, we conclude there is sufficient evidence to suggest that the proportion of U.S. adults this year who rate 'reducing health care costs' as a national priority is higher than 68%.

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The P-value of 0.045 provides statistically significant evidence to reject the null hypothesis and support the alternative hypothesis that the proportion of U.S. adults who rate 'reducing healthcare costs' as a national priority has increased from the previous 68%.

In the context of this issue, the P-value of 0.045 represents the probability of observing a sample proportion as extreme as 70%, or more, given that the null hypothesis is true - the null hypothesis being that the true proportion of U.S. adults who rate 'reducing healthcare costs' as a national priority is 68%.

If the P-value is less than or equal to the significance level (in this case, 5% or 0.05), we reject the null hypothesis. Since 0.045 is indeed less than 0.05, we reject the null hypothesis, supporting the alternative hypothesis that the proportion of U.S. adults who view reducing healthcare costs as a national priority is greater than 68%.

Therefore, the P-value of 0.045 suggests there is statistically significant evidence to conclude that the perception of 'reducing healthcare costs' as a national priority has increased amongst U.S. adults recently.

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Answer:

The null hypothesis failed to be rejected.

There is  not enough evidence to support the claim that the proportion of alumni that assist to at least a one class reunion is different for privates university and public university (p-value=0.222).  

Step-by-step explanation:

This is a hypothesis test for the difference between proportions.

The claim is that the proportion of alumni that assist to at least a one class reunion is different for privates university and public university.  

Then, the null and alternative hypothesis are:

[tex]H_0: \pi_1-\pi_2=0\\\\H_a:\pi_1-\pi_2\neq 0[/tex]

where π1: proportion of private university alunmi that have attended at least one class reunion, and π2: proportion of public university alunmi that have attended at least one class reunion.

The significance level is 0.05.

The sample 1 (private), of size n1=1042 has a proportion of p1=0.6286.

[tex]p_1=X_1/n_1=655/1042=0.6286[/tex]

The sample 2 (public), of size n2=1318 has a proportion of p2=0.6039.

[tex]p_2=X_2/n_2=796/1318=0.6039[/tex]

The difference between proportions is (p1-p2)=0.0247.

[tex]p_d=p_1-p_2=0.6286-0.6039=0.0247[/tex]

The pooled proportion, needed to calculate the standard error, is:

[tex]p=\dfrac{X_1+X_2}{n_1+n_2}=\dfrac{655+796}{1042+1318}=\dfrac{1451}{2360}=0.6148[/tex]

The estimated standard error of the difference between means is computed using the formula:

[tex]s_{p1-p2}=\sqrt{\dfrac{p(1-p)}{n_1}+\dfrac{p(1-p)}{n_2}}=\sqrt{\dfrac{0.6148*0.3852}{1042}+\dfrac{0.6148*0.3852}{1318}}\\\\\\s_{p1-p2}=\sqrt{0.00023+0.00018}=\sqrt{0.00041}=0.0202[/tex]

Then, we can calculate the z-statistic as:

[tex]z=\dfrac{p_d-(\pi_1-\pi_2)}{s_{p1-p2}}=\dfrac{0.0247-0}{0.0202}=\dfrac{0.0247}{0.0202}=1.222[/tex]

This test is a two-tailed test, so the P-value for this test is calculated as (using a z-table):

[tex]P-value=2\cdot P(t>1.222)=0.222[/tex]

As the P-value (0.222) is bigger than the significance level (0.05), the effect is not significant.

The null hypothesis failed to be rejected.

There is  not enough evidence to support the claim that the proportion of alumni that assist to at least a one class reunion is different for privates university and public university.  

To determine if the difference in sample proportions is statistically significant, we can perform a hypothesis test.

To determine if the difference in sample proportions is statistically significant, we can perform a hypothesis test.

In this case, the test statistic is calculated as [tex]\frac{\frac{655}{1042} - \frac{796}{1318}}{\sqrt{\frac{655}{1042}\left(1 - \frac{655}{1042}\right)/1042 + \frac{796}{1318}\left(1 - \frac{796}{1318}\right)/1318}}[/tex]  By comparing the test statistic to the critical value at a significance level of 0.05, we can determine if the difference in sample proportions is statistically significant.

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Answer:

A

Step-by-step explanation:

Answer:

D

Step-by-step explanation:

When dividing fractions, multiply the first number by the reciprocal of the second number

2/5 ÷ 1/3

First, find the reciprocal of the second number: 1/3

To find the reciprocal, flip the numerator (top number) and denominator (bottom number)

1/3-->3/1

Now, multiply 2/5 and 3/1

2/5 * 3/1

Multiply across the numerators and denominators

6/5

Answer:

1

Step-by-step explanation:

Answer: (5, -9)

Step-by-step explanation:

Rewrite the equation in term of  x and y.

Answer:

68%

Step-by-step explanation:

Final answer:

Using the empirical rule for normal distribution, approximately 68% of the boys at the all boys school with a mean height of 70 inches and a standard deviation of 5 inches are between 65 and 75 inches tall.

Explanation:

According to the empirical rule (also known as the 68-95-99.7 rule), for a normal distribution:

Approximately 68% of the data falls within one standard deviation of the mean.

About 95% of the data falls within two standard deviations of the mean.

And around 99.7% falls within three standard deviations of the mean.

In the case of the all boys school with a mean height of 70 inches and standard deviation of 5 inches, the range between 65 inches (70 - 5) and 75 inches (70 + 5) represents one standard deviation from the mean on either side. Thus, using the empirical rule, approximately 68% of the boys are between 65 and 75 inches tall.

Answer:

7 bags red onions

10 bags yellow onions

Step-by-step explanation:

If r is the number of bags of red onions, and y is the number of bags of yellow onions, then:

r + y = 17

4r + 6y = 88

Solve the system of equations using substitution or elimination.  Using substitution:

r = 17 − y

4(17 − y) + 6y = 88

68 − 4y + 6y = 88

2y = 20

y = 10

r = 7

The chef received 5 bags of red onions and 12 bags of yellow onions.

The question involves solving a system of linear equations to find the number of bags of red onions and yellow onions the chef received. Let's denote the number of bags of red onions as R and the number of bags of yellow onions as Y. We know that:

Each bag of red onions contains 4 onions.

Each bag of yellow onions contains 6 onions.

The chef got a total of 17 bags.

The chef got a total of 88 onions.

Based on this information, we can set up the following equations:

R + Y = 17
(This represents the total number of bags.)

4R + 6Y = 88
(This represents the total number of onions.)

We can solve this system of equations using substitution or elimination. Let's use substitution. First, we can express Y as 17 - R from the first equation:

Y = 17 - R

Now, we substitute Y in the second equation:

4R + 6(17 - R) = 88

By solving this equation, we find that R = 5 and Y = 12. Therefore, the chef got 5 bags of red onions and 12 bags of yellow onions.