Does the following linear programming problem exhibit infeasibility, unboundedness, or alternate optimal solutions? Explain.
Min 1X + 1Y
s.t. 5X + 3Y < 30
3X + 4Y > 36
Y < 7
X , Y > 0

a) We can say with 95% confidence that the true mean kilowatt-hours used by all six-room homes falls between 6.005 and 9.195 thousand kilowatt-hours.

b) We can say with 95% confidence that a particular six-room home will use between 3.283 and 11.917 thousand kilowatt-hours.

a. The first question asks us to determine a 95% confidence interval for the mean kilowatt-hours used by all six-room homes. To do this, we need to calculate the sample mean (x) and the sample standard deviation (s) for the kilowatt-hours used by the six-room homes in our sample. We can then use the t-distribution and the formula:

x ± tα/2 (s/√n)

where tα/2 is the t-value for our desired confidence level (in this case, 95% with 9 degrees of freedom), s is the sample standard deviation, and n is the sample size.

Using the data given, we can calculate x = 7.6 and s = 1.551. We can then find the t-value using a t-table or a calculator, which is approximately 2.306. Plugging these values into the formula gives us:

7.6 ± 2.306 x (1.551/√10)

which simplifies to:

(6.005, 9.195)

b. The second question asks us to determine a 95% prediction interval for a particular six-room home. A prediction interval is similar to a confidence interval, but it takes into account both the variability of the sample and the variability of a new observation. To calculate the prediction interval, we can use the formula:

x ± tα/2 (s√1 + 1/n + (x₀ - x)²/((n-1)s²))

where x is the predicted value of kilowatt-hours for a new observation, x₀ is the number of rooms for that observation, and all other variables are the same as in the previous formula.

Using the data given, we can calculate x and s for all six-room homes as before. We can also assume that the predicted value for a new observation with six rooms is simply the sample mean for six-room homes (i.e., x = 7.6). We can then find the t-value using a t-table or a calculator, which is approximately 2.306. Plugging these values into the formula and setting n=10 (the sample size) gives us:

7.6 ± 2.306 x (1.551√1 + 1/10 + (6-7.6)²/((10-1)(1.551)²))

which simplifies to:

(3.283, 11.917)

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there are 117 different combinations of red and blue balloons that can be chosen from the store. Combinations describe the various ways that a group of objects can be chosen without taking into account their sequence.

A combination is a choice of elements from a bigger set where the order in which they are chosen is irrelevant. let's find out the possible combinations of red and blue balloons that can be chosen.

1. First, we'll calculate the number of combinations for red balloons. Since there are 12 red balloons, we can choose from 0 to 12 red balloons. That's 13 possible choices for red balloons.

2. Next, we'll calculate the number of combinations for blue balloons. Since there are 8 blue balloons, we can choose from 0 to 8 blue balloons. That's 9 possible choices for blue balloons.

3. Now, we'll multiply the number of choices for red balloons by the number of choices for blue balloons. This will give us the total number of combinations for red and blue balloons.

13 choices (red) x 9 choices (blue) = 117 combinations

So, there are 117 different combinations of red and blue balloons that can be chosen from the store.

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Answer:

B

Step-by-step explanation:

The quadratic formula is used to solve quadratic equations in the form ax^2 + bx + c = 0.

Looking at the given options, we can see that option B can be written in this form as 3x^2 - 8x - 14 = 0. Therefore, the equation that could be solved using the quadratic formula is option B.

The 15 times the probability of pulling socks of different colors every day is 6/5 or 1.2.

What is probability?

Probability is a measure of the likelihood or chance of an event occurring. It is a number between 0 and 1, with 0 representing an impossible event and 1 representing a certain event. The probability of an event is calculated by dividing the number of ways the event can occur by the total number of possible outcomes.

The total number of socks in the drawer is 2 + 2 + 2 = 6.

On the first day, any sock can be chosen, so the probability of selecting a sock of a particular color is 2/6 = 1/3.

On the second day, there are only 5 socks left, so the probability of selecting a sock of a different color from the first day is 4/5.

On the third day, there are only 4 socks left, so the probability of selecting a sock of a different color from the first two days is 2/4 = 1/2.

To find the probability of pulling socks of different colors on all three days, we need to multiply the probabilities of each day:

P(different colors for all 3 days) = (1/3) × (4/5) × (1/2)

P(different colors for all 3 days) = 2/25

To get the 15 times probability, we multiply by 15:

15 × 2/25 = 6/5

Therefore, the answer is 6/5 or 1.2.

Hence, the 15 times the probability of pulling socks of different colors every day is 6/5 or 1.2.

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Answer: 121/209= 57.89% rounded

Step-by-step explanation:

total golf balls are 209

add red +  yellow balls = 121

121/209= 57.89% rounded

The correct option is D, we can simplify the expression as:

[tex]\sqrt{125p^2} = 5p\sqrt{5}[/tex]

Remember two things, the square root can be distributed under the product, and it is the inverse of the square exponent.

Then we can rewrite our expression as follows:

[tex]\sqrt{125p^2} = \sqrt{125}*\sqrt{p^2}[/tex]

Now we can simplify both of the square roots to get:

[tex]\sqrt{125}*\sqrt{p^2} = p*\sqrt{125} = p*\sqrt{5*25} = p*\sqrt{5} *\sqrt{25} \\\\= 5p\sqrt{5}[/tex]

Thus, we can see that the correct option is D.

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It should be noted that the values of x and y in the trapezium will be

x=118

y= 35

A trapezoid, also known as a trapezium, is a closed, flat object with four straight sides and one pair of parallel sides. A trapezium's parallel sides are known as the bases, while its non-parallel sides are known as the legs. A trapezium might have parallel legs as well.  A trapezium is a quadrilateral with one parallel pair of opposite sides.

Based on the information, x will be:

= 180-62

= 118

y will be:

= 180-145=35

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The statement "for 1000 trials of simulation, the simulation result will not always be equal to the analytical results" is true.

True. For 1000 trials of simulation, the simulation result will not always be equal to the analytical results. Simulation is a method of generating data by running a model or process multiple times to observe the outcomes. Analytical results, on the other hand, are obtained through mathematical or statistical calculations. While simulation can provide valuable insights into the behavior of a system, it is subject to random variation and may not always produce the same results as analytical methods. Therefore, it is important to use both simulation and analytical methods to validate and verify the results of a study.

Therefore, the statement "for 1000 trials of simulation, the simulation result will not always be equal to the analytical results" is true.

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The responses that must be included are the population is all high school students, the sample contains freshmen, sophomores, juniors, and seniors, and the sample is representative of students in the entire high school.

Hence, options A, B, and D are correct.

To ensure that a sample is representative of the population, it must be selected in a way that accurately reflects the characteristics of the population. In this case, the population is all high school students in the town, and the possible sample includes an equal number of students from each grade level - freshman, sophomores, juniors, and seniors.

This is a good approach to ensure that the sample is representative of the entire population, as it captures the diversity of the population by including students from each grade level. Additionally, by having an equal number of students from each grade level, the sample is not biased towards any particular group.

It is also important to ensure that the sample is not too small, as a small sample size may not accurately reflect the characteristics of the entire population. Finally, if the sample is truly representative of the entire high school, any conclusions drawn from the sample should be applicable to the population as a whole.

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If cholesterol level were changed to being measured in grams instead of milligrams, the correlation between fat content and cholesterol level would not be affected.

This is because correlation is a measure of the strength and direction of the linear relationship between two variables, and converting the units of measurement does not change the underlying relationship between the variables. So, the correlation coefficient of 0.75 would remain the same whether cholesterol level is measured in milligrams or grams.

The correlation between fat content and cholesterol level for the 20 different brands of American cheese slices is 0.75. If you change the measurement of cholesterol level from milligrams to grams (1 gram = 1000 milligrams), it will not affect the correlation. The correlation coefficient will remain 0.75, as it is unit-less and only represents the strength and direction of the relationship between the two variables.

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The value of a²+4b ÷c -d is 31

Substitution of variable is a process of replacing an unknown( variable) with known values. For example the value of x²+y² when x is 2 and y is 3 is calculated by replacing 2 with x and 3 with y

i.e 2²+3² = 4+9 = 13

Also, The value of a²+4b ÷c -d when a = 4, b = 9 and c = 2 , d = 3 is calculated as;

4²+4 ×9 ÷ 2 -3

= 16+36÷2-3

using PEDMAS

= 16+18 -3

= 34-3

= 31

therefore the value of a²+4b ÷c -d is 31

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The conclusion of conjuncture that robyn will be 6 ft , 2 in. tall when she has completed 22 years age is not valid. So, option(b) is right one.

We have some statement or conjectures. We have to determine the final or produced conjecture is valid or not. According to statement, In fast five years,

The growth rate of Robyn's height = 2 in. per year

But for next five years the growth rate may or may not be remain same, i.e, 2 in. every year. Now, Age of Robyn = 15 years and Height of Robyn = 5 feet, 4 in. = 64 inches ( from conversion factor, 1 ft = 16 inches)

The final statement is that she will be 6 ft , 2 in. that is 74 inches tall when she's 22 years old. After 7 years from now, the increase in her height = 2 × 7 = 14 inches

and new height = 64 inches + 14 inches

= 78 in. = 6 feet, 6 inch.

So, this is not valid conjecture.

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Complete question

determine if the following conjecture is valid. given: for the past five years, robyn has grown 2 in . every year. she is now 15 years old and is 5 ft , 4 in . tall. conclusion: robyn will be 6 ft , 2 in . tall when she's 22 years old.

a) valid

b) not valid

c) valid only if she is male

The expression that can be added to both sides of the given quadratic equation to change it to a perfect square trinomial is [tex]\frac{b^2}{4a^2}[/tex] .

The standard form of perfect square trinomial is given as:

[tex]ax^2 + bx + c[/tex]

here,

a = coefficient of x² .

b = coefficient of x.

c = constant .

Given the quadratic equation:

[tex]x^2 + \dfrac{b}{a}x\ +\ ?= -\dfrac{c}{a}\ +\ ?[/tex]

The above equation is needed to be changed to a perfect square trinomial.

To change the quadratic equation into the perfect square, the squared of half the value of the coefficient of degree one variable can be added to both sides of the equation.

Therefore, the term to be that is needed to be added to the given quadratic equation is [tex]\frac{b^2}{4a^2}[/tex] .

Now, the quadratic equation can be written as:

[tex]x^{2} + \frac{b}{a} x + \frac{ b^{2}}{4a^{2}} = \frac{-c}{a} + \frac{ b^{2}}{4a^{2}}[/tex]

Therefore, [tex]\dfrac{b^2}{4a^2}[/tex] should be added to both sides to convert it into the perfect polynomial.

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The given question is incomplete. Probably the complete question is:

When deriving the quadratic formula by completing the square what expression can be added to both sides of the given equation to create a perfect square trinomial?

[tex]x^2 + \dfrac{b}{a}x\ +\ ?= -\dfrac{c}{a}\ +\ ?[/tex]

If 3 men build a garage in 8 days, then 4 men will be needed to build the garage in 6 days.

The amount of work required to build the garage is constant, regardless of the number of days or workers involved. We assume that each worker does the same amount of work in a day, then we use the following formula;

⇒ work = rate × time,

where rate is = amount of work done by one worker in a day, and time is = number of days worked,

Let the number of workers needed be "x". If 3-workers can build the garage in 8 days, then we have:

⇒ work = 3 workers × 8 days = 24 worker-days,

If "x" workers are needed to build the garage in 6 days, then we have:

⇒ work = (x workers) × (6 days),

Since the amount of work is same in both cases, we equate the two expressions;

⇒ 3 workers × 8 days = x workers × 6 days

⇒ x = (3 workers × 8 days)/6 days = 4 workers;

Therefore, 4 workers are needed to build the garage in 6 days.

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The probability that a student lives within 3 miles of the school is 75%, and the probability that a student who lives within 3 miles walks to school is 20%. We can find the probability that a student both lives within 3 miles and walks to school by multiplying these probabilities:

0.75 x 0.20 = 0.15

Therefore, the probability that a student lives within 3 miles and walks to school is 0.15 or 15%.

Answer:

0.15  or   15%

Step-by-step explanation:

The proportion of infants with birth weights under 95 oz is about 0.159oz

68−95−99.7 Rule, also known as the empirical rule conveys that for a normal distribution, mostly all of the data will fall within three (68%,95%,99.7%) standard deviations of the mean. Empirical rule is an approximate so it is not recommended to use unless a question specifically asks you to solve using it.

Let X be the Birthweights

X ~ N( = 110, [tex]\sigma^2 = 15^2[/tex])

The probability of X is less than 95 is,

P(X < 95) = [tex]P(\frac{X-\mu}{\sigma} < \frac{95-\mu}{\sigma} )[/tex]

               [tex]=P(Z < \frac{95-110}{15} )[/tex]

              [tex]=P(Z < \frac{-15}{15} )[/tex]

              = P (Z < -1)

P(X < 95)    = 0.159 (using the normal table)

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To estimate the fraction of people who black out at 6 or more gs at 98% confidence level and an error of at most 0.03, a sample size of 1079 is required using the formula n = (2.33² * 0.5 * (1-0.5)) / 0.03², rounded up to the nearest integer.

To determine the sample size required to estimate the fraction of people who black out at 6 or more gs with an error of at most 0.03 and a 98% confidence level, we need to use the formula

n = (z² * p * (1-p)) / E²

where

n is the sample size

z is the z-score for the desired confidence level (2.33 for 98% confidence)

p is the estimated proportion of people who black out at 6 or more gs (unknown)

E is the maximum error (0.03)

We don't know the estimated proportion p, but we can assume that it is 0.5, which is the value that will give the maximum sample size. So, substituting the values into the formula, we get

n = (2.33² * 0.5 * (1-0.5)) / 0.03² = 1078.8

Rounding up to the nearest integer, we get a sample size of 1079. Therefore, a sample size of 1079 is required to estimate the fraction of people who black out at 6 or more gs at the 98% confidence level with an error of at most 0.03.

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To complete the square for the equation x² + 7x = 4, we need to add (7/2)² or (7/2)² + 4 to both sides. (option d)

To start, let's review what it means to complete the square. Suppose we have an equation of the form x² + bx = c, where b and c are constants. Our goal is to find a value d such that the equation can be rewritten in the form (x + e)² = f, where e and f are also constants. To do this, we add and subtract the quantity (b/2)² on the left-hand side of the equation:

x² + bx + (b/2)² - (b/2)² = c

We can simplify the left-hand side by factoring the first three terms as a perfect square trinomial:

(x + b/2)² = c + (b/2)²

x² + 7x - 4 = 0

Next, we add and subtract the quantity (7/2)² on the left-hand side:

x² + 7x + (7/2)² - (7/2)² - 4 = 0

Again, we can simplify the left-hand side by factoring the first three terms as a perfect square trinomial:

(x + 7/2)² - (7/2)² - 4 = 0

We can simplify further by adding (7/2)² and 4 to both sides:

(x + 7/2)² = 33/4

Now we have completed the square, and the equation is in the form (x + e)² = f, where e = 7/2 and f = 33/4. To solve for x, we take the square root of both sides:

x + 7/2 = ±√(33/4)

Finally, we can solve for x by subtracting 7/2 from both sides:

x = -7/2 ± √(33)/2

Option (d), (7/2)², is the correct answer.

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Answer:

reflectional symmetry

Step-by-step explanation:

According to the given inductive definition of Ackermann's function, we can find the values of the function as follows:

A(2,1) = A(1, A(2,0)) = A(1, 1) = A(0, A(1,0)) = A(0, 2) = 2

A(1,2) = A(0, A(1,1)) = A(0, A(0, A(1,0))) = A(0, A(0, 2)) = A(0, 4) = 6

A(1,0) = A(0, A(1,-1)) = A(0, A(0, 0)) = A(0, 1) = 2^1 = 2

A(0,1) = 2^1 = 2

A(3,0) = A(2, A(3,-1)) = A(2, A(2, A(3,-2))) = A(2, A(2, A(2, A(3,-3)))) = A(2, A(2, A(2, 1))) = A(2, A(2, 2)) = A(2, 2^2) = A(2, 4) = 2^4 = 16

A(3,3) = A(2, A(3,2)) = A(2, A(2, A(3,1))) = A(2, A(2, A(2, A(3,0)))) = A(2, A(2, A(2, 1))) = A(2, A(2, 2)) = A(2, 2^2) = A(2, 4) = 2^4 = 16

Therefore, the values of Ackermann's function are:

A(2,1) = 2

A(1,2) = 6

A(1,0) = 2

A(0,1) = 2

A(3,0) = 16

A(3,3) = 16
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The probability that you and your friend both have the same two numbers is approximately 0.4823, or about 48.23%.

If you and your friend each roll two dice, there are two possible cases:

You both roll doubles (i.e., both dice show the same number).

You both roll non-doubles (i.e., the two dice show different numbers).

Let's calculate the probability of each case separately:

The probability of rolling doubles on one die is 1/6, since there are six possible outcomes (1, 2, 3, 4, 5, or 6) and only one of them will result in doubles. The probability of rolling doubles on both dice is the product of the probabilities of rolling doubles on each die, which is (1/6) * (1/6) = 1/36. Therefore, the probability that you and your friend both roll doubles is (1/36) * (1/36) = 1/1296.

The probability of rolling non-doubles on one die is 5/6, since there are five possible outcomes (2, 3, 4, 5, or 6) that will result in non-doubles, out of a total of six possible outcomes. The probability of rolling non-doubles on both dice is the product of the probabilities of rolling non-doubles on each die, which is (5/6) * (5/6) = 25/36. Therefore, the probability that you and your friend both roll non-doubles is (25/36) * (25/36) = 625/1296.

Therefore, the overall probability that you and your friend both have the same two numbers is the sum of the probabilities of the two cases:

1/1296 + 625/1296

= 626/1296

= 0.4823 (rounded to four decimal places)

So, the probability that you and your friend both have the same two numbers is approximately 0.4823, or about 48.23%.

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Answer:

The quadratic equation with roots -3 and -6 and leading coefficient 2 is:

2x2 - 8x - 12 = 0

To derive this, we use the quadratic formula:

ax2 + bx + c = 0

With:

a = 2 (leading coefficient)

b = -4 (calculated from roots: b = -2(root1 + root2) = -2(-3 - 6) = -4)

c = -12 (calculated from discriminant: c = b2/4a = -42/8 = -12)

So the full equation is:

2x2 - 4x - 12 = 0

Which simplifies to:

2x2 - 8x - 12 = 0

This has the roots -3 and -6 as requested, with a leading coefficient of 2.

Step-by-step explanation:

The measure of location which is the most likely to be influenced by extreme values in the data set is mean.

The measure of location which is the most likely to be influenced by extreme values in the data set is the mean. The range is a measure of dispersion and is calculated as the difference between the highest and lowest values in the data set.

The median, on the other hand, is the middle value in a sorted list of data and is not affected by extreme values. The mode is the most frequently occurring value and is also not affected by extreme values. Therefore, the answer is the mean.

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The standard deviation of the resulting data set would be 14.

If each value in a data set is multiplied by a constant, the standard deviation is also multiplied by that constant.

Therefore, if each value in a data set with a standard deviation of 8 is multiplied by 1.75, the standard deviation of the resulting data set would be:

New standard deviation = 8 x 1.75 = 14

Standard deviation is a measure of the amount of variation or dispersion in a set of data. It is calculated by taking the square root of the variance, which is the average of the squared differences from the mean.

Multiplying each value in a data set by a constant will stretch or compress the data set, but it will not change the shape of the distribution. So, if the original data set had a normal distribution (i.e., a bell-shaped curve), the resulting data set will also have a normal distribution.

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For a researcher's sample of cigarette smoker with average 31 cigarettes a day, as t( critical value) > 0.05, so Null hypothesis can't be rejected and it concludes that the true mean number of cigarettes smoked per day is greater than 31, α=0.05.

We have a researcher who see that a cigarette smoker smokes on average 31 cigarettes a day. So, population or true mean = 31

Now, a sample of smokers is considered with Sample size, n = 10

Mean number of cigarettes they smoked per day = 28

Standard deviations = 2.7

level of significance = 0.05

We have to check the claim of researcher is true. Consider null and alternative hypothesis as right tailed, [tex]H_ 0 : \mu = 31[/tex]

[tex]H_ a : \mu > 31[/tex]

Using t- test for test statistic value :

[tex]t= \frac{\bar X -\mu}{ \frac{\sigma }{\sqrt{n}}}[/tex]

Substitute all known values,

[tex]t= \frac{ 28 - 31}{ \frac{ 2.7 }{\sqrt{10} }}

[/tex]

[tex]= \frac{ - 3}{ \frac{ 2.7 }{\sqrt{10} }}[/tex]

= - 3.51364184463

degree of freedom, df = n - 1 = 9

From the t distribution table, the critical value for [tex]d_f = 9 \: and \: \alpha = 0.05[/tex] is equals to 1.833. Since our computed t( critical) = 1.833 > 0.05, is not in the rejection region, we do not reject the null hypothesis. Hence, There is not enough evidence to support claim.

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For a researcher's sample of cigarette smoker with average 31 cigarettes a day, as t( critical value) > 0.05, so Null hypothesis can't be rejected and it concludes that the true mean number of cigarettes smoked per day is greater than 31, α=0.05.

We have a researcher who see that a cigarette smoker smokes on average 31 cigarettes a day. So, population or true mean = 31

Now, a sample of smokers is considered with Sample size, n = 10

Mean number of cigarettes they smoked per day = 28

Standard deviations = 2.7

level of significance = 0.05

We have to check the claim of researcher is true. Consider null and alternative hypothesis as right tailed,

Using t- test for test statistic value :

Substitute all known values,

= - 3.51364184463

degree of freedom, df = n - 1 = 9

From the t distribution table, the critical value for  is equals to 1.833. Since our computed t( critical) = 1.833 > 0.05, is not in the rejection region, we do not reject the null hypothesis. Hence, There is not enough evidence to support claim.

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The weight of a small Starbucks coffee that is heavier than 480 grams corresponds to a probability of 0.0062.

To find the weight that corresponds to each event, we'll need to use the normal distribution formula:

Z = (X - μ) / σ where Z is the standard score (or z-score), X is the observed value, μ is the mean, and σ is the standard deviation.

We can use this formula to convert the weight of a small Starbucks coffee into a z-score, and then use a standard normal distribution table (such as Appendix C) to find the corresponding probability (or vice versa).

Here are the specific events and their corresponding weights:

1. The weight of a small Starbucks coffee that is lighter than 400 grams. First, we need to convert the weight of 400 grams into a z-score:

Z = (400 - 420) / 24 = -0.83 Using Appendix C or Excel.

we can find that the probability of a z-score being less than -0.83 is 0.2033.

Therefore, the weight of a small Starbucks coffee that is lighter than 400 grams corresponds to a probability of 0.2033.

2. The weight of a small Starbucks coffee that is between 420 and 450 grams. To find the z-scores corresponding to these weights, we need to use the formula twice: For 420 grams: Z = (420 - 420) / 24 = 0 For 450 grams: Z = (450 - 420) / 24 = 1.25 Using Appendix C or Excel, we can find that the probability of a z-score being between 0 and 1.25 is 0.3944.

Therefore, the weight of a small Starbucks coffee that is between 420 and 450 grams corresponds to a probability of 0.3944.

3. The weight of a small Starbucks coffee that is heavier than 480 grams. Again, we need to convert the weight of 480 grams into a z-score:

Z = (480 - 420) / 24 = 2.50 Using Appendix C or Excel, we can find that the probability of a z-score being greater than 2.50 is 0.0062.

Therefore, the weight of a small Starbucks coffee that is heavier than 480 grams corresponds to a probability of 0.0062.

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The resulting interval will contain 95% of all sample means. In conclusion, the interval value is 1.96.

When applying the Central Limit Theorem (CLT) to define an interval within which we expect 95% of all sample means to fall, we would use a z-value of 1.96. This is because 95% of the area under a normal distribution curve falls within 1.96 standard deviations of the mean. Therefore, using a z-value of 1.96 will give us an interval that contains 95% of all sample means.

Identify the desired confidence level. In this case, it is 95%.

Find the corresponding z-value for the desired confidence level. For a 95% confidence level, the z-value is 1.96.

Use the z-value and the standard deviation of the sample means to calculate the interval. The formula for the interval is:

mean ± (z-value)(standard deviation)

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The probability of rolling a pair of fair dice and getting a sum greater than or equal to 8 is 5/12, or approximately 0.4167.

To calculate the probability of rolling a sum greater than or equal to 8 with a pair of fair dice, we need to first find the total number of outcomes that result in a sum of 8 or more, and then divide this by the total possible outcomes of rolling two dice.

Step 1: Determine the total possible outcomes.
Since a fair die has 6 faces, there are 6 possible outcomes for each die. When rolling two dice, the total possible outcomes are 6 outcomes for die 1 multiplied by 6 outcomes for die 2, which is 6 x 6 = 36.

Step 2: Determine the number of outcomes that result in a sum of 8 or more.
Here are the combinations that result in a sum greater than or equal to 8:

(2, 6), (3, 5), (3, 6), (4, 4), (4, 5), (4, 6), (5, 3), (5, 4), (5, 5), (5, 6), (6, 2), (6, 3), (6, 4), (6, 5), (6, 6)

There are a total of 15 combinations.

Step 3: Calculate the probability.
Now, we can find the probability by dividing the number of successful outcomes (sums greater than or equal to 8) by the total possible outcomes:

Probability = (Number of successful outcomes) / (Total possible outcomes) = 15 / 36 = 5/12

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The function that will return the value of x, rounded to the nearest whole number is option (c) round(x)

This function rounds the value of x to the nearest integer. For example, if x = 3.4, round(x) will return 3, and if x = 3.6, round(x) will return 4.

Option (a) abs(x) returns the absolute value of x, which means it returns the positive value of x regardless of its sign. For example, if x = -3, abs(x) will return 3.

Option (b) fmod(x) returns the remainder of x divided by another number, so it does not round x to the nearest whole number.

Option (d) whole(x) is not a standard math function, so it is unclear what it would do.

Option (e) sqrt(x) returns the square root of x, so it does not round x to the nearest whole number.

Therefore, the correct answer to this question is option (c) round(x).

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The equivalent expression of 18 + 30 using distributive property is 6(3) + 6(5)

The distributive property states that for any numbers a, b, and c, a multiplied by (b+c) equals a multiplied by b plus a multiplied by c.

a(b + c) = a(b) + a(c)

The prime factor of 18 + 30 is written as;

18 + 30 = 2 x 3 x 3 + 2 x 3 x 5

18 + 30 = 2 x 3 x (3 + 5)

Simplifying the expression inside the parentheses gives:

2 x 3 x (3 + 5) = 6 (3 + 5)

applying distributive property we will have;

6 (3 + 5) = 6(3) + 6(5)

Thus, the final expression is; 18 + 30 = 6(3) + 6(5)

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