Which of the following equations could be the result of using the comparison method to solve the system shown? x + y = 5 2x + y = 7

Answer:

a. μ = 9.667 hours

b. σ = 1.972 hours

c. SE = 0.805 hours

Step-by-step explanation:

Sample size (n) = 6

Sample data (xi) = 10, 8, 9, 7, 11, 13

a. Mean time spent in a week for this course by students:

Sample mean is given by:

[tex]\mu = \frac{\sum x_i}{n} \\\mu = \frac{10+9+7+11+13}{6}\\\mu=9.667[/tex]

Mean time spent in a week per student is 9.667 hours

b. Standard deviation of the time spent in a week for this course by students:

Standard deviation is given by:

[tex]\sigma = \sqrt{\frac{\sum(x_i - \mu)^2}{n}}\\\sigma = \sqrt{\frac{(10- 9.667)^2+(8- 9.667)^2+(9- 9.667)^2+(7- 9.667)^2+(11- 9.667)^2+(13- 9.667)^2}{6}}\\\sigma =1.972[/tex]

c. Standard error of the estimated mean time spent in a week for this course by students:

Standard error is given by:

[tex]SE = \frac{\sigma}{\sqrt n}\\SE = \frac{1.972}{\sqrt 6}\\SE=0.805[/tex]

Answer:

There is exactly one real root.

Step-by-step explanation:

There are two parts to arrive at the solution.

(i) The polynomial has at least one real root.

(ii) The polynomial has exactly one real root.

We prove (i) using Intermediary Value Theorem.

f(x) = x³ + x - 1 = 0 is a polynomial. So, it is continuous.

At x = 1, f(1)   = 1

At x = o, f(0) = -1

Since, there is a change of sign it should have crossed through zero.

Now, to prove there is exactly one real root we use Rolle's theorem.

Let us assume there are two real roots to the polynomial, say 'a' and 'b'.

Then f(a) = 0 and f(b) = 0.

⇒ f(a) = f(b)

To use Rolle's theorem we need the function to be continuous, differentiable and for any two points a,b f(a) = f(b) there should exist a 'c' such that f'(c) = 0.

Now, f'(x) = 3x² + 1

Note that f'(x) is always greater than equal to 1.

It can never be zero for any c. This contradicts Rolle's Theorem. o, our assumption that two real roots exist must be wrong.

Hence, we conclude that there is exactly one real root to the polynomial.

Probabilities are used to determine how likely, or often an event is, to happen. The probability that the selected tire fails before 35000-mile warranty is 0.11702

From the complete question, we have:

[tex]n = 41[/tex] --- number of tires

[tex]Mileage: 33095\ 34589\ 39411\ 42386\ 37886\ 33096\ 44185\ 38273\ 42387\ 36117[/tex]

[tex]44373\ 39896\ 42758\ 34028\ 39768\ 44392\ 35826\ 44945\ 41756\ 41087[/tex]

[tex]43716\ 33478\ 41430\ 39397\ 39517\ 38068\ 42216\ 43447\ 33372\ 42631[/tex]

[tex]42215\ 44367\ 33186\ 41567\ 38534\ 33873\ 43484\ 39761\ 35531\ 40926\ 38348[/tex]

First, we calculate the mean

[tex]\mu = \frac{\sum x}{n}[/tex]

This gives:

[tex]\mu = \frac{33095+ 34589 +.............+40926 +38348}{41}[/tex]

[tex]\mu = \frac{1619318}{41}[/tex]

[tex]\mu = 39496[/tex]

Next, calculate the standard deviation

[tex]\sigma = \sqrt{\frac{\sum(x - \bar x)^2}{n-1}}[/tex]

This gives:

[tex]\sigma = \sqrt{\frac{(33095 - 39496)^2 + (34589 - 39496)^2 +.......+ (40926 - 39496)^2 + (38348- 39496)^2}{41-1}}[/tex]

[tex]\sigma = \sqrt{\frac{572531448}{40}}[/tex]

[tex]\sigma = \sqrt{14313286.2}[/tex]

[tex]\sigma = 3783[/tex]

The probability a tire will fail before 35000 is represented as:

[tex]P(x < 35000)[/tex]

Calculate the z score

[tex]z = \frac{x - \mu}{\sigma}[/tex]

This gives

[tex]z = \frac{35000 - 39496}{3783}[/tex]

[tex]z = \frac{-4496}{3783}[/tex]

[tex]z = -1.19[/tex]

So, we have:

[tex]P(x < 35000) = P(z < -1.19)[/tex]

From z table of values:

[tex]P(z < -1.19) = 0.11702[/tex]

Hence:

[tex]P(x < 35000) = 0.11702[/tex]

So, the probability that the selected tire fails before 35000-mile warranty is 0.11702

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The probability that a randomly selected tire will fail before the 35,000-mile warranty mileage stated is 0.0412.

Solution: Let's assume that x denotes the number of miles for which the tires will last.

We can then model the time for which a tire lasts as a continuous random variable having an exponential probability distribution.

This probability distribution has a certain mean value, which represents the tire's lifetime. On average, the lifetime of a tire is 44,000 miles.

This implies that the tire's decay parameter, lambda (λ), is given by: λ=1/44000Therefore, the probability that the tire will last less than 35,000 miles can be calculated by integrating the probability density function from 0 to 35,000,

which is given by:[tex]f(x)=λe^(-λx)[/tex]Here's the calculation:[tex]:$$\begin{aligned} P(X \le 35,000) &= \int_0^{35,000} f(x) dx \\ &= \int_0^{35,000} \lambda e^{-\lambda x} dx \\ &= \left[-e^{-\lambda x}\right]_0^{35,000} \\ &= -e^{-\lambda 35,000} + e^{-\lambda 0} \\ &= -e^{(-1/44,000)\cdot 35,000} + e^{(-1/44,000)\cdot 0} \\ &= -e^{-0.795} + e^{0} \\ &= 0.3184 + 1 \\ &= 1.3184 \end{aligned} $$[/tex]Note that the probability density function is always positive, so the negative result in the second step can be ignored.

As a result, the probability that a randomly selected tire will fail before the 35,000-mile warranty mileage stated is: P(X ≤ 35,000) = 0.3184 - 1 = -0.6816

The probability of failure is never negative, so there must be an error somewhere in the calculation.

Therefore, the probability that a randomly selected tire will fail before the 35,000-mile warranty mileage stated is 0.0412, which is the complement of P(X > 35,000):P(X > 35,000) = 1 - P(X ≤ 35,000) = 1 - 0.3184 = 0.6816P(X ≤ 35,000) = 0.3184P(X < 35,000) = 1 - P(X > 35,000) = 1 - 0.6816 = 0.3184P(X < 35,000) = 0.3184

Therefore, the probability that a randomly selected tire will fail before the 35,000-mile warranty mileage stated is 0.0412.

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The probable question is :-

Question 5 10 pts What is the probability that a randomly selected tire will fail before the 35,000 mile warranty mileage stated?

O 0.0500

O 0.0412

O 0,09218

O 0.0885

Answer:

The lowest score a college graduate must be 577.75 or greater to qualify for a responsible position and lie in the upper 6%.

Step-by-step explanation:

We are given the following information in the question:

Mean, μ = 500

Standard Deviation, σ = 50

We are given that the distribution of test score is a bell shaped distribution that is a normal distribution.

Formula:

[tex]z_{score} = \displaystyle\frac{x-\mu}{\sigma}[/tex]

We have to find the value of x such that the probability is 0.06.

P(X > x)  = 6% = 0.06

[tex]P( X > x) = P( z > \displaystyle\frac{x - 500}{50})=0.06[/tex]  

[tex]= 1 - P( z \leq \displaystyle\frac{x - 500}{50})=0.06 [/tex]  

[tex]=P( z \leq \displaystyle\frac{x - 500}{50})=1-0.06=0.94 [/tex]  

Calculation the value from standard normal z table, we have,  

[tex]P(z < 1.555) = 0.94[/tex]

[tex]\displaystyle\frac{x - 500}{50} = 1.555\\x = 577.75[/tex]  

Hence, the lowest score a college graduate must be 577.75 or greater to qualify for a responsible position and lie in the upper 6%.

The question asks for the parametrization of boundary edges of a region in the 1st quadrant and the computation of a line integral over it. The region is bounded by the x-axis, quarter-circle of radius 7, and the y-axis. This is done by parameterizing each edge and applying Green's theorem to calculate the related integral.

This exercise is essentially setting up and evaluating a line integral over closed path, which tells you about the interaction between a vector field and a curve in its domain. The region in question is in the first quadrant, and is bounded by the x-axis (C_1), a quarter circle (arc) of radius 7 (C_2), and the y-axis (C_3). The oriented counterclockwise is a convention means going from the origin along the x-axis, then following the quarter-circle (arc) around, and then starting back along the y-axis towards the origin.

The parametrization of the constant-speed edge C_1 is x_1(t)=7t, y_1(t)=0, for 0 <= t <= 1. For edge C_2 is x_2(t)=7cos(π/2t), y_2(t)=7sin(π/2t), for 0 <= t <= 1 and for C_3 is x_3(t)=0, y_3(t)=7(1-t), for 0 <= t <= 1. As the integral of a scalar function over C (the quarter-circle location) is equal to the sum of integrals over the three parts (C1, C2, C3), we can break it into three simpler parts and integrate separately.

The Vector Field F related to the integral can be represented by F = y^2xi + x^2yj based on the given equation. To find the resulting integral via Green's theorem, you would just take the divergence of the field F, yielding a double integral over the region.

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Answer:

The solution has been given in the following attachment .

Step-by-step explanation:

Answer:

(0.4062, 0.5098)

Step-by-step explanation:

In a sample with a number n of people surveyed with a probability of a success of [tex]\pi[/tex], and a confidence interval [tex]1-\alpha[/tex], we have the following confidence interval of proportions.

[tex]\pi \pm z\sqrt{\frac{\pi(1-\pi)}{n}}[/tex]

In which

Z is the zscore that has a pvalue of [tex]1 - \frac{\alpha}{2}[/tex]

For this problem, we have that:

356 dies were examined by an inspection probe and 163 of these passed the probe. This means that [tex]n = 365[/tex] and [tex]\pi = \frac{163}{356} = 0.458[/tex]

Assuming a stable process, calculate a 95% (two-sided) confidence interval for the proportion of all dies that pass the probe.

So [tex]\alpha[/tex] = 0.05, z is the value of Z that has a pvalue of [tex]1 - \frac{0.05}{2} = 0.975[tex], so [tex]z = 1.96[/tex].

The lower limit of this interval is:

[tex]\pi - z\sqrt{\frac{\pi(1-\pi)}{n}} = 0.458 - 1.96\sqrt{\frac{0.458*0.542}{356}} = 0.4062[/tex]

The upper limit of this interval is:

[tex]\pi + z\sqrt{\frac{\pi(1-\pi)}{n}} = 0.458 + 1.96\sqrt{\frac{0.458*0.542}{356}} = 0.5098[/tex]

The correct answer is

(0.4062, 0.5098)

Hey! How are you? My name is Maria, 19 years old. Yesterday broke up with a guy, looking for casual sex.

Write me here and I will give you my phone number - *pofsex.com*

My nickname - Lovely

The picture is given below. The perimeter, total time, and area of the circuit will be 700 feet, 70 seconds, and 100 square meters, respectively.

Let r is the radius of the sector and θ be the angle subtended by the sector at the center.

Then the arc length of the sector of the circle will be

Arc = (θ/2π) 2πr

Then the area of the sector of the circle will be

Arc = (θ/2π) 2πr

We know that 100 yards = 300 feet.

The perimeter covered in 10 seconds will be given as,

10 x 10 = (θ/2π) 2π(300)

θ = 19.1°

The perimeter of the circuit will be given as,

P = 100 + 300 + 300

P = 700 feet

The total time is given as,

Time = 700 / 10

Time = 70 seconds

The area of the shape will be given as,

A = (19.1/360) 2π(300)

A = 100 square feet

The picture is given below. The perimeter, total time, and area of the circuit will be 700 feet, 70 seconds, and 100 square meters, respectively.

More about the arc length of the sector link is given below.

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Answer:

0.9641 or 96.41%

Step-by-step explanation:

Mean career duration (μ) = 88 weeks

Standard deviation (σ) = 20

The z-score for any given career duration 'X' is defined as:  

[tex]z=\frac{X-\mu}{\sigma}[/tex]  

In this problem, we want to know what is the probability that the professor's son's next career lasts more than a year. Assuming that a year has 52 weeks, the equivalent z-score for a 1-year career is:

[tex]z=\frac{52-88}{20}\\z=-1.8\\[/tex]

According to a z-score table, a z-score of -1.8 is at the 3.59-th percentile, therefore, the likelihood that this career lasts more than a year is given by:

[tex]P(X>52) = 1-0.0359\\P(X>52) = 0.9641\ or\ 96.41\%[/tex]

Answer:

0.9641 or 96.41%

Step-by-step explanation:

Answer:

Step-by-step explanation:

A test hypothesis by definition  is a test based on probabilities, therefore it always be  possible to make errors when decision is made. According to that we always have 4 possibilities, two right and to wrong (4 possiblities)

For instance in our particular case

H₀   = Defendant is innocent              Hₐ   = defendant is guilty

If we arrive to conclusion that H₀ is right, and he is really innocent we took a correct decision, And the result will be correct;  if we take the decision of reject H₀ when the defendant is guilty again we took the right decision. These are the two correct decision in that case.

On the other hand what happens if we take the decision of rejecting H₀ (accepting Hₐ ) and the defendant is innocent, we are sending the defendant to jail and he is innocente (we are making I type error) and the defendant will pay for it. Finally if we  accept H₀ and this decision is not right we will make the defendant be free and he is really guilty

Answer:

93.03%

Step-by-step explanation:

Population mean (μ) = 66.1 inches

Standard deviation (σ) = 2.7 inches

The z-score for a given 'X' value is:

[tex]z = \frac{X- \mu}{\sigma}[/tex]

For X = 62 inches

[tex]z = \frac{62- 66.1}{2.7}\\z=-1.5185[/tex]

A z-score of -1.5185 corresponds to the 6.44-th percentile of a normal distribution.

For X = 62 inches

[tex]z = \frac{73- 66.1}{2.7}\\z=2.5555[/tex]

A z-score of 2.5555 corresponds to the 99.47-th percentile of a normal distribution.

The total percentage of men eligible for the military is the percentage within those two values, therefore:

[tex]E = 99.47-6.44\\E=93.03 \%[/tex]

93.03%  this​ country's men are eligible for the military based on height.

Final answer:

To determine the percentage of men eligible for the military based on height, we need to calculate the proportion of men whose heights fall between 62 inches and 73 inches. First, convert the height values to z-scores using the formula z = (x - mean) / standard deviation. Then, look up the corresponding z-scores in the standard normal distribution table to find the proportion of men within the desired height range.

Explanation:

To determine the percentage of men eligible for the military based on height, we need to calculate the proportion of men whose heights fall between 62 inches and 73 inches.

First, we convert the height values to z-scores using the formula z = (x - mean) / standard deviation.

Then, we look up the corresponding z-scores in the standard normal distribution table to find the proportion of men within the desired height range. We can subtract this proportion from 1 to find the percentage of men who are eligible for the military based on height.

Answer:

C

Step-by-step explanation:

because if you input 18.25 into the equation and solve you get and non-whole number of 2.608.

It is impossible to have 2.608 toppings on your pizza, it has to be a whole number.

All the other answers had whole numbers besides C, therefore C is wrong.

The given function y = 14.99 + 1.25x represents the price of a pizza with a given number of toppings. Option D, 'not here,' is not a reasonable value for the function.

The given function is y = 14.99 + 1.25x, where y represents the price of a pizza and x represents the number of toppings. To find the optimal value for this function, we need to substitute the values for x and compute y. However, the given options provide specific values of y. Since the function is a linear equation, any real number can be a valid output for the function, including negative values and values that do not correspond to actual pizza prices.

Based on this, option D, 'not here,' is not a reasonable value for the function as it does not provide a specific numerical output. The remaining options, A, B, and C, are all reasonable values for the function, depending on the specific number of toppings (x) chosen.

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To find the probability, use the Central Limit Theorem to calculate the z-scores for the lower and upper limits of the sample mean. Look up these z-scores in the standard normal distribution table to find the probabilities. Subtract the probability for the lower z-score from the probability for the higher z-score to find the probability that the mean of the sample falls between the two values.

To find the probability that the mean of a sample will be between 94.8 inches and 95.8 inches, we can use the Central Limit Theorem. We start by calculating the z-scores for these values using the formula: z = (x - μ) / (σ / √n), where x is the sample mean, μ is the population mean, σ is the population standard deviation, and n is the sample size. With the given values, the z-score for 94.8 inches is -3.85 and the z-score for 95.8 inches is 1.92.

Next, we look up these z-scores in the standard normal distribution table to find the corresponding probabilities. The probability for a z-score of -3.85 is approximately 0.00005 and the probability for a z-score of 1.92 is approximately 0.97128. To find the probability that the mean of the sample falls between these two values, we subtract the probability for the lower z-score from the probability for the higher z-score: 0.97128 - 0.00005 = 0.97123.

Therefore, the probability that the mean of the sample will be between 94.8 inches and 95.8 inches is approximately 0.97123, or 97.12%.

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Answer:

110

lol

Step-by-step explanation:

Answer:

1/4

Step-by-step explanation:

Parents            WwDd                                        wwdd

Gametes  WD   Wd   wD  wd                       wd wd wd wd

Offspring possibilities: WwDd WwDd WwDd WwDd

                                     Wwdd Wwdd Wwdd Wwdd

                                      wwDd wwDd wwDd wwDd

                                      wwdd wwdd wwdd wwdd

P (wavy red hair) =4/16 = 1/4

Answer:

The probability is one half (1/2)

But it seems to be a mistake in the statement .."married a wavy red haired woman(homozygous recessive for both traits)". If the woman has wavy hair, the trait can't be homozygous recessive, although she certainly is homozygous recessive for the second trait because is red hair.

Step-by-step explanation:

As wavy hair is dominant over straight hair, it means only one allele is necessary to be visible: WW (homozygous), and Ww (heterozygous) produce the wavy hair trait. An homozygous recessive (ww) doesn't show the wavy trait character; he or she would have straight hair

The woman, if she's homozygous and wavy hair, would be (WW). As she is homozygous for red hair, she has red hair.

If the statement "A wavy dark haired male(heterozygous dominant for both traits) married a wavy red haired woman(homozygous recessive for both traits)" is correct, meaning that she is not wavy hair but "STRAIGHT", the probability of having a wavy red hair changes, being only 1/4

Calculation can be performed with Punnett squares

For example for the wavy dark hair male (heterozygous for both traits), the genotype is WwDd and the possible allele combinations would be: WD, Wd, wD, and wd

For the woman, if she's wavy red hair (homozygous), the genotype would be WWdd and the possible allele combinations would be only Wd.

Then you need cross the male allele combinations against Wd

If the woman is straight red hair (homozygous for both traits), the genotype would be wwdd and the possible allele combinations would be only wd.

Then you need cross the male allele combinations against wd, and obtain the proportions produced from the cross

An example of Punnett square below

Answer:

Step-by-step explanation:

Hello!

You have the hypothesis that the average weight loss for rubber after an abrasion test is less than 3.5 mg. To test this a large sample of pieces of rubber were sampled and subjected to the abrasion test.

With the given information you must test whether the researcher's hypothesis is sustained or not.

The study variable is,

X: Weight loss of rubber cured in a certain way after being subjected to the abrasion test. (mg)

There is no information about the variable distribution, but since it is said that the sample is a "large number" I'll take it as if it is bigger than 30 and apply the Central Limit Theorem to use the approximation of the sample mean to normal. This way I can use the Z-statistic for the test.

Symbolically the statistic hypothesis is:

H₀: μ ≥ 3.5

H₁: μ < 3.5

α: 0.05 (since is not listed, I'll choose one of the most common signification levels)

You have a one-tailed critical region, this means the p-value will also be one-tailed to the left of the distribution (i.e. →-∞)

The formula of the statistic is:

Z= X[bar] - μ ≈ N(0;1)

        δ/√n

To calculate the statistic you have to use the information given.

The sample mean X[bar]= 3.4 mg

Upper bond of 95% CI= 3.45 mg

The basic structure of a CI for the mean is

"estimator" ± "margin of error"

Upper bound is "estimator" + "margin of error"

Using the formula:

Ub= X[bar] + d ⇒ 3.45= 3.4 + d

⇒ d= 3.45 - 3.4 = 0.05

Where d is the margin of error

d= [tex]Z_{1-\alpha /2}[/tex] * (δ/√n)

d= [tex]Z_{0.975}[/tex] * (δ/√n)

d/[tex]Z_{0.975}[/tex]= (δ/√n)

(δ/√n)= 0.05/ 1.96 = 0.0255

(δ/√n) is the denominator in the formula, corresponds to the standard deviation of the distribution.

Now you have all values and can calculate the statistic under the null hypothesis:

Z= 3.4 - 3.5 = -3.92

       0.0255

And the p-value:

P(Z ≤ -3.92) = 0.000044 ⇒ My Z- table goes up to P(Z ≤ -3.00) = 0.001, so using strictly the table I can say that the probability is less than 0.001.

To calculate the exact probability I've used a statistic program.

p-value < 0.001

I hope it helps!

The Laplace transform of the function f(t) = eat sinh bt is F(s) = (e^a)/((s - a)^2 + b^2) after manipulating and simplifying using the standard Laplace transform rules.

The Laplace transform of the function f(t) = eat sinh bt is given by the formula:

F(s) = L{f(t)}(s) = Int0->infinity[ e^(at - s)*sinh(bt) dt]

By splitting the hyperbolic sine function, sinh(bt), into exponentials, we get:

F(s) = 0.5*Int0->infinity [ (e^(-s + a + b)*t - e^(-s + a - b)*t) dt]

As a result, after applying Laplace transform rules and simplifying, we get:

F(s) = (e^a)/((s - a)^2 + b^2).

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The correct answer is: A,

On a coordinate plane, a parabola opens up. It goes through (0, 1), has a vertex at (2, negative 3), and goes through (4, 1).

Step-by-step explanation:

Figure represents graph of [tex]f(x) = x^{2}[/tex] and [tex]g(x) = (x-2)^{2} -3[/tex]

Here, [tex]f(x) = x^{2}[/tex] is " Translated " or " Transformation " to [tex]g(x) = (x-2)^{2} -3[/tex]

In process of transformation,

You should remember that shape of curve remain same and only changes we get in vertex shift

Now, Vertex can be shift in two direction, we are going to discuss both the cases

(A). Shifting of Vertex in X-Axis:

A new function g(x) = f(x - c) represents to X-axis shift and In graph of f(x), Curve is shifted c units along right side of the X-axis

(B). Shifting of Vertex in Y-axis:

A new function g(x) = f(x) + b represents to Y-axis shift and In graph of f(x), Curve is shifted b units along the upward direction of Y-axis

Looking at the figure, You can see that vertex of f(x) is shifted 2 Units in X-axis and Negative 3 units in Y-axis and result into g(x)

Now,  [tex]g(x) = (x-2)^{2} -3[/tex]  = f(x-2) + (-3)

Therefore, new vertex we get is (2,-3)

Also, [tex]g(x) = (x-2)^{2} -3[/tex]

[tex]g(0) = (0-2)^{2} -3[/tex]

[tex]g(0) = (4} -3[/tex]

[tex]g(0) = 1 [/tex]

So. g(x) passes through (0,1)

The correct answer is: A On a coordinate plane, a parabola opens up. It goes through (0, 1), has a vertex at (2, negative 3), and goes through (4, 1).

Answer:

Graph A is the correct answer

Step-by-step explanation:

I juust took the test and got it right :D hope this helps

Answer: 4

Step-by-step explanation:

For given Population standard deviation[tex](\sigma)[/tex] , the formula to sample size is given by :-

[tex]n=(\dfrac{\sigma\cdot z*}{E})^2[/tex]

, where z* = Two-tailed critical value.

E = Margin of error.

Given : [tex]\sigma=1.5[/tex] years

We know that , Critical value for 90​% confidence interval : z* = 1.645

Margin of error : E=1.3 years

Then, the minimum sample size required to construct a 90​% confidence interval  for the population mean will be :-

[tex]n=(\dfrac{1.5\cdot 1.645}{1.3})^2\\\\=(1.89807692308)^2\\\\=3.60269600593\approx4[/tex] [Rounded to the nearest whole number.]

Hence, the minimum sample size required = 4

The minimum sample size required to construct a 90% confidence interval for the population mean age of students at a college, given a population standard deviation of 1.5 years and desired margin of error of 1.3 years, is 3 students.

The admissions director is trying to estimate the mean age of all students enrolled at the college within 1.3 years of the actual population mean with a 90% confidence level. In statistical terms, this means constructing a 90% confidence interval for the population mean within a margin of error of 1.3 years.

The formula to compute sample size for a confidence interval when the population standard deviation is known is:

n = (Zσ/E)^2

where:

Substituting the given values into the formula, we have:

n = (1.645*1.5/1.3)^2

Solving for n, we get approximately 2.41. Because we can't survey a fraction of a student, we round up to the nearest whole number. Thus, the minimum sample size required is 3 students.

In practice, especially for a large population, this sample size might be too small, but based on the strict parameters established (90% confidence level, 1.3 years margin of error, and 1.5 years population standard deviation), this is the minimum sample size required.

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Final answer:

The probability that a randomly selected man from the survey group favors raising the legal drinking age is 5/16, or 0.3125.

Explanation:

The question asks for the probability that a man chosen at random from the survey group favors raising the legal drinking age from 18 to 21. To solve this, we need to look at the numbers provided. We know that of the 540 people who favored raising the age, 390 were female. This means that 540 - 390 = 150 were male. Similarly, from the 460 who opposed, 130 were female, leading to 460 - 130 = 330 males who opposed.

Now, we calculate the total number of men in the survey, which is 150 men who favored raising the age plus 330 men who opposed, giving us a total of 150 + 330 = 480 men. The probability that a randomly selected man favors raising the age is then the number of men who favor it divided by the total number of men, which is 150/480.

To further simplify, we divide both numerator and denominator by 30, yielding an answer of 5/16. Therefore, the probability that a man favors raising the drinking age is 5/16, or approximately 0.3125.

Answer:

Step-by-step explanation:

To answer the question, we need to be able to find the number of each type of cone that needs to be sold. For the purpose, it is convenient to define variables that represent those numbers. We have chosen to use the variables x and y to represent the number of small cones and the number of large cones, respectively ("Let" statement in above answer). One could use "s" and "l" as being more mnemonic, but "l" can be confused with a variety of other symbols, so we like not to use it.

The problem statement puts limits on the numbers of small and large cones sold, so our system of inequalities needs to reflect those limits. (We suspect these are not hard limits, but represent historical data. We doubt the shop would decline to sell more, and we can conceive of conditions under which they might sell fewer.)

The heart of the matter is that the profit from each type of cone must total more than $350 (per day). The problem statement requires the shop make a profit, not just break even, so we use the > symbol for profit, instead of ≥.

___

See above for the "Let" statement and the system of inequalities. (This problem does not require we solve them.)

Answer:

[293.21;372.28]

Step-by-step explanation:

Hello!

You are asked to construct a 90% Confidence Interval for the mean duration of the foreclosure process in Boston.

The Study variable is X: duration of foreclosure in Boston. X≈N(μ;σ²)

Sample n=8

To study the population sample you can use either a Z or a t-statistic. Since the sample is less than 10, I'll choose a Student t-statistic, because it is more potent with small samples than the Z, but either one is a good choice.

X[bar]= 332.75

S= 59.01

[tex]X[bar] ± t_{n-1; 1-\alpha /2}*\frac{S}{\sqrt{n} }[/tex]

[tex]332.75 ± 1.895*\frac{59,01}{\sqrt{8} }[/tex]

[293.21;372.28]

With a confidence level of 90% you'd expect the interval [293.21;372.28] to contain the population mean of the duration of the foreclosure process in Boston.

I hope you have a SUPER day!

Answer:

Bottom 7%: L= 6.04 cm

Top 7%: L= 6.22 cm

Step-by-step explanation:

Mean length of the population (μ) = 6.13 cm

Standard deviation (σ) = 0.06 cm

The z-score for any given length 'X' is:

[tex]z=\frac{X-\mu}{\sigma}[/tex]

What we want to know is the length at the 7-th percentile and at the 93-rd percentile.

According to a z-score table, the 7-th percentile has a correspondent z-score of -1.476 and the 93-rd percentile has a z-score of 1.476. Therefore, the bottom 7% and top 7% are separated by the following lengths:

[tex]z(X_B)=\frac{X_B-\mu}{\sigma}\\-1.476=\frac{X_B-6.13}{0.06}\\X_B = 6.04\\z(X_T)=\frac{X_T-\mu}{\sigma}\\1.476=\frac{X_T-6.13}{0.06}\\X_T = 6.22[/tex]

Bottom 7%: L= 6.04 cm.

Top 7%: L= 6.22 cm.

To find the lengths that separate the top 7% and the bottom 7% of nails produced in the factory, we can use the z-score formula. The z-scores can be calculated using the inverse normal distribution function, and then the lengths can be found using the formula x = μ + zσ. The lengths that separate the top 7% and the bottom 7% are approximately 6.22 cm and 6.04 cm, respectively.

To find the lengths that separate the top 7% and the bottom 7% of nails produced in the factory, we can use the z-score formula. The z-score represents how many standard deviations a value is from the mean. For the top 7%, we can find the z-score by using the formula: z = invNorm(1 - 0.07), where invNorm is the inverse normal distribution function. Similarly, for the bottom 7%, the z-score can be calculated using the formula: z = invNorm(0.07). Once we have the z-scores, we can use the formula x = μ + zσ, where x is the length of the nails, μ is the mean length (6.13 cm), z is the z-score, and σ is the standard deviation (0.06 cm).

Using a calculator or software, we can find the z-scores:

Substituting the values into the x = μ + zσ formula:

Therefore, the two lengths that separate the top 7% and the bottom 7% are approximately 6.22 cm and 6.04 cm, respectively.

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Answer:

See below

Step-by-step explanation:

(a) There is a number whose cube is equal to 2.

[tex]\large \exists x \in \mathbb{R}\;|\;x^3=2[/tex]

(b) The square of every number is at least 0.

[tex]\large \forall x\in \mathbb{R},\;x^2\geq 0[/tex]

(c) There is a number that is equal to its square.

[tex]\large \exists x \in \mathbb{R}\;|\;x^2=x[/tex]

(d) Every number is less than or equal to its square.

[tex]\large \forall x\in \mathbb{R},\;x\leq x^2[/tex]

(By the way, this last statement is not true when 0 < x < 1)

Answer:

B. N(μ, 1.30).

Step-by-step explanation:

The Central Limit Theorem estabilishes that, for a random variable X, with mean [tex]\mu[/tex] and standard deviation [tex]\sigma[/tex], a large sample size can be approximated to a normal distribution with mean [tex]\mu[/tex] and standard deviation [tex]\frac{\sigma}{\sqrt{n}}[/tex].

In this problem, we have that:

An SRS of 25 recent birth records at the local hospital was selected. In the sample, the average birth weight was = 119.6 ounces. Suppose the standard deviation is known to be σ = 6.5 ounces.

Assume that in the population of all babies born in this hospital, the birth weights follow a Normal distribution, with mean μ.

This means that for the sampling distribution, the mean is the mean of the weight of all babies born, so [tex]\mu[/tex] and [tex]s = \frac{6.5}{\sqrt{25}} = 1.30[/tex].

So the correct answer is

B. N(μ, 1.30).

The correct representation of the sampling distribution of the sample mean based on the data from birth records is option C, which is N(119.6, 1.30). This is based on the formula of sampling distribution for mean with known standard deviation.

The question is asking about the sampling distribution of the sample mean which is a statistical concept used in inferential statistics. The sample distribution of the mean is normally distributed, denoted as N(μ, σ/n0.5) where μ is the population mean, σ is the population standard deviation, and n is the sample size (SRS - Simple Random Sample).

Based on the data given, the sample mean after studying the 25 birth records is 119.6 ounces and the standard deviation is known to be 6.5 ounces. So, the standard deviation of the sample mean, often termed as the standard error, would be σ/√n = 6.5/√25 = 1.3 ounces. Hence the correct sampling distribution of the sample mean would be represented by N(119.6, 1.30), which is option C in your question.

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Answer:

E. The area to the left of 5.5 and D. The area to the right of 5.5

Step-by-step explanation:

Please see attachment .

Answer:

30/52 or 0.5769 or 57.69%

Step-by-step explanation:

In a standard deck of 52 cards, the number of face cards (F) and the number of hearts (H) is given by:

[tex]F=4+4+4 =12\\H=\frac{52}{4}=13[/tex]

Out of all hearts, three of them are face cards (jack, king, and queen). Therefore, the probability of a card being EITHER a face card or a heart is:

[tex]P(F \cup H) = P(F) +P(H) - P(F \cap H) \\P(F \cup H)=\frac{12+13-3}{52} =\frac{22}{52}[/tex]

Therefore, the probability of card being NEITHER a face card NOR a heart is:

[tex]P=1-P(F \cup H) \\P=1-\frac{22}{52}=\frac{30}{52}\\\\P=0.5769\ or\ 57.69\%[/tex]

Answer:

c. There is insufficient evidence to conclude that there is a significant difference between the means of the statistics day students and night students on Exam 2

Step-by-step explanation:

Given that a  statistics instructor believes that there is no significant difference between the mean class scores of statistics day students on Exam 2 and statistics night students on Exam 2.

Group   Group One     Group Two  

Mean 75.8600 75.4100

SD 16.9100 19.7300

SEM 2.8583 3.2436

N 35       37      

*SEM is std error/sqrt n

Mean difference = 0.4500

[tex]H_0: \bar x = \bar y\\H_a: \bar x \neq \bar y[/tex]

(two tailed test)

Std error for difference = 4.342

Test statistic t = [tex]\frac{0.45}{4.342} \\=0.1036[/tex]

df =70

p value = 0.9178

Since p >0.05 we accept H0

c. There is insufficient evidence to conclude that there is a significant difference between the means of the statistics day students and night students on Exam 2

Final answer:

The concluding statement is that there is insufficient evidence to conclude a significant difference in the means of statistics day students and statistics night students on Exam 2.

Explanation:

The hypothesis test being conducted in this scenario is a two-sample t-test. The null hypothesis, denoted as H0, states that there is no significant difference between the mean scores of statistics day students and statistics night students on Exam 2. The alternative hypothesis, denoted as Ha, states that there is a significant difference between the means of the two groups. To determine whether there is sufficient evidence to support the alternative hypothesis, we can compare the t-statistic with the critical t-value from a t-distribution table.

In this case, since the standard deviations are assumed to be equal, we can calculate the pooled standard deviation and use it to calculate the t-statistic. With the given sample means, standard deviations, and sample sizes, the calculated t-statistic is -0.246. The critical t-value for a two-tailed test with a significance level of 0.05 and 70 degrees of freedom is approximately 1.994. Since the calculated t-statistic (-0.246) falls within the range between -1.994 and 1.994, we fail to reject the null hypothesis.

Therefore, the concluding statement is:

c. There is insufficient evidence to conclude that there is a significant difference between the means of the statistics day students and night students on Exam 2.

The alternative hypothesis whereby mud = the population mean difference is expressed as; Ha: mud > 0

An alternative hypothesis is defined as one in which the observers or researchers anticipate a difference (or an effect) between two or more variables.

Now, in this case, the null hypothesis is that eating milk chocolate lowered someone's cholesterol levels. This means the alternate hypothesis is that eating milk chocolate increases someone's cholesterol levels.

Thus, alternative hypothesis in this case is expressed as;

Ha: mud > 0

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