A NHANES report gives data for 654 women aged 20–29 years. The mean BMI of these 654 women was x¯=26.8 . We treated these data as an SRS from a normally distributed population with standard deviation ????=7.5 . (a) Suppose that we had an SRS of just 100 young women. What would be the margin of error for 95% confidence?

Answer: 19/12 pounds or 1.583 pounds

Step-by-step explanation:

Let a pound be represented by x. So x = 1 pound.

5/6 of a pound is the same as multiplying 5/6 × x. It becomes 5/6 × x = 5x/6

3/4 of a pound is the same as multiplying 3/4 × x. It becomes 3/4 × x = 3x/4

We want to look for 5/6 of a pound + 3/4 of a pound.

It becomes

5x/6 + 3x/4

Taking lowest common multiple(LCM) of 12, it becomes

= (2×5x + 3×3x) / 12

= (10x +9x)/12

= 19x /12

Substituting x = 1 pound,

It becomes 19/12 pounds

Expressing in decimal, 19/12 = 15.83

We can also solve from the beginning in terms of decimals

5/6 pounds = 0.833 pounds

3/4pounds = 0.75 pounds

5/6 pounds + 3/4 pounds = 0.833 + 0.75 = 1.583 pounds

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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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 .

9514 1404 393

Answer:

  No; 495

Step-by-step explanation:

For many folks, the order of application of toppings will not matter. For those folks, the number of possible 4-topping pizzas is

 C(12,4) = 495 = 12!/(8!×4!)

There are 495 possible 4-topping pizzas.

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 amount of ounce used for paint is [tex]\frac{9}{15} ounce[/tex]  

Step-by-step explanation:

used amount for water is [tex]\frac{2}{3}[/tex] ounce.

used amount for sky is [tex]\frac{3}{5}[/tex] ounce.

thus, the total amount used is [tex]\frac{2}{3}[/tex] +  [tex]\frac{3}{5}[/tex]

= [tex]\frac{19}{15}[/tex]

Thus the total unused amount is 2 - [tex]\frac{19}{15}[/tex] = [tex]\frac{11}{15}[/tex] .

But the remaining amount is [tex]\frac{2}{15}[/tex].

Thus used amount for flag is [tex]\frac{11}{15}[/tex] - [tex]\frac{2}{15}[/tex]

= [tex]\frac{9}{15}[/tex]

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:

See attached image for the graph of the function

Step-by-step explanation:

Notice that this is the product of a power function ([tex]4x^2[/tex]) times the trigonometric and periodic function cos(x). So the zeros (crossings of the x axis will be driven by the values at which they independently give zero. That is the roots of the power function (only x=0) and the many roots of the cos function: [tex]x= \frac{\pi}{2} , \frac{3\pi}{2} ,...[/tex], and their nagetiva values.

Notice that the blue curve in the graph represents the original function f(x), with its appropriate zeros (crossings of the x-axis), while the orange trace is that of "-f(x)". Of course for both the zeroes will be the same, while the rest of the curves will be the reflection over the x-axis since one is the negative of the other.

Answer:

The solution has been given in the following attachment .

Step-by-step explanation:

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:

63

Step-by-step explanation:

The lower limit and upper limit of 99% confidence interval on the proportion of such tears that will heal is (0.477, 0.874)

Suppose we're given that:

Then, the sample proportion of favorable cases is:

[tex]\hat{p} = \dfrac{X}{N}[/tex]

The critical value at the level of significance [tex]\alpha[/tex] is [tex]Z_{1- \alpha/2}[/tex]

The corresponding confidence interval is:

[tex]CI = & \displaystyle \left( \hat p - z_c \sqrt{\frac{\hat p (1-\hat p)}{n}}, \hat p + z_c \sqrt{\frac{\hat p (1-\hat p)}{n}} \right)[/tex]

We need to construct the 99% confidence interval for the population proportion.

We have been provided with the following information about the number of favorable cases(the number of tears located between 3 and 6 mm from the meniscus rim were healed):

The sample proportion is computed as follows, based on the sample size N = 37 and the number of favorable cases X = 25

[tex]\hat p = \displaystyle \frac{X}{N} = \displaystyle \frac{25}{37} = 0.676[/tex]

The critical value for [tex]\alpha = 0.01[/tex] is [tex]z_c = z_{1-\alpha/2} = 2.576[/tex]

The corresponding confidence interval is computed as shown below:

[tex]\begin{array}{ccl} CI & = & \displaystyle \left( \hat p - z_c \sqrt{\frac{\hat p (1-\hat p)}{n}}, \hat p + z_c \sqrt{\frac{\hat p (1-\hat p)}{n}} \right) \\\\ \\\\ & = & \displaystyle \left( 0.676 - 2.576 \times \sqrt{\frac{0.676 (1- 0.676)}{37}}, 0.676 + 2.576 \times \sqrt{\frac{0.676 (1- 0.676)}{37}} \right) \\\\ \\\\ & = & (0.477, 0.874) \end{array}[/tex]

Therefore, based on the data provided, the 99% confidence interval for the population proportion is 0.477<p<0.874, which indicates that we are 99% confident that the true population proportion p is contained by the interval (0.477, 0.874).

Thus, the lower limit and upper limit of 99% confidence interval on the proportion of such tears that will heal is (0.477, 0.874)

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Final answer:

The independent variable is time and the dependent variable is elevation in the context of a linear function modeling climbers ascending a mountain at a rate of 1500 feet per hour.

Explanation:

The situation described by the student can be modeled by a linear function in which the independent variable is time and the dependent variable is elevation.

As the group of climbers begins at an elevation of 5000 feet and climbs at a steady rate of 1500 feet per hour, the relationship between the time spent climbing and the elevation gained is direct.

The function that models this situation is f(t) = 1500t + 5000, where 't' represents the time in hours, and 'f(t)' represents the elevation in feet.

Answer:

Confidence interval for the proportion mean is between 0.0113 and 0.0587. B. Yes, it is reasonable to conclude that 5% of the employees cannot pass the employee test.

Step-by-step explanation:

We have a large sample size of n = 400 random tests conducted. Let p be the true proportion of employees who failed the test. A point estimate of p is [tex]\hat{p} = 14/400 = 0.035[/tex], we can estimate the standard deviation of [tex]\hat{p}[/tex] as [tex]\sqrt{\hat{p}(1-\hat{p})/n}=\sqrt{0.035(1-0.035)/400}=0.0092[/tex]. A [tex]100(1-\alpha)%[/tex] confidence interval is given by [tex]\hat{p}\pm z_{\alpha/2}\sqrt{\hat{p}(1-\hat{p})/n[/tex], then, a 99% confidence interval is [tex]0.035\pm z_{0.005}0.0092[/tex], i.e., [tex]0.035\pm (2.5758)(0.0092)[/tex], i.e., (0.0113, 0.0587). [tex]z_{0.005} = 2.5758[/tex] is the value that satisfies that there is an area of 0.005 above this and under the standard normal curve. B. Yes, it is reasonable to conclude that 5% of the employees cannot pass the employee test, because this inverval contain 0.05.

The 99% confidence interval for the proportion of employees that fail the test is between 0.016 and 0.054. Since 5% is within this range, it is reasonable to conclude that 5% of the employees cannot pass the test.

To compute a 99% confidence interval for the proportion of employees that fail the test, we first need to calculate the sample proportion (p). Here, 14 employees failed the tests out of 400, so p = 14/400 = 0.035. The 99% confidence interval requires Z-score of 2.576 (as 99% of the data lies within 2.576 standard deviations of the mean in a normal distribution).

The estimation error (E) can be calculated using the formula E = Z * √( (p*(1-p)) / n), where n is the total number of tests. Substituting the values, E = 2.576 * √(0.035 * (1 - 0.035) / 400) = 0.019

So, the 99% confidence interval is (p - E, p + E) = (0.035 - 0.019, 0.035 + 0.019) = (0.016, 0.054). Thus, we are 99% confident that the true proportion of employees that fail the test is between 0.016 and 0.054.

Given that 5% (or 0.05) is within the 99% confidence interval we calculated, it would be reasonable to conclude that 5% of the employees cannot pass the employee test.

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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%.

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:

s=29.93m/s

h=22.88m

Step-by-step explanation:

we must find the initial speed,  we will determine its position (x-y).

x component [tex]s=0+v_{0}cos\alpha.t+0=v_{0}cos\alpha.t[/tex]

y component [tex]h=0+v_{0}sin\alpha.t-\frac{1}{2}gt^{2}=v_{0}sin\alpha.t-\frac{1}{2}gt^{2}\\[/tex]  since the ball is caught at the same height then h=0

[tex]h=v_{0}sin\alpha.t-\frac{1}{2}gt^{2}=0\\v_{0}sin\alpha.t-\frac{1}{2}gt^{2}=0;v_{0}sin\alpha.t=\frac{1}{2}gt^{2}\\t=\frac{2v_{0}sin\alpha}{g}\\[/tex]

where t= flight time;[tex]s=v_{0}cos\alpha.t[/tex], replacing t:

[tex]v_{0}=\sqrt{\frac{sg}{sin2\alpha}}[/tex]

[tex]s=v_{0}cos\alpha(\frac{2sin\alpha }{g})=\frac{v_{0} ^{2}2sin\alpha.cos\alpha}{g}=\frac{v_{0} ^{2}sin(2\alpha)}{g}]

: the values ​​must be taken to the same units

[tex]300ft*0.3048m/ft=91.44m[/tex]

[tex]v_{0}=\sqrt{\frac{91.44m*9.8\frac{m}{s^{2}}}{sin2(45)}}=\sqrt{895.112(\frac{m}{s} )^{2} }=29.93\frac{m}{s}[/tex]

To calculate the height you should know that this is achieved when its component at y = 0

[tex]v_{y}=v_{0}sin\alpha-gt=0;gt=v_{0}sin\alpha\\\\ t=\frac{v_{0}sin\alpha  }{g}\\h=v_{0}sin\alpha  .t-\frac{1}{2}gt^{2}[/tex]

replacing t;[tex]h=v_{0}sin\alpha(\frac{v_{0}sin\alpha}{g})-\frac{1}{2}g(\frac{v_{0sin\alpha}}{g}) ^{2}\\[/tex]

finally

[tex]h=\frac{(v_{0}sin\alpha)^{2}}{2g}=\frac{(29.95*sin45)^{2}}{2*9.8}=22.88m[/tex]

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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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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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.

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Answer:

(a) According to the  Albert Michelson's measurements, two-sided 95% confidence interval for velocity of light in the air would be 299,852.4±15.5

(b) Since true velocity of light in the air is not included in the 95% confidence interval, we can say that Michelson’s measurements were not accurate.

Step-by-step explanation:

Confidence Interval can be calculated using M±ME where

And margin of error (ME) from the mean can be calculated using the formula

ME=[tex]\frac{z*s}{\sqrt{N} }[/tex] where

Then ME=[tex]\frac{1.96*79.01}{\sqrt{100} }[/tex] ≈ 15.5

According to the  Albert Michelson's measurements, 95% confidence interval for velocity of light in the air would be 299,852.4±15.5

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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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:

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:

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]

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: Consumption of,

Japan = 4.6 million barrel,

China = 7.8 million barrel,

The US = 19.8 million barrel.

Step-by-step explanation:

Let x be the quantity of oil per day consumed by Japan,

∵ China consumes 3.2 million barrels a day than Japan.

So, the consumption of China = ( x + 3.2) million Barrel,

Also, The United States consumes 12 million more barrels a day than China.

So, the consumption of the US = (x + 3.2 + 12) = (x + 15.2) million barrel,

Thus, the total consumption of these three countries

= x + x + 3.2 + x + 15.2

= (3x + 18.4) million barrel,

According to the question,

3x + 18.4 = 32.2

3x = 32.2 - 18.4

3x = 13.8

⇒ x = 4.6

Hence, Japan, China and the US consume 4.6 million barrel, 7.8 million barrel and 19.8 million barrel respectively.

Answer:

At least 1537 samples needed to estimate the true average number of eggs a queen bee lays with 95 percent confidence

Step-by-step explanation:

Minimum sample size required can be found using the formula

N≥[tex](\frac{z*s}{ME} )^2[/tex] where

then N≥[tex](\frac{1.96*10}{0.5} )^2[/tex] =1536.64

Then, at least 1537 samples needed to estimate the true average number of eggs a queen bee lays with 95 percent confidence

The minimum sample size needed is 1537 for a margin of error of 0.5.

Margin of error is used to determine by what value there is deviation from the real value. Margin of error (E) is given by:

[tex]E=Z_\frac{\alpha}{2} *\frac{standard\ deviation}{\sqrt{sample\ size} }[/tex]

The 95% confidence level have a z score of 1.96. Hence for E = 0.5, standard deviation = 10. hence:

[tex]0.5=1.96*\frac{10}{\sqrt{sample\ size}}\\ \\sample\ size=1537[/tex]

The minimum sample size needed is 1537 for a margin of error of 0.5.

Find out more on margin of error at: https://brainly.com/question/10218601

A) the z score for 95% is 1.96

Multiply by the deviation:

1.96 x 0.005 = 0.0098

Now add and then subtract that from the mean:

4.035 - 0.0098 = 4.0252

4.035 + 0.0098 = 4.0448

The interval is (4.0252, 4.0448)

B) 4.035 +/- 1.96 x sqrt( 0.005/sqrt(25))

= 4.035 +/-0.00196

Answer: ( 4.033, 4.037)

C) the conclusion is that both 4.020 and 4.055 are out of the range.

4.020 is below the lowest range and 5.055 is higher than highest range.

The 95% confidence interval for the sample means is; CI = (4.033, 4.037)

A) We are given;

Mean; x' = 4.035 mm

standard deviation; σ = 0.005 mm

sample size; n = 25

Formula for confidence interval is;

CI = x' ± z(σ/√n)

The z score for 95% confidence level is 1.96. Thus;

CI = 4.035 ± 1.96(0.005)

CI = 4.035 ± 0.0098

CI = (4.025, 4.045)

B) From the formula for confidence interval earlier stated, we have;

CI = 4.035 ± 1.96(0.005/√(25))

CI = 4.035 ± 0.00196

CI = (4.033, 4.037)

C)i) The conclusion is that both 4.020 is out of the range of the confidence interval.

ii) The conclusion is that 4.055 is higher than the confidence interval

Read more about Confidence Interval at; https://brainly.com/question/17097944

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.

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.