The amounts (in ounces) of juice in eight randomly selected juice bottles are: 15.3 15.3 15.7 15.7 15.3 15.9 15.3 15.9 Construct a 98% confidence interval for the mean amount of juice in all such bottles

Answer:

1000000000

Step-by-step explanation:

20000 times 50000 equals 1000000000

when you multiply 20,000 by 50,000, you get the product of 1,000,000,000 or 1 billion.

The The product of 20,000 multiplied by 50,000 is:

20,000 x 50,000 = 1,000,000,000

Therefore, 20,000 multiplied by 50,000 equals 1 billion. of 20,000 multiplied by 50,000 is:

20,000 x 50,000 = 1,000,000,000

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Answer:

a.

[tex]\vec{u}=\frac{\bigtriangledown F(-3,3)}{|\bigtriangledown F(-3,3)|}=\frac{1}{\sqrt{2}}[\hat{i}-\hat{j}][/tex] (ascent)

[tex]\vec{u}=-\frac{\bigtriangledown F(-3,3)}{|\bigtriangledown F(-3,3)|}=-\frac{1}{\sqrt{2}}[\hat{i}-\hat{j}][/tex]  (descent)

b.

[tex]\vec{v}=\frac{1}{\sqrt{2}}[\hat{i}+\hat{j}][/tex]

Step-by-step explanation:

a. The function is given by:

[tex]F(x,y)=e^{-(x^2/6+y^2/6)}[/tex]

the point is P(-3,3)

a. The unit vector that gives the direction of the steepest ascent is necessary to compute the gradient of F(x,y):

[tex]\bigtriangledown F(x,y)=e^{-(x^2/6+y^2/6)}(-\frac{x}{3})\hat{i}+e^{-(x^2/6+y^2/6)}(-\frac{y}{3})\hat{j}\\\\\bigtriangledown F(x,y)=-\frac{1}{3}e^{-(x^2/6+y^2/6)}[x\hat{i}+y\hat{j}][/tex]

The, it is necessary to evaluate in the point P, and to compute the norm of the vector in order to get the unit vector:

[tex]\bigtriangledown F(-3,3)=-\frac{1}{3}e^{-(\frac{9}{6}+\frac{9}{6})}[-3\hat{i}+3\hat{j}]\\\\\bigtriangledown F(-3,3)=e^{-3}[\hat{i}-\hat{j}]\\\\|\bigtriangledown F(-3,3)|=\sqrt{(e^{-3})^2+(e^{-3})^2}=\sqrt{2}e^{-3}\\\\\vec{u}=\frac{\bigtriangledown F(-3,3)}{|\bigtriangledown F(-3,3)|}=\frac{1}{\sqrt{2}}[\hat{i}-\hat{j}][/tex]   (ascent)

for the steepest descend you have

[tex]\vec{u}=-\frac{\bigtriangledown F(-3,3)}{|\bigtriangledown F(-3,3)|}=-\frac{1}{\sqrt{2}}[\hat{i}-\hat{j}][/tex]

b.

the vector with the direction of no change is a vector perpendicular to grad(F):

[tex]\bigtriangledown F(-3,3)\cdot \vec{v}=0\\\\e^{-3}v_1-e^{-3}v_2=0\\\\v_1=v_2[/tex]

furthermore, v is an unit vector:

[tex]\sqrt{v_1^2+v_2^2}=1\\\\v_1 ^2+v_1^2=1\\\\2v_1^2=1\\\\v_1=\frac{1}{\sqrt{2}}=v_2[/tex]

then, the vector is:

[tex]\vec{v}=\frac{1}{\sqrt{2}}[\hat{i}+\hat{j}][/tex]

Answer:

c) 0.932

99% confidence interval for average weights of all packages sold in small meat trays.

(0.932 ,1.071)

Step-by-step explanation:

Explanation:-

Given random sample of 35 packages in small meat trays produced weight with an average of 1.01 lbs. and standard deviation  of 0.18 lbs.

size of the sample 'n' = 35

mean of the sample x⁻= 1.01lbs

standard deviation of the sample 'S' = 0.18lbs

The 99% confidence intervals are given by

[tex](x^{-} - t_{\alpha } \frac{S}{\sqrt{n} } , x^{-} +t_{\alpha } \frac{S}{\sqrt{n} } )[/tex]

The degrees of freedom γ=n-1 =35-1=34

tₐ =  2.0322

99% confidence interval for average weights of all packages sold in small meat trays

[tex](1.01 - 2.0322 \frac{0.18}{\sqrt{35} } , 1.01+2.0322 \frac{0.18}{\sqrt{35} } )[/tex]

( 1.01 - 0.06183 , 1.01+0.06183)

(0.932 ,1.071)

Final answer:-

99% confidence interval for average weights of all packages sold in small meat trays.

(0.932 ,1.071)

To find the area of the surface generated by revolving the curve about the x-axis, you need to evaluate the definite integral of the square of the given function within the specified interval.

Definite integral representing the area:

Given curve: y = 81 - x^2, -5 ≤ x ≤ 5

Revolving this curve about the x-axis generates a surface. The definite integral representing the area is obtained by integrating the square of the function and applying the limits of integration.

The total area of the regions between the curves is 726 square units

Calculating the total area of the regions between the curves

From the question, we have the following parameters that can be used in our computation:

y = 81 - x²

Also, we have the interval to be

−5 ≤ x ≤ 5

So, the area of the regions between the curves is

Area = ∫y dx

This gives

Area = ∫(81 - x²) dx

Integrate

Area =  81x - x³/3

Recall that  −5 ≤ x ≤ 5

So, we have

Area =  |[81(-5) - (-5)³/3] - [81(5) - (5)³/3]|

Evaluate

Area =  726

Hence, the total area of the regions between the curves is 726 square units

Answer:

3(x+5)^2

Step-by-step explanation:

Step 1:

Extract the obvious common constant factor

3(x^2+10x+25)

Step 2:

Recognize the second term as a square

3(x+5)^2

Answer:

$5336.90

Step-by-step explanation:

First step is to filter through what is given And to deduce the important information....

Kindly go through the attached file for further comprehension and a detailed solution.

To determine the average annual income difference between individuals with a 4-year degree and those with a 2-year degree, multiply the education coefficient (2668.45) by 2, resulting in an approximate difference of $5336.90.

Based on the regression analysis provided, the variable coefficient for education is 2668.45. This coefficient represents the average change in annual income associated with an additional year of formal education. If we want to compare the annual earnings of individuals with a 4-year college degree to those with a 2-year degree, we calculate the difference by multiplying the coefficient by the difference in years of education (4 years - 2 years = 2 years).

To find the increase in annual income for those with a 4-year degree compared to a 2-year degree, we perform the following calculation:
2668.45 × 2 = 5336.90.

Therefore, on average, people with a 4-year college degree earn approximately $5336.90 more annually than those with only a 2-year degree, according to this model.

Answer:

i. Point of estimate:

[tex] \hat \mu = \bar X =143.0[/tex]

ii. Margin of error:

[tex] ME = 2.01 *\frac{56.7}{\sqrt{50}}= 16.12[/tex]

iii. The 90% confidence interval

Replacing in the confidence interval formula we got

[tex]143.0-16.12=126.88[/tex]    

[tex]143.0+16.12=159.12[/tex]    

The 90% confidence interval is 126.88 to 159.12

Step-by-step explanation:

Information given

[tex]\bar X=143.0[/tex] represent the sample mean for the variable of interest

[tex]\mu[/tex] population mean

s=56.7 represent the sample standard deviation

n=50 represent the sample size  

Confidence interval

The confidence interval for the true mean when we don't know the deviation is given by the following formula:

[tex]\bar X \pm t_{\alpha/2}\frac{s}{\sqrt{n}}[/tex]   (1)

In order to calculate the critical value for the confidence interval [tex]t_{\alpha/2}[/tex] we need to find the degrees of freedom, with this formula:

[tex]df=n-1=50-1=49[/tex]

The Confidence level provided is 0.90 or 90%, the value for the significance is [tex]\alpha=1-0.9=0.1[/tex] and [tex]\alpha/2 =0.05[/tex], and we can use excel, a calculator or a table to find the critical value. The excel command would be: "=-T.INV(0.05,49)".And we see that [tex]t_{\alpha/2}=2.01[/tex]

i. Point of estimate:

[tex] \hat \mu = \bar X =143.0[/tex]

ii. Margin of error:

[tex] ME = 2.01 *\frac{56.7}{\sqrt{50}}= 16.12[/tex]

iii. The 90% confidence interval

Replacing in the confidence interval formula we got

[tex]143.0-16.12=126.88[/tex]    

[tex]143.0+16.12=159.12[/tex]    

The 90% confidence interval is 126.88 to 159.12

Answer-

You should always check the solution is a rational equation. It would be easier to get an answer... Idk if this helps, but thats what I was taught>>>

Step-by-step explanation:

Answer:

196.6 feet

Step-by-step explanation:

Draw the right triangle formed by the zip-line.  Use SOH-CAH-TOA to write and solve an equation:

tan 11.5° = 40 / x

x = 40 / tan 11.5°

x = 196.6

The horizontal distance between the towers is 196.60 ft.

Distance is the measurement of an object from a specific point in the horizontal direction, and height is the measurement of an object in the vertical direction.

Here, ∅₁ is called the angle of elevation and ∅₂ is called the angle of depression. For one specific type of problem in height and distances, we have a generalized formula. Height = Distance moved / [cot(original angle) – cot(final angle)] => h = d / (cot ∅₁ – cot ∅₂)

Given:

The vertical height of the zip line = 40 ft

The angle of elevation = 11.5°

We know, according to trigonometric values tan n = height / base

∴ tan 11.5 = 40 / base

base = 40/ tan 11.5

base = 40 / 0.203

base = 196. 60 ft

Therefore, the horizontal distance between the towers is 196.60 ft.

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Answer:

Step-by-step explanation:

The length of the shadow cast by the 29-m tall building is approximately 13.9 m. This is derived by using the Pythagorean theorem, considering the details of the problem as elements of a right triangle.

In this problem, we are given a 29-m tall building and the distance from the top of the building to the tip of the shadow which is 34 m. We are asked to find the length of the shadow. To solve for this, we can use the knowledge of right triangles from geometry.

In this case, the height of the building is one side of a right triangle, the distance from the top of the building to the end of the shadow is the hypotenuse, and the shadow length would be the other side. Since we know the length of the hypotenuse (34 m) and one side (29 m), we can solve for the other side (the shadow length) by using the Pythagorean theorem which states that the square of the hypotenuse (side opposite the right angle) is equal to the sum of the squares of the other two sides.

Expressed in the formula: c^2 = a^2 + b^2. Where 'c' is the hypotenuse and 'a' and 'b' are the other two sides.

Rearranging for 'a', which is the length of the shadow, we get: a^2 = c^2 - b^2. Substituting with the given values results in: a = sqrt(34^2 - 29^2). After calculating, the approximate length of the shadow is 13.9 m (rounded to the nearest tenth).

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Answer:

=2(4+9w)

=2(4) 2(+9w)

=8 +18w

=18w+8

Step-by-step explanation:

Answer:

Hence P values of beta becomes smaller(< 0.0001). and doest affect the mean response

Step-by-step explanation:

Given:

AS Predictor  become more highly correlated .

To find:

Descriptive  Nature of high correlated Predictor .

Solution:

A predictor is high correlated means:

1)It means that the two variables are strongly related to each other.

2)This is also called as problem of multicollinearity when two variables are

in Regression.

Effects when predictor are highly correlated ;

In short ,Mulitcollinearity does not affect the mean response and  new response of the model.

Hence P values of beta becomes smaller(< 0.0001). and doest affect the mean response

Final answer:

The margin of error for a 95% confidence interval, given a standard error of 1.91, is approximately 3.74 when rounded to two decimal places.

Explanation:

To find the margin of error for a 95% confidence interval when the standard error is given, we will use the concept of the z-score that corresponds to our desired confidence level. For a 95% confidence interval, the z-score is typically 1.96, which is the critical value for a normal distribution that leaves 2.5% in each tail. The margin of error (MoE) is calculated by multiplying the standard error (SE) by the z-score.

Margin of Error = z * SE

Marginal Error for our scenario = 1.96 * 1.91

Therefore, the Margin of Error = 3.7436. Rounding this to two decimal places gives us a Margin of Error of approximately 3.74.

Answer:

that is the solution to the question

The percentage change to 2 decimal places when a price of £60 is increased to £89.99 is 49.98%.

Given that, a price of £60 is increased to £89.99.

Percentage is defined as a given part or amount in every hundred. It is a fraction with 100 as the denominator and is represented by the symbol "%".

Here, old price =£60 and new price =£89.99

Change in price = 89.99-60

= 29.99

Percentage = Change in price/Old price ×100

= 29.99/60 ×100

= 0.4998 ×100

= 49.98%

Therefore, the percentage change to 2 decimal places when a price of £60 is increased to £89.99 is 49.98%.

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Answer:

Check the explanation

Step-by-step explanation:

The odds are 4 to 1 against, so we can estimate the probability of success (p) as

[tex]\frac{p}{q}=\frac{p}{1-p}=\frac{1}{4}\\\\4p=1-p\\\\5p=1\\\\p=0.2[/tex]

The expected pay for every success is 3 to 1, so we lose $1 for every lose and we gain $3 for every win.

The number of winnings in the 100 rounds to be even can be calculated as:

[tex]W+L=100\\\\L=100-W\\\\\\Payoff=0=3*W-1*L=3W-1*(100-W)=3W+W-100\\\\0=4W-100\\\\W=25[/tex]

We have to win at least 25 rounds to have a positive payoff.

As the number of rounds is big, we will approximate the binomial distribution to a normal distribution with parameters:

[tex]\mu=np=100*0.2=20\\\\\ \sigma=\sqrt{npq}=\sqrt{100*0.2*0.8}=4[/tex]

The z-value for x=25 is

[tex]z=\frac{X-\mu}{\sigma}=\frac{25-20}{4}=1.25[/tex]

The probability of z>1.25 is

P(X>25)=P(z>1.25)=0.10565

There is a 10.5% chance of having a positive payoff.

NOTE: if we do all the calculations for the binomial distribution, the chances of having a net payoff are 13.1%.

The maximum number of students that attend the trip is 160.

To determine the number of students that go on the trip, to consider the cost of theatre tickets, train tickets, and the allotted budget.

Let's denote the number of students as S. We know that the cost of each theatre ticket is £36 and that there are 2 teachers attending. Therefore, the total cost of theatre tickets for the teachers is 2 × £36 = £72.

The remaining budget after accounting for the cost of theatre tickets for the teachers is £2000 - £72 = £1928.

Each student will require a train ticket costing £12. So, the cost of train tickets for S students will be 12S.

to find the maximum number of students (S) that can be accommodated within the remaining budget.

From the remaining budget, the cost of train tickets for the students should be less than or equal to the available funds:

12S ≤ £1928

To solve for S, we divide both sides of the inequality by 12:

S ≤ £1928 / 12

S ≤ 160.67

Since the number of students must be a whole number, the maximum number of students that can go on the trip is 160.

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Answer:

[tex]a_{n} = 32(\frac{1}{4})^{n-1}[/tex]

Step-by-step explanation:

The nth term of a geometric sequence is given by the following equation.

[tex]a_{n+1} = ra_{n}[/tex]

In which r is the common ratio.

This can be expanded for the nth term in the following way:

[tex]a_{n} = a_{1}r^{n-1}[/tex]

In which [tex]a_{1}[/tex] is the first term.

This means that for example:

[tex]a_{3} = a_{1}r^{3-1}[/tex]

So

[tex]a_{3} = a_{1}r^{2}[/tex]

[tex]2 = a_{1}(\frac{1}{4})^{2}[/tex]

[tex]2 = \frac{a_{1}}{16}[/tex]

[tex]a_{1} = 32[/tex]

Then

[tex]a_{n} = 32(\frac{1}{4})^{n-1}[/tex]

Answer:

Step-by-step explanation:

This is a test of 2 independent groups. The population standard deviations are not known. it is a two-tailed test. Let c b the subscript for students using computer and n be the subscript for students not using computer.

Therefore, the population means would be μc and μn.

The random variable is xc - xn = difference in the sample mean scores of students who used computers and those who didn't use computers

We would set up the hypothesis.

The null hypothesis is

H0 : μc = μn H0 : μc - μn = 0

The alternative hypothesis is

Ha : μc ≠ μn Ha : μc - μn ≠ 0

Since sample standard deviation is known, we would determine the test statistic by using the t test. The formula is

(xc - xn)/√(sc²/nc + sn²/nn)

From the information given,

xc = 309

xn = 303

sc = 29

sn = 32

nc = 60

nn = 40

t = (309 - 303)/√(29²/60 + 32²/40)

t = 0.953

The formula for determining the degree of freedom is

df = [sc²/nc + sn²/nn]²/(1/nc - 1)(sc²/nc)² + (1/nn - 1)(sn²/nn)²

df = [29²/60 + 32²/40]²/(1/60 - 1)(29²/60)² + (1/40 - 1)(32²/40)² = 1569.48/20.13

df = 78

We would determine the probability value from the t test calculator. It becomes

p value = 0.344

Assuming a level of significance of 0.05, we would not reject the null hypothesis because the p value, 0.344 is > 0.05

Therefore, we cannot conclude that the population mean scores differ.

Answer:

a. possible sample size is 10

b. mean is 3

c standard deviation is 0.9

Step-by-step explanation:

Mean of any given set of numbers is the "average" you're used to, where you add up all the numbers and then divide by the number of numbers.

Standard deviation is a measure of the amount of variation or dispersion of a set of values.

Go to attachment for detailed analysis.

The number of possible samples of size 2 without replacement from a population of 5 objects is 10. The means of these samples can be calculated by taking the combinations of 2 objects and finding their mean. The mean of these means is equal to the population mean. The standard error of these means can be calculated using the variance of the means and the finite population correction factor.

a. To calculate the number of possible samples of size 2 without replacement from a population of 5 objects, we use the combination formula: C(n, r) = n! / (r! * (n-r)!), where n is the population size and r is the sample size. In this case, n = 5 and r = 2, so the number of possible samples is C(5, 2) = 5! / (2! * (5-2)!) = 10.

b. To list all the means of these samples, we take each combination of 2 objects from the population and calculate their mean. For example, one possible sample is {1, 2}, and its mean is (1 + 2) / 2 = 1.5. Similarly, we calculate the means for the other 9 possible samples.

c. To find the mean of these means, we calculate the average of all the means calculated in part b. d. The mean of these means, denoted as E(X), is equal to the population mean, denoted as Mu, when the sampling is done without replacement.

e. To find the standard error sigma(X), we need to calculate the standard deviation of all these means. We can do this by calculating the variance of the means and taking its square root. f. The standard error sigma(X) equals the square root of the variance of the means divided by the square root of the sample size, multiplied by the finite population correction factor.

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Answer: -4x⁴ + x² + 6

Step-by-step explanation:

Remove parentheses:   (-a) = -a

-4x⁴ + 6x²+3 - (5x²-3)

-4x⁴ + 6x² + 3 - 5x² + 3

Simplify -4x⁴+ 6x² + 3 - 5x² + 3:   -4x⁴ + x² + 6

-4x⁴ + x² + 6

Answer:$6250

Step-by-step explanation:

selling price(sp)=12000

Original price(op)=?

sp+500=2 x op

12000+500=2 x op

12500=2 x op

Divide both sides by 2

12500/2=(2 x op)/2

6250=op

Therefore original price is $6250

To find the original price of the car, set up an equation with the given information and solve for X. The original price of the car was $6,250.

To find the original price of the car, we need to set up an equation based on the given information. Let's assume the original price of the car is X. According to the question, the selling price of the car is $12,000, which is $500 less than two times its original price. So, we can write the equation as:

2X - $500 = $12,000

To solve the equation, we add $500 to both sides:

2X = $12,500

Finally, we divide both sides by 2 to find the original price of the car:

X = $6,250

Therefore, the original price of the car was $6,250.

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Answer:

There will be required 590 repair persons.

Step-by-step explanation:

Lets count the amount of minutes that are going to be used per week repairing. The amount of minutes of repair work required per week is

100*23 + 100*42+ 100*19 + 150*27 + 150*35 = 17700.

Each worker works 6 hours per day in average and 5 times per week. This means that each worker will do 30 hours of work each week. Therefore, we will need 17700/30 = 590 workers (In practice you should contract a few more, like 600, just in case).

Answer:

5 hours

Step-by-step explanation:

227.5+97.5=325

325/65=5

Answer:

the total workdone required to to lift the chain from its top end so that its bottom is 2 meters above the ground = 661 J

Step-by-step explanation:

Given that:

The mass density of the chain is [tex]\rho (x) = 2x( 4-x) \ kg/m[/tex]

It is pertinent and crucial to consider the determination of the work-done in lifting the chain from its front in that its bottom is 2 meters away from the ground.

Consider a cross section portion of the chain of length  Δx that has to be lifted to a height [tex]x_k[/tex]

The required wok to be done for this work is [tex]W_k = \rho(x_k)g \delta x(x_k)[/tex]

combining the segments of the chain and taking the [tex]\delta x[/tex]→0

Integrally, the work-done can be illustrated as :

[tex]\int\limits^3_0 \rho {(x_k)} \, gxdx \ \ = \ \ \int\limits^3_0 (9.8) 2x^2 (4-x)dx \\\\= 19.6 \int\limits^3_0 (4x^2-x^3)dx\\\\\\= 19.6 [ \frac{4}{3}x^2- \frac {x^4}{4}]^3__0}}dx\\\\\\= 19.6 [ \frac{4}{3}(3)^2- \frac {3^4}{4}]}dx\\\\= 19.6 (36- \frac{81}{4})\\\\\\= 308.7 \ \ J[/tex]

Furthermore, there is need to lift the chain  up to 2 meters . So, calculating the weight of the chain ; we have:

Weight = [tex]\int\limits^3_0 \rho {x} \, gdx[/tex]

[tex]= \int\limits^3_0 (9.8) {2x}(4-x) \, dx\\ \\\\= 19.6\int\limits^3_0 {(4x-x^2)} \, dx \\\\= 19.6 [ 2x^2 - \frac{x^3}{3}]^3_0\\\\= 19.6 [2(3)^2 - \frac{3^3}{3}]\\\\=19.6 [18-9]\\\\= 176.4 \ \ J[/tex]

Finally .the work-done is said to be equal to the potential energy

∴ W = mgh

W = (176.4)×2

W = 352.8 J

Total workdone = (308.7 + 352.8 ) J

Total workdone = 661 J

Thus, the total workdone required to to lift the chain from its top end so that its bottom is 2 meters above the ground = 661 J

Answer:

Step-by-step explanation:

Answer:

Step-by-step explanation:

Given that,

Length of chain

L = 3m

Linear mass density is

ρ(x) = 2x(4 — x) kg/m lies on the ground

When x = 0, is top of the chain

Work done to lift the chain from top end so that the bottom is 2m above the ground.

Considered the segment of the chain of length ∆x that will be lifted in the positive y direction (+j)m from the foot.

The work needed to lift this segment is given as

Work = mass density × ∆x × gravity

W = ρ × ∆x × g

g is acting downward = 9.8j

Summing over all segment of the chain and passing to the limit as ∆x→0.

Therefore, the total work done needed to full extend the chain is

W = ∫ ρ × ∆x × g x = 0 to 3

Since g is constant

∆x = xdx

Then,

W = g ∫ 2x(4—x)x dx. x= 0 to x = 3

W = 9.81 ∫ (8x² — 2x³)x dx

W = 9.81 ( 8x³/3 — 2x⁴/4)

W=9.81(8x³/3— ½x⁴) from x=0 to x=3

W = 9.81[8(3)³/3 — ½(3⁴)] — 0

W = 9.81 × (72 —40.5)

W = 9.81 × 31.5

Work done= 309.015 J

Lifting the entire chain requires to light the weight

Weight = ∫ρgdx

Weight = g ∫ρ dx. From x=0 to x=3

Weight = g ∫ 2x(4-x) dx

Weight = 9.81 ∫(8x-2x²)dx

Weight = 9.81 [ 8x²/2 - 2x³/3]

Weight = 9.81[4x²-⅔x³] x=0 to x=3

Weight = 9.81[4(3²) — ⅔(3³)]

Weight = 9.81(36—18)

Weight = 9.81 × 18

Weight = 176.58N

Now, this weight is lifted to a height of 2m, then using potential energy formula, we have

P.E = Work = mgh = Weight ×height

Work = W×h = 176.58 × 2

Work = 353.16 J

Then, total workdone is

W = 353.16 + 309.015

W = 662.18 J

The required Work done required to lift the chain from top so that it's bottom is 2m from the ground is 662.18J

Answer:

Mailing preparation takes 38.29 min max time to prepare the mails.

Step-by-step explanation:

Given:

Mean:35 min

standard deviation:2 min

and 95%  confidence interval.

To Find:

In normal distribution mailing preparation time  taken less than.

i.eP(t<x)=?

Solution:

Here t -time and x -required time

mean time 35 min

5 % will not have true mean value . with 95 % confidence.

Question is asked as ,preparation takes less than  time means what is max time that preparation will take to prepare mails.

No mail take more time than that time .

by Z-score or by confidence interval is

Z=(X-mean)/standard deviation.

Z=1.96 at  95 % confidence interval.

1.96=(X-35)/2

3.92=(x-35)

X=38.29 min

or

Confidence interval =35±Z*standard deviation

=35±1.96*2

=35±3.92

=38.29 or 31.71 min

But we require the max time i.e 38.29 min

And by observation we can also conclude the max time from options as 38.29 min.

In a normal distribution, the 95th percentile is calculated by adding twice the standard deviation to the mean. Based on the given mean (35 minutes) and standard deviation (2 minutes), 95% of the time the envelope preparation should take less than 39 minutes.

The problem asks what time, in percentage terms, the preparation for mailing the weekly report to the executives is less than.

Given that the time taken to prepare the envelopes follows a normal distribution with a mean of 35 minutes and a standard deviation of 2 minutes, we need to determine the time that occurs at the 95th percentile of this distribution.

The 95th percentile is found by adding 2 standard deviations to the mean in a normal distribution (this is due to the empirical rule, which states that about 95% of values lie within 2 standard deviations of the mean in a normal distribution).

Here's how you calculate it:

Determine the mean: The mean is given as 35 minutes.

Determine the standard deviation: The standard deviation is given as 2 minutes.

Calculate the 95th percentile: Mean + 2(Standard Deviation) = 35 + 2(2) = 39 minutes.

So, on 95% of occasions the mailing preparation takes less than 39 minutes. None of the options (a, b, c, and d) provided are correct based on the calculations.

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Answer:

One sample z-test for population mean would be the best approach to conduct a hypothesis test.

Step-by-step explanation:

Following is the data available to us:

Mean amount of water = u = 3

Sample mean = x = 2.5

Sample Size = n = 200

Sample Standard Deviation = s = 1

Population Standard Deviation = [tex]\sigma[/tex] = 1.2

Population is normally distributed.

We need to find the best approach to conduct a hypothesis test. Since only one sample is involved, it is a One-Sample test about population mean. for conducting hypothesis test for One-Sample about population mean we have following two options:

Selecting the best approach:

The first thing to check is if our data from a population which is normally distributed. Which in this case is. Next we check if the value of population standard deviation is known or unknown. the rule is:

Since, in this case we know the value of Population Standard Deviation which is 1.2, One sample z-test for population mean would be the best approach to conduct a hypothesis test.

The appropriate approach to conduct a hypothesis test for the claim that the AI, or Adequate Intake of water, for pregnant women is a mean of 3L/d, liters per day, is to use a one-sample t-test.

The one-sample t-test is a statistical test that is used to compare the sample mean to a known population mean. It is appropriate to use this test when the sample size is small (n < 30) and the population standard deviation is unknown.

In this case, the sample size is 200, which is greater than 30, but the population standard deviation is known (1.2). However, it is still appropriate to use the one-sample t-test because the sample size is not large enough to reliably estimate the population standard deviation.

To conduct the one-sample t-test, we will follow these steps:

State the null and alternative hypotheses.

Null hypothesis (H0): The mean water intake for pregnant women is 3L/d.

Alternative hypothesis (Ha): The mean water intake for pregnant women is not 3L/d.

Calculate the test statistic.

The test statistic for the one-sample t-test is calculated as follows:

t = (x - μ) / (s / √n)

where:

x is the sample mean

μ is the known population mean

s is the sample standard deviation

n is the sample size

In this case, the test statistic is calculated as follows:

t = (2.5 - 3) / (1 / √200) = -4.17

Find the p-value.

The p-value is the probability of obtaining a test statistic as extreme or more extreme than the one observed, assuming that the null hypothesis is true.

We can use a t-table to find the p-value. The p-value for a t-statistic of -4.17 with 199 degrees of freedom is less than 0.001.

Make a decision.

Since the p-value is less than the significance level (α = 0.05), we reject the null hypothesis and conclude that there is sufficient evidence to support the claim that the mean water intake for pregnant women is not 3L/d.

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Answer:

14

Step-by-step explanation:

Answer:

a) P (Y  less than or equal to 79 ) = 0.226  

b) Z = 1.008  less than or equal to Zc = 1.64

So, it means null hypothesis is not rejected.

Step-by-step explanation:

a) Probability that fewer than 80 patients will recover.

As we have:

p = 88% = 0.88 = population success proportion

n = 93 = Sample size

mean = n x p , mean = 93 x 0.88 , mean = 81.84

So, we need P (Y less than or equal to 79) fewer than 80 patients.

Standard Deviation of the population =

SD of population = [tex]\sqrt{n . p . (1-p)}[/tex] , SD of population = [tex]\sqrt{93 .0.88 .(1-0.88)}[/tex]

SD of population = 3.13

As we know that,

probability in binomial distribution is approximately equal to normal distribution so, we will use normal distribution from here. In addition, due to this shift 79 will be equal to 79.5 in normal distribution.

P(Y less than or equal to 79) ≈ P(Y less than or equal to 79.5)

P([tex]\frac{Y - mean }{SD}[/tex]) , P([tex]\frac{79.5 - 81.84}{3.1336}[/tex]) , P(Z less than or equal to -0.75)

So, when will you check the Z score of -0.75 in the Z table you will get the probability which is :

P (Y less than or equal to 79 ) = 0.226

So, 0.226 is the probability that fewer than 80 patients will recover.

b) Z  test is used in this part to check the claim:

Favorable Cases sample (recovered) = 85 = Y

Sample size = N = 93

Significance level = 0.05

Proportion Sample = p = Y/N = 85/93 = 0.914.

1. Null hypothesis and Alternative Hypothesis:

H1: p less than or equal to 88% or p less than or equal to 0.88

H2: p greater than 88% or p greater than 0.88

2. Rejection Region:

Zc value is = 1.64 for a right tail test.

Rejection region is such that z value must be greater than Zc value i.e 1.64. If it is not greater, it will not lie in rejection region.

3. Statistics Test:

In this we have to calculate value of Z and compare it with value of Zc.

Z = [tex]\frac{P-p1}{\sqrt{p1(1-p1)/n} }[/tex]

Small p1 = 0.88 , Capital P = 0.914  Z = [tex]\frac{0.914-0.88}{\sqrt{0.88.(1-0.88)/93} }[/tex]

Z = 1.008

4. Final Decision about the null hypothesis:

As we can see, Z value is less than Zc value, hence it does not lie in the rejection region.

Z = 1.008  less than or equal to Zc = 1.64

So, it means null hypothesis is not rejected.

H1 is not rejected,  it means there is not sufficient proof to claim that population proportion is larger than population success proportion p1.

a. Estimate the probability that fewer than 80 patients will recover: Approximately 0.2743.

b. Confirm/disprove the director's claim:

The probability of observing more than 85 patients recovering out of 93 treated with the new technique is approximately 0.1515. Therefore, there isn't sufficient evidence to support the director's claim that all hospitals should adopt the new technique based solely on this sample data.

To solve this problem, we'll use the normal approximation to the binomial distribution since the sample size (93) is relatively large.

Let's break it down step by step.

Given:

Probability of traditional medical treatment curing the disorder = 0.88

Number of patients treated with the new technique = 93

a. Estimate the probability that fewer than 80 patients will recover.

To estimate this probability, we'll use the normal approximation to the binomial distribution.

1. Calculate the mean [tex](\(\mu\))[/tex] and standard deviation [tex](\(\sigma\))[/tex] of the binomial distribution:

[tex]\[\mu = np = 93 \times 0.88 = 81.84\][/tex]

[tex]\[\sigma = \sqrt{np(1-p)} = \sqrt{93 \times 0.88 \times 0.12} \approx 3.076\][/tex]

2. Standardize the value of 80 using the z-score formula:

[tex]\[z = \frac{x - \mu}{\sigma} = \frac{80 - 81.84}{3.076} \approx -0.600\][/tex]

3. Use the standard normal distribution table or calculator to find the probability corresponding to z = -0.600.

[tex]\[P(Z < -0.600) \approx 0.2743\][/tex]

So, the estimated probability that fewer than 80 patients will recover is approximately 0.2743.

b. The director of the hospital recommends that all hospitals adopt this new technique since more than 85 patients in the sample have recovered after being treated with the new technique.

Confirm/disprove the director's claim.

To confirm/disprove the director's claim

We'll calculate the probability of observing more than 85 successes (patients recovering) out of 93 trials (patients treated) under the assumption that the success rate is the same as the traditional treatment (0.88).

1. Calculate the mean [tex](\(\mu\))[/tex] and standard deviation [tex](\(\sigma\))[/tex] of the binomial distribution:

[tex]\[\mu = np = 93 \times 0.88 = 81.84\][/tex]

[tex]\[\sigma = \sqrt{np(1-p)} = \sqrt{93 \times 0.88 \times 0.12} \approx 3.076\][/tex]

2. Standardize the value of 85 using the z-score formula:

[tex]\[z = \frac{x - \mu}{\sigma} = \frac{85 - 81.84}{3.076} \approx 1.031\][/tex]

3. Calculate the probability of observing more than 85 successes:

[tex]\[P(Z > 1.031) \approx 1 - P(Z < 1.031) \approx 1 - 0.8485 \approx 0.1515\][/tex]

The probability of observing more than 85 successes out of 93 trials is approximately 0.1515, which means there's about a 15.15% chance of observing this many or more successes if the true success rate is 0.88.

Since [tex]\(15.15\% > 5\%\)[/tex]  (common significance level),

We fail to reject the null hypothesis that the success rate of the new technique is the same as the traditional treatment.

Therefore, there isn't sufficient evidence to support the director's claim that all hospitals should adopt the new technique based solely on this sample data.

Answer:

1:B the ballet club

2.B the ballet club.

Step-by-step explanation:

It is correct on khan :)

The chess club accepts 5 new members each week, and had 37 members after 6 weeks. The club started with 7 members. Without more data, a similar function for the ballet club cannot be determined.

This question requires a bait to understand the concept of linear functions. The chess club at Nahk University accepts 5 new members each week and had 37 members after 6 weeks. We can express this as a linear function, with the number of weeks as the independent variable and the number of members as the dependent variable:

Members = 5 * Weeks + Initial Members

Here, we know from the information given that after 6 weeks, the chess club had 37 members. So if we plug those numbers into the equation:

37 = 5 * 6 + Initial Members

Then solving for the number of initial members gives us 7. Hence, the chess club started with 7 members.

To find a similar function for the ballet club, we would need more specific data to map time (in weeks) to the number of members in the same way.

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