The population of a town has grown at an annual rate of approximately 2.7%. How long will it take for its population of 14,450 people to double at this growth rate?

The probability of meeting at least one person with the flu in twelve random encounters on campus when the infection rate is 4% is 0.391 or approximately 39.1%.

To determine the probability of meeting at least one person with the flu in twelve random encounters on campus when the infection rate is 4%, we can use the binomial distribution formula:
P(X ≥ 1) = 1 - P(X = 0)
where X is the number of people with the flu in twelve random encounters, and P(X = 0) is the probability of meeting zero people with the flu.

The probability of meeting zero people with the flu in one random encounter is:
P(X = 0) = (96/100)^1 * (4/100)^0 = 0.96
where 96/100 represents the probability of not meeting someone with the flu, and 4/100 represents the probability of meeting someone with the flu.
Therefore, the probability of meeting at least one person with the flu in twelve random encounters is:
P(X ≥ 1) = 1 - P(X = 0)
P(X ≥ 1) = 1 - 0.96^12
P(X ≥ 1) = 0.391

Therefore, the probability of meeting at least one person with the flu in twelve random encounters on campus when the infection rate is 4% is 0.391 or approximately 39.1%.

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If f is defined by a power series that has a positive radius of convergence, then this means that the power series converges to f for all values of x within a certain interval centered at 0. Specifically, the radius of convergence R tells us the size of this interval.

To see why this is the case, let's recall the definition of a power series:
f(x) = a0 + a1x + a2x^2 + a3x^3 + a4x^4 + ...
This expression tells us that the function f can be written as an infinite sum of terms involving powers of x. The coefficients a0, a1, a2, etc. are constants that determine the size of each term. The important thing to note here is that this series only converges if the limit of its terms as n approaches infinity goes to zero. Now, the radius of convergence R can be calculated using the ratio test:
R = 1/lim sup(|an|^(1/n)) as n approaches infinity
This formula tells us that R is the inverse of the limit superior of the nth root of the absolute value of the coefficients. Intuitively, this means that if the coefficients of the power series grow too quickly, then the series will not converge for any value of x. On the other hand, if the coefficients grow very slowly, then the series will converge for a wide range of values of x. So, if f has a positive radius of convergence, this means that the limit superior of the nth root of the coefficients goes to zero, which implies that the series converges for all values of x within an interval of size 2R centered at 0. In other words, we can plug in any value of x within this interval and get a well-defined value for f(x).

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If we reference the given temperature data, the village where the temperature fell in the morning and then rose over the afternoon is Village B.

Temperature is described as  a physical quantity that expresses quantitatively the perceptions of hotness and coldness. Temperature is measured with a thermometer.

In Village B, the daytime high is 15 degrees, followed by lows of 10 degrees at 10:00 and 5 degrees at 12:00 (morning).

Nevertheless, the temperature begins to rise from 12:00 and eventually rises to +5 degrees at 18:00 (afternoon), reaching 0 degrees at 14:00.

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Answer and Explanation:

1. The left column should be labeled coin toss because we can see that there are two outcomes from a coin toss: heads or tails.

We know that the right column should be labeled spinner because it has three outcomes (red, yellow, or blue) for every previous outcome.

2. We can label the top box of the left column as H (for Heads) and the lower box as T (for Tails). Then, we can label the top, middle, and bottom boxes in the right column as R, Y, and B, respectively (for Red, Yellow, Blue).

3. Next to each box on the right, label the two events that lead to that outcome. For example, "HR" means that you tossed a heads, then spun a red.

4. There is only 1 outcome in the event "toss a tails, spin a yellow" because there is only one way (out of two ways) to toss a tails, and from there, there is only one way to spin a yellow (out of three ways).

5. The probability of tossing tails, then spinning a yellow is:

[tex]\dfrac{1}{2} \cdot \dfrac{1}{3} = \boxed{\dfrac{1}{6}}[/tex]

We do not have sufficient evidence to claim that the vans outperform the manufacturer's mpg rating at the 0.01 level of significance, the null and alternative hypotheses are H₀: μ = 25.3, Hₐ: μ > 25.3.

To determine if there is sufficient evidence that the vans outperform the manufacturer's mpg rating, we need to conduct a hypothesis test. The null hypothesis (H₀) is that the vans have an average mpg of 25.3, while the alternative hypothesis (Hₐ) is that the vans have an average mpg greater than 25.3.

H₀: μ = 25.3

Hₐ: μ > 25.3

We will use a one-tailed t-test since the alternative hypothesis is directional (greater than). To conduct the test, we need to calculate the t-statistic and compare it to the critical value at the 0.01 level of significance with 18 degrees of freedom (19 vans - 1).

t = (x' - μ) / (s / √n)

t = (25.4 - 25.3) / (√(5.29 / 19)) = 0.90

Using a t-distribution table or a statistical software, we find that the critical value for a one-tailed test at the 0.01 level of significance with 18 degrees of freedom is 2.552.

Since the calculated t-statistic (0.90) is less than the critical value (2.552), we fail to reject the null hypothesis. Therefore, we do not have sufficient evidence to claim that the vans outperform the manufacturer's mpg rating at the 0.01 level of significance.

In summary, we can use a hypothesis test to determine if there is sufficient evidence that the vans outperform the manufacturer's mpg rating. We set up the null and alternative hypotheses, calculate the t-statistic, compare it to the critical value, and make a decision based on the level of significance.

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The taylor series for f(x) centered at the given value of a. f(x) = 10 x - 4 x^3 text(, ) a=-2 is:
f(x) = -56 + 34(x+2) + 12(x+2)^2 - 4(x+2)^3/3 + ...



The Taylor series for f(x) centered at a=-2 is:

f(x) = f(a) + f'(a)(x-a) + f''(a)(x-a)^2/2! + f'''(a)*(x-a)^3/3! + ...

Plugging in the given function and the value of a:

f(-2) = 10(-2) - 4(-2)^3 = -56

f'(-2) = 10 - 4(3)(-2)^2 = 34

f''(-2) = -4(6)(-2) = 48

f'''(-2) = -4(6) = -24

Thus, the Taylor series for f(x) centered at a=-2 is:

f(x) = -56 + 34(x+2) + 24(x+2)^2/2! - 24(x+2)^3/3! + ...

Simplifying:

Therefore the final equation is:
f(x) = -56 + 34(x+2) + 12(x+2)^2 - 4(x+2)^3/3 + ...

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Approximately 58.28 cm of wire is needed to make the skeleton model of the pyramid.

The length of wire needed to make a skeleton model of the triangular-based pyramid with all edges of length 10 cm need to find the length of the wire required to outline the edges of the pyramid.

The pyramid has four triangular faces and one triangular base.

Each of the triangular faces is an isosceles triangle with two sides of length 10 cm and one base of length "b" (which we need to find).

To do this, we can use the Pythagorean tells us that for any right triangle the sum of the squares of the lengths of the legs (the two shorter sides) is equal to the square of the length of the hypotenuse (the longest side).

The two legs of the right triangles are each 5 cm (half of the length of one of the edges of the pyramid), and the hypotenuse is the base of the triangular face, which we are calling "b".

b² = 5² + 5²

b² = 50

b = √(50)

≈ 7.07

So the length of each base of the triangular face is approximately 7.07 cm.

The triangular base of the pyramid is an equilateral triangle with all sides of length 10 cm.

The total length of wire needed to make the skeleton model of the pyramid is:

3 × 10 cm + 4 × 7.07 cm = 30 cm + 28.28 cm

= 58.28 cm

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The quotient of expression is,

⇒ 7x² - x/7 + 92/49

We have to given that;

Expression is,

⇒ (7x³ - 8x² - 13x + 2) / (7x - 1)

By using division method as;

(7x - 1) ) 7x³ - 8x² - 13x + 2 ( 7x² - x/7 + 92/49

           7x³ - 7x²

          --------------

                 - x² - 13x

                 - x² + x/7

                 ------------

                      92/7x + 2

                       92/7x - 92/49

                     ----------------------

                                 190/49

Thus, The quotient of expression is,

⇒ 7x² - x/7 + 92/49

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The post-war period of the 1950s and 1960s saw the emergence of a number of important voices in literature, art, and politics. Here are some examples:

Ginsberg was a prominent poet and a leading figure in the Beat Generation, a group of writers who rejected conventional norms and values. His most famous work, "Howl," was published in 1956 and became a landmark in American literature.

Baldwin was an African-American writer whose novels and essays explored issues of race and sexuality in America. His most famous works include "Go Tell It on the Mountain" (1953) and "The Fire Next Time" (1963).

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Explain the Post-War voices that emerge in (1950s-1960s)

The area of the top surface of the wedge is 9 × pi square inches.

To find the area of the top surface of the wedge, we first need to find the area of the whole circle with radius 6 inches. The formula for the area of a circle is A = pi × [tex]r^2[/tex], where A is the area and r is the radius.

So, A = pi × [tex](6)^2[/tex] = 36 × pi square inches.

Since the central angle of the wedge is pi/2 radians, we can find the fraction of the circle that is represented by the wedge by dividing pi/2 by 2 × pi (the total number of radians in a circle).

So, the fraction of the circle represented by the wedge is pi/2 / 2 × pi = 1/4.

To find the area of the top surface of the wedge, we simply multiply the area of the whole circle by this fraction:

Area of top surface = (1/4) × 36 × pi = 9 × pi square inches.

Therefore, the area of the top surface of the wedge is 9 × pi square inches.

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Option C (chi-square distribution) and Option D (F distribution) have only positive values.

The chi-square distribution is a continuous probability distribution that is used in statistical tests to compare observed data with expected data. It has only positive values and is right-skewed. It is commonly used in hypothesis testing, goodness-of-fit tests, and in confidence interval calculations.

The F distribution is also a continuous probability distribution that arises in the analysis of variance (ANOVA) and regression analysis. It is used to test the equality of variances of two or more populations. Like the chi-square distribution, it is also right-skewed and has only positive values.

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The general solution of the given differential equation is y(t) = [tex]c_1 e^{2t} + c_2 e^{-t} -2t^2 + 6t -5[/tex]

We first solve the associated homogeneous equation y'' − y' − 2y = −8t + 4t² to find the general solution of the given differential equation y'' − y' − 2y = 0.

The characteristic equation is r² - r - 2 = 0, which factors as (r - 2)(r + 1) = 0. Therefore, the roots are r = 2 and r = -1.

The general solution of the associated homogeneous equation is y_h(t) = [tex]c_1 e^{2t} + c_2 e^{-t}[/tex], where c_1 and c_2 are constants.

To find a particular solution of the given non-homogeneous equation, we use the method of undetermined coefficients. Since the right-hand side of the equation is a polynomial of degree 2, we assume a particular solution of the form y_p(t) = At² + Bt + C. Substituting this into the differential equation, we get:

2A - 2A t - 2At² - B - 2Bt - 2C = -8t + 4t²

2A -B -2C - (2A + 2B)t -2At²= -8t + 4t²

Equating coefficients of like terms, we get:

2A -B -2C = 0

- (2A + 2B) = -8

-2A = 4

Therefore, A = -2, B = 6, and C = -5. Thus, a particular solution of the given non-homogeneous equation is y_p(t) = -2t² + 6t -5.

The general solution of the given differential equation is the sum of the general solution of the associated homogeneous equation and a particular solution of the non-homogeneous equation. Therefore, the general solution is:

y(t) = y_h(t) + y_p(t)

= [tex]c_1 e^{2t} + c_2 e^{-t} -2t^2 + 6t -5[/tex]

where c_1 and c_2 are constants.

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The sample size should be 41.

We can use the formula for the margin of error for a population standard deviation:

m = z*sigma/sqrt(n)

where z is the z-score corresponding to the desired level of confidence, sigma is the population standard deviation, and n is the sample size.

Plugging in the given values, we have:

1.347 = z*4/sqrt(n)

Solving for n, we get:

n = (z*sigma/m)^2

At a 95% confidence level, the z-score is approximately 1.96. Plugging in the values, we get:

n = (1.96*4/1.347)^2

n = 40.28

Rounding up to the nearest whole number, the sample size should be 41.

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The exponential function y = 990(0.95) represents exponential decay with a 5% decrease per unit increase in x.

The given exponential function is y = 990(0.95). To determine whether it represents growth or decay, we need to examine the base of the exponent, which is 0.95 in this case.

When the base of an exponential function is between 0 and 1, such as 0.95, it represents exponential decay. This means that as x increases, the corresponding y-values decrease exponentially.

To calculate the percentage rate of decrease, we can compare the base (0.95) to 1. A decrease from 1 to 0.95 represents a difference of 0.05. To convert this difference into a percentage, we multiply by 100.

Percentage rate of decrease = 0.05 * 100 = 5%

Therefore, the given exponential function y = 990(0.95) represents exponential decay with a rate of 5% decrease per unit increase in x. This implies that for each unit increase in x, the y-value will decrease by 5% of its previous value.

It's important to note that the rate of decrease remains constant throughout the function. As x increases, the value of y will continue to decrease by 5% with each unit increase.

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Using the mean value theorem, we can find an upper bound for f(7) given the information provided. The mean value theorem states that for a differentiable function f(x) on the interval [a,b], there exists at least one point c in the interval such that:

f'(c) = (f(b) - f(a))/(b - a)

If we apply this theorem to the interval [4,7], we get:

f'(c) = (f(7) - f(4))/(7 - 4)

Since f '(x) ≥ 3 for 4 ≤ x ≤ 7, we know that f'(c) ≥ 3. We can use this inequality to find an upper bound for f(7):

3 ≤ (f(7) - 6)/3

9 ≤ f(7) - 6

f(7) ≥ 15

Therefore, the smallest possible value for f(7) is 15. This means that f(x) must be increasing at a rate of at least 3 between x=4 and x=7, and the smallest possible value of f(7) occurs when f(x) is increasing at a constant rate of 3.

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Answer: 9x^2+16x+1  (I'm assuming the 6x2 & 3x2 were supposed to be exponents?)

Step-by-step explanation:

Lets rearrange this first

6x^2+7x+11+3x^2+8x-8+x-3+1

now combine the x^2 coefficients:

9x^2+7x+11+8x-8+x-3+1

now combine the x coefficients:

9x^2+16x+11-8-3+1

now combine the remaining constants:

9x^2+16x+1

We can use the formula for the area enclosed by a parametric curve. Therefore, the area enclosed by the parametric equation is given by

A = 27[(1/5)b^5 - (8/3)b^3] - 27[(1/5)a^5 - (8/3)a^3]

A = ∫[a,b] y(t) x'(t) dt

where x'(t) is the derivative of x(t) with respect to t.

Using the given parametric equations, we have:

x(t) = t^3 - 8t

y(t) = 9t^2

Taking the derivative of x(t), we have:

x'(t) = 3t^2 - 8

Substituting these expressions into the formula for A, we get:

A = ∫[a,b] y(t) x'(t) dt

= ∫[a,b] 9t^2 (3t^2 - 8) dt

= 27∫[a,b] t^4 - 8t^2 dt

= 27[(1/5)t^5 - (8/3)t^3]∣[a,b]

Using the bounds of the integral, which are not given in the problem, we get:

A = 27[(1/5)b^5 - (8/3)b^3] - 27[(1/5)a^5 - (8/3)a^3]

Therefore, the area enclosed by the parametric equation is given by:

A = 27[(1/5)b^5 - (8/3)b^3] - 27[(1/5)a^5 - (8/3)a^3]

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The given equation dy/dx - (1/x)y = (xcosx)/x³ is linear with y as the dependent variable.

Given differential equation is (x²y+x⁴ cosx)dx -x³dy = 0

The given equation can indeed be classified as linear with y as the dependent variable.

A linear equation with respect to the dependent variable y is of the form:

dy/dx + P(x)y = Q(x),

where P(x) and Q(x) are functions of x. In this case, we have:

(x²y + x⁴cosx)dx - x³dy = 0.

By rearranging the terms, we can write it as:

x²ydx - x³dy + x⁴cosxdx = 0.

Now, we can rewrite the equation in the form:

dy/dx + (-x²/x³)y = x⁴cosx/x³.

Simplifying further, we get:

dy/dx - (1/x)y = (xcosx)/x³.

As you can see, the equation is in the form of a linear equation with respect to y. The coefficient of y, (-1/x), is a function of x, while the right-hand side (RHS) is also a function of x. Therefore, the given equation is linear with y as the dependent variable.

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Given a vector field F on R3 and non-intersecting curves s(t) and c(t) over t ∈ [0, 1] with s(0) = c(0) and s(1) = c(1), if we have ∮s F · d = ∮c F · d, then the value of F · dr where r is given by s(t) for t ∈ [0, 1] is the same as the value of F · dr where r is given by c(t) for t ∈ [0, 1].

The given equation, ∮s F · d = ∮c F · d, is a statement of the conservative nature of the vector field F. It means that the line integral of F around a closed curve is zero, which implies that F is a conservative vector field.

Since s(t) and c(t) are non-intersecting curves with the same endpoints, they form a closed loop. By the conservative property of F, the line integral of F along both curves will be equal.

The value of F · dr where r is given by s(t) for t ∈ [0, 1] is the line integral of F along the curve s(t). Since s(t) and c(t) form a closed loop, we can apply the equation ∮s F · d = ∮c F · d to conclude that the value of F · dr along s(t) is the same as the value of F · dr along c(t). Thus, we can evaluate F · dr by choosing either curve and applying the line integral formula.

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To estimate (mu1 - mu2) with a sampling error of 3.6 and 95% confidence, n1 and n2 should be determined based on the known standard deviations (sigma1 = 13, sigma2 = 22), to estimate (mu1 - mu2) with a sampling error of 6 and 99% confidence, n1 and n2 should be determined based on the range of each population (60 and to achieve a 90% confidence interval of width 1.3, the appropriate values of n1 and n2 need to be calculated.

a) To estimate (mu1 - mu2) with a sampling error of 3.6 and 95% confidence, we can use the formula:

\[ n = \left(\frac{{Z * \sqrt{{\sigma_1^2 + \sigma_2^2}}}}{{E}}\right)^2 \]

where Z is the Z-score corresponding to the desired confidence level (95% corresponds to Z = 1.96), sigma1 and sigma2 are the known standard deviations (sigma1 = 13, sigma2 = 22), and E is the desired sampling error (E = 3.6).

By plugging in the values, we get:

\[ n = \left(\frac{{1.96 * \sqrt{{13^2 + 22^2}}}}{{3.6}}\right)^2 \]

Simplifying this expression will give us the appropriate value for n1 and n2.

b) To estimate (mu1 - mu2) with a sampling error of 6 and 99% confidence, we can use the formula:

\[ n = \left(\frac{{Z * R}}{{2 * E}}\right)^2 \]

where Z is the Z-score corresponding to the desired confidence level (99% corresponds to Z = 2.58), R is the range of each population (R = 60), and E is the desired sampling error (E = 6).

By substituting the values, we get:

\[ n = \left(\frac{{2.58 * 60}}{{2 * 6}}\right)^2 \]

Simplifying this expression will give us the appropriate value for n1 and n2.

c) To achieve a 90% confidence interval of width 1.3, we can use the formula:

\[ n = \left(\frac{{Z * \sigma}}{{E}}\right)^2 \]

where Z is the Z-score corresponding to the desired confidence level (90% corresponds to Z = 1.645), sigma is the unknown standard deviation, and E is the desired interval width (E = 1.3).

Since the standard deviation (sigma) is unknown, we don't have enough information to calculate the appropriate values for n1 and n2.

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The probability of event a, P(a), is 4/7 for the given sample set.

To find P(a), we need to use the given information about the probabilities and the fact that the total probability of all outcomes in a sample space S is equal to 1. We have:
S = {a, b, c}
P(a) = 2P(b) = 4P(c)

First, we can express P(b) and P(c) in terms of P(a):
P(b) = P(a) / 2
P(c) = P(a) / 4

Now we use the fact that the sum of probabilities of all outcomes in S equals 1:
P(a) + P(b) + P(c) = 1

Substitute P(b) and P(c) with their expressions in terms of P(a):
P(a) + (P(a) / 2) + (P(a) / 4) = 1

To solve for P(a), combine the terms:
P(a) * (1 + 1/2 + 1/4) = 1
P(a) * (4/4 + 2/4 + 1/4) = 1
P(a) * (7/4) = 1

Now, divide both sides by (7/4) to isolate P(a):
P(a) = 1 / (7/4)
P(a) = 4/7

So, the probability of event a, P(a), is 4/7.

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Answer:

rcritical = tα/2 sq(t2α/2+n−2)

Step-by-step explanation:

The arrow reaches a maximum height of 245 meters and stays in flight for 14 seconds.

To find the maximum height and flight time for the arrow, we'll use the given height equation and concepts of projectile motion.
Maximum height:
1. First, we need to find the time (t) at which the arrow reaches its maximum height. This occurs when the vertical velocity is zero. The velocity equation is the derivative of the height equation: v = dh/dt = -10t + 70.
2. Set the velocity equation to zero and solve for t: 0 = -10t + 70. Solve for t: t = 7 seconds.
3. Now that we have the time, plug it into the height equation to find the maximum height: h = -5(7^2) + 70(7) = -245 + 490 = 245 meters.
Flight time:
1. The arrow's flight time is the time it takes to reach the ground (h = 0) after reaching its maximum height.
2. Set the height equation to zero and solve for t: 0 = -5t^2 + 70t. Factor out a common term: 0 = 5t(-t + 14). This gives two possible solutions for t: t = 0 seconds (initial time) and t = 14 seconds.
3. The arrow stays in flight for 14 seconds, as the 0 seconds correspond to the initial time.
In summary, the arrow reaches a maximum height of 245 meters and stays in flight for 14 seconds.

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No, because P(A|B) = 0.79 and the P(A) = 0.48 they are not equal.

The probability formula defines the likelihood of the happening of an event. It is the ratio of favorable outcomes to the total favorable outcomes.

We have the information:

P(A)= 0.48  

P(B) = 0.42

P(A∩B) = 0.38

To find out if watching a movie and buying a popcorn are independent,

The formula is used:

P(A|B) = P(A∩B)/P(A)

Plug all the values in above formula:

P(A|B) = 0.38/0.48 = 0.79166

P(A|B) = 0.79

From the deductions above;

Hence, the answer is

No, because P(A|B) = 0.79 and the P(A) = 0.48 they are not equal.

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For complete question, to see the attachment

Answer:

x= 3/2

x= 1/5

Step-by-step explanation:

3.1 The function that models the pairs sold (s) over time (t) is:

[tex]s(t) = -3t^2 + 6t + 2.[/tex]

3.2 The estimated number of pairs of shoes sold after 6 weeks is -70.

3.1 To write a function that models the pairs sold (s) over time (t), we can use the given data points to find the pattern or relationship between the weeks (t) and the pairs sold (s).

From the table:

Week (t) Pairs sold (s)

1 5

3 2

5 48

By observing the data, we can see that the pairs sold (s) increases by a certain amount after each week. Let's calculate the difference between consecutive pairs sold:

Difference between pairs sold at week 3 and week 1: 2 - 5 = -3

Difference between pairs sold at week 5 and week 3: 48 - 2 = 46

We notice that the difference is not constant, which suggests a nonlinear relationship. To model this, we can use a quadratic function.

Let's assume the function is of the form s(t) = at^2 + bt + c, where a, b, and c are constants to be determined.

Substituting the given data point (t, s) = (1, 5) into the function, we get:

[tex]5 = a(1)^2 + b(1) + c[/tex]

5 = a + b + c (Equation 1)

Substituting the data point (t, s) = (3, 2) into the function, we get:

[tex]2 = a(3)^2 + b(3) + c[/tex]

2 = 9a + 3b + c (Equation 2)

Substituting the data point (t, s) = (5, 48) into the function, we get:

[tex]48 = a(5)^2 + b(5) + c[/tex]

48 = 25a + 5b + c (Equation 3)

Now we have a system of three equations (Equations 1, 2, and 3) that we can solve to find the values of a, b, and c.

Solving the system of equations, we find:

a = -3

b = 6

c = 2

Therefore, the function that models the pairs sold (s) over time (t) is:

[tex]s(t) = -3t^2 + 6t + 2.[/tex]

3.2 To estimate the number of pairs of shoes sold after 6 weeks, we can substitute t = 6 into the function [tex]s(t) = -3t^2 + 6t + 2.[/tex]

[tex]s(6) = -3(6)^2 + 6(6) + 2[/tex]

s(6) = -3(36) + 36 + 2

s(6) = -108 + 36 + 2

s(6) = -70

Therefore, the estimated number of pairs of shoes sold after 6 weeks is -70.

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Answer:

AB = √(4^2 + 1^2) = √(16 + 1) = √17

GH = √(4^2 + 4^2) = √(16 + 16) = √32

= 4√2

120

Step-by-step explanation:

turning $54 to cents

$1= 100c

$54=54×100= 5400

calling the number of 20c coins a and 50c coins b

20a + 50b= 5400 ...equ(1)

since there are twice as many 20c coins as 50c coins

a=2b ...equ(2)

substituting a=2b in equ(1)

20(2b) + 50b = 5400

40b + 50b = 5400

90b = 5400

dividing both sides by 90

b= 60

to get the number of 20c coins I'm substituting b=60 in equ(2)

a= 2×60

a=120

therefore the number of 20c coins is 120

There is a 4% chance that both the man and the woman like In-N-Out the best if we assume that their burger tastes are independent and which the probability of liking In-N-Out the best is around 20%.

Let a man and a woman who are dating have independent burger tastes then the probability which both of them like In-N-Out the best can be calculated as follows,

We can let that the probability that the man likes In-N-Out the best is p1 and the probability that the woman likes In-N-Out the best is [tex]p_2[/tex]

So, the probability that both of them like In-N-Out the best can be calculated as the product of their individual probabilities:

P(both like In-N-Out) = P(man likes In-N-Out) × P(woman likes In-N-Out) [tex]= p_1 × p_2[/tex] of them like In-N-Out the best.

Let's the probability that any person likes In-N-Out the best is around 20% (which is roughly the percentage of people who ranked In-N-Out as their favorite burger chain in the Harris Poll).

Then the probability that both the man and the woman like In-N-Out the best can be calculated as:

P(both like In-N-Out) = 0.2 × 0.2 = 0.04

Hence, there is a 4% chance that both the man and the woman like In-N-Out the best if we assume that their burger tastes are independent and which the probability of liking In-N-Out the best is around 20%.

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Correct question is "What is the probability that a man and a woman who are dating both like in-n-out the best? assuming their burger tastes are independent."

Answer:

15

Step-by-step explanation:

Since the angles are complementary, that means that when the angles are added together, they are equal to 90*. With this information, we can make the equation: x + (3x + 30) = 90

From this we can add like terms and get 4x + 30 = 90

After this, subtract 30 from both sides: 4x = 60

Divide both sides by 4

x = 15

(Ignore the degree sign in my picture, sorry!)