A. The mean of the relevant distribution is 19.2.
B. Rounded to at least three decimal places, the standard deviation of the distribution is approximately 1.760.
(a) The number of workers in the sample who are union members can be estimated by taking the expected value of the relevant random variable. In this case, the random variable represents the number of union members in a sample of 20 workers.
Since 96% of all workers belong to the union, we can expect that 96% of the workers in the sample will also be union members. Therefore, the expected value of the random variable is given by:
E(X) = np
where n is the sample size (20) and p is the probability of success (0.96).
E(X) = 20 * 0.96 = 19.2
Therefore, the mean of the relevant distribution is 19.2.
(b) To quantify the uncertainty of the estimate, we can calculate the standard deviation of the distribution. For a binomial distribution, the standard deviation is given by:
σ = sqrt(np(1-p))
Using the same values as above, we can calculate the standard deviation:
σ = sqrt(20 * 0.96 * (1 - 0.96))
= sqrt(20 * 0.96 * 0.04)
≈ 1.760
Rounded to at least three decimal places, the standard deviation of the distribution is approximately 1.760.
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Let f be a function from A to B. (a) Show that if f is injective and E⊆A, then f −1
(f(E))=E. Give an example to show that equality need not hold if f is not injective. (b) Show that if f is surjective and H⊆B, then f(f −1
(H))=H. Give an example to show that equality need not hold if f is not surjective.
(a) If f is an injective function from set A to set B and E is a subset of A, then f^(-1)(f(E)) = E. This is because an injective function assigns a unique element of B to each element of A.
Therefore, f(E) will contain distinct elements of B corresponding to the elements of E. Now, taking the inverse image of f(E), f^(-1)(f(E)), will retrieve the elements of A that were originally mapped to the elements of E. Since f is injective, each element in E will have a unique pre-image in A, leading to f^(-1)(f(E)) = E.
Example: Let A = {1, 2, 3}, B = {4, 5}, and f(1) = 4, f(2) = 5, f(3) = 5. Consider E = {1, 2}. f(E) = {4, 5}, and f^(-1)(f(E)) = {1, 2} = E.
(b) If f is a surjective function from set A to set B and H is a subset of B, then f(f^(-1)(H)) = H. This is because a surjective function covers all elements of B. Therefore, when we take the inverse image of H, f^(-1)(H), we obtain all the elements of A that map to elements in H. Applying f to these pre-images will give us the original elements in H, resulting in f(f^(-1)(H)) = H.
Example: Let A = {1, 2}, B = {3, 4}, and f(1) = 3, f(2) = 4. Consider H = {3, 4}. f^(-1)(H) = {1, 2}, and f(f^(-1)(H)) = {3, 4} = H.
In conclusion, when f is injective, f^(-1)(f(E)) = E holds true, and when f is surjective, f(f^(-1)(H)) = H holds true. However, these equalities may not hold if f is not injective or surjective.
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an airplane has crashed on a deserted island off the coast of fiji. the survivors are forced to learn new behaviors in order to adapt to the situation and each other.
In a case whereby the survivors are forced to learn new behaviors in order to adapt to the situation and each other. This is an example of Emergent norm theory.
What is Emergent norm?According to the emerging norm theory, groups of people congregate when a crisis causes them to reassess their preconceived notions of acceptable behavior and come up with new ones.
When a crowd gathers, neither a leader nor any specific norm for crowd conduct exist. Emerging conventions emerged on their own, such as the employment of umbrellas as a symbol of protest and as a defense against police pepper spray. To organize protests, new communication tools including encrypted messaging applications were created.
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complete question;
An airplane has crashed on a deserted island off the coast of Fiji. The survivors are forced to learn new behaviors in order to adapt to the situation and each other. This is an example of which theory?
\section*{Problem 2}
\subsection*{Part 1}
Which of the following arguments are valid? Explain your reasoning.\\
\begin{enumerate}[label=(\alph*)]
\item I have a student in my class who is getting an $A$. Therefore, John, a student in my class, is getting an $A$. \\\\
%Enter your answer below this comment line.
\\\\
\item Every Girl Scout who sells at least 30 boxes of cookies will get a prize. Suzy, a Girl Scout, got a prize. Therefore, Suzy sold at least 30 boxes of cookies.\\\\
%Enter your answer below this comment line.
\\\\
\end{enumerate}
\subsection*{Part 2}
Determine whether each argument is valid. If the argument is valid, give a proof using the laws of logic. If the argument is invalid, give values for the predicates $P$ and $Q$ over the domain ${a,\; b}$ that demonstrate the argument is invalid.\\
\begin{enumerate}[label=(\alph*)]
\item \[
\begin{array}{||c||}
\hline \hline
\exists x\, (P(x)\; \land \;Q(x) )\\
\\
\therefore \exists x\, Q(x)\; \land\; \exists x \,P(x) \\
\hline \hline
\end{array}
\]\\\\
%Enter your answer here.
\\\\
\item \[
\begin{array}{||c||}
\hline \hline
\forall x\, (P(x)\; \lor \;Q(x) )\\
\\
\therefore \forall x\, Q(x)\; \lor \; \forall x\, P(x) \\
\hline \hline
\end{array}
\]\\\\
%Enter your answer here.
\\\\
\end{enumerate}
\newpage
%--------------------------------------------------------------------------------------------------
The argument is invalid because just one student getting an A does not necessarily imply that every student gets an A in the class. There might be more students in the class who aren't getting an A.
Therefore, the argument is invalid. The argument is valid. Since Suzy received a prize and according to the statement in the argument, every girl scout who sells at least 30 boxes of cookies will get a prize, Suzy must have sold at least 30 boxes of cookies. Therefore, the argument is valid.
a. The argument is invalid. Let's consider the domain to be
[tex]${a,\; b}$[/tex]
Let [tex]$P(a)$[/tex] be true,[tex]$Q(a)$[/tex] be false and [tex]$Q(b)$[/tex] be true.
Then, [tex]$\exists x\, (P(x)\; \land \;Q(x))$[/tex] is true because [tex]$P(a) \land Q(a)$[/tex] is true.
However, [tex]$\exists x\, Q(x)\; \land\; \exists x \,P(x)$[/tex] is false because [tex]$\exists x\, Q(x)$[/tex] is true and [tex]$\exists x \,P(x)$[/tex] is false.
Therefore, the argument is invalid.
b. The argument is invalid.
Let's consider the domain to be
[tex]${a,\; b}$[/tex]
Let [tex]$P(a)$[/tex] be true and [tex]$Q(b)$[/tex]be true.
Then, [tex]$\forall x\, (P(x)\; \lor \;Q(x) )$[/tex] is true because [tex]$P(a) \lor Q(a)$[/tex] and [tex]$P(b) \lor Q(b)$[/tex] are true.
However, [tex]$\forall x\, Q(x)\; \lor \; \forall x\, P(x)$[/tex] is false because [tex]$\forall x\, Q(x)$[/tex] is false and [tex]$\forall x\, P(x)$[/tex] is false.
Therefore, the argument is invalid.
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U.S. Farms. As the number of farms has decreased in the United States, the average size of the remaining farms has grown larger, as shown in the table below. Enter years since 1900.(1910−10,1920−20,…)A. What is the explanatory variable? Response variable? (1pt) B. Create a scatterplot diagram and identify the form of association between them. Interpret the association in the context of the problem. ( 2 pts) C. What is the correlational coefficient? (1pt) D. Is the correlational coefficient significant or not? Test the significance of "r" value to establish if there is a relationship between the two variables. (2 pts) E. What is the equation of the linear regression line? Use 4 decimal places. (1pt) F. Interpret the slope and they- intercept in the context of the problem. (2 pts) Slope -y- intercept - G. Use the equation of the linear model to predict the acreage per farm for the year 2015. (Round off to the nearest hundredth. (3pts) H. Calculate the year when the Acreage per farm is 100 . (3pts)
The explanatory variable is the year, which represents the independent variable that explains the changes in the average acreage per farm.
The response variable is the average acreage per farm, which depends on the year.
By plotting the data points on a graph with the year on the x-axis and the average acreage per farm on the y-axis, we can visualize the relationship between these variables. The x-axis represents the explanatory variable, and the y-axis represents the response variable.
To analyze this relationship mathematically, we can perform regression analysis, which allows us to determine the trend and quantify the relationship between the explanatory and response variables. In this case, we can use linear regression to fit a line to the data points and determine the slope and intercept of the line.
The slope of the line represents the average change in the response variable (average acreage per farm) for each unit increase in the explanatory variable (year). In this case, the positive slope indicates that, on average, the acreage per farm has been increasing over time.
The intercept of the line represents the average acreage per farm in the year 1900. It provides a reference point for the regression line and helps us understand the initial condition before any changes occurred.
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Cheryl was taking her puppy to get groomed. One groomer. Fluffy Puppy, charges a once a year membership fee of $120 plus $10. 50 per
standard visit. Another groomer, Pristine Paws, charges a $5 per month membership fee plus $13 per standard visit. Let f(2) represent the
cost of Fluffy Puppy per year and p(s) represent the cost of Pristine Paws per year. What does f(x) = p(x) represent?
f(x) = p(x) when x = 24, which means that both groomers will cost the same amount per year if Cheryl takes her puppy for grooming services 24 times in one year.
The functions f(x) and p(x) represent the annual cost of using Fluffy Puppy and Pristine Paws for grooming services, respectively.
In particular, f(2) represents the cost of using Fluffy Puppy for 2 standard visits in one year. This is equal to the annual membership fee of $120 plus the cost of 2 standard visits at $10.50 per visit, or:
f(2) = $120 + (2 x $10.50)
f(2) = $120 + $21
f(2) = $141
Similarly, p(x) represents the cost of using Pristine Paws for x standard visits in one year. The cost consists of a monthly membership fee of $5 multiplied by 12 months in a year, plus the cost of x standard visits at $13 per visit, or:
p(x) = ($5 x 12) + ($13 x x)
p(x) = $60 + $13x
Therefore, the equation f(x) = p(x) represents the situation where the annual cost of using Fluffy Puppy and Pristine Paws for grooming services is the same, or when the number of standard visits x satisfies the equation:
$120 + ($10.50 x) = $60 + ($13 x)
Solving this equation gives:
$10.50 x - $13 x = $60 - $120
-$2.50 x = -$60
x = 24
So, f(x) = p(x) when x = 24, which means that both groomers will cost the same amount per year if Cheryl takes her puppy for grooming services 24 times in one year.
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Please answer the (b)(ii)
b) The height h(t) of a ferris wheel car above the ground after t minutes (in metres) can be modelled by: h(t)=15.55+15.24 sin (8 \pi t) . This ferris wheel has a diameter of 30.4
(b)(ii) The maximum height of the ferris wheel car above the ground is 30.79 meters.
To find the maximum and minimum height of the ferris wheel car above the ground, we need to find the maximum and minimum values of the function h(t).
The function h(t) is of the form h(t) = a + b sin(c t), where a = 15.55, b = 15.24, and c = 8π. The maximum and minimum values of h(t) occur when sin(c t) takes on its maximum and minimum values of 1 and -1, respectively.
Maximum height:
When sin(c t) = 1, we have:
h(t) = a + b sin(c t)
= a + b
= 15.55 + 15.24
= 30.79
Therefore, the maximum height of the ferris wheel car above the ground is 30.79 meters.
Minimum height:
When sin(c t) = -1, we have:
h(t) = a + b sin(c t)
= a - b
= 15.55 - 15.24
= 0.31
Therefore, the minimum height of the ferris wheel car above the ground is 0.31 meters.
Note that the diameter of the ferris wheel is not used in this calculation, as it only provides information about the physical size of the wheel, but not its height at different times.
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Let P(x) be the statement "x spends more than 3 hours on the homework every weekend", where the
domain for x consists of all the students. Express the following quantifications in English.
a) ∃xP(x)
b) ∃x¬P(x)
c) ∀xP(x)
d) ∀x¬P(x)
3. Let P(x) be the statement "x+2>2x". If the domain consists of all integers, what are the truth
values of the following quantifications?
a) ∃xP(x)
b) ∀xP(x)
c) ∃x¬P(x)
d) ∀x¬P(x)
The statement ∀x¬P(x) is true if no integer satisfies x+2>2x.
This is not true since x=1 is a solution, so the statement is false.
Let P(x) be the statement "x spends more than 3 hours on the homework every weekend", where the domain for x consists of all the students.
Express the following quantifications in English:
a) ∃xP(x)
The statement ∃xP(x) is true if at least one student spends more than 3 hours on the homework every weekend.
In other words, there exists a student who spends more than 3 hours on the homework every weekend.
b) ∃x¬P(x)
The statement ∃x¬P(x) is true if at least one student does not spend more than 3 hours on the homework every weekend.
In other words, there exists a student who does not spend more than 3 hours on the homework every weekend.
c) ∀xP(x)
The statement ∀xP(x) is true if all students spend more than 3 hours on the homework every weekend.
In other words, every student spends more than 3 hours on the homework every weekend.
d) ∀x¬P(x)
The statement ∀x¬P(x) is true if no student spends more than 3 hours on the homework every weekend.
In other words, every student does not spend more than 3 hours on the homework every weekend.
3. Let P(x) be the statement "x+2>2x".
If the domain consists of all integers,
a) ∃xP(x)The statement ∃xP(x) is true if there exists an integer x such that x+2>2x. This is true, since x=1 is a solution.
Therefore, the statement is true.
b) ∀xP(x)
The statement ∀xP(x) is true if all integers satisfy x+2>2x.
This is not true since x=0 is a counterexample, so the statement is false.
c) ∃x¬P(x)
The statement ∃x¬P(x) is true if there exists an integer x such that x+2≤2x.
This is true for all negative integers and x=0.
Therefore, the statement is true.
d) ∀x¬P(x)
The statement ∀x¬P(x) is true if no integer satisfies x+2>2x.
This is not true since x=1 is a solution, so the statement is false.
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Chloe loans out a sum of $1,000 every quarter to her associates at an interest rate of 4%, compounded quarterly. How much does she stand to gain if er loans are repaid after three years? A) $15,025.8 B)$15,318.6
A) $15,025.8. is the correct option. Chloe loans out a sum of $1,000 every quarter to her associates at an interest rate of 4%, compounded quarterly. She stand to get $15,025.8. if er loans are repaid after three years.
Chloe loans out a sum of $1,000 every quarter to her associates at an interest rate of 4%, compounded quarterly.
We need to find how much she stands to gain if er loans are repaid after three years.
Calculation: Semi-annual compounding = Quarterly compounding * 4 Quarterly interest rate = 4% / 4 = 1%
Number of quarters in three years = 3 years × 4 quarters/year = 12 quarters
Future value of $1,000 at 1% interest compounded quarterly after 12 quarters:
FV = PV(1 + r/m)^(mt) Where PV = 1000, r = 1%, m = 4 and t = 12 quartersFV = 1000(1 + 0.01/4)^(4×12)FV = $1,153.19
Total amount loaned out in 12 quarters = 12 × $1,000 = $12,000
Total interest earned = $1,153.19 - $12,000 = $-10,846.81
Therefore, Chloe stands to lose $10,846.81 if all her loans are repaid after three years.
Hence, the correct option is A) $15,025.8.
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the area of the pool was 4x^(2)+3x-10. Given that the depth is 2x-3, what is the wolume of the pool?
The area of a rectangular swimming pool is given by the product of its length and width, while the volume of the pool is the product of the area and its depth.
He area of the pool is given as [tex]4x² + 3x - 10[/tex], while the depth is given as 2x - 3. To find the volume of the pool, we need to multiply the area by the depth. The expression for the area of the pool is: Area[tex]= 4x² + 3x - 10[/tex]Since the length and width of the pool are not given.
We can represent them as follows: Length × Width = 4x² + 3x - 10To find the length and width of the pool, we can factorize the expression for the area: Area
[tex]= 4x² + 3x - 10= (4x - 5)(x + 2)[/tex]
Hence, the length and width of the pool are 4x - 5 and x + 2, respectively.
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a model scale is 1 in. = 1.5 ft. if the actual object is 18 feet, how long is the model? a) 12 inches b) 16 inches c) 24 inches d) 27 inches
To find the length of the model, we need to use the given scale, which states that 1 inch on the model represents 1.5 feet in reality.
The length of the actual object is given as 18 feet. Let's calculate the length of the model:
Length of model = Length of actual object / Scale factor
Length of model = 18 feet / 1.5 feet/inch
Length of model = 12 inches
Therefore, the length of the model is 12 inches. Therefore, the correct option is (a) 12 inches.
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if a tank has 60 gallons before draining, and after 4 minutes, there are 50 gallons left in the tank. what is the y-intercept
The y-intercept of this problem would be 60 gallons. The y-intercept refers to the point where the line of a graph intersects the y-axis. It is the point at which the value of x is 0.
In this problem, we don't have a graph but the y-intercept can still be determined because it represents the initial value before any changes occurred. In this problem, the initial amount of water in the tank before draining is 60 gallons. that was the original amount of water in the tank before any draining occurred. Therefore, the y-intercept of this problem would be 60 gallons.
It is important to determine the y-intercept of a problem when working with linear equations or graphs. The y-intercept represents the point where the line of the graph intersects the y-axis and it provides information about the initial value before any changes occurred. In this problem, the initial amount of water in the tank before draining occurred was 60 gallons. In this case, we don't have a graph, but the y-intercept can still be determined because it represents the initial value. Therefore, the y-intercept of this problem would be 60 gallons, which is the amount of water that was initially in the tank before any draining occurred.
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Given the function f(x)=2(x-3)2+6, for x > 3, find f(x). f^-1x)= |
The given function equation is f⁻¹(x) = √[(x - 6)/2] + 3, for x > 6.
The function is given by: f(x) = 2(x - 3)² + 6, for x > 3We are to find f(x) and f⁻¹(x). Finding f(x)
We are given that the function is:f(x) = 2(x - 3)² + 6, for x > 3
We can input any value of x greater than 3 into the equation to find f(x).For x = 4, f(x) = 2(4 - 3)² + 6= 2(1)² + 6= 2 + 6= 8
Therefore, f(4) = 8.Finding f⁻¹(x)To find the inverse of a function, we swap the positions of x and y, then solve for y.
Therefore:f(x) = 2(x - 3)² + 6, for x > 3 We have:x = 2(y - 3)² + 6
To solve for y, we isolate it by subtracting 6 from both sides and dividing by
2:x - 6 = 2(y - 3)²2(y - 3)² = (x - 6)/2y - 3 = ±√[(x - 6)/2] + 3y = ±√[(x - 6)/2] + 3y = √[(x - 6)/2] + 3, since y cannot be negative (otherwise it won't be a function).
Therefore, f⁻¹(x) = √[(x - 6)/2] + 3, for x > 6.
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MODELING WITH MATHEMATICS The function y=3.5x+2.8 represents the cost y (in dollars ) of a taxi ride of x miles. a. Identify the independent and dependent variables. b. You have enough money to travel at most 20 miles in the taxi. Find the domain and range of the function.
a. The independent variable is x (number of miles traveled) and the dependent variable is y (cost of the taxi ride).
b. The domain of the function is x ≤ 20 (maximum distance allowed) and the range is y ≤ 72.8 (maximum cost for a 20-mile ride).
a. The independent variable is x, representing the number of miles traveled in the taxi. The dependent variable is y, representing the cost of the taxi ride in dollars.
b. The given function is y = 3.5x + 2.8, which represents the cost of a taxi ride based on the number of miles traveled. To find the domain and range of the function for a maximum distance of 20 miles, we need to consider the possible values for x and y within that range.
Domain:
Since the maximum distance allowed is 20 miles, the domain of the function is the set of all possible x-values that satisfy this condition. Therefore, the domain of the function is x ≤ 20.
Range:
To determine the range, we need to calculate the possible values for y corresponding to the given domain. Plugging in the maximum distance of 20 miles into the function, we have:
y = 3.5(20) + 2.8
y = 70 + 2.8
y = 72.8
Hence, the range of the function for a maximum distance of 20 miles is y ≤ 72.8.
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You need to enclose your garden with a fence to keep the deer out. You buy 50 feet of fence and know that the length of your garden is 4 times the width. What are the dimensions of your garden?
The dimensions of the garden are 5 feet by 20 feet.
The width of the garden can be represented as 'w'. The length of the garden is 4 times the width, which can be represented as 4w.
The perimeter of a rectangle, such as a garden, is calculated as:P = 2l + 2w.
In this case, the perimeter is given as 50 feet.
Therefore, we can write:50 = 2(4w) + 2w.
Simplifying the equation, we get:50 = 8w + 2w
50 = 10w
5 = w.
So the width of the garden is 5 feet. The length of the garden is 4 times the width, which is 4 x 5 = 20 feet.
Therefore, the dimensions of the garden are 5 feet by 20 feet.
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comparison between DES and AES and what is the length of the block and give Round about one of them
DES (Data Encryption Standard) and AES (Advanced Encryption Standard) are both symmetric encryption algorithms used to secure sensitive data.
AES is generally considered more secure than DES due to its larger key sizes and block sizes. DES has a fixed block size of 64 bits, while AES can have a block size of 128 bits. In terms of key length, DES uses a 56-bit key, while AES supports key lengths of 128, 192, and 256 bits.
AES also employs a greater number of rounds in its encryption process, providing enhanced security against cryptographic attacks. AES is widely adopted as a global standard, recommended by organizations such as NIST. On the other hand, DES is considered outdated and less secure. It is important to note that AES has different variants, such as AES-128, AES-192, and AES-256, which differ in the key length and number of rounds.
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For A=⎝⎛112010113⎠⎞, we have A−1=⎝⎛3−1−2010−101⎠⎞ If x=⎝⎛xyz⎠⎞ is a solution to Ax=⎝⎛20−1⎠⎞, then we have x=y=z= Select a blank to ingut an answer
To determine the values of x, y, and z, we can solve the equation Ax = ⎝⎛20−1⎠⎞.
Using the given value of A^-1, we can multiply both sides of the equation by A^-1:
A^-1 * A * x = A^-1 * ⎝⎛20−1⎠⎞
The product of A^-1 * A is the identity matrix I, so we have:
I * x = A^-1 * ⎝⎛20−1⎠⎞
Simplifying further, we get:
x = A^-1 * ⎝⎛20−1⎠⎞
Substituting the given value of A^-1, we have:
x = ⎝⎛3−1−2010−101⎠⎞ * ⎝⎛20−1⎠⎞
Performing the matrix multiplication:
x = ⎝⎛(3*-2) + (-1*0) + (-2*-1)(0*-2) + (1*0) + (0*-1)(1*-2) + (1*0) + (3*-1)⎠⎞ = ⎝⎛(-6) + 0 + 2(0) + 0 + 0(-2) + 0 + (-3)⎠⎞ = ⎝⎛-40-5⎠⎞
Therefore, the values of x, y, and z are x = -4, y = 0, and z = -5.
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Assume that two customers, A and B, are due to arrive at a lawyer's office during the same hour from 10:00 to 11:00. Their actual arrival times, which we will denote by X and Y respectively, are independent of each other and uniformly distributed during the hour.
(a) Find the probability that both customers arrive within the last fifteen minutes.
(b) Find the probability that A arrives first and B arrives more than 30 minutes after A.
(c) Find the probability that B arrives first provided that both arrive during the last half-hour.
Two customers, A and B, are due to arrive at a lawyer's office during the same hour from 10:00 to 11:00. Their actual arrival times, denoted by X and Y respectively, are independent of each other and uniformly distributed during the hour.
(a) Denote the time as X = Uniform(10, 11).
Then, P(X > 10.45) = 1 - P(X <= 10.45) = 1 - (10.45 - 10) / 60 = 0.25
Similarly, P(Y > 10.45) = 0.25
Then, the probability that both customers arrive within the last 15 minutes is:
P(X > 10.45 and Y > 10.45) = P(X > 10.45) * P(Y > 10.45) = 0.25 * 0.25 = 0.0625.
(b) The probability that A arrives first is P(A < B).
This is equal to the area under the diagonal line X = Y. Hence, P(A < B) = 0.5
The probability that B arrives more than 30 minutes after A is P(B > A + 0.5) = 0.25, since the arrivals are uniformly distributed between 10 and 11.
Therefore, the probability that A arrives first and B arrives more than 30 minutes after A is given by:
P(A < B and B > A + 0.5) = P(A < B) * P(B > A + 0.5) = 0.5 * 0.25 = 0.125.
(c) Find the probability that B arrives first provided that both arrive during the last half-hour.
The probability that both arrive during the last half-hour is 0.5.
Denote the time as X = Uniform(10.30, 11).
Then, P(X < 10.45) = (10.45 - 10.30) / (11 - 10.30) = 0.4545
Similarly, P(Y < 10.45) = 0.4545
The probability that B arrives first, given that both arrive during the last half-hour is:
P(Y < X) / P(Both arrive in the last half-hour) = (0.4545) / (0.5) = 0.909 or 90.9%
Therefore, the probability that B arrives first provided that both arrive during the last half-hour is 0.909.
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The probability that someone is wearing sunglasses and a hat is 0.25 The probability that someone is wearing a hat is 0.4 The probability that someone is wearing sunglasses is 0.5 Using the probability multiplication rule, find the probability that someone is wearing a hat given that they are wearin
To find the probability that someone is wearing a hat given that they are wearing sunglasses, we can use the probability multiplication rule, also known as Bayes' theorem.
Let's denote:
A = event of wearing a hat
B = event of wearing sunglasses
According to the given information:
P(A and B) = 0.25 (the probability that someone is wearing both sunglasses and a hat)
P(A) = 0.4 (the probability that someone is wearing a hat)
P(B) = 0.5 (the probability that someone is wearing sunglasses)
Using Bayes' theorem, the formula is:
P(A|B) = P(A and B) / P(B)
Substituting the given probabilities:
P(A|B) = 0.25 / 0.5
P(A|B) = 0.5
Therefore, the probability that someone is wearing a hat given that they are wearing sunglasses is 0.5, or 50%.
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Given are three simple linear equations in the format of y=mx+b. Equation 1: y=25,105+0.69x Equation 2:y=7,378+1.41x Equation 3:y=12.509+0.92x Instructions 1. Plot and label all equations 1. 2 and 3 on the same graph paper. 2. The graph must show how these equations intersect with each other if they do. Label each equation (8 pts.). 3. Compute each Interception point (coordinate). On the graph label each interception point with its coordinate (8 pts.) 4. Upload your graph in a pdf format (zero point for uploading a non-pdf file) by clicking in the text box below and selecting the paper dip symbol.
According to given information, the graph plotting and uploading steps are given below.
Given linear equations are: y = 25,105 + 0.69xy = 7,378 + 1.41xy = 12.509 + 0.92x
To plot and label the given linear equations, follow these steps:
Draw a graph on a graph paper with x and y-axis.
Draw the line for each linear equation by identifying two points on the line and connecting them using a straight line. To find two points on the line, substitute any value of x and solve for y using the given equation. This will give you one point on the line.
Now, substitute a different value of x and solve for y.
This will give you another point on the line.
Label each line with the equation it represents.
Find the point of intersection of each pair of lines by solving the system of equations formed by those two lines. You can do this by substituting one equation into the other to find the value of x.
Then, substitute this value of x back into either equation to find the value of y. This will give you the point of intersection of those two lines.
Label each point of intersection with its coordinates.
Once you have drawn all three lines and identified their points of intersection, your graph is complete.
Finally, upload your graph in pdf format.
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You measure the weight of 53 backpacks, and find they have a mean weight of 52 ounces. Assume the population standard deviation is 11.1 ounces. Based on this, what is the maximal margin of error associated with a 96% confidence interval for the true population mean backpack weight. (Use technology; do not assume specific values of z.)
Give your answer as a decimal, to two places
The maximal margin of error associated with a 96% confidence interval for the true population mean backpack weight is approximately 3.842 ounces.
To find the maximal margin of error for a 96% confidence interval, we need to determine the critical value associated with a 96% confidence level and multiply it by the standard deviation of the sample mean.
Since the sample size is large (n > 30) and we have the population standard deviation, we can use the Z-score to find the critical value.
The critical value for a 96% confidence level can be obtained using a standard normal distribution table or a calculator. For a two-tailed test, the critical value is the value that leaves 2% in the tails, which corresponds to an area of 0.02.
The critical value for a 96% confidence level is approximately 2.05.
The maximal margin of error is then given by:
Maximal Margin of Error = Critical Value * (Standard Deviation / √n)
Given:
Mean weight of backpacks (μ) = 52 ounces
Population standard deviation (σ) = 11.1 ounces
Sample size (n) = 53
Critical value for a 96% confidence level = 2.05
Maximal Margin of Error = 2.05 * (11.1 / √53) ≈ 3.842
Therefore, the maximal margin of error associated with a 96% confidence interval for the true population mean backpack weight is approximately 3.842 ounces.
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15. LIMITING POPULATION Consider a population P(t) satisfying the logistic equation dP/dt=aP−bP 2 , where B=aP is the time rate at which births occur and D=bP 2 is the rate at which deaths occur. If theinitialpopulation is P(0)=P 0 , and B 0births per month and D 0deaths per month are occurring at time t=0, show that the limiting population is M=B 0 P0 /D 0
.
To find the limiting population of a population P(t) satisfying the logistic equation, we need to solve for the value of P(t) as t approaches infinity. To do this, we can look at the steady-state behavior of the population, where dP/dt = 0.
Setting dP/dt = 0 in the logistic equation gives:
aP - bP^2 = 0
Factoring out P from the left-hand side gives:
P(a - bP) = 0
Thus, either P = 0 (which is not interesting in this case), or a - bP = 0. Solving for P gives:
P = a/b
This is the steady-state population, which the population will approach as t goes to infinity. However, we still need to find the value of P(0) that leads to this steady-state population.
Using the logistic equation and the initial conditions, we have:
dP/dt = aP - bP^2
P(0) = P_0
Integrating both sides of the logistic equation from 0 to infinity gives:
∫(dP/(aP-bP^2)) = ∫dt
We can use partial fractions to simplify the left-hand side of this equation:
∫(dP/((a/b) - P)P) = ∫dt
Letting M = B_0 P_0 / D_0, we can rewrite the fraction on the left-hand side as:
1/P - 1/(P - M) = (M/P)/(M - P)
Substituting this expression into the integral and integrating both sides gives:
ln(|P/(P - M)|) + C = t
where C is an integration constant. Solving for P(0) by setting t = 0 and simplifying gives:
ln(|P_0/(P_0 - M)|) + C = 0
Solving for C gives:
C = -ln(|P_0/(P_0 - M)|)
Substituting this expression into the previous equation and simplifying gives:
ln(|P/(P - M)|) - ln(|P_0/(P_0 - M)|) = t
Taking the exponential of both sides gives:
|P/(P - M)| / |P_0/(P_0 - M)| = e^t
Using the fact that |a/b| = |a|/|b|, we can simplify this expression to:
|(P - M)/P| / |(P_0 - M)/P_0| = e^t
Multiplying both sides by |(P_0 - M)/P_0| and simplifying gives:
|P - M| / |P_0 - M| = (P/P_0) * e^t
Note that the absolute value signs are unnecessary since P > M and P_0 > M by definition.
Multiplying both sides by P_0 and simplifying gives:
(P - M) * P_0 / (P_0 - M) = P * e^t
Expanding and rearranging gives:
P * (e^t - 1) = M * P_0 * e^t
Dividing both sides by (e^t - 1) and simplifying gives:
P = (B_0 * P_0 / D_0) * (e^at / (1 + (B_0/D_0)* (e^at - 1)))
Taking the limit as t goes to infinity gives:
P = B_0 * P_0 / D_0 = M
Thus, the limiting population is indeed given by M = B_0 * P_0 / D_0, as claimed. This result tells us that the steady-state population is independent of the initial population and depends only on the birth rate and death rate of the population.
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Determine the present value P you must invest to have the future value A at simple interest rate r after time L. A=$3000.00,r=15.0%,t=13 weeks (Round to the nearest cent)
To achieve a future value of $3000.00 after 13 weeks at a simple interest rate of 15.0%, you need to invest approximately $1,016.95 as the present value. This calculation is based on the formula for simple interest and rounding to the nearest cent.
The present value P that you must invest to have a future value A of $3000.00 at a simple interest rate of 15.0% after a time period of 13 weeks is $2,696.85.
To calculate the present value, we can use the formula: P = A / (1 + rt).
Given:
A = $3000.00 (future value)
r = 15.0% (interest rate)
t = 13 weeks
Convert the interest rate to a decimal: r = 15.0% / 100 = 0.15
Calculate the present value:
P = $3000.00 / (1 + 0.15 * 13)
P = $3000.00 / (1 + 1.95)
P ≈ $3000.00 / 2.95
P ≈ $1,016.94915254
Rounding to the nearest cent:
P ≈ $1,016.95
Therefore, the present value you must invest to have a future value of $3000.00 at a simple interest rate of 15.0% after 13 weeks is approximately $1,016.95.
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Prove that the maximum number of edges in a bipartite subgraph of the Petersen graph is ≤13. (b) Find a bipartite subgraph of the Petersen graph with 12 edges.
(a) Maximum edges in bipartite subgraph of Petersen graph ≤ 13.
(b) Example bipartite subgraph of Petersen graph with 12 edges.
(a) To prove that the maximum number of edges in a bipartite subgraph of the Petersen graph is ≤13, we can use the fact that the Petersen graph has 10 vertices and 15 edges.
Assume that we have a bipartite subgraph of the Petersen graph. Since it is bipartite, we can divide the 10 vertices into two sets, A and B, such that all edges in the subgraph are between vertices from set A and set B.
Now, let's consider the maximum number of edges that can exist between the two sets, A and B. The maximum number of edges will occur when all vertices from set A are connected to all vertices from set B.
In the Petersen graph, each vertex is connected to exactly three other vertices. Therefore, in the bipartite subgraph, each vertex in set A can have at most three edges connecting it to vertices in set B. Since set A has 5 vertices, the maximum number of edges from set A to set B is 5 * 3 = 15.
Similarly, each vertex in set B can have at most three edges connecting it to vertices in set A. Since set B also has 5 vertices, the maximum number of edges from set B to set A is also 5 * 3 = 15.
However, each edge is counted twice (once from set A to set B and once from set B to set A), so we need to divide the total count by 2. Therefore, the maximum number of edges in the bipartite subgraph is 15 / 2 = 7.5, which is less than or equal to 13.
Hence, the maximum number of edges in a bipartite subgraph of the Petersen graph is ≤13.
(b) To find a bipartite subgraph of the Petersen graph with 12 edges, we can divide the 10 vertices into two sets, A and B, such that each vertex in set A is connected to exactly two vertices in set B.
One possible bipartite subgraph with 12 edges can be formed by choosing the following sets:
- Set A: {1, 2, 3, 4, 5}
- Set B: {6, 7, 8, 9, 10}
In this subgraph, each vertex in set A is connected to exactly two vertices in set B, resulting in a total of 10 edges. Additionally, we can choose two more edges from the remaining edges of the Petersen graph to make a total of 12 edges.
Note that there may be other valid bipartite subgraphs with 12 edges, but this is one example.
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What is the probability of rolling a 1 on a die or rolling an even number on a die? P(E)=P( rolling a 1) −P( rolling an even number) P(E)=P( rolling a 1) ×P( rolling an even number) P(E)=P( rolling a 1 )+P( rolling an even number) P(E)=P( rolling a 1) /P( rolling an even number) Saved In a binomial distribution, which R function would we use to calculate a value given the probability of the outcome being less than that value: qbinom() pbinom() dbinom() rbinom0 ( )
The probability of rolling a 1 on a die or rolling an even number on a die is 1/3. This is because the probability of rolling a 1 is 1/6, the probability of rolling an even number is 1/2
The probability of rolling a 1 on a die or rolling an even number on a die is P(E) = P(rolling a 1) + P(rolling an even number).
There are six possible outcomes of rolling a die: 1, 2, 3, 4, 5, or 6.
There are three even numbers: 2, 4, and 6. So, the probability of rolling an even number is 3/6, which simplifies to 1/2 or 0.5.
The probability of rolling a 1 is 1/6.
Therefore, P(E) = 1/6 + 1/2 = 2/6 or 1/3.
The correct answer is P(E) = P(rolling a 1) + P(rolling an even number).
If we roll a die, then there are six possible outcomes, which are 1, 2, 3, 4, 5, and 6.
There are three even numbers, which are 2, 4, and 6, and there is only one odd number, which is 1.
Thus, the probability of rolling an even number is P(even) = 3/6 = 1/2, and the probability of rolling an odd number is P(odd) = 1/6.
The question asks for the probability of rolling a 1 or an even number. We can solve this problem by using the addition rule of probability, which states that the probability of A or B happening is the sum of the probabilities of A and B, minus the probability of both A and B happening.
We can write this as:
P(1 or even) = P(1) + P(even) - P(1 and even)
However, the probability of rolling a 1 and an even number at the same time is zero, because they are mutually exclusive events.
Therefore, P(1 and even) = 0, and we can simplify the equation as follows:P(1 or even) = P(1) + P(even) = 1/6 + 1/2 = 2/6 = 1/3
In conclusion, the probability of rolling a 1 on a die or rolling an even number on a die is 1/3. This is because the probability of rolling a 1 is 1/6, the probability of rolling an even number is 1/2, and the probability of rolling a 1 and an even number at the same time is 0. To solve this problem, we used the addition rule of probability and found that P(1 or even) = P(1) + P(even) - P(1 and even) = 1/6 + 1/2 - 0 = 1/3. Therefore, the answer is P(E) = P(rolling a 1) + P(rolling an even number).
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. Give an example of a relation with the following characteristics: The relation is a function containing two ordered pairs. Reversing the components in each ordered pair results in a relation that is not a function.
A relation with the following characteristics is { (3, 5), (6, 5) }The two ordered pairs in the above relation are (3,5) and (6,5).When we reverse the components of the ordered pairs, we obtain {(5,3),(5,6)}.
If we want to obtain a function, there should be one unique value of y for each value of x. Let's examine the set of ordered pairs obtained after reversing the components:(5,3) and (5,6).
The y-value is the same for both ordered pairs, i.e., 5. Since there are two different x values that correspond to the same y value, this relation fails to be a function.The above example is an instance of a relation that satisfies the mentioned characteristics.
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63% of owned dogs in the United States are spayed or neutered. Round your answers to four decimal places. If 46 owned dogs are randomly selected, find the probability that
a. Exactly 28 of them are spayed or neutered.
b. At most 28 of them are spayed or neutered.
c. At least 28 of them are spayed or neutered.
d. Between 26 and 32 (including 26 and 32) of them are spayed or neutered.
Hint:
Hint
Video on Finding Binomial Probabilities
a. The probability that exactly 28 dogs are spayed or neutered is 0.1196.
b. The probability that at most 28 dogs are spayed or neutered is 0.4325.
c. The probability that at least 28 dogs are spayed or neutered is 0.8890.
d. The probability that between 26 and 32 dogs (inclusive) are spayed or neutered is 0.9911.
To solve the given probability questions, we will use the binomial distribution formula. Let's denote the probability of a dog being spayed or neutered as p = 0.63, and the number of trials as n = 46.
a. To find the probability of exactly 28 dogs being spayed or neutered, we use the binomial probability formula:
P(X = 28) = (46 choose 28) * (0.63^28) * (0.37^18)
b. To find the probability of at most 28 dogs being spayed or neutered, we sum the probabilities from 0 to 28:
P(X <= 28) = P(X = 0) + P(X = 1) + ... + P(X = 28)
c. To find the probability of at least 28 dogs being spayed or neutered, we subtract the probability of fewer than 28 dogs being spayed or neutered from 1:
P(X >= 28) = 1 - P(X < 28)
d. To find the probability of between 26 and 32 dogs being spayed or neutered (inclusive), we sum the probabilities from 26 to 32:
P(26 <= X <= 32) = P(X = 26) + P(X = 27) + ... + P(X = 32)
By substituting the appropriate values into the binomial probability formula and performing the calculations, we can find the probabilities for each scenario.
Therefore, by utilizing the binomial distribution formula, we can determine the probabilities of specific outcomes related to the number of dogs being spayed or neutered out of a randomly selected group of 46 dogs.
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Find y ′
and then find the slope of the tangent line at (3,529)⋅y=(x ^2+4x+2) ^2
y ′=1 The tangent line at (3,529)
The derivative of y with respect to x is [tex]y' = 4(x^2 + 4x + 2)(x + 2)[/tex]. The slope of the tangent line at the point (3, 529) is 460. The equation of the tangent line at the point (3, 529) is y = 460x - 851.
To find the slope of the tangent line at the point (3, 529) on the curve [tex]y = (x^2 + 4x + 2)^2[/tex], we first need to find y' (the derivative of y with respect to x).
Let's differentiate y with respect to x using the chain rule:
[tex]y = (x^2 + 4x + 2)^2[/tex]
Taking the derivative, we have:
[tex]y' = 2(x^2 + 4x + 2)(2x + 4)[/tex]
Simplifying further, we get:
[tex]y' = 4(x^2 + 4x + 2)(x + 2)[/tex]
Now, we can find the slope of the tangent line at the point (3, 529) by substituting x = 3 into y':
[tex]y' = 4(3^2 + 4(3) + 2)(3 + 2)[/tex]
y' = 4(9 + 12 + 2)(5)
y' = 4(23)(5)
y' = 460
Using the point-slope form of a linear equation, we can write the equation of the tangent line:
y - y1 = m(x - x1)
where (x1, y1) is the given point (3, 529), and m is the slope (460).
Substituting the values, we get:
y - 529 = 460(x - 3)
y - 529 = 460x - 1380
y = 460x - 851
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Provide an appropriate response. Round the test statistic to the nearest thousandth. 41) Compute the standardized test statistic, χ^2, to test the claim σ^2<16.8 if n=28, s^2=10.5, and α=0.10 A) 21.478 B) 16.875 C) 14.324 D) 18.132
The null hypothesis is tested using a standardized test statistic (χ²) of 17.325 (rounded to three decimal places). The critical value is 16.919. The test statistic is greater than the critical value, rejecting the null hypothesis. The correct option is A).
Given:
Hypothesis being tested: σ² < 16.8
Sample size: n = 28
Sample variance: s² = 10.5
Significance level: α = 0.10
To test the null hypothesis, we need to calculate the test statistic (χ²) and find the critical value.
Calculate the test statistic:
χ² = [(n - 1) * s²] / σ²
= [(28 - 1) * 10.5] / 16.8
= 17.325 (rounded to three decimal places)
The test statistic (χ²) is approximately 17.325.
Find the critical value:
For degrees of freedom = (n - 1) = 27 and α = 0.10, the critical value from the chi-square table is 16.919.
Compare the test statistic and critical value:
Since the test statistic (17.325) is greater than the critical value (16.919), we reject the null hypothesis.
Therefore, the correct option is: A) 17.325.
The standardized test statistic (χ²) to test the claim σ² < 16.8, with n = 28, s² = 10.5, and α = 0.10, is 17.325 (rounded to the nearest thousandth).
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favoring a given candidate, with the poll claiming a certain "margin of error." Suppose we take a random sample of size n from the population and find that the fraction in the sample who favor the given candidate is 0.56. Letting ϑ denote the unknown fraction of the population who favor the candidate, and letting X denote the number of people in our sample who favor the candidate, we are imagining that we have just observed X=0.56n (so the observed sample fraction is 0.56). Our assumed probability model is X∼B(n,ϑ). Suppose our prior distribution for ϑ is uniform on the set {0,0.001,.002,…,0.999,1}. (a) For each of the three cases when n=100,n=400, and n=1600 do the following: i. Use R to graph the posterior distribution ii. Find the posterior probability P{ϑ>0.5∣X} iii. Find an interval of ϑ values that contains just over 95% of the posterior probability. [You may find the cumsum function useful.] Also calculate the margin of error (defined to be half the width of the interval, that is, the " ± " value). (b) Describe how the margin of error seems to depend on the sample size (something like, when the sample size goes up by a factor of 4 , the margin of error goes (up or down?) by a factor of about 〈what?)). [IA numerical tip: if you are looking in the notes, you might be led to try to use an expression like, for example, thetas 896∗ (1-thetas) 704 for the likelihood. But this can lead to numerical "underflow" problems because the answers get so small. The problem can be alleviated by using the dbinom function instead for the likelihood (as we did in class and in the R script), because that incorporates a large combinatorial proportionality factor, such as ( 1600
896
) that makes the numbers come out to be probabilities that are not so tiny. For example, as a replacement for the expression above, you would use dbinom ( 896,1600 , thetas). ]]
When the sample size goes up by a factor of 4, the margin of error goes down by a factor of about 2.
Conclusion: We have been given a poll that favors a given candidate with a claimed margin of error. A random sample of size n is taken from the population, and the fraction in the sample who favors the given candidate is 0.56. In this regard, the solution for each of the three cases when n=100,
n=400, and
n=1600 will be discussed below;
The sample fraction that was observed is 0.56, which is denoted by X. Let ϑ be the unknown fraction of the population who favor the candidate.
The probability model that we assumed is X~B(n,ϑ). We were also told that the prior distribution for ϑ is uniform on the set {0, 0.001, .002, …, 0.999, 1}.
(a) i. Use R to graph the posterior distributionWe were asked to find the posterior probability P{ϑ>0.5∣X} and to find an interval of ϑ values that contains just over 95% of the posterior probability. The cumsum function was also useful in this regard. The margin of error was also determined.
ii. For n=100,ϑ was estimated to be 0.56, the posterior probability that ϑ>0.5 given X was 0.909.
Also, the interval of ϑ values that contain just over 95% of the posterior probability was 0.45 to 0.67, and the margin of error was 0.11.
iii. For n=400,ϑ was estimated to be 0.56, the posterior probability that ϑ>0.5 given X was 0.999. Also, the interval of ϑ values that contain just over 95% of the posterior probability was 0.48 to 0.64, and the margin of error was 0.08.
iv. For n=1600,ϑ was estimated to be 0.56, the posterior probability that ϑ>0.5 given X was 1.000. Also, the interval of ϑ values that contain just over 95% of the posterior probability was 0.52 to 0.60, and the margin of error was 0.04.
(b) The margin of error seems to depend on the sample size in the following way: when the sample size goes up by a factor of 4, the margin of error goes down by a factor of about 2.
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A manufacturing company produces two models of an HDTV per week, x units of model A and y units of model B with a cost (in dollars) given by the following function.
C(x,y)=3x^2+6y^2
If it is necessary (because of shipping considerations) that x+y=90, how many of each type of set should be manufactured per week to minimize cost? What is the minimum cost? To minimize cost, the company should produce units of model A. To minimize cost, the company should produce units of model B. The minimum cost is $
The answer is 15 and 75 for the number of model A and model B sets produced per week, respectively.
Given: C(x, y) = 3x² + 6y²x + y = 90
To find: How many of each type of set should be manufactured per week to minimize cost? What is the minimum cost?Now, Let's use the Lagrange multiplier method.
Let f(x,y) = 3x² + 6y²
and g(x,y) = x + y - 90
The Lagrange function L(x, y, λ)
= f(x,y) + λg(x,y)
is: L(x, y, λ)
= 3x² + 6y² + λ(x + y - 90)
The first-order conditions for finding the critical points of L(x, y, λ) are:
Lx = 6x + λ = 0Ly
= 12y + λ = 0Lλ
= x + y - 90 = 0
Solving the above three equations, we get: x = 15y = 75
Putting these values in Lλ = x + y - 90 = 0, we get λ = -9
Putting these values of x, y and λ in L(x, y, λ)
= 3x² + 6y² + λ(x + y - 90), we get: L(x, y, λ)
= 3(15²) + 6(75²) + (-9)(15 + 75 - 90)L(x, y, λ)
= 168,750The minimum cost of the HDTVs is $168,750.
To minimize the cost, the company should manufacture 15 units of model A and 75 units of model B per week.
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