Excuse me, for the person who answers this right I will brain list. (if you can't quiet understand the question, I left an image)
Complete: For the function ()=−2‾‾‾‾‾√3, the average rate of change to the nearest hundredth over the interval −2 ≤ x ≤ 4 is ______.

Using Euler's method we get:

y_1 = 1

using the analytical solution:

y = 1.225

We can estimate the accuracy of the result of the first calculation to be approximately `0.0003927`.

Let's begin by solving the given differential equation using Euler's method.

Using Euler's method we can estimate the value of `y` at a point using the following equation:

y_n+1 = y_n + h*f(x_n,y_n), where h is the step size given by

`h=x_(n+1)-x_n`.

Given that `dy/dx = x/y` we have that `y dy = x dx`. Integrating both sides we get:

(1/2)y^2 = (1/2)x^2 + C where C is the constant of integration.

To find `C` we use the initial condition `y(0)=1`.

This gives:

(1/2)(1)^2 = (1/2)(0)^2 + C => C = 1/2

Therefore the solution is given by: y^2 = x^2 + 1/2 => y = sqrt(x^2 + 1/2)

Now to estimate `y(1)` using the Euler's method, we have:

x_0 = 0, y_0 = 1, h = 0.1

Using Euler's method we get:

y_1 = y_0 + h*(x_0/y_0) = 1 + 0.1*(0/1) = 1

Now, we will improve the result using h= 0.05 and compare both results with the analytical solution.

x_0 = 0, y_0 = 1, h = 0.05

Using Euler's method we get:

y_1 = y_0 + h*(x_0/y_0) = 1 + 0.05*(0/1) = 1

Now, using the analytical solution:

y = sqrt(x^2 + 1/2) => y(1) = sqrt(1 + 1/2) = sqrt(3/2) = 1.225

Using Euler's method we get y(1) = 1.0 (with h = 0.1) and 1.0 (with h = 0.05). As we can see the result is not accurate. To improve the result we can use a more accurate method like the Runge-Kutta method.

Next, we will use the predictor-corrector method to solve the given differential equation.

dy/dx = x^2+y^2 ; y(0)=0

with h = 0.01

To use the predictor-corrector method we need to first use a predictor method to estimate the value of `y` at `x_(n+1)`. For that we can use the Euler's method. Then, using the estimate, we correct the result using a better approximation method like the Runge-Kutta method.

The Euler's method gives:

y_n+1(predicted) = y_n + h*f(x_n,y_n) = y_n + h*(x_n^2 + y_n^2)y_1(predicted)

= y_0 + h*(x_0^2 + y_0^2) = 0 + 0.01*(0^2 + 0^2) = 0

Next, we will correct this result using the Runge-Kutta method of order 4.

The Runge-Kutta method of order 4 is given by: y_n+1 = y_n + (1/6)*(k1 + 2*k2 + 2*k3 + k4)

where k1 = h*f(x_n,y_n)

k2 = h*f(x_n + h/2, y_n + k1/2)

k3 = h*f(x_n + h/2, y_n + k2/2)

k4 = h*f(x_n + h, y_n + k3)

Using the given differential equation: f(x,y) = x^2 + y^2y_1 = y_0 + (1/6)*(k1 + 2*k2 + 2*k3 + k4)

where k1 = h*f(x_0,y_0) = 0

k2 = h*f(x_0 + h/2, y_0 + k1/2) = h*f(0.005, 0) = 0.000025

k3 = h*f(x_0 + h/2, y_0 + k2/2) = h*f(0.005, 0.0000125) = 0.000025

k4 = h*f(x_0 + h, y_0 + k3) = h*f(0.01, 0.0000125) = 0.000100y_1 = 0 + (1/6)*(0 + 2*0.000025 + 2*0.000025 + 0.000100) = 0.0000583

Now, we will repeat this process for `h=0.05`.

h = 0.05

The Euler's method gives:

y_1(predicted) = y_0 + h*(x_0^2 + y_0^2) = 0 + 0.05*(0^2 + 0^2) = 0

The Runge-Kutta method of order 4 gives:

y_1 = y_0 + (1/6)*(k1 + 2*k2 + 2*k3 + k4)

where k1 = h*f(x_0,y_0) = 0

k2 = h*f(x_0 + h/2, y_0 + k1/2) = h*f(0.025, 0) = 0.000313

k3 = h*f(x_0 + h/2, y_0 + k2/2) = h*f(0.025, 0.000156) = 0.000312

k4 = h*f(x_0 + h, y_0 + k3) = h*f(0.05, 0.000156) = 0.001242y_1 = 0 + (1/6)*(0 + 2*0.000313 + 2*0.000312 + 0.001242) = 0.000451

The estimate of the accuracy of the result of the first calculation is given by the difference between the two results obtained using `h=0.01` and `h=0.05`. This is:

y_1(h=0.05) - y_1(h=0.01) = 0.000451 - 0.0000583 = 0.0003927

Therefore, we can estimate the accuracy of the result of the first calculation to be approximately `0.0003927`.

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To sketch an approximate solution curve passing through specific points, integrate the differential equation numerically Euler's method, Runge-Kutta methods, or solve the equation analytically if possible

To generate a direction field for a given differential equation using computer software, you can use mathematical software packages such as MATLAB, Python with libraries like NumPy and Matplotlib, or dedicated software like Wolfram Mathematica. Here, I will explain the general procedure using Python and Matplotlib.

Define the differential equation: Write down the differential equation you want to work with. For example, let's say we have a first-order ordinary differential equation dy/dx = x - y.

Import the necessary libraries: In Python, you'll need to import the required libraries, such as NumPy and Matplotlib. You can do this with the following code:

python

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import numpy as np

import matplotlib.pyplot as plt

Define the direction field function: Create a Python function that calculates the slope at each point (x, y) based on the given differential equation. For our example equation dy/dx = x - y, the function can be defined as follows:

python

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def direction_field(x, y):

   return x - y

Generate a grid of points: Define the range of x and y values over which you want to generate the direction field. Create a mesh grid using NumPy's meshgrid function to generate a grid of points (x, y). For example:

python

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x = np.linspace(-5, 5, 20)

y = np.linspace(-5, 5, 20)

X, Y = np.meshgrid(x, y)

Calculate the slopes: Use the direction_field function to calculate the slopes (dy/dx) at each point in the grid. Store the result in a variable, such as dy_dx:

python

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dy_dx = direction_field(X, Y)

Plot the direction field: Use Matplotlib's quiver function to plot the direction field. This function creates arrows at each point (x, y) in the grid, indicating the direction of the slope (dy/dx). Here's an example:

python

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plt.figure(figsize=(8, 8))

plt.quiver(X, Y, np.ones_like(dy_dx), dy_dx)

plt.xlabel('x')

plt.ylabel('y')

plt.title('Direction Field')

plt.grid(True)

plt.show()

This code will display the direction field for the given differential equation.

To sketch an approximate solution curve passing through specific points, you can integrate the differential equation numerically using numerical integration methods such as Euler's method, Runge-Kutta methods, or solve the equation analytically if possible. Once you have the solution, you can plot it on top of the direction field using Matplotlib to compare it with the given points.

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Answer:

scale factor = 20%

Step-by-step explanation:

A scale factor is a multiplier which can be used to determine the rate between two or more dimensions.

In the given figure, the sides of the original triangle is 5 times greater than that of the copy. Thus,

the scale factor = [tex]\frac{copy length}{original length}[/tex] x 100%

                          = [tex]\frac{1}{5}[/tex] x 100%

                          = 20%

The scale factor of the original triangle to its copy is 20%. This implies that the dimensions of the original triangle are multiplied by 20% to determine that of the corresponding copy triangle. Thus, we scale down the dimensions of the original triangle by 20%.

The 90% confidence level means that we are 90% confident that the true median household income for Arizona falls within the given interval.

In statistical analysis, a confidence interval provides a range of values within which the true population parameter is likely to lie. In this case, the 90% confidence interval for the 2015 median household income in Arizona is stated as $51,492 ± $431.

Interpreting this confidence interval, we can say that we are 90% confident that the true median household income for Arizona in 2015 falls between $51,061 ($51,492 - $431) and $51,923 ($51,492 + $431). This means that if we were to take multiple samples and calculate their respective confidence intervals, approximately 90% of these intervals would contain the true median household income.

The confidence level represents the degree of certainty associated with the interval. In this case, a confidence level of 90% means that there is a 90% probability that the true median household income lies within the given range. It indicates a high level of confidence but allows for a 10% chance of the true value falling outside the interval.

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The phrases used to describe an upper-tail test are "is greater than," "is bigger than," "is above," and "is not equal to." and the phrases used to describe a two-tail test are "is not the same as," "is different from," and "is not equal to."

a) The hypothesis testing process is based on the assumption that the null hypothesis is true, which means that there is no significant difference between the observed and expected data. The alternative hypothesis is a statement that contradicts the null hypothesis and suggests that the observed data is different from the expected data. A hypothesis test involves testing the null hypothesis against the alternative hypothesis using a test statistic and a significance level. The significance level is the probability of rejecting the null hypothesis when it is true. If the p-value is less than the significance level, then we reject the null hypothesis and accept the alternative hypothesis. Otherwise, we fail to reject the null hypothesis.

b) The phrases used to describe an upper-tail test are "is greater than," "is bigger than," "is above," and "is not equal to." An upper-tail test is a one-tailed test that is used to determine if the sample mean is significantly greater than the population mean. The null hypothesis for an upper-tail test is that the population mean is less than or equal to the sample mean. The alternative hypothesis is that the population mean is greater than the sample mean.

c) The phrases used to describe a two-tail test are "is not the same as," "is different from," and "is not equal to." A two-tail test is used to determine if the sample mean is significantly different from the population mean. The null hypothesis for a two-tail test is that the population mean is equal to the sample mean. The alternative hypothesis is that the population mean is not equal to the sample mean.

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Answer:

4 months

Step-by-step explanation:

Equation:

y = 55 + 30x

y = gym membership

x = per month

Work:

y = 55 + 30x

175 = 55 + 30x

30x = 175 - 55

30x = 120

x = 4

4 months

Answer:

○ B) m∠XOA = m∠OYC

Step-by-step explanation:

Since Point O is circumcentered into the square, we keep it in the midst when we are making comparisons, and since this was simple to identify, this automatically is a false statement. Point O stays in the midst at all times.

- Hope this helps!

Answer:

your answer is 1/2 hope this helped have a good day :3

Step-by-step explanation:

Answer:

1/2 or 0.5

Step-by-step explanation:

5/3 + (- 7/6)​ = 5/3 - 7/6

Answer:

1,8,27,64,125

Step-by-step explanation:

Answer:

Any number raised to zero is one.

Examples:

[tex]4^{0} = 1\\\\178^0=1\\\\15^0 = 1[/tex]

Any number raised to a negative integer is 1 over that exponential term without the negative exponent.

Examples:

[tex]5^{-1} = \frac{1}{5}\\\\324^{-13}=\frac{1}{324^{13}}\\\\17^{-6} = \frac{1}{17^{6}}[/tex]

According to the question You have two coins, a blue and a red one. You choose one of the coins at random, each being chosen with probability are as follows :

(a) To compute P(H1), we need to consider the probability of getting heads on the first toss. There are two scenarios: either we picked the blue coin and got heads, or we picked the red coin and got heads.

[tex]\(P(H1) = P(H1 \cap B) + P(H1 \cap \bar{B}) = P(H1 | B)P(B) + P(H1 | \bar{B})P(\bar{B}) = 0.8 \times \frac{1}{2} + 0.2 \times \frac{1}{2} = 0.4 + 0.1 = 0.5\)[/tex]

(b) To compute P(H2), we can use the same reasoning as in part (a), considering the probability of getting heads on the second toss.

[tex]\(P(H2) = P(H2 \cap B) + P(H2 \cap \bar{B}) = P(H2 | B)P(B) + P(H2 | \bar{B})P(\bar{B}) = 0.8 \times \frac{1}{2} + 0.2 \times \frac{1}{2} = 0.4 + 0.1 = 0.5\)[/tex]

(c) To compute P(H1 ∩ B), we consider the probability of getting heads on the first toss given that we picked the blue coin.

[tex]\(P(H1 \cap B) = 0.8 \times \frac{1}{2} = 0.4\)[/tex]

(d) To compute P(H1 ∩ H2), we need to consider the probability of getting heads on both the first and second toss.

[tex]\(P(H1 \cap H2) = P(H1)P(H2 | H1)\)[/tex]

(e) The independence of events H1 and H2 can be determined by comparing [tex]\(P(H2 | H1)\)[/tex]  and [tex]\(P(H2)\)[/tex] .

(f) To compute P(H2 | H1), we use the formula for conditional probability:

[tex]\(P(H2 | H1) = \frac{P(H1 \cap H2)}{P(H1)} = \frac{0.4}{0.5} = 0.8\)[/tex]

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a. The function in graph q is classified as an odd function, as f(-x) = -f(x).

b. The function in graph q is classified as an even function, as f(-x) = f(x).

c. The function [tex]y = 3^x - 4[/tex] is classified as neither an odd function nor an even function.

For the third function, [tex]y = 3^x - 4[/tex], we have that:

No relation between f(1) and f(-1), hence the function is neither even nor odd.

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Given that IQ is normally distributed with a mean of 100 and a standard deviation of 15 and the sample size, n=16

We need to find the probability that a randomly selected sample has a mean greater than 105.

The z-score is given by;[tex]z = \frac{\bar{x}-\mu}{\frac{\sigma}{\sqrt{n}}}\[/tex]

The sample mean of size n=16 is [tex]\bar{x}=105[/tex], the population mean is [tex]\mu=100[/tex] and the standard deviation is [tex]\sigma=15[/tex]. Now we need to find the probability that the z-score is greater than z. Since the sample size is greater than 30, we can use the z-distribution to approximate the standard normal distribution. Since the sample size is n=16 which is less than 30, we cannot use the z-distribution to approximate the standard normal distribution. Instead, we use the t-distribution, which is a set of distributions that are similar to the standard normal distribution but are dependent on the sample size. The formula for the t-score is given by;

[tex]t=\frac{\bar{x}-\mu}{\frac{s}{\sqrt{n}}}\[/tex]Where [tex]s[/tex] is the sample standard deviation. Since the population standard deviation is unknown, we use the sample standard deviation instead.

The sample mean of size n=16 is [tex]\bar{x}=105[/tex],

the population mean is [tex]\mu=100[/tex],

the sample standard deviation is [tex]s=\frac{\sigma}{\sqrt{n}}=\frac{15}{\sqrt{16}}=3.75[/tex].

The t-score is given by[tex]t=\frac{105-100}{\frac{3.75}{\sqrt{16}}}=4.267\[/tex]

The degrees of freedom is given by n-1 = 16-1 = 15. Using the t-table with 15 degrees of freedom and a two-tailed test at the 0.05 level of significance, the critical value is 2.131. Since the t-score is greater than the critical value, we reject the null hypothesis and conclude that there is sufficient evidence to suggest that the sample mean is greater than the population mean. The p-value is the area under the curve to the right of the t-score. We find the p-value using the t-table. The p-value is 0.0002. Hence, the probability that a randomly selected sample has a mean greater than 105 is 0.0002 (or 0.02%). Therefore, the option with the correct answer is 0.0002.

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Answer:

k = 4

Step-by-step explanation:

11 - 7 = 4

Answer:

Circumference ~ 106.8

Step-by-step explanation:

Use the distance formula to find the radius of the circle with the two coordinates given. The radius of this circle is 17. Plug 17 into the formula 2(pi)r to find the Circumference. The answer is 106.8 rounded to the nearest tenth.

Answer:

d

Step-by-step explanation:

i did the math

a) The derivative of a constant times a constant is zero, so the derivative of da ([8] c*) with respect to c* is zero. b) there is no value of k that satisfies this equation. c) k = 2/e

a. The derivative of a constant times a constant is zero, so the derivative of da ([8] c*) with respect to c* is zero.

b. To find a value of k such that ekt is a solution to a = -4, we substitute ekt into the equation:

a = -4

ekt = -4

Since ekt is always positive, there is no value of k that satisfies this equation.

c. To find a value of k such that ekt is a solution to ' = 2, we substitute ekt into the equation:

' = 2

d(ekt)/dt = 2

Differentiating ekt with respect to t, we get:

kekt = 2

Dividing both sides by ek, we have:

k = 2/e

d. The general solution for the system of differential equations in the form x₁(t) = ? and x₂(t) = ? can be obtained by solving the system using the initial conditions and finding the values of the arbitrary constants A and B.

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Answer:

1/2

Step-by-step explanation:

The total number of marbles in the jar is equal to 6

Since we have 3 purple marbles and 3 green marbles,

The probability of picking a purple marble =

Number of purple marbles in the jar / total number of marbles

= 3/6

= 1/2

From this calculation the probability of picking a purple marble is 1/2 or 0.5

Answer:

39.4 divided by (-7.2)= -5.472

6.7-39.31=-32.61

Step-by-step explanation:

If Sº f(a)dz f(x)dx = 35 35 and o [*p12 g(x)dx = 12, find [3f(x) + 59(2)]da. The value of indefinite integral [3f(x) + 59(2)]da If Sº f(a)dz f(x)dx = 35 35 and o [*p12 g(x)dx = 12 is 223.

We are given the following conditions:

Sº f(a)dz f(x)dx = 35

35o [*p12 g(x)dx = 12

First, we need to evaluate the indefinite integral.

Hence, integrating (x² + x + 17)34c + x² with respect to x, we get,

x³/3 + 17x² + 34cx + x³/3 + C= (2/3) x³ + 17x² + 34cx + C

To find [3f(x) + 59(2)]da,

we need to integrate the same with respect to a.

[3f(x) + 59(2)]da= 3Sº

f(x)da + 59(2)a= 3Sº f(a)dz f(x)dx + 118

Therefore,[3f(x) + 59(2)]da= 3 × 35 + 118= 223

Therefore, [3f(x) + 59(2)]da= 223.

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A.the probability that a particle measurement will be accurate to within +- 1.0 cm is  0.25 B. the mean of the measurement errors is 1 cm. C.  the standard deviation of the measurement errors is 2.311 cm.

a. For a uniform distribution of -3.0 to + 5.0 cm, the total distance is 5.0 - (-3.0) = 8.0 cm.

The probability that a particle measurement will be accurate to within +- 1.0 cm is given by:

P(-1.0 ≤ X ≤ 1.0) = (1/(8.0)) * (1.0 - (-1.0))= (1/8.0) * 2= 0.25

b. Find the mean of the measurement errors

The formula to calculate the mean (μ) of the measurement errors is:μ = (a + b) / 2

where, a is the lower limit of the distribution, and b is the upper limit of the distribution.μ = (-3.0 + 5.0) / 2= 2 / 2= 1

Therefore, the mean of the measurement errors is 1 cm.

c. Find the standard deviation of the measurement errors

The formula to calculate the standard deviation (σ) of a uniform distribution is:σ = (b - a) / √12

where, a is the lower limit of the distribution, b is the upper limit of the distribution, and √12 is the square root of 12.σ = (5.0 - (-3.0)) / √12= 8 / 3.464= 2.311

Therefore, the standard deviation of the measurement errors is 2.311 cm.

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Step-by-step explanation:

Let's find the difference between 7508 and the other numbers.

A. 5706 - 7508 = -1802

B. 6993 - 7508 = -515

C. 8108 - 7508 = 600

D. 8522 - 7508 = 1014

E. 1050 - 7508 = -6458

If we do not take +ve / -ve into account,

the closest number to 7508 = the smallest difference

which in this case, is B, 6993.

Answer:

B. 6993

Step-by-step explanation:

hope this helps :)

The system has infinitely many solutions.

The given augmented matrix represents a system of linear equations. To find the reduced row-echelon form, we perform row operations to transform the matrix into a triangular form with leading entries (1s) and zeros below each leading entry. Let's denote the variables as x, y, and z.

After performing the necessary row operations, we arrive at the reduced row-echelon form:

From the reduced row-echelon form, we can deduce the following equations:

These equations reveal that the system contains infinitely many solutions. By assigning a parameter, such as z = t, where t represents any real number, we can express the solutions in terms of the parameter. Thus, the coordinates of the solution are given by:

In this case, there is an infinite number of possible solutions, and each solution is represented by a unique value of t. Therefore, the main answer is that the system has infinitely many solutions.

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The cost of sugar per pound can be found by dividing the total cost of sugar by the number of pounds purchased. In this case, the correct option is C).

To calculate the cost of sugar per pound, we divide the total cost by the number of pounds purchased. In this case, Jamie bought 4 pounds of sugar for $2.56. Therefore, the cost per pound is given by:

Cost per pound = Total cost / Number of pounds

Cost per pound = $2.56 / 4 pounds

Simplifying this calculation, we find:

Cost per pound = $0.64

Hence, the cost of sugar per pound is $0.64, which corresponds to option c. $0.64 in the given choices. This means that Jamie paid $0.64 for each pound of sugar purchased.

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Answer:

(a)

Step-by-step explanation:

Given

See attachment for sets A and B

Required

The true statement about both sets

First, we calculate the typical values (mean) of set A and set B.

This is calculated as:

[tex]Mean = \frac{\sum fx}{\sum f}[/tex]

For A:

[tex]A= \frac{0*1+2*1+5*1+6*1+7*1}{1+1+1+1+1}[/tex]

[tex]A= \frac{20}{5}[/tex]

[tex]A =4[/tex]

For B:

[tex]B = \frac{7 * 1 + 8 *1 + 9 * 2 + 10 * 1}{1+1+2+1}[/tex]

[tex]B = \frac{43}{5}[/tex]

[tex]B = 8.6[/tex]

Here, we can conclude that B has a larger typical value

Next calculate the spread (range) of sets A and B

This is calculated as:

[tex]Range = Highest -Least[/tex]

For A:

[tex]A = 7 - 0[/tex]

[tex]A = 7[/tex]

For B

[tex]B=10 - 7[/tex]

[tex]B = 3[/tex]

Here, we can conclude that A has a larger spread.

Hence, (a) is true

Answer:

The answer should be B

[tex]3c \: + \: 43 \: \geqslant \: 100[/tex]

To determine whether the set W is a vector space, we need to check if it satisfies the properties of a vector space. In this case, W represents the set of all vectors of the form:

W = {(a, b, -2a + 3b) | a, b ∈ ℝ}

To show that W is a vector space, we need to demonstrate that it is closed under vector addition and scalar multiplication, and that it contains the zero vector. Let's verify each of these properties.

Consider two arbitrary vectors in W, (a₁, b₁, -2a₁ + 3b₁) and (a₂, b₂, -2a₂ + 3b₂). Their sum is given by:

(a₁, b₁, -2a₁ + 3b₁) + (a₂, b₂, -2a₂ + 3b₂) = (a₁ + a₂, b₁ + b₂, -2(a₁ + a₂) + 3(b₁ + b₂))

We can rewrite the last expression as:

(a₁ + a₂, b₁ + b₂, -2a₁ - 2a₂ + 3b₁ + 3b₂) = (a₁ + a₂, b₁ + b₂, -2(a₁ + a₂) + 3(b₁ + b₂))

This shows that the sum of two arbitrary vectors in W is also in W. Therefore, W is closed under vector addition.

Consider an arbitrary vector in W, (a, b, -2a + 3b), and a scalar c ∈ ℝ. The scalar multiple of this vector is given by:

c(a, b, -2a + 3b) = (ca, cb, c(-2a + 3b)) = (ca, cb, -2ca + 3cb)

This expression can be rewritten as:

(ca, cb, -2(ca) + 3(cb))

Thus, the scalar multiple of an arbitrary vector in W is also in W. Therefore, W is closed under scalar multiplication.

To check if W contains the zero vector, we need to find values of a and b that make the expression (-2a + 3b) equal to zero. If we set a = 0 and b = 0, then (-2a + 3b) = (-2(0) + 3(0)) = 0. Thus, the zero vector (0, 0, 0) is in W.

Since W satisfies all the properties of a vector space, we can conclude that W is indeed a vector space.

To find a set S that spans W, we can choose two arbitrary vectors that are linearly independent. One possible set is:

S = {(1, 0, -2), (0, 1, 3)}

These vectors can be expressed in the form of W:

(1, 0, -2) = (a, b, -2a + 3b) when a = 1 and b = 0

(0, 1, 3) = (a, b, -2a + 3b) when a = 0 and b = 1

Any vector in W can be represented as a linear combination of these two vectors, which demonstrates that S spans W.

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