Find the area of the figure.

When a person walking [tex] \frac{1}{5}[/tex] mile in [tex] \frac{1}{15}[/tex] hour, then the speed ( distance travelled per unit time) of person is equals to the 3 miles per hour.

The speed is defined as the rate at which a particular object covers a certain amount of distance. This speed can be fast or slow. That is simply defined as the rate of change in distance per unit of time. It is defined as Speed=[tex]\frac{d}{t }[/tex]

We have a person who is walks along the road. The distance travelled by person =

[tex] \frac{1}{5}[/tex] mile

Time taken by him to travel the distance 1/5 mile [tex]= \frac{ 1}{15}[/tex] hours.

We have to determine the persons speed per hour. Using the speed formula, now the speed of person, [tex]s = \frac{ \frac{ 1}{5} }{\frac{1}{15} }[/tex]

= 3 miles/hour

Hence, required value of speed is 3 miles per hour.

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The z-score of the norminal distribution is 10.14.

Z-score is a statistical measurement that describes a value's relationship to the mean of a group of values.

To calculate the z-score of the norminal distribution, we use the formula below

Formula:

Where:

From the question,

Given:

Substitute these values into equation 1

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Answer:

The cost before rebates is: $69.95 + $49.95 = $119.90

The total rebate amount is: $20 + $30 = $50

The cost of two envelopes is: 2 x $0.20 = $0.40

The cost of two stamps is: 2 x $0.394 = $0.788

The total cost after rebates and including expenses is: $119.90 - $50 + $0.40 + $0.788 = $70.078

Rounding to two decimal places, the total cost after rebates and including expenses is $70.08

The actual rebate after expenses is: $50 - $0.40 - $0.788 = $48.812

Rounding to two decimal places, the actual rebate after expenses is $48.81

The mean of this distribution is (a+b)/2 = $500, and the standard deviation is (b-a)/sqrt(12) = $288.68. The probability of winning more than $600 on one spin of the wheel is the area under the uniform distribution curve from $600 to $1000, which is (1000-600)/(1000-0) = 0.4 or 40%.

In a uniform distribution, all possible outcomes have an equal probability of occurring, and the range of values is defined by the minimum value (a) and the maximum value (b). In this case, the range is from $0 to $1000, so a=0 and b=1000.

The mean of a uniform distribution is the average of the minimum and maximum values, which is (a+ b)/2 = $500. The standard deviation of a uniform distribution is calculated using the formula (b-a)/sqrt(12), which gives a value of $288.68 for this distribution.

To find the probability of winning more than $600, we need to calculate the area under the uniform distribution curve from $600 to $1000. Since the total area under the curve is 1, we can calculate the probability by dividing the width of the interval by the total width of the distribution, which is (1000-600)/(1000-0) = 0.4 or 40%.

Therefore, the probability of winning more than $600 on one spin of the wheel is 0.4.

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The standardized residuals are -0.11, 0.75, -0.38, 2.08, and 2.16, respectively.

(a) The estimated regression equation can be found by first calculating the sample means and sample standard deviations for both x and y, as well as the sample correlation coefficient, and then using these values to calculate the slope and intercept of the regression line:

x = (6 + 11 + 15 + 18 + 20)/5 = 14

y = (7 + 7 + 13 + 21 + 30)/5 = 15.6

sx = sqrt(((6-14)^2 + (11-14)^2 + (15-14)^2 + (18-14)^2 + (20-14)^2)/4) = 4.49

sy = sqrt(((7-15.6)^2 + (7-15.6)^2 + (13-15.6)^2 + (21-15.6)^2 + (30-15.6)^2)/4) = 8.25

r = ((6-14)(7-15.6) + (11-14)(7-15.6) + (15-14)(13-15.6) + (18-14)(21-15.6) + (20-14)(30-15.6))/4sxsy

= 0.962

b = r(sy/sx) = 1.71

a = y - b(x) = -9.23

Therefore, the estimated regression equation is y = -9.23 + 1.71x.

(b) The residuals can be found by subtracting the predicted values of y (based on the regression line) from the actual values of y:

xi

yi

y

Residuals

6

7

0.09

-0.09

11

7

6.38

0.62

15

13

13.31

-0.31

18

21

19.29

1.71

20

30

23.57

6.43

(c) The standardized residuals can be found by dividing each residual by its estimated standard deviation:

xi

yi

ŷ

Residuals

Standardized Residuals

6

7

0.09

-0.09

-0.11

11

7

6.38

0.62

0.75

15

13

13.31

-0.31

-0.38

18

21

19.29

1.71

2.08

20

30

23.57

6.43

2.16

The estimated standard deviation of the residuals can be found by calculating the root mean squared error (RMSE):

RMSE = sqrt(((-0.09)^2 + (0.62)^2 + (-0.31)^2 + (1.71)^2 + (6.43)^2)/4) = 3.04

Therefore, the standardized residuals are -0.11, 0.75, -0.38, 2.08, and 2.16, respectively.

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The given absolute value function is f(x) = |x - 3x|.

To evaluate absolute value function f(-2), we substitute -2 in place of x:

f(-2) = |-2 - 3(-2)|

= |-2 + 6|

= |4|

= 4

Therefore, f(-2) = 4.

To evaluate f(0), we substitute 0 in place of x:

f(0) = |0 - 3(0)|

= |0|

= 0

Therefore, f(0) = 0.

To evaluate f(2), we substitute 2 in place of x:

f(2) = |2 - 3(2)|

= |-4|

= 4

Therefore, f(2) = 4.

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The graph of the function g(x) = −|x + 3| − 2 is added as an attachment

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

g(x) = −|x + 3| − 2

The above expression is an absolute value function that hs the following properties

Next, we plot the graph

See attachment for the graph of the function

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We can say with 95% confidence that the true population mean of the growing season is between 176.3 and 212.9 days.

We can use a t-distribution since the population standard deviation is unknown and the sample size is small (n < 30).

The formula for a confidence interval with a t-distribution is:

CI = x ± tα/2 * (s/√n)

Where:

x = sample mean

s = sample standard deviation

n = sample size

tα/2 = t-value with degrees of freedom (df = n-1) and α/2 level of significance

Using the given information, we have:

x = 194.6

s = 55.6

n = 32

df = n-1 = 31

α/2 = 0.05/2 = 0.025 (since it's a 95% confidence interval)

We can find the t-value using a t-distribution table or a calculator. For df = 31 and α/2 = 0.025, we get:

tα/2 = 2.0395

Substituting the values into the formula, we get:

CI = 194.6 ± 2.0395 * (55.6/√32)

CI = (176.3, 212.9)

Therefore, we can say with 95% confidence that the true population mean of the growing season is between 176.3 and 212.9 days.

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The standard deviation of the population is 8.

We have,

The mean of the sampling distribution is the mean of the population, so we have:

[tex]$\bar{x} = \frac{1}{n}\sum_{i=1}^{n}x_i = \mu$[/tex]

where [tex]$\bar{x}$[/tex] is the sample mean, n is the sample size, [tex]x_1[/tex] are the individual samples, and [tex]$\mu$[/tex] is the population mean.

In this case,

We are given the sample size n = 100 and the sample mean [tex]\bar{x}[/tex]

We also know that the sampling distribution comes from a normally distributed population.

The standard error of the mean.

[tex]$SE = \frac{s}{\sqrt{n}}$[/tex]

where s is the sample standard deviation.

The standard error of the mean represents the standard deviation of the sampling distribution.

In this case,

We don't have the sample standard deviation s, but we can estimate it using the sample variance:

[tex]$s^2 = \frac{1}{n-1}\sum_{i=1}^{n}(x_i - \bar{x})^2$[/tex]

Substituting the values we have:

[tex]$s^2 = \frac{1}{99}\sum_{i=1}^{100}(x_i - 145)^2 = 64$[/tex]

Therefore:

[tex]$s = \sqrt{64} = 8$[/tex]

Substituting this value into the standard error equation:

[tex]$SE = \frac{s}{\sqrt{n}} = \frac{8}{\sqrt{100}} = 0.8$[/tex]

The standard deviation of the population is related to the standard error by the following equation:

[tex]$SE = \frac{\sigma}{\sqrt{n}}$[/tex]

Rearranging this equation, we get:

[tex]$\sigma = SE \times \sqrt{n} = 0.8 \times \sqrt{100} = 8$[/tex]

Therefore,

The standard deviation of the population is 8.

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For a matrix, [tex]A = \begin{bmatrix} 3 & 2& 4 \\ 1& -2& 3\\ 2 &3 &2 \end{bmatrix}\\[/tex]

a) The value of det(M₂₁), det(M₂₂), and det(M₂₃) are -8, -2 and 5 respectively.

b) The values of cofactors of matrix A, A₂₁, A₂₂ and A₂₃ are 8, -2 and -5 respectively.

c) The computed value of determinant, det(A) is equals to -3.

Matrix is a set of elements arranged in rows and columns in order to form a rectangular array. We have a matrix A, defined as [tex]A = \begin{bmatrix} 3 & 2& 4 \\ 1& -2& 3\\ 2 &3 &2 \end{bmatrix}\\[/tex]

We have to determine the following values :

a) The minor of matrix is exist for each element of matrix and is equal to the part of the matrix remained after removing the row and the column containing. First we determine the value Minors and then their determinant. [tex]M_{21} = \begin{bmatrix} 2& 4 \\ 3& 2\\ \end{bmatrix}\\[/tex]

det(M₂₁ ) = | M₂₁ | = 2× 2 - 4× 3 = - 8

[tex]M_{22} = \begin{bmatrix} 3& 4 \\ 2& 2\\ \end{bmatrix}\\[/tex]

det(M₂₂) = | M₂₂| = 2× 3 - 4× 2 = - 2

[tex]M_{23} = \begin{bmatrix} 3& 2 \\ 2& 3\\ \end{bmatrix}\\[/tex]

det(M₂₃ ) = | M₂₃ | = 3×3 - 2×2= 5

b) The value of cofactors of matrix A are A₂₁ = (-1)²⁺¹ M₂₁

= -(-8) = 8

A₂₂ = (-1)²⁺² M₂₂ = -2

A₂₃ = (-)²⁺³ M₂₃ = -5

c) Now, we determine the value of determinant of matrix A from part (b).

det(A) = a₂₁ A₂₁ + a₂₂ A₂₂ + a₂₃ A₂₃

= 1× 8 - 2 (-2) + 3( -5)

= -3

Hence, required value is -3.

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Answer:

Original Money Earned: $10,000

To increase this by 5 percent, we need to multiply $10,000 by 0.05 (5%).

$10,000 x 0.05 = $500

Allison makes $500 (5% of $10,000) per month, so you would add that to the sum of your answer after every previous month.

Now, let's add that.

Feb : $10,000 + 500 = $10,500

Mar : $10,500 + 500 = $11,000

Apr : $11,000 + 500 = $11,500

May : $11,500 + 500 = $12,000

Jun : $12,000 + 500 = $12,500

Jul : $12,500 + 500 = $13,000

Aug : $13,000 + 500 = $13,500

Sep : $13,500 + 500 = $14,000

Oct : $14,000 + 500 = $14,500

Nov : $14,500 + 500 = $15,000

Dec : $15,000 + 500 = $15,500

Allison can fill a series with a trend function in excel to calculate monthly income.

To calculate Allison's monthly income from February to December using Excel, you can use the fill series with a trend function.

1. Open a new Excel spreadsheet.
2. In cell A1, type "January" and in cell B1, type "$10,000" (without quotes) as Allison's January income.
3. In cell A2, type "February".
4. In cell B2, type the formula "=B1*1.05" (without quotes). This formula calculates the income for February by increasing January's income by 5 percent.
5. Click on cell B2 to select it, then move your cursor to the bottom right corner of the cell until the cursor changes into a small black cross.
6. Click and hold the left mouse button, then drag the cursor down to cell B12, which corresponds to December.
7. Release the left mouse button. Excel will fill the series with a trend, calculating the income for each month from February to December.

Hence, Excel is used to calculate Allison's monthly income from February to December, taking into account the expected 5 percent increase per month.

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The max-flow min-cut theorem helps to demonstrate that the maximum number of independent 1's in the matrix H equals the minimum number of lines in a cover.

To show that the maximum number of independent 1's in a 0-1 matrix H with $n_{1}$ rows and $n_{2}$ columns equals the minimum number of lines in a cover, we can use the max-flow min-cut theorem on an appropriately defined network.

First, create a bipartite graph G, where one set of vertices represents the rows and the other set represents the columns of the matrix H. Connect an edge between a row vertex and a column vertex if there is a 1 in the corresponding entry of the matrix H.

Next, add a source vertex s connected to all row vertices and a sink vertex t connected to all column vertices. Assign capacities of 1 to all edges in the network.

Now, apply the max-flow min-cut theorem on this network. The maximum flow in the network represents the maximum number of independent 1's in the matrix H. The minimum cut corresponds to the minimum number of lines in a cover of H, as it separates the source and sink while minimizing the number of crossing edges.

Hence, the max-flow min-cut theorem helps to demonstrate that the maximum number of independent 1's in the matrix H equals the minimum number of lines in a cover.

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True, the outer loop in each of the three sorting algorithms (e.g., Bubble Sort, Selection Sort, Insertion Sort) is responsible for ensuring the number of passes required are completed. It iterates through the entire list to make sure the sorting process is executed correctly.

An algorithm is a standard formula or set of instructions that has a finite number of steps or instructions for using a computer to solve a problem.

The time complexity is a measurement of how long an algorithm takes to run until it completes the task in relation to the size of the input.

Flowcharts can be used to illustrate algorithms, an algorithm for a process or workflow can be graphically represented in a flowchart.

Therefore, an algorithm is a collection of guidelines for resolving a dilemma or carrying out a task.

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The transition probability matrix is:

[tex]\left[\begin{array}{cccc}0.5&0.1&0.2&0.2\\0.75&0&0.6 &0.3\\0.1&0.5&0 &0.1\\0.05&0.1&0.25&0\end{array}\right][/tex]

What is matrix?

A matrix is a rectangular array of numbers or other mathematical objects for which operations such as addition, subtraction, multiplication, and scalar multiplication are defined.

The Markov chain for housing movements can be formulated as follows:

State 1: Apartment

State 2: Condominium

State 3: Own House

State 4: Rented House

The transition probability matrix P is given by:

[tex]\left[\begin{array}{cccc}P_{11}&P_{12}&P_{13} &P_{14}\\P_{21}&P_{22}&P_{23} &P_{24}\\P_{31}&P_{32}&P_{33} &P_{34}\\P_{41}&P_{42}&P_{43} &P_{44}\end{array}\right][/tex]

where [tex]$P_{ij}$[/tex] is the probability of moving from state i to state j. To calculate these probabilities, we need to use the data from the survey.

For example, [tex]$P_{12}$[/tex] is the probability of moving from an apartment to a condominium. From the survey data, we can see that out of 200 apartment dwellers, 100 moved to another apartment, 20 moved to a condominium, 40 moved to their own house, and 40 moved to a rented house. Therefore, [tex]$P_{12} = 20/200 = 0.1$[/tex].

Similarly, we can calculate the other transition probabilities:

[tex]P_{13} = 40/200 = 0.2$\\$P_{14} = 40/200 = 0.2$\\$P_{21} = 150/200 = 0.75$\\$P_{23} = 120/200 = 0.6$\\$P_{24} = 60/200 = 0.3$\\$P_{31} = 20/200 = 0.1$\\$P_{32} = 100/200 = 0.5$\\$P_{34} = 20/200 = 0.1$\\$P_{41} = 10/200 = 0.05$\\$P_{42} = 20/200 = 0.1$\\$P_{43} = 50/200 = 0.25$[/tex]

Therefore, the transition probability matrix is:

[tex]\left[\begin{array}{cccc}0.5&0.1&0.2&0.2\\0.75&0&0.6 &0.3\\0.1&0.5&0 &0.1\\0.05&0.1&0.25&0\end{array}\right][/tex]

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Here, we can say with 95% confidence that the annual profit for the new restaurant in Kamloops with the given characteristics will fall between $1,065,200 and $1,264,800.

Based on the given characteristics of the new restaurant in Kamloops, we can estimate the annual profit by subtracting the total costs from the total revenue generated by the restaurant.
Total revenue = 15,000 covers x $100 per cover = $1,500,000
Total costs = $150k food cost + $85k overhead costs + $100k lab = $335,000
Annual profit = Total revenue - Total costs = $1,500,000 - $335,000 = $1,165,000
To compute the 95% prediction interval for annual profit, we need to consider the variability in the data and assume that the annual profit follows a normal distribution. Assuming a standard deviation of $50,000, the 95% prediction interval can be calculated as follows:  95% prediction interval = $1,165,000 +/- (1.96 x $50,000) = $1,065,200 to $1,264,800
Therefore, we can say with 95% confidence that the annual profit for the new restaurant in Kamloops with the given characteristics will fall between $1,065,200 and $1,264,800.

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The surface area of the composite figure is 1474 square feet.

In the composite figure, there are two shapes rectangular prism and triangular prism.

Surface area of rectangular prism = 2(lb+bh+hl)

= 2(19×9+9×11+11×19)

= 958 square feet

Surface area of triangular prism = (Perimeter of the base × Length of the prism) + (2 × Base Area)

= (S1 +S2 + S3)L + bh

= (13+13+10)×11+10×12

= 516 square feet

Total surface area = 958+516

= 1474 square feet

Therefore, the surface area of the composite figure is 1474 square feet.

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The number is -4. We can solve for it using algebraic equations and multiplication.

The problem states that when we multiply our number by four and add twenty, we get 4. We can represent this relationship using an algebraic equation with the variable x representing our unknown number:

4x + 20 = 4

To solve for x, we need to isolate it on one side of the equation. We start by subtracting 20 from both sides of the equation:

4x = -16

Now, we divide both sides by 4:

x = -4

Therefore, our number is -4. We can check this answer by plugging it back into the original equation:

Simplifying the left side of the equation, we get:

-16 + 20 = 4

4 = 4

This confirms that our answer, x = -4, is correct.

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The printer that has a better rate of cost per page would be = printer B.

For printer A:

The number of pages printed = 100

The cost for the printed pages = $26.99

Cost per page = 26.99/100 = $0.27/page

For printer B:

The number of pages printed = 275

The cost for the printed pages = $67.99

Cost per page =67.99/275

= 0.25

Therefore the printer that has a better rate of cost per page would be = printer B

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To find the volume of Tammy's storage shed, which is in the shape of a rectangular prism, we need to use the formula: Volume = Length × Width × Height.

Given the dimensions of the rectangular prism storage shed are:
Length = 10 feet
Width = 9 feet
Height = 9 feet

We can calculate the volume as follows:

Step 1: Multiply the length, width, and height.
Volume = 10 × 9 × 9

Step 2: Perform the multiplication.
Volume = 810 cubic feet

So, Tammy's storage shed has a volume of 810 cubic feet.

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Based on the given premises, we know that either the butler, maid, or cook committed the crime. If it was the cook, poison was used, and if poison was used, it wasn't done swiftly, but we know that the crime was swift. Therefore, we can conclude that the cook is not the culprit. This leaves us with the butler and the maid. However, there is no information given about the maid that would implicate her in the crime.

On the other hand, the butler is the only suspect left who has not been ruled out by the given premises. Therefore, we can conclude that the butler did it. However, to prove his guilt, we need more information or evidence. The given premises only allow us to eliminate the other suspects and narrow down the list of possible culprits to one.

In order to prove the butler's guilt, we would need additional information or evidence that directly implicates him in the crime. This could come in the form of eyewitness testimony, forensic evidence, or a confession. Without further information, we cannot definitively prove that the butler is guilty.
To prove that the butler is guilty, we can use the given statements and logical reasoning. Here is a step-by-step explanation for the sequent:

1. Either it was the butler or the maid, or it was the cook. [(BvM)vC]
2. If the cook did it, poison was used. [(CvM)-->P]
3. If it was done with poison then it wasn't done swiftly, but it was swift. [(P--> ~S)&S]

From statement 3, we know that it was done swiftly (S), so it can't be poison (~P):
4. ~P (from 3)

Now, since it wasn't poison, it means the cook couldn't have done it (~C):
5. ~C (from 2 and 4, using Modus Tollens)

We're left with either the butler or the maid:
6. BvM (from 1 and 5, using Disjunction Elimination)

Since we know it was done swiftly, and the only available options are the butler and the maid, we can conclude:
7. B (Butler)

So, we must conclude that the butler did it. The butler is guilty based on the given sequent and logical reasoning.

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The value of angle O is 11 degrees

It is important to note that the properties of a triangle are;

From the information given, we have the angles;

m<N = 5x - 8

m<O = x - 5

m<P = 6x + 1

Also, the sum of the angles in a triangle is 180 degrees

Now, substitute the angles

m<O + m<P + m<O = 180

5x - 8 + x - 5 + 6x + 1 = 180

collect the like terms

5x + x + 6x = 180 + 12

add the terms

12x = 192

x = 16

For the angle, m<O = x - 5 = 16 -5 = 11 degrees

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The value of the expectation E[aX1 + bX2 + cX3] is aE[X1] + bE[X2] + cE[X3]

The value of the variance Var[aX1 + bX2 + cX3] is a² Var[X1] + b² Var[X2] + c² Var[X3]

We have,

Using the linearity of expectation and the fact that the variables are independent, we have:

E[aX1 + bX2 + cX3]

= aE[X1] + bE[X2] + cE[X3]

And for the variance, using the fact that the variables are independent and using the property Var[aX] = a^2 Var[X], we have:

Var[aX1 + bX2 + cX3]

= Var[aX1] + Var[bX2] + Var[cX3]

= a^2 Var[X1] + b^2 Var[X2] + c^2 Var[X3]

Note that we have used the fact that the covariance between any two distinct X_i, X_j is zero since they are independent,

i.e., Cov[X_i, X_j] = E[X_iX_j] - E[X_i]E[X_j] = E[X_i]E[X_j] - E[X_i]E[X_j] = 0.

Thus,

The value of the expectation E[aX1 + bX2 + cX3] is aE[X1] + bE[X2] + cE[X3]

The value of the variance Var[aX1 + bX2 + cX3] is a² Var[X1] + b² Var[X2] + c² Var[X3]

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Based on the information, we can see from the line plots that Bus 14 tends to have longer travel times than Bus 18 for most of the data points, except for a few outliers.

In terms of travel time, Bus 14 and Bus 18 each have a median of 16 minutes. As such, it cannot be inferred from this information alone which mode of transportation tends to arrive more rapidly.

Nevertheless, the line plots reveal that Bus 14's journey takes slightly longer than Bus 18's for most of the data points, except for a few outliers.

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a) Counter-example: x=1

When x=1, 7x^3=7 and x^4=1, which means 7x^3>x^4 is not true for all positive integers.

b) Counter-example: x=-2 and y=-3

When x=-2 and y=-3, x^2=4 and y^2=9, which means x^2<y^2, then x^2>y^2 is not true for all real numbers.

c) Counter-example: n=0

When n=0, n^2=0 and n=0, which means n^2=n. Therefore, the statement "If n is an integer, then n^2≠n" is not true for all integers.

a) The conjecture states that if x is a positive integer, then 7x^3>x^4. To find a counter-example, we need to find a value of x that is a positive integer for which this statement is false. Let's try x=1. Substituting this value in the conjecture, we get 7x^3=7 and x^4=1. Therefore, 7x^3>x^4 becomes 7>1, which is false. Thus, we have found a counter-example, and the conjecture is false.

b) The conjecture states that if x and y are real numbers and x>y, then x^2>y^2. To find a counter-example, we need to find two real numbers x and y such that x>y but x^2<y^2. Let's try x=-2 and y=-3. Substituting these values in the conjecture, we get x^2=4 and y^2=9. Therefore, x^2<y^2 becomes 4<9, which is true. But x^2>y^2 becomes 4>9, which is false. Thus, we have found a counter-example, and the conjecture is false.

c) The conjecture states that if n is an integer, then n^2≠n. To find a counter-example, we need to find an integer n for which n^2=n. Let's try n=0. Substituting this value in the conjecture, we get n^2=0 and n=0. Therefore, n^2=n becomes 0=0, which is true. Thus, we have found a counter-example, and the conjecture is false.

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With 99% confidence, it can be said that the population proportion of adults who say they have started paying bills online in the last year is between the endpoints of the given confidence interval.

Part 1:

To construct a 99% confidence interval for the population proportion, we can use the following formula:

CI = P ± z*√(P(1-P)/n)

where P is the sample proportion, z is the z-score corresponding to the desired level of confidence (in this case, 99%), and n is the sample size.

In this case, P = 1446/2728 = 0.5298, z = 2.576 (from a standard normal distribution table), and n = 2728. Plugging these values into the formula, we get:

CI = 0.5298 ± 2.576√(0.5298(1-0.5298)/2728)

CI = (0.5085, 0.5511)

So the 99% confidence interval for the population proportion is (0.5085, 0.5511).

Part 2:

The correct interpretation is A. With 99% confidence, it can be said that the population proportion of adults who say they have started paying bills online in the last year is between the endpoints of the given confidence interval.

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The equations are;

H = 24 + 9x

H = 40 + 5x

In a word problem, there are usually one or more unknown quantities that you need to find. Identify these unknowns and assign them a variable.

We have to know that Tree A was 24 inches tall and Tree B was 40 inches tall. Each year thereafter, Tree A grew by 9 inches per year and Tree B grew by 5 inches per year.

Then for tree A;

H = 24 + 9x

For tree B

H = 40 + 5x

Where x is the number of years that the trees stay.

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