please solve this question step by step with solution
9 7. If Chebyshev's Theorem asserts that P (34 < X < 54) > variable X, then its standard deviation is 25 a) 10 b) 16 c) 8 for a random d) 9

The diameter of the cylindrical pipe is approximately 68 centimeters.

Let's consider the situation described. The pipe touches the wall and the ceiling of the room, and it is supported by a brace. The ends of the brace are 85 centimeters away from the wall and the ceiling.

To determine the diameter of the pipe, we can imagine a right triangle formed by the wall, the ceiling, and the brace. The distance from the wall to the ceiling is the hypotenuse of this triangle, and the ends of the brace are the two legs.

Using the Pythagorean theorem, we can find the length of the hypotenuse:

hypotenuse^2 = leg1^2 + leg2^2

Let's denote the diameter of the pipe as d. Since the ends of the brace are 85 centimeters away from the wall and the ceiling, each leg of the right triangle is half the diameter of the pipe, which is d/2.

Now we can substitute these values into the Pythagorean theorem equation:

85^2 = (d/2)^2 + (d/2)^2

Simplifying the equation:

7225 = 2(d/2)^2

7225 = 2(d^2/4)

14450 = d^2

Taking the square root of both sides:

d = √14450 ≈ 120.21

Therefore, the diameter of the cylindrical pipe is approximately 120.21 centimeters or rounded to the nearest whole number, 120 centimeters.


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Answer:

Step-by-step explanation:

1000g=1kg

xg=95.2kg

x=95200g

Heat of vapourisation=the thermal energy required for vaporization divided by the mass of the substance that is vaporizing.

mass of the substance=95200g

thermal energy=4.8* 107J

Heat of vapourisation= 4.8*10^7j / 95200g = 504.201680672

To 2 decimal places= 504.20J/g

3) ∅ = 53.1°

4.) ∅= 33.6°

5.) ∅ = 22.5°

6.) ∅ = 30°

A acute angle is defined as the angle that is less than 90. That is angles of 45°,23°,14° and 67° are all less than 90° and therefore a typical example of an acute angle.

3.) When sin∅ = 4/5

Ø = sin-1(0.8)

= 53.1°

4.) when cos∅ = 5/6

∅ = cos-1(0.8333)

= 33.6°

5.) when sec ∅ = √75/8

But sec∅ = 1/cos∅

1/cos∅ = √75/8

make Cos∅ the subject of formula;

cos∅ = 8/√75

= 0.9238

∅ = Cos-1 (0.9238)

= 22.5°

6.) cot ∅ = √3

But cot ∅ = 1/tan∅

tan∅ = 1/√3

= 0.5774

∅ = tan-1(0.5774 )

= 30°

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By using linear approximation we can find The value of Δz is 0.0556 and the value of dz is 0.0406.

What is linear approximation?

Linear approximation, also known as the tangent line approximation or first-order approximation, is a method used to estimate the value of a function near a specific point using the equation of a straight line.

To find the values of Δz and dz, we need to calculate the change in z and the differential of z when the variables x and y change from (2, -1) to (2.04, -0.95).

First, we calculate the change in z (Δz) by subtracting the initial value of z from the final value of z:

Δz = z(final) - z(initial)

Substituting the given values into the expression for z:

z(final) = (2.04)² - (2.04)(-0.95) + 8(-0.95)²

z(initial) = (2)² - (2)(-1) + 8(-1)²

Calculating these values, we find:

z(final) ≈ 4.1616

z(initial) = 5

Therefore, Δz ≈ 4.1616 - 5 ≈ -0.8384 (rounded to four decimal places).

Next, we calculate the differential of z (dz) using partial derivatives:

dz = (∂z/∂x)dx + (∂z/∂y)dy

Taking the partial derivatives of z with respect to x and y:

∂z/∂x = 2x - y

∂z/∂y = -x + 16y

Substituting the given values:

∂z/∂x ≈ 2(2.04) - (-0.95) ≈ 4.08 + 0.95 ≈ 5.03

∂z/∂y ≈ -(2.04) + 16(-0.95) ≈ -2.04 - 15.2 ≈ -17.24

Substituting these values into the expression for dz:

dz ≈ (5.03)dx + (-17.24)dy

Since dx = 2.04 - 2 ≈ 0.04 and dy = -0.95 - (-1) ≈ 0.05, we can calculate dz:

dz ≈ (5.03)(0.04) + (-17.24)(0.05) ≈ 0.2012 - 0.862 ≈ -0.6608 (rounded to four decimal places).

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The probability of getting a not even number is 0.5.

The given outcomes are 3, 4, 5, 6.

We know that, probability of an event = Number of favourable outcomes/Total number of outcomes.

Total number of outcomes = 4

Number of favorable outcomes = 2

Now, P (not even) = 2/4

= 1/2

= 0.5

Therefore, the probability of getting a not even number is 0.5.

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1. It is True The sets of whole numbers, integers, and rational numbers are indeed proper subsets of the set of real numbers. This is because the set of real numbers encompasses all possible numbers, including the subsets mentioned. Whole numbers consist of positive integers including zero, integers include both positive and negative numbers (including zero), and rational numbers include numbers that can be expressed as a fraction of two integers.

2. It is True The set of real numbers does have the closure properties of addition, subtraction, and multiplication. Closure property means that when two real numbers are added, subtracted, or multiplied, the result will also be a real number. For example, if we add two real numbers, the sum will be a real number. Similarly, subtracting or multiplying two real numbers will always yield a real number. The set of real numbers is closed under these operations, which means that the operations can be performed on real numbers without leaving the set.

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The range of the number of books that the friends own is {6,10,50}.

Tiffany and Robert own the same number of books.

The number of books own by both of them are x each.

Joe owns 4 fewer books than Tiffany.

Joe= x-4

Eva owns 5 times as many books as Robert.

Eva =5x

Mean =x+x+x-4+5x/4

=3x-4+5x/4

=8x-4/4

=2x-1

Modal number is x.

2x-1=9+x

x=10

So the range of the number of books that the friends own is {6,10,50}.

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The formula of the function is f(x) = -1/2sin(π(x + 1/2)) + 3 and the graph is attached

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

Midline = (π, 5)

Minimum = (0, 3)

A sinusoidal function is represented as

f(x) = Asin(B(x + C)) + D

Where

Amplitude = A

Period = 2π/B

C = Phase shift

D = Vertical shift

x is in radians.

The minimum is (0, 3)

So, we have

D = 3.5

i.e. f(x) = Asin(B(x + C)) + 3.5

Using the midline, we have

Asin(B(x + C)) + 3.5 = 5

Evaluate the difference

Asin(B(x + C)) = 1.5

Next, we assume values for B and C

This gives

Asin(πx + π/2)) = 1.5

So, we have

Asin(π(x + 1/2)) = 1.5

Set sin(π(x + 1/2)) = -3

So, we have

A = -1/2

This means that the equation is

f(x) = -1/2sin(π(x + 1/2)) + 3

The graph of the function is attached

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The p-value for the t-statistic of 2.13 with 15 degrees of freedom is 0.022. Based on our sample, we have evidence to suggest that the population mean is greater than 100 with a level of significance of 0.05.

In this hypothesis test, we are testing whether the population mean is greater than 100. We are given that the sample size is 16 and the t-statistic is 2.13. To find the p-value, we need to find the area to the right of the t-statistic under the t-distribution curve with 15 degrees of freedom. Using a t-table or calculator, we find that the area is 0.022.

To perform this hypothesis test, we can use the following steps:
1. State the null and alternative hypotheses:
H0: μ = 100
Ha: μ > 100
2. Choose the level of significance α:
Assuming a level of significance of 0.05, which is a common choice, we have α = 0.05.
3. Calculate the test statistic:
We are given that the t-statistic is 2.13.
4. Find the p-value:
To find the p-value, we need to find the area to the right of the t-statistic under the t-distribution curve with 15 degrees of freedom. Using a t-table or calculator, we find that the area is 0.022.
5. Make a decision:
Since the p-value is less than the level of significance, we reject the null hypothesis. We have evidence to suggest that the population mean is greater than 100.

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The statement cross-tabulation with two variables is known as twice-tabulation is false because there is no such term as "twice-tabulation" in the context of statistical analysis.

Cross-tabulation, also known as contingency table analysis, involves the analysis of categorical variables by creating a table that shows the frequency or distribution of one variable based on the levels of another variable.

It allows for the examination of the relationship between two variables, highlighting any associations or patterns that may exist. Each cell in the cross-tabulation table represents the count or proportion of observations that fall into specific combinations of categories from the two variables.

The term "twice-tabulation" does not exist in statistical literature and is not commonly used to refer to cross-tabulation with two variables. The correct term to describe this statistical technique is simply cross-tabulation or contingency table analysis.

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The given statement "In the Klein model, two open chords are interpreted to be 'perpendicular' if and only if they are perpendicular in the usual Euclidean sense" is False because in the Klein model of hyperbolic geometry, perpendicularity is not preserved in the same way as in Euclidean geometry.

In the Klein model, angles and perpendicularity are defined by geodesics, which are curves that minimize distance on the hyperbolic plane. Geodesics in the Klein model are represented by straight lines.

Therefore, two open chords in the Klein model can be interpreted as perpendicular if their corresponding geodesic lines intersect at a right angle on the hyperbolic plane, but this may not correspond to perpendicularity in the usual Euclidean sense. Hence, the given statement is false.

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Matrix-vector multiplication is a linear operation because it satisfies the properties of scalar multiplication and vector addition, which are a(u+v) = au + av and a(cu) = cau, where a is a scalar and u and v are vectors.

Matrix-vector multiplication is a fundamental operation in linear algebra, where a matrix is multiplied by a vector to produce another vector. This operation is considered linear because it adheres to certain properties.

The first property is scalar multiplication, which states that multiplying a vector u by a scalar a and adding it to another vector v (a(u+v)) is equivalent to multiplying u by a (au) and v by a (av) separately and then adding the results (au + av). In other words, the operation distributes over vector addition.

The second property is the distributive property of scalar multiplication, which states that multiplying a vector u by a scalar c and then multiplying the resulting vector (cu) by another scalar a is equivalent to multiplying u by the product of the two scalars (cau). This property allows the scalar multiplication to be distributed over scalar multiplication.

These properties ensure that matrix-vector multiplication preserves the linearity of the underlying vector space. They enable the manipulation and analysis of systems of linear equations, transformations, and other mathematical operations involving matrices and vectors.

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The binomial coefficient (9 3) can be evaluated using the formula (k n) = k(k - 1)(k - 2)(k - 3) ... (k - n + 1)/n!, where k is a real number and n is a positive integer. Applying this formula, we find (9 3) = 9 * 8 * 7 / 3! = 84.

The binomial coefficient (k n) represents the number of ways to choose n items from a set of k distinct items, without considering their order. It can be calculated using the formula (k n) = k(k - 1)(k - 2)(k - 3) ... (k - n + 1)/n!, where n! denotes the factorial of n.

In this case, we are evaluating (9 3), which means choosing 3 items from a set of 9. Applying the formula, we have (9 3) = 9 * 8 * 7 / 3!, where 3! = 3 * 2 * 1 = 6.

Simplifying the expression, we get (9 3) = 9 * 8 * 7 / 6 = 504 / 6 = 84. Therefore, the binomial coefficient (9 3) is equal to 84.

In summary, using the given formula for binomial coefficients, we find that (9 3) is equal to 84, representing the number of ways to choose 3 items from a set of 9.

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In this situation, the investment of $800 at a rate of 4% compounded monthly represents exponential growth.

As, Exponential growth occurs when a quantity increases over time at a constant percentage rate.

Here, the 4% interest rate represents the growth factor or rate of increase. Each month, the investment grows by 4% of its current value.

As time progresses, the investment will continue to grow at an increasing rate due to the compounding effect. The longer the investment remains, the greater the growth becomes.

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The general solution to the differential equation is then given by y(x) = e⁽⁻ˣ⁾(C₁ + 1)cos(2x) + (C₂ + C)sin(2x).

To find the general solution to the differential equation y + 2y' + 5y - 2e⁽⁻ˣ⁾cos(2x), we can use either Variation of Parameters or Reduction of Order.

a. Variation of Parameters:

The first step is to find the general solution to the homogeneous equation y + 2y' + 5y = 0, which is given by

y_h(x) = e⁽⁻ˣ⁾(C₁cos(2x) + C₂sin(2x)).

Next, we need to find the particular solution y_p(x) using the method of Variation of Parameters.

We assume that y_p(x) has the form

y_p(x) = u₁(x)cos(2x) + u₂(x)sin(2x), where u₁(x) and u₂(x) are functions to be determined.

Substituting this into the differential equation and solving for u₁'(x) and u₂'(x), we get the following system of equations:

u₁'(x)cos(2x) + u₂'(x)sin(2x) = 0

-2u₁'(x)sin(2x) + 2u₂'(x)cos(2x) = 2e⁽⁻ˣ⁾cos(2x)

Solving this system of equations, we get

u₁'(x) = -e⁽⁻ˣ⁾ and u₂'(x) = 0.

Integrating these, we get

u₁(x) = e⁽⁻ˣ⁾ and u₂(x) = C.

Therefore, the particular solution is

y_p(x) = e⁽⁻ˣ⁾cos(2x) + Csin(2x).

The general solution to the differential equation is then given by y(x) = y_h(x) + y_p(x) = e⁽⁻ˣ⁾(C₁cos(2x) + C₂sin(2x)) + e⁽⁻ˣ⁾cos(2x) + Csin(2x)

= e⁽⁻ˣ⁾(C₁ + 1)cos(2x) + (C₂ + C)sin(2x).

b. Reduction of Order:

We can use the method of Reduction of Order to find the second linearly independent solution to the homogeneous equation y + 2y' + 5y = 0.

Let y₂(x) = v(x)y₁(x), where y₁(x) = e⁽⁻ˣ⁾cos(2x).

Substituting this into the differential equation and simplifying, we get v''(x) + 3v'(x) + 4v(x) = 0.

This is a first-order linear homogeneous differential equation, which can be solved using the integrating factor e⁽³ˣ/²⁾.

Multiplying both sides by this factor and integrating, we get

v(x) = C₁e⁽⁻³ˣ/²⁾ + C₂e⁽⁻ˣ/²⁾.

Therefore, the general solution to the homogeneous equation is

y_h(x) = e⁽⁻ˣ⁾(C₁cos(2x) + C₂sin(2x)) + e⁽⁻³ˣ/²⁾(C₃cos(2x) + C₄sin(2x)).

To find the particular solution, we assume that y_p(x) has the form

y_p(x) = u(x)e⁽⁻ˣ⁾cos(2x), where u(x) is a function to be determined.

Substituting this into the differential equation and solving for u(x), we get

u(x) = -1/2 + e⁽ˣ/²⁾∫e⁽⁻³ˣ/²⁾cos(2x)e⁽⁻ˣ/²⁾dx.

Using integration by parts, we can evaluate this integral to get

u(x) = -1/2 + e⁽ˣ/²⁾(-3/13)cos(2x) + (2/13)sin(2x).

Therefore, the general solution to the differential equation is

y(x) = y_h(x) + y_p(x)

= e⁽⁻ˣ⁾(C₁cos(2x) + C₂sin(2x)) + e⁽⁻³ˣ/²⁾(C₃cos(2x) + C₄sin(2x)) - (1/2)e⁽⁻ˣ⁾cos(2x) + e⁽ˣ/²⁾(-3/13)cos(2x) + (2/13)sin(2x).

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The outward flux of F across the closed curve C is -210.

Option D is the correct answer.

We have,

To find the outward flux of F across the closed curve C using Green's Theorem, we need to evaluate the line integral of F around the boundary of the region enclosed by C.

The given vector field is F = (x^2 + y^2)i + (x - y)j.

Curve C is a rectangle with vertices at (0, 0), (5, 0), (5, 7), and (0, 7).

Applying Green's Theorem, the outward flux can be calculated as:

Flux = ∬R (curl F) · n dA

where R is the region enclosed by the curve C, curl F is the curl of F, n is the unit outward normal vector to the curve C, and dA represents the area element.

First, let's calculate the curl of F:

curl F = (∂Fz/∂y - ∂Fy/∂z)i + (∂Fx/∂z - ∂Fz/∂x)j + (∂Fy/∂x - ∂Fx/∂y)k

In this case, Fz = 0, so the curl simplifies to:

curl F = (∂Fy/∂x - ∂Fx/∂y)j

Now, let's compute the partial derivatives of F:

∂Fx/∂y = 0 - 1 = -1

∂Fy/∂x = 2x

Substituting these values into the curl expression:

curl F = (-1 - 2x)j

The unit outward normal vector n for a rectangle is either the positive or negative y-axis direction, depending on the orientation.

Since the flux is defined as outward, we'll choose the positive y-axis direction.

The area element dA is equal to dx dy, where dx is the infinitesimal change in the x-direction and dy is the infinitesimal change in the y-direction.

Now, let's set up the integral to calculate the outward flux:

Flux = ∬R (curl F) · n dA

= ∫∫R (-1 - 2x)j · j dx dy

= ∫∫R (-1 - 2x) dy dx

To integrate over the rectangle region R, we set the limits of integration:

x: 0 to 5

y: 0 to 7

Flux = ∫[tex]0^5[/tex] ∫[tex]0^7[/tex] (-1 - 2x) dy dx

Evaluating the integral:

Flux = ∫[tex]0^5[/tex] [(-1 - 2x)y][tex]0^7[/tex] dx

= ∫[tex]0^5[/tex] (-1 - 2x)(7 - 0) dx

= -7∫[tex]0^5[/tex] (1 + 2x) dx

= -7[x + x^2][tex]0^5[/tex]

= -7[(5 + 25) - (0 + 0)]

= -7(30)

= -210

Therefore,

The outward flux of F across the closed curve C is -210.

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The function f(x) = cot(x) is an odd function, algebraically

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

f(x) = cot(x)

A function is said to be even if

f(x) = f(-x)

Using the above as a guide, we have the following:

f(-x) = cot(-x)

-f(x) = -cot(x)

A function is said to be odd if

-f(x) = f(-x)

So, we have

-f(x) = -cot(x)

-f(x) = cot(x)

By comparing the functions:

f(x) = -f(x) = cot(x)

Hence, the function is odd

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The equivalent weight of sodium bicarbonate stated in grams is 4.5 grams.

To convert the weight of sodium bicarbonate from ounces to grams, we can use the conversion factor given: 1 gram = 0.035 ounce.

Let's perform the conversion:

Weight in grams = Weight in ounces × Conversion factor

Weight in grams = 0.1575 ounce × 1 gram / 0.035 ounce

Weight in grams = 4.5 grams

Therefore, the equivalent weight of sodium bicarbonate stated in grams is 4.5 grams.

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The Pearson correlation coefficient for these data is 0.1. This suggests a weak, positive linear relationship between X and Y variables.

The Pearson correlation coefficient, also denoted as r, is a measure of the strength and direction of the linear relationship between two variables. In this case, we have a set of 5 pairs of X and Y values, with SSx = 16, SSy = 4 and SP = 2.
The sample standard deviations (Sx and Sy) for the X and Y variables. The sample covariance is defined as:
Sxy = (SP/n) - (SX/n)(SY/n)
Sxy = (2/5) - (sqrt(16)/5)(sqrt(4)/5) = 0.2

Next, we can calculate the sample standard deviations for X and Y using the formulas:
Sx = sqrt(SSx/(n-1)) = sqrt(16/4) = 2
Sy = sqrt(SSy/(n-1)) = sqrt(4/4) = 1
Finally, we can calculate the Pearson correlation coefficient using the formula:
r = Sxy/(SxSy) = 0.2/(2*1) = 0.1

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Here's a picture I found:

Special thanks to FarmerLing6429 on The Art of Problem-Solving for sending me this image.

The graph of the boxplot is plotted and attached.

To calculate these values, we first need to arrange the data in ascending order:

12, 12, 14, 14, 15, 15, 17, 19, 23, 24, 26, 26, 27, 27, 27

Now, we can find the important points:

Minimum (Min): 12

First Quartile (Q1): The median of the lower half of the data (excluding the overall median if the total number of data points is odd):

Q1 = 14 (median of 12, 12, 14, 14, 15)

Median (Q2): The overall median (the middle value):

Q2 = 19

Third Quartile (Q3): The median of the upper half of the data (excluding the overall median if the total number of data points is odd):

Q3 = 26 (median of 23, 24, 26, 26, 27)

Maximum (Max): 27

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Answer:

Plots that can detect violations in regression assumptions, while response plots help analyse the relationship between the response variable and predictors are Residual plots and scatterplot matrix.

Step-by-step explanation:

There are several graphical plots commonly used to detect violations of regression model assumptions and analyze estimated regression models. Here are a few examples:

Residual plot: A residual plot shows the difference between the observed values of the response variable and the predicted values from the regression model. Patterns in the residuals, such as nonlinearity, heteroscedasticity (unequal variances), or outliers, can indicate violations of assumptions.

Response plot: This plot examines the relationship between the response variable and one of the predictor variables while holding other predictors constant. It helps identify nonlinearity or other issues in the relationship between the response and predictors.

Scatterplot matrix: A scatterplot matrix displays scatterplots between pairs of predictor variables. It can help detect issues like perfect multicollinearity, which occurs when two or more predictors are highly correlated, leading to problems in estimating the regression coefficients.

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The answer to the question is (a) 1.440.The critical value that would be used to obtain an 80% confidence interval for the mean of a Normally distributed population, given an SRS of six observations, would be 1.440.

To calculate the critical value for an 80% confidence interval, we need to use the t-distribution. The t-distribution is used when the sample size is small (less than 30) or when the population standard deviation is unknown.  For an 80% confidence interval with 5 degrees of freedom (6-1=5), the critical value is 1.440, according to the t-distribution table. This means that if we take multiple samples of the same size from the same population and construct 80% confidence intervals for each sample, approximately 80% of the intervals would contain the true population mean.

To obtain the critical value for an 80% confidence interval for the mean of a Normally distributed population, given an SRS of six observations, we need to use the t-distribution. The t-distribution is used when the sample size is small (less than 30) or when the population standard deviation is unknown.  To determine the critical value, we first need to calculate the degrees of freedom, which is the sample size minus one. In this case, the degrees of freedom would be 5 (6-1=5). We then need to look up the corresponding t-value from the t-distribution table for an 80% confidence level and 5 degrees of freedom. Using the t-distribution table, we can find that the critical value for an 80% confidence interval with 5 degrees of freedom is 1.440.

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The value of l is 75cm

Similar figures are two figures having the same shape. The objects which are of exactly the same shape and size are known as congruent objects.

The ratio of corresponding sides of similar shapes are equal

In the diagram, It consist of bigger cone to smaller cone.

Represent the slant height of the smaller cone by x, the remaining part is 35cm

Therefore l = 35 + x

Therefore (35+x)/x = 15/8

= 8( 35+x) = 15x

280+8x = 15x

280 = 15-8x

280 = 7x

divide both sides by 7

x = 280/7

x = 40

Therefore l = 35+x

l = 35 + 40

l = 75cm

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There are 27 outcomes that could be an even number

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

Rolling of two dice

The outcomes of the dice are then multiplied

So, we have

Die 1 = {1, 2, 3, 4, 5, 6}

Die 2 = {1, 2, 3, 4, 5, 6}

Rolls involving 2, 4 and 6 would always be even numbers

So, we have

Outcomes 1 = 3 * 2 * 3 = 18

Rolls involving 1, 3 and 5 and ending in 2, 4 and 6 would always be even numbers

So, we have

Outcomes 2 = 3 * 2 * 3/2 = 9

So, we have

Total = 18 + 9

Evaluate

Total = 27

Hence, there are 27 outcomes that could be an even number

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The car traveled 53.33ft before coming to a complete stop.

What is the velocity?

The velocity of an object is its speed and direction of motion. The idea of velocity is crucial in kinematics, the part of classical mechanics that explains the motion of bodies. Velocity is a physical vector quantity that requires both magnitude and direction to define.

Here, we have

Given: A car traveling at 40 ft/sec begins decelerating (time is zero here, as is position) at a constant 15 feet per second squared.

Initial velocity = 40 ft/sec

Acceleration(a) = dv/dt, adt = dv

Now, we integrate

∫adt = ∫dv

at + c = v

At t = 0, v = 40,  we get c = 40

v = at + 40

It is given that car decelerates at 15sec/ft means acceleration is negative i.e a = -15ft/sec

Now,

v = (-15)t + 40

When a car is stopped the velocity becomes zero

v = -15t + 40 = 0

15t = 40

t = 40/15

t = 8/3sec.

Now, the position function is given by

∫vdt = ∫(-15t+40)dt

When t = 0

x(t) = 0

x(t) = -15t²/2 + 40t + c

0 = 0 + c

c = 0

x(t) = -15t²/2 + 40t

Now, we find x(t) when t = 8/3 and we get

x(8/3) = -15(8/3)²/2 + 40(8/3)

x(8/3) = 53.33ft

Hence, the car traveled 53.33ft before coming to a complete stop.

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The mean of the scores on the Statistics midterm exam is 47.67.

The mean is the average of a data set.

To find the mean, we need to sum up all the scores and divide the sum by the total number of scores.

Sum of scores = 24 + 27 + 30 + 30 + 31 + 34 + 36 + 38 + 40 + 43 + 44 + 44 + 44 + 46 + 49 + 50 + 52 + 56 + 59 + 60 + 60 + 61 + 62 + 65 + 66 + 68 + 68

Sum of scores = 1287

Total number of scores = 27

Mean = Sum of scores / Total number of scores

Mean = 1287 / 27

Mean = 47.6666666667

Mean = 47.67.

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Answer:

square root of 4 is 2.

√4 =2

square of 2 is 4.

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Answer:

To determine how long it will take for the car to be worth half the price, we need to find the value of x when V is equal to $10,000 (half of $20,000).

We can set up the equation:

10,000 = 20,000(0.8)^x

To solve for x, we can take the logarithm of both sides of the equation.

log(10,000) = log(20,000(0.8)^x)

Using logarithmic properties, we can simplify the equation:

log(10,000) = log(20,000) + x * log(0.8)

We can now calculate x:

x = (log(10,000) - log(20,000)) / log(0.8)

Using a calculator, we find that x is approximately 3.17.

Therefore, it will take approximately 3.17 years for the car to be worth half the price.