I NEED. HELP ASAP please!!!

Answer:

bbv

Step-by-step explanation:

Recurring decimal is decimal representation of a number whose digits are periodic and infinite. Proved algebraically that the recurring decimal 0.178 can be written as the fraction 59/330 below.

Given number in the decimal form is [tex]0. 1 \overline 7 \overline 8[/tex]

Suppose the number is equal to the x,

[tex]x=0. 1 \overline 7 \overline 8[/tex]

Recurring decimal is decimal representation of a number whose digits are periodic and infinite.

As the number 78 is the recurring number. Thus the recurring number can be written as,

[tex]x=0.1787878.....[/tex]                      .......equation 1.

Suppose this is equation number 1.

Multiply the above equation with 100 both the sides,

[tex]100\times x=100\times0. 1 787878....[/tex]

[tex]100x=100\times0.1787878...[/tex]

[tex]100x=17.87878...[/tex]

Subtract the above equation from equation number 1. Thus,

[tex]\begin{aligned}\ 100x-x&=17.87878-0.1787878\\ 99x&=17.7\\ \end[/tex]

Solve for x ,

[tex]x=\dfrac{17.7}{99} [/tex]

Multiply with 10 in both numerator and denominator,

[tex]x=\dfrac{177}{990} \\ x=\dfrac{59}{330} \\[/tex]

Hence proved algebraically that the recurring decimal 0.178 can be written as the fraction 59/330

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The equation of the regression line for the relationship between job performance (X) and attitude ratings (Y) is Y = 57.124 + 0.352X.

To find the equation of the regression line, we will use a technique called simple linear regression. This method allows us to model the relationship between two variables using a straight line equation. In our case, the variables are job performance (denoted as Perf) and attitude ratings (denoted as Att).

The equation of a regression line is typically represented as: Y = a + bX

To find the equation of the regression line, we need to calculate the values of 'a' and 'b' using the given data points. Let's go step by step:

Mean of Perf (X): (59 + 63 + 65 + 69 + 58 + 77 + 76 + 69 + 70 + 64) / 10 = 66.0

Mean of Att (Y): (75 + 64 + 81 + 79 + 78 + 84 + 95 + 80 + 91 + 75) / 10 = 80.2

Perf differences:

(59 - 66.0), (63 - 66.0), (65 - 66.0), (69 - 66.0), (58 - 66.0), (77 - 66.0), (76 - 66.0), (69 - 66.0), (70 - 66.0), (64 - 66.0)

Att differences:

(75 - 80.2), (64 - 80.2), (81 - 80.2), (79 - 80.2), (78 - 80.2), (84 - 80.2), (95 - 80.2), (80 - 80.2), (91 - 80.2), (75 - 80.2)

Squared Perf differences:

(-7)², (-3)², (-1)², (3)², (-8)², (11)², (10)², (3)², (4)², (-2)²

Squared Att differences:

(-5.2)², (-16.2)², (0.8)², (-1.2)², (-2.2)², (3.8)², (14.8)², (-0.2)², (10.8)², (-5.2)²

Step 3: Calculate the sum of the squared Perf differences and the sum of the squared Att differences.

Sum of squared Perf differences:

7² + 3² + 1² + 3² + 8² + 11² + 10² + 3² + 4² + 2² = 369

Sum of squared Att differences:

5.2² + 16.2² + 0.8² + 1.2² + 2.2² + 3.8² + 14.8² + 0.2² + 10.8² + 5.2² = 734.72

Sum of Perf differences multiplied by Att differences:

(-7)(-5.2) + (-3)(-16.2) + (-1)(0.8) + (3)(-1.2) + (-8)(-2.2) + (11)(3.8) + (10)(14.8) + (3)(-0.2) + (4)(10.8) + (-2)(-5.2) = 129.8

Calculate the slope (b) using the following formula:

b = sum of Perf differences multiplied by Att differences / sum of squared Perf differences

b = 129.8 / 369 = 0.352

a = Mean of Att (Y) - b * Mean of Perf (X)

a = 80.2 - 0.352 * 66.0 = 57.124

Y = a + bX

Y = 57.124 + 0.352X

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(i) True.

(ii) False.

The first statement is true.

It is a well-known fact that for any random variable X, the covariance of X with itself is equal to the variance of X. This can be easily shown by applying the definition of covariance and variance and using the fact that the correlation between X and X is always 1.

The second statement is false. The mean of an exponential random variable is equal to 1/λ, where λ is the rate parameter. On the other hand, the standard deviation of an exponential random variable is equal to 1/λ as well. These two values are not equal, unless λ=1. Therefore, the statement is false.

In summary, the first statement is true, while the second statement is false. The covariance of a random variable with itself is equal to its variance, but the mean and standard deviation of an exponential random variable are not equal unless λ=1.

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The charge on the capacitor at time t is given by Q(t) = 6C - 6C * e^(-t/2)---- Eqn. (1) and the current at time t is given by I(t) = 3A * e^(-t/2) --- Eqn. (2).

R(dQ/dt) + (1/C)Q = E(t)

The general solution of the above equation (when E(t) is a constant E) is:

Q(t) = CE + (Q(0) - CE)e^(-t/RC)

Plugging in the given values:

Q(t) = 0.1F * 60V + (0C - 0.1F * 60V)e^(-t/(20Ω * 0.1F))

Simplify this to get:

Q(t) = 6C - 6C * e^(-t/2)      Eqn. (1)

The current is the derivative of the charge with respect to time:

I(t) = dQ(t)/dt = d/dt [6C - 6C * e^(-t/2)]

Taking the derivative and simplifying gives:

I(t) = 3A * e^(-t/2)     Eqn. (2)

So the charge on the capacitor at time t is given by Eqn. (1) and the current at time t is given by Eqn. (2).

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Answer:

12^3

Step-by-step explanation:

Answer:

12 ^3

Step-by-step explanation:

Answer:  

588245.  

Step-by-step explanation:  

nth term = an = a1 r^(n-1)   where a1 = the first term  

a3 = 35 = a1  7^(3 - 1)  

35 = a1* 49  

a1 = 35/49 = 5/7  

So the 8th term = (5/7)* (7)^7

= 588245

The solution of the initial-value problem is:

y(t) = C1e^(-4t) + C2e^(4t) + Y(t)

where C1 and C2 are constants determined by the initial conditions, and Y(t) is the particular solution given by the formula provided.

To find the solution of the initial-value problem, we can use the given fundamental set of solutions of the homogeneous equation and the particular solution.

The fundamental set of solutions is y1(t) = e^at, where a = -4 and y2(t) = e^bt, where b = 4.

The particular solution is Y(t) = ds-y1(t) to + y2(t) • y3(t), where y3(t) is another function that satisfies the non-homogeneous equation.

Combining the solutions, the general solution of the non-homogeneous equation is y(t) = C1e^(-4t) + C2e^(4t) + Y(t), where C1 and C2 are constants

To determine the specific solution, we need to use the initial conditions. Given y(0) = 2, y'(0) = 2, and y''(0) = 88, we can substitute these values into the general solution and solve for the constants C1 and C2.

Finally, the solution of the initial-value problem is y(t) = C1e^(-4t) + C2e^(4t) + Y(t), where C1 and C2 are the constants determined from the initial conditions and Y(t) is the particular solution.

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Answer:

The approximate age of the bone is approximately 11552 years.

Step-by-step explanation:

The current proportion of carbon-14 with respect to its original amount is defined by following formula:

[tex]\frac{y}{a} = e^{-0.00012\cdot t}[/tex] (1)

Where:

[tex]y[/tex] - Current amount of carbon-14, no unit.

[tex]a[/tex] - Initial amount of carbon-14, no unit.

[tex]t[/tex] - Time, in years.

If we know that [tex]\frac{y}{a} = 0.25[/tex], then the approximate age of the bone is:

[tex]t = -8333.333\cdot \ln \frac{y}{a}[/tex]

[tex]t\approx 11552.453\,yr[/tex]

The approximate age of the bone is approximately 11552 years.

Step-by-step explanation:

54, 54, 72

[tex]180 - 2 \times 54 = \\ = 180 - 108 = \\ = 72[/tex]

Answer:

y = 2/3x - 6

Step-by-step explanation:

Use the slope intercept equation, y = mx + b

Plug in the slope and given point, then solve for b

y = mx + b

-4 = 2/3(3) + b

-4 = 2 + b

-6 = b

Plug in the slope and b into the equation

y = 2/3x - 6

So, the equation of the line is y = 2/3x - 6

The simplified ratio for 0.875 is 7:8 .

To express the ratio for 0.875, we need to convert the decimal value to a ratio form.

0.875 can be written as 875 / 1000 because the decimal value is equivalent to the fraction obtained by dividing the numerator by the denominator.

To simplify the fraction, we can divide both the numerator and denominator by their greatest common divisor, which is 125 in this case.

Dividing 875 and 1000 by 125, we get:

875 / 125 = 7

1000 / 125 = 8

So, the simplified ratio for 0.875 is 7:8 .

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The correct answer is D to identify the problem.

What is the decision-making process?

The decision-making process is the method by which a judgment or a decision is reached. It involves identifying the problem or decision to be made, analyzing potential courses of action, evaluating alternatives, and selecting the best possible solution.

It's a structured process that assists in making effective decisions, and it can be useful in both personal and professional contexts. What is the first step in the decision-making process?

The first step in the decision-making process is to identify the problem. This entails defining the issue that requires a decision to be made. It's a crucial step because without accurately identifying the issue or problem, it's impossible to make the best decision.

Analyzing the problem and its causes (A), generating alternatives (B), and soliciting and analyzing feedback (C) are all critical components of the decision-making process, but they come after the problem has been identified.

As a result, option D, identifying the problem, is the first step in the decision-making process.

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Step-by-step explanation:

2.) -20× + 24

maaf kalo salah

Answer:

-1/3x + 5 = y

Step-by-step explanation:

Answer:

x = y = 2√2

Step-by-step explanation:

Find the diagram attached

To get the unknown side x and y, we need to use the SOH CAH TOA identity

Opposite side = x

Adjacent = y

Hypotenuse = 4

Sin theta = opposite/hypotenuse

sin 45 = x/4

x = 4 sin 45

x = 4 * 1/√2

x = 4 * 1/√2 * √2/√2

x = 4 * √2/√4

x = 4 * √2/2

x = 2√2

Similarly;

cos theta = adjacent/hypotenuse

cos 45 = y/4

y = 4cos45

y = 4 * 1/√2

y = 4 * 1/√2 * √2/√2

y = 4 * √2/√4

y = 4 * √2/2

y = 2√2

Answer:

For [tex]x = 3[/tex] the value of [tex]f(x) = 2\cdot x^{2}+9[/tex] will be the same of [tex]g(x) = 3^{x}[/tex].

Step-by-step explanation:

To determine for which value of [tex]x[/tex], we need to apply the following identity ([tex]f(x) = g(x)[/tex]) and solve numerically the resulting expression:

[tex]2\cdot x^{2}+9 = 3^{x}[/tex]

[tex]3^{x}-2\cdot x^{2}-9=0[/tex] (1)

A quick approach is using graphic tool and looking for the value of [tex]x[/tex] such that  [tex]3^{x}-2\cdot x^{2}-9=0[/tex]. The result of the analysis is included below in the attached image. We find the following result:

[tex]x = 3[/tex]

For [tex]x = 3[/tex] the value of [tex]f(x) = 2\cdot x^{2}+9[/tex] will be the same of [tex]g(x) = 3^{x}[/tex].

The explicit formula for the beetle population after n weeks can be determined using the given data. The formula is Pn = 4 + (n - 0) * ((67 - 4) / (7 - 0)), where Pn represents the population after n weeks. It will take 28 weeks for the beetle population to reach 256.

The linear growth model assumes that the beetle population increases by a fixed amount each week. To find the explicit formula, we start by calculating the growth rate per week. We know that in 7 weeks, the population increased from 4 to 67. The change in population is 67 - 4 = 63, and the change in weeks is 7 - 0 = 7. Therefore, the growth rate per week is (67 - 4) / (7 - 0) = 9.

Using this growth rate, we can express the population after n weeks using the formula Pn = 4 + (n - 0) * 9. This simplifies to Pn = 4 + 9n. Now, to determine how many weeks it takes for the population to reach 256, we substitute Pn = 256 into the formula. Solving for n, we get 256 = 4 + 9n. By rearranging the equation, we find 9n = 252, and dividing both sides by 9 yields n = 28. Therefore, it will take 28 weeks for the beetle population to reach 256.

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Answer:

Let X be the random variable representing the amount of beer poured by the filling machine. Since X follows a normal distribution with mean μ = 12.10 and standard deviation σ = 0.05, we can use the standard normal distribution to find the probability that a bottle contains between 12.00 and 12.06 ounces.

First, we need to standardize the values 12.00 and 12.06 by subtracting the mean and dividing by the standard deviation:

z1 = (12.00 - 12.10) / 0.05 = -2 z2 = (12.06 - 12.10) / 0.05 = -0.8

Now we can use a standard normal distribution table to find the probability that a standard normal random variable Z is between -2 and -0.8:

P(-2 < Z < -0.8) = P(Z < -0.8) - P(Z < -2) ≈ 0.2119 - 0.0228 ≈ 0.1891

So, the probability that a bottle contains between 12.00 and 12.06 ounces of beer is approximately 0.1891.

Step-by-step explanation:

The approximation of the integral ∫cos(x³ + 10) dx using composite Simpson's rule with n = 3 is 0.126. When approximating the integral using Romberg integration, R₃,₃ gives an approximation of order h⁶.

To calculate the approximation using composite Simpson's rule, we divide the interval of integration into subintervals and apply Simpson's rule to each subinterval. The formula for Simpson's rule is:

S = h/3 * (f(x₀) + 4f(x₁) + 2f(x₂) + 4f(x₃) + ... + 2f(x_{n-2}) + 4f(x_{n-1}) + f(xₙ))

where h is the step size and n is the number of subintervals. In this case, we have n = 3, so we divide the interval into three equal subintervals, and the step size is h = (b - a) / n = (π - 0) / 3 = π/3.

Evaluating the function cos(x³ + 10) at the points x₀ = 0, x₁ = π/3, x₂ = 2π/3, and x₃ = π, we get:

f(x₀) = cos((0)³ + 10) = cos(10) ≈ -0.8391

f(x₁) = cos((π/3)³ + 10) = cos(π³/27 + 10) ≈ -0.4586

f(x₂) = cos((2π/3)³ + 10) = cos(8π³/27 + 10) ≈ -0.8391

f(x₃) = cos((π)³ + 10) = cos(π³ + 10) ≈ -0.3473

Using the Simpson's rule formula, we can now calculate the approximation:

S ≈ π/3 * (f(x₀) + 4f(x₁) + 2f(x₂) + 4f(x₃))

 ≈ π/3 * (-0.8391 + 4(-0.4586) + 2(-0.8391) + 4(-0.3473))

 ≈ 0.126

To calculate the order of approximation using Romberg integration, we use the formula:

Rₙ,ₖ = Rₙ₋₁,ₖ₋₁ + (Rₙ₋₁,ₖ₋₁ - Rₙ,ₖ₋₁) / (4ₖ - 1)

where Rₙ,ₖ represents the Romberg approximation at level n and column k. The order of approximation is determined by the highest power of h in the error term. In this case, we have R₃,₃, so the order is h⁶.

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Answer:

A

Step-by-step explanation:

Answer:

the one that is a horizontal straight line on x axis that lies on 2

Answer:

x = 93.7ft or x = 94ft

Step-by-step explanation:

Hope that helps :)

The correct answer is D. 68 inches. The trend line equation y = x + 2 indicates that there is a linear relationship between height and arm span. The coefficient of 1 on x suggests that, on average, for every increase of 1 inch in height, the arm span increases by 1 inch as well.

The intercept of 2 indicates that even at a height of 0 inches, there is a minimum arm span of 2 inches. By substituting the given height value into the equation, we can accurately predict the corresponding arm span

The equation of the trend line given is y = x + 2, where y represents the arm span and x represents the height of the players. We need to predict the arm span for a player who is 66 inches tall.

To make the prediction, we substitute x = 66 into the equation and solve for y:

y = 66 + 2

y = 68

Therefore, the predicted arm span for a player who is 66 inches tall is 68 inches.

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The null and alternative hypotheses for testing the claim that the proportion of people who own cats is significantly different from 90% at the 0.02 significance level are:

H0: p = 0.9 (proportion of cat owners is 90%)

H1: p ≠ 0.9 (proportion of cat owners is not equal to 90%)

Based on a sample of 500 people, where 82% owned cats, we can conduct a hypothesis test to determine the p-value at the 0.02 significance level. The p-value is the probability of obtaining a sample proportion as extreme as the observed proportion (82%) assuming the null hypothesis is true.

The p-value for this test is the probability of observing a sample proportion as different from 90% as 82%. Since the p-value is not provided in the question, it needs to be calculated based on the sample data and the assumed null distribution.

If the p-value is less than 0.02, we would reject the null hypothesis and conclude that the proportion of cat owners is significantly different from 90%. However, if the p-value is greater than or equal to 0.02, we would fail to reject the null hypothesis and conclude that there is not enough evidence to suggest a significant difference in the proportion of cat owners from 90%.

Without the calculated p-value, we cannot make a definitive conclusion about rejecting or failing to reject the null hypothesis.


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