A round cake has a diameter of 30 cm. What is the circumference of the
cake?

Step-by-step explanation:

-d=0.8-1.5

-d=-0.7

d=0.7

Answer:

d=0.7

Step-by-step explanation:

1.5=d+0.8

d=1.5-0.8

d=0.7

Answer:

\(\\\sf7x^3 - 2x^2 - 5\)

Step-by-step explanation:

\(\\\sf(6x^3 + 3x^2 - 2) + (x^3 - 5x^2 - 3)\)

Remove parenthesis.

6x^3 + 3x^2 - 2 + x^3 - 5x^2 - 3

Rearrange:

6x^3 + x^3 + 3x^2 - 5x^2 - 2 - 3

Combine like terms to get:

----------------------------------------

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Answer:

7x³ - 2x² - 5

Step-by-step explanation:

(6x³ + 3x² - 2) + (x³ - 5x² - 3)

Remove the round brackets.

= 6x³ + 3x² - 2 + x³ - 5x² - 3

Put like terms together.

= 6x³ + x³ + 3x² - 5x² - 2 - 3

Do the operations.

= 7x³ - 2x² - 5

____________

hope this helps!

The area of the segment is 210.25π - 420.5

A segment is an interior region of a circle. It is the space between a chord and an arc.

The area of segment is expressed as;

area of segment = area of sector - area of triangle

The shaded part is a segment.

A sector is a region between two radii and an arc.

Area of triangle = 1/2 × 29 × 29

= 841/2

= 420.5

The area of the sector is given as 210.25

Therefore the area of the segment = 210.25π - 420.5

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we can replace y on the second equation to find x

to find X we need to use this formula to factor

where a=3, b=2 and c=-6

so, replace

we have 2 solutions for x

Answer:

c = 0

Step-by-step explanation:

Step 1: Write equation

15 - 5(4c - 7) = 50

Step 2: Solve for c

Step 3: Check

Plug in c to verify it's a solution.

15 - 5(4(0) - 7) = 50

15 - 5(-7) = 50

15 + 35 = 50

50 = 50

The impact propensity can be interpreted as the slope coefficient for the tax personal exemption (pe) or its first lag in

the regression equation.

To determine the impact propensity in the regression of the general fertility rate (GFR) on the tax personal exemption

(PE) and its first lag, you should follow these steps:

Estimate the regression model using the available data. The model should look like this:

GFR = β0 + β1 × PE + β2 × PE_lag + ε

Where GFR is the general fertility rate, PE is the tax personal exemption, PE_lag is the tax personal exemption's first

lag, and ε is the error term.

Obtain the estimated coefficients (β0, β1, and β2) from the fitted regression model.

These coefficients will help you determine the impact propensity.

Calculate the impact propensity. The impact propensity in this context refers to the change in the general fertility rate

resulting from a one-unit increase in the tax personal exemption, taking into account both its current and lagged

effects.

To find the impact propensity, sum the coefficients for the tax personal exemption and its first lag:

Impact propensity = β1 + β2

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By applying the principle of inclusion-exclusion, we can show that for any events E1, E2,..., EM, the inequality P(E1 or E2 or ... or EM) < P(EM) holds. This result holds true for any integer M ≥ 1.

To prove the statement by induction, we will assume that for M = 1, the inequality holds true. Then we will show that if the statement holds for M, it also holds for M + 1.

Base case (M = 1):

For M = 1, we have P(E1) ≤ P(E1), which is true.

Inductive step:

Assuming that the inequality holds for M, we need to show that it holds for M + 1. That is, we need to prove P(E1 or E2 or ... or EM or EM+1) < P(EM+1).

Using the principle of inclusion-exclusion, we can express the probability of the union of events as follows: P(E1 or E2 or ... or EM or EM+1) = P(E1 or E2 or ... or EM) + P(EM+1) - P((E1 or E2 or ... or EM) and EM+1). Since events E1, E2, ..., EM, and EM+1 are mutually exclusive, the last term on the right-hand side becomes zero: P(E1 or E2 or ... or EM or EM+1) = P(E1 or E2 or ... or EM) + P(EM+1)

Since we assumed that P(E1 or E2 or ... or EM) < P(EM), we can rewrite the inequality as: P(E1 or E2 or ... or EM or EM+1) < P(EM) + P(EM+1)

Now we need to show that P(EM) + P(EM+1) < P(EM+1) for the inequality to hold. Simplifying the expression, we have: P(EM) + P(EM+1) < P(EM+1)

Since P(EM+1) is a probability and is always non-negative, this inequality holds true. Therefore, by the principle of mathematical induction, we have shown that for any integer M ≥ 1, the inequality P(E1 or E2 or ... or EM) < P(EM) holds.

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The paragraph proof that is written to prove that if PQ=4(x−3)+1, QR=x+10, and x=7, then PQ≅QR is given below.

The midpoint of a straight line is a point that divides then line into two equal portions.

Therefore to prove that PQ ≅ QR, the required steps are stated below:

A point on a given straight line which divide it into two equal parts is termed midpoint. So that any mathematical expression for the first fraction or part, is the same as that for the second fraction or part.

In the given question, point Q is the midpoint of line PR.

So that:

PR = PQ + QR     (Midpoint theorem)

Thus, PQ = QR     (Midpoint theorem)

But, PQ=4(x−3)+1,

QR=x+10, and x=7.

Substitute the value of x in both PQ and QR to have;

PQ = 4(x−3)+1      = 4(7 - 3) + 1      = 4*4 + 1PQ = 17  

(Midpoint theorem) QR = x+10      

= &amp; + 10QR = 17  

(Midpoint theorem)

Therefore, PQ ≅ QR

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The correct answer is A) The interval over which the function is concave up is `(1/2, ∞)` and the interval over which the function is concave down is `(-∞, 1/2)`.B) There is no inflection point.

Given function is `f(x)= x - 4x`.

To determine the intervals over which the function is concave up and concave down, we need to find the second derivative of the function and solve it for 0, then we can find the values of x at which the function is concave up or down.f(x) = x - 4x  =  -3x

First derivative, f'(x) = -3Second derivative,

f''(x) = 0 (constant)The second derivative is a constant, which means the function is either concave up or concave down at every point. To determine whether the function is concave up or down, we take the second derivative of a point in each interval, such as the midpoint.

Midpoint of the function is `(0 + 1) / 2 = 1/2` When x < 1/2, f''(x) < 0, which means the function is concave down.

When x > 1/2, f''(x) > 0, which means the function is concave up.

Therefore, the interval over which the function is concave up is `(1/2, ∞)` and the interval over which the function is concave down is `(-∞, 1/2)`.

We can find inflection points by equating the second derivative to 0: f''(x) = 0 -3 = 0  x = 0

There is no inflection point because the second derivative is constant and is never 0.

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Answer:

x = 47/10

Step-by-step explanation:

Step 1: Write equation

51 - 14x = 4 - 4x

Step 2: Solve for x

Answer:

\(\boxed {x = \frac{47}{10}}\)

Step-by-step explanation:

Solve for the value of \(x\):

\(51 - 14x = 4 - 4x\)

-Take \(4x\) and add it to \(-14x\):

\(51 - 14x + 4x = 4 - 4x + 4x\)

\(51 - 10x = 4\)

-Subtract \(51\) to both sides:

\(51 - 51 - 10x = 4 - 51\)

\(-10x = -47\)

-Divide both sides by \(-10\):

\(\frac{-10x}{-10} = \frac{-47}{-10}\)

\(\boxed {x = \frac{47}{10}}\)

So, the value of \(x\) is \(\frac{47}{10}\).

Answer:

Two Real Solutions

Step-by-step explanation:

Use quadratic equation discriminant5t^2+3t-7=0

\(\sqrt{b^2-4ac} \\\sqrt{9-4*5*-7}\\\sqrt{149}\\discriminant>0 two real solutons\):

Since the discriminant is greater then 0, it two real solutions.

Answer:

5

Step-by-step explanation:

read your book so that you can know your book

Your lack of cogscitis has a previous probability of 0.5 according to Dr. B.

Prior probability quantifies how likely a theory is to be correct before any supporting data are taken into account. Your lack of cognitis has a previous probability of 0.5 according to Dr. B. This means that according to Doctor B, there is a 50/50 likelihood that you do not have cognitis before any supporting evidence is considered. This impartial viewpoint is frequently employed in scientific research and medical diagnostics. Prior probability is a crucial component of Bayesian inference, which is used to update a hypothesis' probability after all available data has been considered. Prior probability can help scientists and medical practitioners make better decisions.

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Answer:

Step-by-step explanation:

kkkkk 55 67888000

a. The value of C(g(m)) = 0.11m

b. The coeffient of C(g(m)) is the cost per mile of travel of the vehicle.

The value of C(g(m)) = 0.11m

Since the function C(g) = 2.75g represents the cost (in dollars) of g gallons of gasoline at a gas station and the function g(m) = 0.04m approximates the number of gallons of gasoline a vehicle uses to travel m miles.

We need to find the function C(g(m))

So, substituting g(m) = 0.04m into C(g), we have

C(g) = 2.75g

C(g(m)) = 2.75(0.04m)

C(g(m)) = 0.11m

The value of C(g(m)) = 0.11m

The coeffient of C(g(m)) is the cost per mile of travel of the vehicle.

The coeffient of C(g) is cost per gallon, since C(g)/g = 2.75 cost/gallon and the coefficient of g(m) is gallon per miles since g(m)/m = 0.04 gallon per mile.

Since the coeffient of C(g(m)) = coefficient of C(g) × coefficient of g(m), then

coeffient of C(g(m)) = 2.75 dollars/gallon × 0.04 gallon/mile = 0.11 dollars/mile

So, the coeffient of C(g(m)) is the cost per mile of travel of the vehicle.

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The concept of inductive reasoning is based on the fact that people generate information through general observations and evidence. In the decision-making process, inductive reasoning involves selecting the bike segments based on observations. On the other hand, the deductive approach would involve starting with a general idea and creating specific conclusions based on it.  

Inductive Reasoning: Inductive reasoning involves using specific pieces of evidence or observations to generate general conclusions. In the decision-making process, inductive reasoning can be used to select the most suitable bike segments for a company. This is based on a combination of observations and a general idea of the characteristics that the company is looking for. To select the bike segments, an inductive approach would begin with the observation of different bike segments in the market and the characteristics of the potential customers that the company is targeting. The company would then use this information to develop an understanding of the key features that are important to these customers. After generating the initial set of ideas, the company would then narrow down the bike segments that meet these criteria to arrive at a final decision.
Deductive Reasoning: Deductive reasoning involves starting with general ideas and then using specific evidence to create specific conclusions. In the decision-making process, a deductive approach can be used to select bike segments based on specific premises. This would involve starting with a general idea of what the company is looking for and then breaking this down into specific criteria. The company would then use these criteria to evaluate the different bike segments in the market and select the most suitable segments based on their specific characteristics. The major premise would be the initial idea of what the company is looking for, while the minor premise would be the specific characteristics that the company is evaluating. The company would then use these two premises to arrive at a final decision.

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The observed proportion of correct diagnoses in a random sample of mechanics is not necessarily the same as the MLE, as the MLE involves a more formal estimation procedure that considers the underlying probability distribution and maximizes the likelihood function based on the observed data.

The Maximum Likelihood Estimation (MLE) and the observed proportion of correct diagnoses in a random sample of mechanics are related concepts but not the same.

The MLE is a statistical method used to estimate the parameters of a probability distribution based on observed data. It seeks to find the parameter values that maximize the likelihood of observing the given data. In the case of a binomial distribution, which could be used to model the number of correct diagnoses, the parameter of interest is the probability of success (correct diagnosis) for each trial (mechanic).

In this context, if we have a random sample of 20 mechanics and observe that 15 of them made correct diagnoses, we can calculate the observed proportion of correct diagnoses as 15/20 = 0.75.

While the observed proportion can be considered an estimate of the underlying probability of success, it is not necessarily the same as the MLE. The MLE would involve maximizing the likelihood function, taking into account the specific assumptions and model chosen to represent the data. The MLE estimate may or may not coincide with the observed proportion, depending on the distributional assumptions and the specific form of the likelihood function.

In summary, the observed proportion of correct diagnoses in a random sample of mechanics is not necessarily the same as the MLE, as the MLE involves a more formal estimation procedure that considers the underlying probability distribution and maximizes the likelihood function based on the observed data.

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Answer:

It is important to analyze the relationships between a sample and full population for several reasons:

To help make more informed statements about a population: Analyzing the relationships between a sample and full population helps us to make more accurate and informed statements about the population. By examining a sample, we can draw conclusions about the characteristics of the larger population.

To bring diverging data into analysis: Sometimes, a sample may not accurately reflect the larger population, particularly if the sample is small or biased. Analyzing the relationship between the sample and population can help to identify any diverging data and adjust the analysis accordingly.

To limit variance: Variance is a measure of how much the data varies from the mean. By analyzing the relationship between the sample and population, we can better understand the variance and take steps to limit it.

To find out how many data points are necessary to be statistically valid: Analyzing the relationship between the sample and population can help us to determine the sample size needed to be statistically valid. This can help us to avoid drawing inaccurate conclusions based on a sample that is too small.

Overall, analyzing the relationship between a sample and full population is important for ensuring that our analysis is accurate, representative, and informative.

It is important to analyze the relationships between a sample and the full population to limit variance and to help make more informed statements about a population.

Analyzing the relationships between a sample and the full population is important because it helps limit variance in the data and brings diverging data into the analysis. It also helps to determine how many data points are necessary to be statistically valid and make more informed statements about the population.

By examining a sample and comparing it to the full population, we can identify any biases or errors in our data and ensure that our conclusions are accurate and representative of the entire population. Additionally, understanding the relationship between the sample and the population can help us make predictions and draw conclusions with greater confidence and precision.
By understanding the relationship, you can ensure that the sample accurately represents the population, leading to better inferences and decision-making.

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a) The average amount of time a customer spends in process at the campus deli is approximately 2 minutes, and b) the average number of skiers staying in Madesimo is 1,200 skiers.

a) The average amount of time that a customer spends in process can be determined using Little's formula. According to the formula, the work-in-process (WIP) is equal to the flow rate multiplied by the flow time. In this case, the flow rate is the number of customers served over the lunch period, which is 300. The WIP is the average number of customers in process at any point in time, which is 10. By rearranging the formula, we can solve for the flow time, which represents the average time a customer spends in process.

Thus, the flow time is equal to WIP divided by the flow rate. Therefore, the average amount of time that a customer spends in process is 10/300, or approximately 0.0333 hours (or 2 minutes).

b) To determine the average number of skiers staying in Madesimo, we can again apply Little's formula. The flow rate in this case is the average number of skiers coming to the resort per day, which is 120. The flow time represents the average duration of stay for skiers, which is given as 10 days. Using Little's formula, we can calculate the work-in-process (WIP), which represents the average number of skiers staying in Madesimo.

WIP is equal to the flow rate multiplied by the flow time. Therefore, the average number of skiers staying in Madesimo is 120 skiers/day multiplied by 10 days, resulting in 1,200 skiers on average.

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Answer:

triangle

Step-by-step explanation:

Answersheeeeeeeeeeeeeeeeeeeeeeeeeesssh

Step-by-step explanation:

The probability that the insurance representative will sell a policy to three out of four prospective clients is 0.0256.

The probability that the insurance representative sells to more than two prospective clients is 0.0272.

To calculate the probability that the insurance representative will sell a policy to a specific number of prospective clients out of four, we can use the binomial probability formula.

The binomial probability formula is given by:

P(X = k) = \((n C k) * p^k * (1 - p)^(n - k)\)

Where:

P(X = k) is the probability of exactly k successes (selling a policy) out of n trials (appointments).

(n C k) represents the combination or "n choose k" which calculates the number of ways to choose k successes out of n trials.

p is the probability of success on a single trial (probability of making a sale).

(1 - p) is the probability of failure on a single trial (probability of not making a sale).

Using this formula, we can calculate the probability of selling a policy to three out of four prospective clients.

Probability of selling a policy to three clients:

P(X = 3) = (4 C 3) * (0.20)^3 * (1 - 0.20)^(4 - 3)

Calculating:

P(X = 3) = 4 * 0.008 * 0.80

P(X = 3) = 0.0256

To calculate the probability that she sells to more than two clients, we need to sum the probabilities of selling to three clients and selling to all four clients.

Probability of selling to more than two clients:

P(X > 2) = P(X = 3) + P(X = 4)

Substituting the values:

P(X > 2) = \(0.0256 + (4 C 4) * (0.20)^4 * (1 - 0.20)^(4 - 4)\)

Calculating:

P(X > 2) = 0.0256 + 1 × 0.0016 × 1

P(X > 2) = 0.0272

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Answer:

\(9\)

Step-by-step explanation:

\( {2}^{3} + 4 \times 2 - 7 \\ 8 + 8 - 7 \\ 16 - 7 \\ 9\)

Hope this helps you.

Can I have the brainliest please?

Have a nice day !!!!!!!!!!!!!

Answer:

i am from brazil

Step-by-step explanation:

i can not answer

If andre lives with his wife and three children. 22 pounds of municipal solid waste his family generates each day.

It is defined as the ratio of the population to the total area. The population density represents the average number of people living in each square unit.

It is given that and lives with his wife and three children.

We have to find the mass of municipal solid waste Andre and his family generate each day.

From the data research 22 pounds of municipal solid waste, his family generates each day.

Thus, if Andre lives with his wife and three children. 22 pounds of municipal solid waste his family generates each day.

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Answer:

6x + 4y = 16

Step-by-step explanation:

2(3x + 2y = 8)

2(3x + 2y) = 2(8)

6x + 4y = 16

Answer:

6x+4y=16

Step-by-step explanation:

2 (3x+2y=8)

2×3x+2×2y=2×8

=6x+4y=16 ans.

The statement you provided mentions two separate events involving different companies lower rates

1. Hertz runs a sale: Hertz, a car rental company, is having a sale. This implies that they are offering in  discounted prices or promotional deals on their rental services.

2. Avis buys new cars and Budget lowers rates: Avis, another car rental company, is purchasing that  new cars to add to their fleet. On the other hand, Budget, yet another car rental   the company, is reducing their rental rates.

These events indicate of independent actions taken by the respective companies and are not directly connected to each other.

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=====================================================

Explanation:

x is some positive number such that the earnings of Paul, Collin, and Brian are 2x, 3x and 4x in that order

The ratio 2x:3x:4x reduces to 2:3:4 when we divide all parts by x

The total amount of tips was 77.40 pounds

This means,

(Paul's Share)+(Collin's Share)+(Brian's Share) = Total Tips

(2x)+(3x)+(4x) = 77.40

9x = 77.40

x = 77.40/9

x = 8.6

Use that x value to find each of their share of tips

As a check, note how 17.20+25.80+34.40 = 77.40

The last step is to subtract the values for Brian and Paul

34.40 - 17.20 = 17.20

Brian earned £17.20 more in tips compared to Paul.

Answer:2 pounds per hour

Step-by-step explanation:

Answer: 2b+12

Step-by-step explanation:

Because the number is in front of the bracket you have to times everything in the bracket by the number on the outside.

So,

2 times b is 2b

And

2 times 6 is 12

So it would be 2b+12

Answer:

Area refers to the space occupied by a shape or an object or a surface. Perimeter refers to the measure of the length of the outline or boundary of a shape, an object or a surface. Area is measured in square units.

Answer:

Perimeter :the continuous line forming the boundary of a closed geometrical figure.

Area is the quantity that expresses the extent of a two-dimensional region, shape, or planar lamina, in the plane