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A loaf of bread is cut into slices.

1. If each slice is 1/2 of a loaf, how many slices are there?

:

2. If each slice is 1/5 of a loaf, how many slices are there?

:

3. What happens to the number of slices as each slice gets smaller?

:

Answer:

area of netball court ≈ 467 m²

Step-by-step explanation:

Each of the 3 sections making up the netball court are the same size , then

area of netball court = 3 × 155.55 m² = 466.65 m² ≈ 467 m² ( nearest m² )

Answer:

X>_1 and x<_7

Step-by-step explanation:

A list of steps, in order, that will solve the following equation include the following:

In order to create a list of steps and determine the solution to the equation, we would have to add 5 to both sides and divide both sides by 2 in order to open the parenthesis as follows;

2(x + 3) – 5 = 123

2(x + 3) – 5 + 5 = 123 + 5

2(x + 3) = 128

By dividing both sides of the equation by 2, we have the following:

2(x + 3)/2 = 128/2

x + 3 = 64

By subtracting 3 from both sides of the equation, we have the following:

x + 3 - 3 = 64 - 3

x = 61

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A) The value of  x = 45 degrees

B) Lines AD and BC are not parallel when ABCD is drawn to scale.

To solve this problem, we can use the fact that the sum of the angles in a quadrilateral is 360 degrees.

A) angle A + angle B + angle C + angle D = 360

2x + 90 + x + 3x = 360

6x + 90 = 360

6x = 270

x = 45

Therefore, x = 45 degrees.

B) To determine if lines AD and BC are parallel, we can look at the opposite angles of the quadrilateral. If they are supplementary (add up to 180 degrees), then the lines are parallel.

angle A + angle C = 2x + x = 3x = 135 degrees

angle B + angle D = 90 + 3x = 90 + 135 = 225 degrees

Since angle A + angle C and angle B + angle D do not add up to 180 degrees, the opposite angles are not supplementary, and therefore, lines AD and BC are not parallel.

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The given question is incomplete, the complete question is:

ABCD is a quadrilateral A) Calculate the value of x. B) When ABCD is drawn to scale, would the lines AD and BC be parallel or not?

Answer:

b=sqrt7

Step-by-step explanation:

16=9+b^2

The probability mass function (PMF) for x is {0.0004, 0.0588, 0.3432, 0.941192}, and the cumulative mass function (CMF) for x is {0.0004, 0.0592, 0.4024, 1.0}.

The probability mass function (PMF) and cumulative mass function (CMF) for the random variable x, which denotes the number of parts correctly classified in an optical inspection system, can be determined.

Since the classifications of the parts are independent, we can use the binomial probability distribution to model this scenario. The PMF gives the probability of obtaining a specific value of x, and the CMF gives the probability of obtaining a value less than or equal to x.

The PMF of x is given by the binomial probability formula:

P(x) = (n C x) * p^x * (1 - p)^(n - x)

where n is the number of trials (number of parts inspected), x is the number of successes (number of parts correctly classified), and p is the probability of success (probability of correct classification of any part).

In this case, n = 3 (three parts inspected) and p = 0.98 (probability of correct classification).

Let's calculate the PMF for x:

P(x = 0) = (3 C 0) * (0.98^0) * (1 - 0.98)^(3 - 0) = 0.0004

P(x = 1) = (3 C 1) * (0.98^1) * (1 - 0.98)^(3 - 1) = 0.0588

P(x = 2) = (3 C 2) * (0.98^2) * (1 - 0.98)^(3 - 2) = 0.3432

P(x = 3) = (3 C 3) * (0.98^3) * (1 - 0.98)^(3 - 3) = 0.941192

The PMF for x is:

P(x = 0) = 0.0004

P(x = 1) = 0.0588

P(x = 2) = 0.3432

P(x = 3) = 0.941192

To calculate the CMF, we sum up the probabilities up to x:

F(x) = P(X ≤ x) = P(x = 0) + P(x = 1) + ... + P(x = x)

Using the calculated probabilities, the CMF for x is:

F(x = 0) = 0.0004

F(x = 1) = 0.0592

F(x = 2) = 0.4024

F(x = 3) = 1.0

Therefore, the probability mass function (PMF) for x is {0.0004, 0.0588, 0.3432, 0.941192}, and the cumulative mass function (CMF) for x is {0.0004, 0.0592, 0.4024, 1.0}.

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Answer:

POINTFARMER.EXE=EZ

Step-by-step explanation:

Answer:

120 feet^2

Step-by-step explanation:

Answer:

the answer is 9025

Step-by-step explanation:

\( \sqrt{95 \times 19 \times 5 \\} 95\) it equal 95

The estimated crop yield from the simulation is 443 (option b).

To estimate the crop yield from the given random numbers, we need to assign a specific meaning to each random number. Let's assume that each random number represents the crop yield for a particular year.

Given random numbers: 37, 23, 92, 01, 69, 50, 72, 12, 46, 81

To find the estimated crop yield, we sum up all the random numbers:

37 + 23 + 92 + 01 + 69 + 50 + 72 + 12 + 46 + 81 = 443

Therefore, the estimated crop yield from the simulation is 443. The correct option is b.

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The slope of the regression line is  b=61.02 and y-intercept is a=3.12

Regression analysis is a set of statistical processes used to estimate the relationships between a dependent variable and one or more independent variables. Linear regression is the most popular type of regression analysis, in which one finds the line (or a more sophisticated linear combination) that best fits the data according to a certain mathematical criterion. The ordinary least squares method, for example, finds the unique line (or hyperplane) that minimizes the sum of squared discrepancies between the genuine data and that line (or hyperplane). This enables the researcher to estimate the

Given,

The regression line is y=3.12+61.02 s

Here the slope is b=61.02

The slope indicates that the distance is increased by 61.02 yards for every one mile per hour of the speed.

And the y-intercept is a=3.12

It is the distance estimated by this model if the speed is zero miles per hour.

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Answer:

5x- 10

20x=5

3= 17x

25-10x

6x=12

Step-by-step explanation:

Answer:

1/2 = 3/6 and 1/3 = 2/6

Step-by-step explanation:

we need to make the denominators the same to be able to compare

Answer:

The answer is 25 weeks

Step-by-step explanation:

divide 50 by 2. :D

56/120

Step-by-step explanation:

The body masses of the two children in terms of their father's body mass, x, are:

First child's body mass = 5x/6 kg

Second child's body mass = 4x/5 kg

To express the body mass of the man's two children in terms of their father's body mass, we can use the given ratios.

Let the body mass of the man be x kg.

The first child's body mass is five-sixths of their father's body mass:

Body mass of the first child = (5/6) * x

= 5x/6 kg.

The second child's body mass is four-fifths of their father's body mass:

Body mass of the second child = (4/5) * x

= 4x/5 kg.

Therefore, the body masses of the two children in terms of their father's body mass, x, are:

First child's body mass = 5x/6 kg

Second child's body mass = 4x/5 kg

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A sampling distribution is a way of representing the frequency distribution of a statistic. It can be generated by taking repeated random samples of the same size from a population and calculating the statistic of interest for each sample.

The resulting distribution of these statistics is called the sampling distribution of the statistic.

A sampling distribution with high bias but low variability is one where the sample statistics are consistently biased but not spread out widely. In other words, the sample statistics are clustered around a certain value that is different from the true population parameter, but there is not much variation among the sample statistics.

For example, if we were to take repeated random samples of size 10 from a population with a mean of 50 and a standard deviation of 10, and calculate the mean for each sample, we might get a sampling distribution like this:

In this case, the sampling distribution has a high bias because the sample means are consistently underestimating the population mean by about 5 points.

However, the variability of the sample means is relatively low because they are not spread out widely around the biased value. This might be due to some systematic error in the sampling process or measurement technique that is causing the bias but not introducing much additional variation.

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Assume a stick of length 1 is randomly broken into two portions. The smaller piece's estimated length is 0.25.

The random variable L (the length for the shorter stick) has a uniform distribution.

Thus the probability density function for L is as follows:

This means 2 for all length in the range 0 - 0.5

Now, we calculate the expected value for random variable L as seen on the attachment. Based on the calculation, 0.25 is the smaller piece's estimated length for the randomly broken stick.

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Answer

omgggggggg same i dont know how to do it either

Step-by-step explanation:

Answer:

there is 11 min all to together for sage and tom so they did not have the same cell phone line

Step-by-step explanation:

its easy

To compute the line integral ∫F·dr, where F(t) = (ez, xzez, xyez) and C is a path starting at (1,2,3) and ending at (4,5,6), we can use the Fundamental Theorem of Line Integrals.

First, we parameterize the path C. Let's choose a parameterization r(t) = (x(t), y(t), z(t)) such that r(0) = (1,2,3) and r(1) = (4,5,6). One possible parameterization is:

x(t) = 1 + 3t
y(t) = 2 + 3t
z(t) = 3 + 3t

Now, we can compute F(r(t)) and dr/dt:

F(r(t)) = (e^(3 + 3t), (1 + 3t)(e^(3 + 3t)), (1 + 3t)(2 + 3t)(e^(3 + 3t)))
dr/dt = (3, 3, 3)

Next, we calculate the dot product F(r(t))·dr/dt:

F(r(t))·dr/dt = (e^(3 + 3t), (1 + 3t)(e^(3 + 3t)), (1 + 3t)(2 + 3t)(e^(3 + 3t)))·(3, 3, 3)

Taking the dot product and integrating over the interval [0,1], we have:

∫[0,1] F(r(t))·dr/dt dt = ∫[0,1] (3e^(3 + 3t) + 3(1 + 3t)e^(3 + 3t) + 3(1 + 3t)(2 + 3t)e^(3 + 3t)) dt

Evaluating this integral will yield the numerical value of the line integral over the given path.

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Answer:

-1.42857

Step-by-step explanation:

firm 2's quantity at the Cournot equilibrium is q2=5/3.the inverse demand function: p=10−Q=10−(q1+q2)=10−(11/5+4/5)=4. firm 2's quantity at the new Cournot equilibrium is q2=4/5.

The best response of firm 1 is given by 1q1=5−0.5q21+q2, and the best response of firm 2 is given by 2q2=5−0.5q12+q1. These best response functions are illustrated in the following diagram:
At the Cournot equilibrium, the two firms' quantities will be such that they are both choosing the best response to the other's quantity, which means that they will be producing where the two functions intersect.

Therefore, solving the two best response functions simultaneously for q1 and q2 gives:
q1=q2=5/3
Therefore, firm 2's quantity at the Cournot equilibrium is q2=5/3.
The new best response function for firm 2 is given by 2q2=5−q1+2q22. This is obtained by replacing c2=2 with c2=4 in firm 2's profit function. The new best response function is illustrated below:

At the new Cournot equilibrium, the two firms' quantities will be such that they are both choosing the best response to the other's quantity, which means that they will be producing where the two functions intersect. Therefore, solving the two best response functions simultaneously for q1 and q2 gives:
q1=11/5
q2=4/5
Therefore, firm 2's quantity at the new Cournot equilibrium is q2=4/5.

To determine the market price at the new Cournot equilibrium, we substitute the new equilibrium quantities into the inverse demand function:
p=10−Q=10−(q1+q2)=10−(11/5+4/5)=4
Therefore, the market price at the new Cournot equilibrium is p=4.

In this exercise, we consider a market with two firms, 1 and 2, facing the inverse demand p()=10− p(Q)=10-Q where =1+2Q=q1+q2.

The two firms incur a marginal cost of production c1=c2=2c1=c2=2, produce a homogeneous good, and are Cournot competitors. The best response of firm 1 is given by 1q1=5−0.5q21+q2, and the best response of firm 2 is given by 2q2=5−0.5q12+q1. These best response functions are illustrated in the diagram below:

At the Cournot equilibrium, the two firms' quantities will be such that they are both choosing the best response to the other's quantity, which means that they will be producing where the two functions intersect

. Therefore, solving the two best response functions simultaneously for q1 and q2 gives q1=q2=5/3. Therefore, firm 2's quantity at the Cournot equilibrium is q2=5/3.The new best response function for firm 2 is given by 2q2=5−q1+2q22. This is obtained by replacing c2=2 with c2=4 in firm 2's profit function.

The new best response function is illustrated in the following diagram:At the new Cournot equilibrium, the two firms' quantities will be such that they are both choosing the best response to the other's quantity, which means that they will be producing where the two functions intersect.

Therefore, solving the two best response functions simultaneously for q1 and q2 gives q1=11/5 and q2=4/5. Therefore, firm 2's quantity at the new Cournot equilibrium is q2=4/5

To determine the market price at the new Cournot equilibrium, we substitute the new equilibrium quantities into the inverse demand function: p=10−Q=10−(q1+q2)=10−(11/5+4/5)=4. Therefore, the market price at the new Cournot equilibrium is p=4.

In conclusion, we have analyzed the effect of a raising rival's cost strategy on a Cournot duopoly. We have shown that the strategy reduces firm 2's quantity and increases the market price, but does not affect firm 1's quantity.

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This means that out of all possible outcomes, there is only one outcome where both dice show a one, and this outcome has a relatively low probability compared to other outcomes.

When two dice are rolled, the probability of getting a one on both is 1/36. This means that out of all possible outcomes, the chance of rolling a one on both dice is 1 out of 36. This can be further explained as follows:
1. When two dice are rolled, there are a total of 36 possible outcomes since each die has 6 possible outcomes (1, 2, 3, 4, 5, or 6) and there are 6 possibilities for the first die and 6 possibilities for the second die (6 x 6 = 36).
2. Out of these 36 possible outcomes, there is only one outcome where both dice show a one (1). Therefore, the probability of getting a one on both dice is 1 out of 36 (1/36).
3. This probability is relatively low, indicating that it is less likely to roll a one on both dice compared to other outcomes. The probability of getting a one on both dice can be considered a rare event.
In conclusion, when two dice are rolled, the probability of getting a one on both is 1/36. This means that out of all possible outcomes, there is only one outcome where both dice show a one, and this outcome has a relatively low probability compared to other outcomes.

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Answer:

The number is 76

Step-by-step explanation:

Answer:

w=7  5/8

l = 29.875

Step-by-step explanation:

We have to use system of equations here

Let l be length and w be width

l = 3w+7

w=l(1/3)-7

We can use the equation:

l+w+l+w = 75

2l+2w = 75

Now we substitute l for what we said above

2(3w+7)+2w=75

6w+14+2w=75

Combine like terms

8w+14=75

Subtract 14 on both sides

8w=61

w=7  5/8

We can use what we know about the relationship of the width and the length to find the length

7  5/8 * 3 + 7 = 29.875

l = 29.875

1. The variable used to predict another variable is called the dependent variable.

2. The predicted attendance if the temperature is 90 degrees would be 9,750.

3. The computed test statistic, rounded to three decimal places, would be approximately 2.071.

1. The variable used to predict another variable is called the dependent variable. It is the variable that is being predicted or explained by the independent variable.

The variable used to predict another variable is called the dependent variable.

2. The given equation is Attendance = 16,500 - 75 * Temperature.

If Temperature is 90 degrees, we can substitute this value into the equation to find the predicted attendance.

Attendance = 16,500 - 75 * 90

Attendance = 16,500 - 6,750

Attendance = 9,750

Therefore, the predicted attendance if the temperature is 90 degrees would be 9,750.

The predicted attendance if the temperature is 90 degrees would be 9,750.

3. To calculate the test statistic, we need to use the formula:

test statistic = (sample correlation coefficient * sqrt(sample size - 2)) / sqrt(1 - sample correlation coefficient^2)

Given:

Sample size (n) = 25

Sample correlation coefficient (r) = 0.60

Substituting these values into the formula:

test statistic = (0.60 * sqrt(25 - 2)) / sqrt(1 - 0.60^2)

test statistic ≈ (0.60 * sqrt(23)) / sqrt(1 - 0.36)

test statistic ≈ (0.60 * sqrt(23)) / sqrt(0.64)

test statistic ≈ (0.60 * sqrt(23)) / 0.8

test statistic ≈ 1.380 / 0.8

test statistic ≈ 1.725

Rounding to three decimal places, the computed test statistic would be approximately 2.071.

The computed test statistic, rounded to three decimal places, would be approximately 2.071.

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Answer:

1/3

Step-by-step explanation:

10 students have a brother.

12 students have a sister.

4 students have a brother and a sister.

6 students have only a brother.

8 students have only a sister.

4 students have a brother and a sister.

Given that a student has a sister, there are 12 such students (8 with only a sister and 4 with a brother and a sister).

Of those 12 with a sister, there are 4 with a brother and a sister, so there are 4 out of 12 with a sister who also have a brother.

p = 4/12 = 1/3

Mary and her father traveled 105 miles in the morning.

So, from the equation:

Then, solve as follows:

Therefore, they traveled 105 miles in the morning.

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In this case, the experimental probability is D. 0.860

First, note that Experimental probability, also known as Empirical probability, is founded on real experiments and adequate documentation of events.

In the table we can see that he rolled the cube 1000 times, and he recorded that 140 of those times he rolled a 5.

Then, the probability of rolling a 5 will be equal to:

P1 = 140/1000 = 0.14

Now, the probabilty of NOT rolling a 5, is equal to the rest of the probabilities, this is:

P2 = 1 - 0.14 = 0.86

then the correct option is D

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Full Question:

Although part of your question is missing, you might be referring to this full question:

Jimmy rolls a number cube multiple times and records the data in the table above. At the end of the experiment, he counts that he had rolled 140 fives. Find the experimental probability of not rolling a five, based in Jimmy’s experiment. Round the answer to the nearest thousandth.

A. 0.140

B. 0.167

C. 0.667

D. 0.860