In a company's survey of 500 employees, 25% said they go to the gym at least twice a week. The margin of error is ±2%. If the company has approximately 2,500 employees, what is the estimated maximum number of employees going to the gym at least twice a week?

If two rows of a 3×3 matrix A are the same, then detA = 0.

True, consider a 3 × 3 matrix A with two identical rows.

The determinant of a matrix is a scalar value that encodes various properties of the matrix.

One property of the determinant is that it changes sign if two rows (or two columns) of the matrix are interchanged.

Another property is that if two rows (or two columns) of the matrix are the same, then the determinant is zero.

Without loss of generality, assume that the first and second rows of A are the same.

Interchange the first and third rows of A using an elementary row operation without changing the value of the determinant, since this operation changes the sign of the determinant.

Then, we obtain a matrix B of the form:

[ a11 a12 a13 ]

[ a11 a12 a13 ]

[ a31 a32 a33 ]

Now, we can expand the determinant of B along the first column to get:

det(B) = a11 × det(B11) - a31 × det(B31)

B11 and B31 are the 2x2 matrices obtained by deleting the first row and the first column, and the third row and the first column of B, respectively.

Since the first and second rows of B are identical, we have det(B11) = 0. Hence, we obtain:

det(B) = a11 × det(B11) - a31 × det(B31) = -a31 × det(B31)

Now, we can expand the determinant of B31 along its first column to get:

det(B31) = a12 × a33 - a32 × a13

Substituting this into the previous expression, we obtain:

det(B) = -a31 × det(B31) = -a31 × (a12 × a33 - a32 × a13)

This shows that the determinant of A is zero, since det(A) = det(B) by elementary row operations.

If two rows of a 3 × 3 matrix A are the same, then detA = 0.

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30% of the scores in the dataset fall within one standard deviation of the mean.

To determine the percent of scores in a data table that fall within one standard deviation of the mean, you need to follow these steps:

Calculate the mean and standard deviation of the dataset.

Determine the lower and upper bounds of one standard deviation by subtracting the standard deviation from the mean to get the lower bound, and adding the standard deviation to the mean to get the upper bound.

Count the number of data points in the dataset that fall within the lower and upper bounds of one standard deviation.

Divide the number of data points within one standard deviation by the total number of data points in the dataset, and multiply the result by 100 to get the percentage of scores that fall within one standard deviation of the mean.

Let's say you have a dataset with a mean of 50 and a standard deviation of 10.

To determine the percent of scores that fall within one standard deviation of the mean, you would calculate the lower and upper bounds of one standard deviation as follows:

Lower Bound = 50 - 10 = 40

Upper Bound = 50 + 10 = 60

The number of data points in the dataset that fall within the lower and upper bounds of one standard deviation.

Let's say there are 30 data points that fall within this range.

The number of data points within one standard deviation by the total number of data points in the dataset, and multiply the result by 100 to get the percentage of scores that fall within one standard deviation of the mean:

Percent Within One Standard Deviation

= (30/100) × 100 = 30%

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The force on the square loop is F = i(a²)(B), where B is the strength of the magnetic field in the x direction.

A current-carrying loop refers to a closed circuit consisting of a conductor (usually a wire) bent into the shape of a closed loop, through which an electric current flows. The loop may be any shape or size, but its geometry and the amount of current flowing through it determine the magnetic field it produces.

When a current-carrying loop is placed in a magnetic field, a force is exerted on the loop due to the interaction between the magnetic field and the current. The force on the loop can be calculated using the following formula:

=> F = iABsinθ

where F is the force on the loop, i is the current flowing through the loop, A is the area of the loop, B is the magnetic field, and θ is the angle between the normal to the loop and the direction of the magnetic field.

In this case, the loop is in the yz plane and centered at the origin. The magnetic field is assumed to be in the x direction. Since the loop is perpendicular to the x direction, the angle between the normal to the loop and the magnetic field is 90 degrees.

The area of the loop is given by A = a².

The current flowing through the loop is i.

Thus, the force on the loop is given by:

F = iABsinθ = i(a^2)(B)(sin90) = i(a^2)(B)

Therefore, the force on the square loop is F = i(a^2)(B), where B is the strength of the magnetic field in the x direction.

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The length of AB is 10m.

Let's assume that the length of AC = x,

According to the problem statement, AB= 2x ...........(1)

Also, BC is 10 cm shorter than AB, so,

BC=AB-10 .......(2)

The perimeter of the triangle is the sum of its three sides:

therefore,

1.95=AB+AC+BC

We can put the value from eq(1) & eq(2)

1.95=2x+x+(AB-10)

AB= 3x+8.05............... (3)

Now, we need to find the value of x

1.95= 2x+x+(AB-10)

or, 1.95 = 3x+ AB-10

Adding 10 to both sides

11.95 = 3x+AB.......(4)

Substituting the expression, we found for AB:

11.95=3x+3x=8.05

11.95= 6x

x= 0.65

So, the value of AB is

AB = 3x+8.05

AB = 3(0.65)+8.05

AB = 10 m

Therefore, The length of AB is 10m.

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Once you have the required numerical values, plug them into the formula and calculate the minimum sample size needed for Big Corporation to be confident in their estimate of the mean amount of coffee dispensed per cup

We need to determine the minimum sample size required for Big Corporation to estimate the mean amount of coffee dispensed per cup with a certain level of confidence and margin of error.

Unfortunately, some of the numerical values are missing in question.

Step 1: Determine the desired confidence level (e.g., 90%, 95%, 99%).
Step 2: Identify the desired margin of error (e.g., within 0.1 ounces).
Step 3: Given the standard deviation of cup amounts (missing in the question, let's assume it as "σ").

Now, use the formula for determining the minimum sample size needed:

n = (Z² * σ²) / E²

where:
n = minimum sample size
Z = Z-score corresponding to the desired confidence level (e.g., 1.96 for 95% confidence)
σ = standard deviation of cup amounts
E = margin of error

Step 4: Calculate the minimum sample size (n) using the formula and round up to the nearest whole number.

By numerical values, plug them into the formula and calculate the minimum sample size needed for Big Corporation to be confident in their estimate of the mean amount of coffee dispensed per cup.

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The hypothesis used for consumer panel test are null hypothesis when μ = 8 and alternative hypothesis when μ < 8.

And α = 0.01 make it easier for consumer panel to conclude that manufacturer is cheating its customers

Bottles are label with 8 oz.

Random sample contained an average of 7.8 oz.

The consumer panel should test the following hypotheses,

Null hypothesis,

H₀: The population mean amount of liquid in the bottles is equal to 8 oz.  μ = 8.

Alternative hypothesis,

Hₐ, The population mean amount of liquid in the bottles is less than 8 oz.  μ < 8.

Here, H₀ represents the null hypothesis that the manufacturer is not cheating customers,

while Hₐ represents the alternative hypothesis that the manufacturer is cheating customers by underfilling the bottles.

To determine which value of α would make it easier for the consumer panel to conclude that the manufacturer is cheating its customers,

Consider the level of significance of the test.

The level of significance α is the probability of rejecting the null hypothesis when it is actually true a Type I error.

A smaller value of α makes it less likely to reject the null hypothesis and more difficult to conclude that the manufacturer is cheating customers.

Conversely, a larger value of α makes it more likely to reject the null hypothesis.

And easier to conclude that the manufacturer is cheating customers.

Given the serious nature of the accusation,

A conservative approach would be appropriate, and the consumer panel may want to use a lower value of α to minimize the risk of a Type I error.

This implies, of the three given values α = 0.01 would make it easier for  consumer panel to conclude that manufacturer is cheating its customers.

As it represents the smallest value of α and the most conservative approach.

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The standard deviation of the number of patients who are successfully treated is approximately 2.53.

To find the standard deviation of the number of patients who are successfully treated, we need to use the formula for the standard deviation of a binomial distribution:

σ = √(np(1-p))

where σ is the standard deviation, n is the number of trials, p is the probability of success on a single trial.

In this case, n = 40 (the number of patients) and p = 0.80 (the probability of success). So we can plug these values into the formula:

σ = √(40 x 0.80 x (1 - 0.80))
  = √(40 x 0.80 x 0.20)
  = √(6.4)
  = 2.53

Therefore, the standard deviation of the number of patients who are successfully treated is 2.53.

To find the standard deviation of the number of patients who are successfully treated, we need to use the binomial distribution formula, where n is the number of trials (patients), and p is the probability of success (successful treatment).

In this case, n = 40 and p = 0.80. The formula for the standard deviation (σ) of a binomial distribution is:

σ = √(n × p × (1 - p))

Plugging in the values, we get:

σ = √(40 × 0.80 × (1 - 0.80))
σ = √(40 × 0.80 × 0.20)
σ = √(6.4)

Therefore, the standard deviation of the number of patients who are successfully treated is approximately 2.53.

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The range of the function p(x) = {x² +5x+6, x<-4  ,and x+4, x ≥ -4} for the domain {-6, -4, -2, 0, 2} is equal to {0, 2, 6, 12, 20}.

The function is equal to,

p(x) = {x² +5x+6, x<-4  ,

          x+4, x ≥ -4}

Domain of the function is equal to {-6, -4, -2, 0, 2}

First, let us evaluate the function for each value to get the range in the domain,

For x = -6 ,

p(x) = (-6)² + 5(-6) + 6

      = 12,

For x = -2

p(x) =  (-2)² + 5(-2) + 6

       = 0.

For x = 0,

p(x) = 0² + 5(0) + 6

      = 6.

For x = -4,

p(x) = (-4)² + 5(-4) + 6

      = 2

also p(x) = x+ 4 ,  x ≥ -4

p(x) = -4 + 4

      = 0

For x = 2,

p(x) = 2² + 5(2) + 6

       = 20.

Therefore, the range of the function is {0, 2, 6, 12, 20} for the given domain.

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To calculate the minimum sample size needed, we can use the formula: n = (z * σ / E)^2

Where:

z = the z-score associated with the desired level of confidence (let's assume 95% confidence, so z = 1.96)

σ = the standard deviation of the population (given in the problem statement)

E = the maximum error margin (given in the problem statement)

Plugging in the values, we get:

n = (1.96 * σ / E)^2

To determine the value of σ/E, we need more information. Let's assume that the maximum error margin E is 0.5, which means that the psychologist wants to be within 0.5 points of the true population mean si score.

Now, let's say that σ = 10 (just as an example). Then, σ/E = 10/0.5 = 20.

Plugging this into the formula, we get:

n = (1.96 * 10 / 0.5)^2 = 384.16

Rounding up to the nearest whole number, the minimum sample size needed is 385.

Therefore, the psychologist would need to take a random sample of at least 385 si scores from smokers to be confident that her estimate of the population mean si score is within 0.5 points.

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The approximate probability that the random sample of 50 will contain 38% or more orange candies is 0.966 or about 96.6%.

The number of orange candies in a sample of 50 candies as a binomial random variable with n = 50 and p = 0.30, p is the probability of selecting an orange candy.

The probability that the sample contains 38% or more orange candies, we need to calculate the cumulative probability of the binomial distribution from x = 0 to x = 19, and subtract it from 1.

Here, x is the number of orange candies in the sample, and 19 is the largest integer less than or equal to 0.38 × 50=19.

Using a binomial calculator or software, we can find that the cumulative probability of the binomial distribution from x = 0 to x = 19 is approximately 0.034.

The probability that the sample contains 38% or more orange candies is approximately:

1 - 0.034 = 0.966

The approximate probability that the random sample of 50 will contain 38% or more orange candies is 0.966 or about 96.6%.

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Some examples of nutrient- and energy-dense foods that can be beneficial for people who are underweight are nuts and nut butters, Avocado, whole eggs, Full Fat Dairy, Fatty Fish, Dried Fruit, Whole grain bread and pasta, olive oil, sweet potatoes.

Nuts are high in healthy fats, protein, and calories, making them an excellent choice for people who are underweight. Nut butters, such as almond butter, peanut butter, and cashew butter, are also good options.

Avocado is high in healthy fats, fiber, and calories, making it a great choice for adding extra nutrients and calories to meals.

Whole eggs are a good source of protein, healthy fats, and calories, making them a nutrient-dense food that can help with weight gain.

Full-fat dairy products, such as whole milk, cheese, and yogurt, are high in calories and protein, making them a good choice for people who are underweight.

Fatty fish, such as salmon, tuna, and mackerel, are high in protein and healthy fats, making them an excellent choice for weight gain.

Dried fruit is a concentrated source of calories and nutrients, making it a good option for adding extra energy to meals or as a snack.

Whole-grain bread and pasta are higher in nutrients and fiber than their white counterparts and can help increase caloric intake.

Olive oil is high in healthy fats and calories, making it a good choice for adding flavor and nutrition to meals.

Sweet potatoes are a nutrient-dense carbohydrate source that can provide additional calories and nutrients to meals.

Overall, choosing nutrient- and energy-dense foods can be an effective way for people who are underweight to increase their caloric intake and promote healthy weight gain.

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-- The given question is incomplete, the complete question is

"Give some examples of nutrient- and energy-dense foods that can be beneficial for people who are underweight." --

Based on these z-scores, we can say that Jenny performed better relative to her classmates on the English test than the statistics test, despite the lower numerical score.

Jenny's performance on her statistics and English tests based on the given z-scores.

Let me explain the concept of z-scores and how they can be used to interpret her performance.
Z-scores are a measure of how many standard deviations a data point is from the mean of a distribution.

Jenny's z-scores tell us how far her test scores are from the average scores of her classmates in each subject.
Jenny's statistics test score is 86, with a z-score of 0.99.

Her English test score is 82, with a z-score of 1.5.

A z-score of 1.5 indicates that her English test score is 1.5 standard deviations above the mean of her English class, A z-score of 0.99 indicates that her statistics test score is 0.99 standard deviations above the mean of her statistics class.

A higher z-score means that her performance in that subject is further above the class average.
Even though Jenny scored 86 on her statistics test and 82 on her English test, she actually performed better relative to her classmates on the English test.

This is because her English test z-score (1.5) is higher than her statistics test z-score (0.99), indicating a better performance in English compared to her peers.

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For a = 1 and m = 1 does f(x) have a horizontal asymptote at y = 2 and a vertical asymptote at x = 1.

To find the values of a and m that give the function f(x) a horizontal asymptote at y = 2 and a vertical asymptote at x = 1, we need to analyze the behavior of the function as x approaches 1 and as x goes to infinity.

When x approaches 1 from the left and right sides, the denominator of f(x) approaches 0, so there is a vertical asymptote at x = 1. To have a vertical asymptote at x = 1, the numerator of f(x) cannot approach 0 as x approaches 1, so m must be greater than or equal to 1.

When x goes to infinity or negative infinity, the function f(x) approaches 2, which means there is a horizontal asymptote at y = 2. To have a horizontal asymptote at y = 2, the degree of the numerator must be equal to or less than the degree of the denominator. The degree of the numerator is m, and the degree of the denominator is 1.

So, the values of a and m that give f(x) a horizontal asymptote at y = 2 and a vertical asymptote at x = 1 are:

a = 1, m = 1

Substituting a = 1 and m = 1 into f(x), we get:

f(x) = 2x/(x+1)

which has a vertical asymptote at x = 1 and a horizontal asymptote at y = 2.

Therefore, the answer is a = 1, m = 1. The other values of a and m do not give a vertical asymptote at x = 1 and/or a horizontal asymptote at y = 2.

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Answer:

CCCCCCCC

Step-by-step explanation:

Edg 2023

The most likely to happen to "job-market" in a weak economy is (a) The unemployment rate rises, and companies go out of business or "lay-off" employees due to insufficient profits.

In a weak economy, the companies experience a decrease in their sales and revenue, which leads to financial difficulties. So, in an attempt to reduce costs and remain profitable, the companies "lay-off" employees or go out of business, resulting in an increase in the unemployment rate.

The weak economy also means that there are fewer job opportunities available, which make it harder for people who are unemployed to find work. So, a weak economy generally has a negative impact on the job market.

Therefore, the correct option is (a).

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The given question is incomplete, the complete question is

What is most likely to happen to the job market in a weak economy?

(a) The unemployment rate rises, and companies go out of business or lay off employees due to insufficient profits.

(b) The unemployment rate decreases because people have no money and want to take any job.

(c) Businesses usually hire more people to help make the economy strong and people have more money to spend on luxuries.

(d) Employment rates rise and employers have more people to choose from when they need to hire new employees.

I would be rich

$ 3904000 would be my net wealth

The value of net wealth after 40 years is,

= $304,000

To multiply means to add a number to itself a particular number of times. Multiplication can be viewed as a process of repeated addition.

Given that;

When you spent $200 per month on lottery tickets, and you were very lucky and won $100,000 once every 10 years.

Hence, Total spent money in 10 years is,

= $200 x 10 x 12

= $24,000

And, Total earning in 10 years = $100,000

So, The value of net worth in 10 years = $100,000 - $24,000

                                                           = $76,000

Thus, The value of net wealth after 40 years is,

= 4 x 76,000

= $304,000

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The true statements are:

Each ticket costs $12.25. (option b)

The one-time processing fee costs $3.75. (option c)

A linear equation is an equation with a single variable raised to the power of one.

y = mx + c

m = variable cost

c = fixed cost

Fixed cost is a cost that remains constant regardless of the number of tickets bought. Variable cost is a cost that changes with the number of tickets bought.

C = 12.25n + 3.75

Where:

12.25 - variable cost - cost of each ticket. This cost increases with the number of tickets that is purchased. As more tickets are purchased, the cost increases by 12.25.

3.75 - fixed cost - one time online processing fee. This is because this payment is made once and it does not increase with the number of tickets bought.

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The unit rate of the juice is 0.5 carrot juice per green apple juice

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

This means that

Carrot juice = 2

Green apple juice = 4

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

Unit rate = Carrot juice/Green apple juice

Substitute the known values in the above equation, so, we have the following representation

Unit rate = 2/4

Evaluate

Unit rate = 0.5

Hence, the unit rate is 0.5 carrot juice per green apple juice

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K-fold cross-validation generally produces an estimate with lower bias compared to leave-one-out cross-validation.(option b).

Cross-validation is a popular technique used in machine learning to estimate the performance of a model on unseen data. There are different methods of cross-validation, including leave-one-out and K-fold. In leave-one-out, only one data point is left out for testing, while in K-fold, a Kth portion of the data is held out.

Now, to answer the question of which method generally produces an estimate with higher bias, we need to look at the formula for bias in cross-validation. The bias is given by the difference between the expected value of the cross-validation estimate and the true performance of the model.

For K-fold cross-validation, the expected value of the estimate is computed as the average of the estimates obtained in each of the K folds. This means that the bias is dependent on the number of folds used. The more folds used, the lower the bias is likely to be.

Hence the correct option is (b).

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Answer: 3

Step-by-step explanation:

The scale factor is the ratio of corresponding side lengths of the two triangles.

In the given triangles, the length of the corresponding sides of the triangles are:

For the first triangle:

Side A = 15 units

Side B = 15 units

For the second triangle:

Side A = 45/2 units

Side B = 45/2 units

To find the scale factor, we can divide the length of one side of the second triangle by the corresponding length of the first triangle.

For example, we can use Side A:

Scale factor = (length of Side A of the second triangle) / (length of Side A of the first triangle)

Scale factor = (45/2) / 15

Scale factor = (45/2) * (1/15)

Scale factor = 3/2

Since the two triangles are scaled copies of each other, the scale factor should be the same for all corresponding sides.

Thus, the scale factor is 3 (in simplest form).

Answer: x=-7 y=7

Step-by-step explanation:

The solutions of the equation f(x) = g(x) include the following: B. -3, 0, 2, -4.

In Mathematics and Geometry, a point of intersection simply refers to the location on a graph where two (2) lines intersect or cross each other, which is primarily represented as an ordered pair containing the point that corresponds to the x-coordinate (x-axis) and y-coordinate (y-axis) on a cartesian coordinate.

By critically observing the graph of the lines representing the given system of equations, we can reasonably and logically deduce that the correct solution set lies in both Quadrant II and IV and it is denoted by the point of intersection of both the x-coordinate (x-axis) and y-coordinate (y-axis), which is given by this ordered pair (-3, 0) and (2, -4).

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The fraction of people on the band's mailing list who have seen the band play at least once is 3/4.

To find the fraction of people who have seen the band play at least once, we need to add the fraction of people who have seen the band exactly once to the fraction of people who have seen the band more than once.

Fraction of people who have seen the band exactly once = 3/10

Fraction of people who have seen the band more than once = 9/20

To find the fraction of people who have seen the band play at least once, we need to add these two fractions

Fraction of people who have seen the band at least once = 3/10 + 9/20

We need to find a common denominator to add these two fractions. The common denominator of 10 and 20 is 20.

Fraction of people who have seen the band at least once = (3/10) * (2/2) + (9/20) * (1/1)

= 6/20 + 9/20

= 15/20

= 3/4

Therefore, 3/4 of the people on the band's mailing list have seen the band play at least once.

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There will be 1, 2, 6, 21 non-isomorphic simple graphs for 2, 3, 4 and 5 vertices.

What are non-isomorphic simple graphs?

A non-isomorphic simple graph is a graph that is distinct from another graph, even if the two graphs have the same number of vertices and edges, and the same connectivity pattern. In other words, two graphs are non-isomorphic if they cannot be transformed into each other by a relabeling of their vertices.

For a small number of vertices, we can enumerate all non-isomorphic simple graphs by hand.

For n = 2, there is only one possible graph, which is the edge connecting the two vertices.

For n = 3, there are only two possible graphs: a triangle (complete graph on 3 vertices) and a single edge with an isolated vertex.

For n = 4, there are six possible graphs:

Complete graph on 4 vertices

Cycle graph on 4 vertices

Complete bipartite graph K2,2

Graph with a central vertex adjacent to all other vertices

Graph with two vertices of degree 3 and two vertices of degree 1

Graph with one vertex of degree 3 and three vertices of degree 1

For n = 5, there are 21 possible graphs, which can be generated by adding edges to the graphs for n = 4.

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The exponential model for this situation is: P(t) = 1,872(1 + 0.016)^t

To write an exponential model representing the given situation, we'll use the formula: P(t) = P₀(1 + r)^t, where P(t) is the population at time t, P₀ is the initial population, r is the growth rate, and t is the number of years.

Where p is the population at any given year t, 1,872 is the initial population, and 1.6% increase is represented by multiplying 1.016 to the power of t. This model assumes continuous and unrestricted growth of the population.

So, the exponential model for this situation is:

P(t) = 1,872(1 + 0.016)^t

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A) the error term is 0.16039
B) number of households required to get a 95% confidence interval, with an error of ± 0.09 is 21.
C) The probability of 12 random households recycling more than 45% of their waste is approximately 0.045 or 4.5%.

Error term = z√(p(1-p)/n)

Error term = 1.75 √(0.30(1-0.30)/25)
= 0.16039

Thus, the error term is 0.16039

2 ) to find the ample size required to estimate the proportion of recycling in poor neighborhoods with an error of +/-0.09 and a 95% confidence level,

n = (z² * p * (1-p)) / E²

n = (1.96² * 0.5 * (1-0.5)) / 0.09²

= 21

So the number of households required to get a 95% confidence interval, with an error of ± 0.09 is 21.

3)  To calculate the probability of 12 random households recycling more than 45% of their waste,

P(X > 12) = 1 - P(X <= 12)

P(X > 12) = 1 - P(X <= 12)

= 1 - 0.955

= 0.045

So the the probability of 12 random households recycling more than 45% of their waste is 0.045.

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Full Question:

In a small town, households recycle on average 30% of their waste. The new recycling committee wants to increase this proportion and study the relationship between recycling and income. They select 25 households from the two wealthier neighborhoods and estimate a 92% confidence interval for the true proportion of recycling. What is the error term of this interval?

The same recycling committee also focuses on poor neighborhoods. How many households do they need to sample to get a 95% confidence interval, with an error of +/-0. 09?

Finally, the same recycling committee wants to know the probability of 12 random households recycling more than 45% of their waste. This probability is: ?

Answer:

Approximately 20.33 (rounded to two decimal places)

Step-by-step explanation:

To find the standard deviation, we will first find the mean (average) of the scores, then find the variance, and finally take the square root of the variance to get the standard deviation.

Calculate the mean:

(80 + 30 + 34 + 32 + 55 + 27 + 38 + 41 + 84) / 9 = 421 / 9 = 46.78 (rounded to two decimal places)

Calculate the variance:

a. Find the difference between each score and the mean, and then square the differences (rounded to two decimal places).

(80 - 46.78)^2 = 1103.57

(30 - 46.78)^2 = 281.57

(34 - 46.78)^2 = 163.33

(32 - 46.78)^2 = 218.45

(55 - 46.78)^2 = 67.57

(27 - 46.78)^2 = 391.25

(38 - 46.78)^2 = 77.09

(41 - 46.78)^2 = 33.41

(84 - 46.78)^2 = 1385.33

b. Find the sum of the squared differences.

1103.57 + 281.57 + 163.33 + 218.45 + 67.57 + 391.25 + 77.09 + 33.41 + 1385.33 = 3721.57

c. Divide the sum of the squared differences by the number of scores (n = 9).

3721.57 / 9 = 413.51 (rounded to two decimal places)

Calculate the standard deviation:

Take the square root of the variance.

√413.51 = 20.33 (rounded to two decimal places)

The standard deviation of the scores is approximately 20.33 (rounded to two decimal places).

We would expect to get 225 purple, smooth kernels, 75 yellow, smooth kernels, 75 purple, wrinkled kernels, and 25 yellow

If the parent plants are heterozygous for both recessive traits, we would expect a 9:3:3:1 ratio of phenotypes in their offspring. This means that out of 400 kernels, we would expect:

Purple, smooth: 9/16 x 400 = 225
Yellow, smooth: 3/16 x 400 = 75
Purple, wrinkled: 3/16 x 400 = 75
Yellow, wrinkled: 1/16 x 400 = 25

Therefore, we would expect to get 225 purple, smooth kernels, 75 yellow, smooth kernels, 75 purple, wrinkled kernels, and 25 yellow, wrinkled kernels in a sample of 400 kernels if the parent plants are heterozygous for both recessive traits.

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The area of the figure below is 58 in².

Area is the region bounded by a plane shape.

To calculate the area of the figure, we use the formula below

Formula:

Where:

Substitute these values into equation 1

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The circumference of a circle is given by $C = \pi d$, where $d$ is the diameter of the circle. In this case, the diameter of Annie's bicycle wheels is $20$ inches, so the circumference of each wheel is:

$C = \pi d = \pi (20\text{ in}) \approx 62.83 \text{ in}$

Since each wheel makes 3 full revolutions every second, the distance that Annie travels in one second is:

$distance = 2C \cdot \text{revolutions per second} = 2 \cdot 62.83 \text{ in} \cdot 3 \approx 377 \text{ in}$

Converting inches to feet, we get:

$distance = 377 \text{ in} \cdot \frac{1 \text{ ft}}{12 \text{ in}} \approx 31 \text{ ft}$

Rounding to the nearest foot, Annie travels approximately $\boxed{31}$ feet in one second.

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Answer:

Answer is 76

Step-by-step explanation: