A survey of 1720 parents of 13- to 17- year-olds found that 646 of the 1720 parents have checked their teen's social media profile. What is the population

The probability that a randomly selected tire will have a life of at least 35,000 miles is 0.1587 or about 15.87%.

To solve this problem, we need to use the concept of probability and the normal distribution. We are given that the life expectancy of a particular brand of tire is normally distributed with a mean of 40,000 and a standard deviation of 5,000 miles. We want to find the probability that a randomly selected tire will have a life of at least 35,000 miles.

We can use the standard normal distribution to find the probability. We first need to standardize the value of 35,000 using the formula:

z = (x - μ) / σ

where z is the standard score, x is the value we want to standardize (in this case, 35,000), μ is the mean, and σ is the standard deviation. Plugging in the values, we get:

z = (35,000 - 40,000) / 5,000 = -1

Now we can use a standard normal distribution table to find the probability that a randomly selected tire will have a life of at least 35,000 miles. We look up the value of -1 in the table and find that the corresponding probability is 0.1587. Therefore, the answer is:

0.1587

So the probability that a randomly selected tire will have a life of at least 35,000 miles is 0.1587 or about 15.87%.

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Answer:

Step-by-step explanation:

the answer is 18 :)

The amount he needs to earn to buy the video game of 60 dollars is 18 dollars.

Jack made 25 dollars on Monday by cutting the grass and 17 dollars on Tuesday by raking leaves. Therefore, the amount of money he needs to earn to buy a video game of 60 dollars can be calculated as follows:

He earns 25 dollars on Monday by cutting grass.

He also earns 17 dollars on Tuesday by raking leaves.

Therefore,

amount he needs to earn to buy a 60 dollars video game = 60 - 25 - 17

amount he needs to earn to buy a 60 dollars video game = 60 - 42

amount he needs to earn to buy a 60 dollars video game = 18 dollars

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Answer: Option: >

Step-by-step explanation: To compare the fractions 34/40 and 5/8, we can convert them to a common denominator. The least common multiple of 40 and 8 is 40, so we can convert 5/8 to 25/40. Now we can compare the fractions:

34/40 is equivalent to 17/20

17/20 is greater than 25/40

Therefore, the correct answer is >.

We need to add 4 blue marbles to the bag to make the probability of drawing a blue marble 1/2.

Currently, there are two blue marbles out of a total of eight marbles in the bag, so the probability of drawing a blue marble is 2/8 or 1/4.

Let x be the number of blue marbles we need to add to the bag. After adding x blue marbles, there will be a total of 2 + x blue marbles in the bag, out of a total of 8 + x marbles.

We want the probability of drawing a blue marble to be 1/2, so we can set up the equation:

(2 + x) / (8 + x) = 1/2

Multiplying both sides by (8 + x), we get:

2 + x = (8 + x) / 2

Multiplying both sides by 2, we get:

4 + 2x = 8 + x

Subtracting x from both sides, we get:

4 + x = 8

Subtracting 4 from both sides, we get:

x = 4

Therefore, we need to add 4 blue marbles to the bag to make the probability of drawing a blue marble 1/2.

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To calculate the number of MADs that the means are apart, we need to find the difference between the two means and then divide that by the MAD.

The difference between the means is 20 - 16 = 4.

Dividing that by the MAD of 2.5, we get:

4 / 2.5 = 1.6

Therefore, the means are 1.6 MADs apart.

1. Find the difference between the two mean ages: 20 years (mean age of second sample) - 16 years (mean age of first sample) = 4 years.

2. Divide the difference by the MAD: 4 years (difference between means) / 2.5 (MAD) = 1.6.

So, the means are 1.6 MADs apart.

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The equation r^2 = sin(2θ) can be rewritten as: r = ± √(sin(2θ))

Since r is always non-negative, we only need to consider the positive square root:

r = √(sin(2θ))

The shaded region is given by the area inside the curve r = √(sin(2θ)) and outside the curve r = 0. This region is symmetric about the polar axis, so we can find the area of one half and multiply by 2.

Using the formula for the area of a polar region, we have:

A = 2∫[0,π/4] 1/2 (r(θ))^2 dθ

Substituting r = √(sin(2θ)), we get:

A = 2∫[0,π/4] 1/2 sin(2θ) dθ

Using the identity sin(2θ) = 2sin(θ)cos(θ), we can simplify the integral:

A = 2∫[0,π/4] 1/2 (2sin(θ)cos(θ)) dθ

A = ∫[0,π/4] sin(θ)cos(θ) dθ

Using the double angle formula, we have:

A = 1/2 ∫[0,π/4] sin(2θ) dθ

Integrating with respect to θ, we get:

A = 1/4 [-cos(2θ)]|[0,π/4]

A = 1/4 (-cos(π/2) + cos(0))

A = 1/4 (0 + 1)

A = 1/4

Therefore, the area of the shaded region is 1/4 square units.

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To calculate the area of a shaded region defined by a polar curve, use the formula Area = 1/2 ∫ (from α to β) ([tex]r(\theta)^2[/tex]) dθ. The polar function is the square root of the given function, and the boundaries of the shaded region mark limits of integration. Without those values, we can't provide a numerical answer.

The question asks us to calculate the area of a shaded region defined by the polar equation [tex]r^2[/tex]=sin 2(theta). This equation falls under the category of a polar curve. To find the area of a region defined by a polar curve, we use the formula Area = 1/2 ∫ (from α to β) ([tex]r(\theta)^2[/tex]) dθ. Here, r(θ) is the polar function (in this case, since [tex]r^2[/tex] =[tex]sin^2(theta)[/tex], r(θ) = sqrt([tex]sin^2(theta)[/tex])), and α and β are the boundaries of the shaded region.

Without knowing the exact values for α and β, we can't provide a numerical answer, but you would integrate the resulting equation from the lower bound to the upper bound. Before integrating, it is vital to ensure that the function is only taking positive values, otherwise, it could lead to miscalculations. Hence, it's important that when you square root sin2(theta), you use the absolute value of sin(theta).

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The null hypothesis refers to the population, whereas the research hypothesis refers to the sample.

We have,

In statistics, the null hypothesis is a statement that assumes there is no significant difference between two groups or variables being compared.

It represents the default position that there is no relationship or effect between the variables of interest.

The null hypothesis is typically formulated as a statement about the population parameter.

On the other hand, the research hypothesis (also known as the alternative hypothesis) is a statement that proposes a significant difference or relationship between the variables being studied.

The research hypothesis is typically formulated as a statement about the sample, which is a subset of the population.

Thus,

The null hypothesis refers to the population, whereas the research hypothesis refers to the sample.

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When you roll a six sided die two times. You know the sum of the two rolls is 4. The probability that you rolled two 2s in a row (2, 2) is 1/3.

To find the probability of rolling two 2s in a row given that the sum of the two rolls is 4, we first need to find all the possible combinations of two rolls that add up to 4. These combinations are (1, 3), (2, 2), and (3, 1).

However, we only want to consider the probability of rolling two 2s in a row, so we can eliminate the other two combinations. This means we are left with only one possible outcome, which is rolling two 2s in a row.

Therefore, the probability of rolling two 2s in a row given that the sum of the two rolls is 4 is 1/3.

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The sample may not be perfectly representative of the entire U.S. population, and any differences observed between men and women in the sample may not necessarily reflect gender differences in the population as a whole.

Random sample of U.S. residents, while the comparison of responses between men and women is a comparison of subgroups within the sample.

By using a random sample of U.S. residents, the study aims to obtain a representative sample of the population's opinions on stem cell research. This is important because it allows the study to draw conclusions about the opinions of the entire population rather than just a specific group of people.

Comparing the responses of men and women within the sample can provide insights into any gender differences in opinions about stem cell research. However, it is important to note that the sample may not be perfectly representative of the entire U.S. population, and any differences observed between men and women in the sample may not necessarily reflect gender differences in the population as a whole.

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The integral for the mass of the shell becomes  ∫₀^π ∫₀^{2π} ∫₃^₅ ρ(r, θ, φ) r^2 sin φ dr dθ dφ.

To find the mass of the spherical shell, we need to integrate the density over its volume. Let's assume that the density of the shell is constant, denoted by rho.

Using spherical coordinates, the integral for the mass of the shell can be written as:

M = ∫∫∫ ρ(r, θ, φ) r^2 sin φ dr dθ dφ

where,

ρ(r, θ, φ) is the density of the shell, which is assumed to be constant,

r is the radial distance from the origin,

θ is the azimuthal angle, which measures the angle in the xy-plane from the positive x-axis,

φ is the polar angle, which measures the angle from the positive z-axis.

Since the shell is centered at the origin and has an inner radius of 3 cm and an outer radius of 5 cm, the limits of integration are:

3 ≤ r ≤ 5

0 ≤ θ ≤ 2π

0 ≤ φ ≤ π

Thus, the integral for the mass of the shell becomes:

M = ∫∫∫ ρ(r, θ, φ) r^2 sin φ dr dθ dφ

= ∫₀^π ∫₀^{2π} ∫₃^₅ ρ(r, θ, φ) r^2 sin φ dr dθ dφ

the symmetry of the shell, which means that we are integrating over the entire volume of the shell. If the shell had some symmetry, we could have reduced the domain of the integral by exploiting that symmetry.

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The probability of approximately 0.611, or 61.1%. Therefore, there is a 61.1% chance that you will receive a cookie from me that contains 4 to 8 chocolate chips.

To determine the probability of receiving a cookie from you that contains 4 to 8 chocolate chips, we need to first calculate the average number of chocolate chips per cookie.

We can do this by dividing the total number of chocolate chips (1,000) by the number of cookies (200), giving us an average of 5 chocolate chips per cookie.

Next, we need to calculate the probability of a cookie containing 4 to 8 chocolate chips. To do this, we can use the binomial distribution formula, which is:

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

where P(x) is the probability of getting x chocolate chips per cookie, n is the number of trials (in this case, the number of cookies), p is the probability of success (getting a chocolate chip), and (n choose x) is the number of ways to choose x cookies from n.

Plugging in the values, we get:

P(4 <= x <= 8) = (200 choose 4) * (0.05)^4 * (0.95)^196 + (200 choose 5) * (0.05)^5 * (0.95)^195 + (200 choose 6) * (0.05)^6 * (0.95)^194 + (200 choose 7) * (0.05)^7 * (0.95)^193 + (200 choose 8) * (0.05)^8 * (0.95)^192

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Answer:

Let b = price of the bike and h = price of the helmet.

b + h = 155---------->8b + 8h = 1,240

.6b + .8h = 100---->6b + 8h = 1,000

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

2b = 240

b = 120, h = 35

Juan paid $120 for the bike and $35 for the helmet.

In the table that shows the length, in inches, of fish in a pond, there are no outlier.

we know, a value that differs significantly from the other values in a dataset is an outlier in mathematics. Measurement errors, data entry errors, or extreme results that are actually outliers from the majority of the data can all lead to outliers.

Here according to question,

A box-and-whisker plot, which depicts the distribution of a dataset by presenting the minimum, first quartile, median, third quartile, and maximum values, is one method for identifying outliers.

Thus, there are no outlier.

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The rotor has turned approximately 0.0906 revolutions or 9.06% of a full revolution.

To determine the number of revolutions the rotor has turned, we need to know the angle traversed by the rotor in one revolution. This value is dependent on the motor and can vary based on its design and the gear ratio used.

Assuming we have this information, we can use the angle traversed by the rotor in one revolution to calculate the number of revolutions it has made based on the angle rotated by the probe. Let's assume that the angle traversed by the rotor in one revolution is 360 degrees. In this case, we can calculate the number of revolutions as follows:

Number of revolutions = Angle rotated by probe / Angle traversed in one revolution

= 32.6 degrees / 360 degrees

= 0.0906 revolutions

It's important to note that this calculation assumes that the rotation of the probe is directly proportional to the rotation of the rotor. If there is any slippage or other factors that affect the relationship between the two, the result may not be accurate.

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It is important to choose the appropriate statistical test that takes into account the nature of the data and the study design to minimize the risk of making a type II error.

Using a t-test for two independent samples instead of a paired t-test when the samples are related (i.e., paired or dependent) can increase the probability of a type II error.

In a paired sample design, each individual or object in one sample is matched or related to a corresponding individual or object in the other sample. Because of this pairing, the observations in the two samples are not independent of each other, and using a t test for independent samples may not account for the dependency between the samples.

By ignoring the relatedness of the samples, the investigator may be introducing additional variability and error into the analysis, which can reduce the statistical power of the test and increase the probability of a type II error (i.e., failing to reject a null hypothesis that is actually false).

Therefore, it is important to choose the appropriate statistical test that takes into account the nature of the data and the study design to minimize the risk of making a type II error.

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Using Pearson's correlation coefficient test is an effective way to determine if there is a relationship between two variables, and can help inform decisions and policies related to education and employment.

To test for a correlation between subjects' level in college and their annual income, you would use a correlation coefficient test. Specifically, you would use Pearson's correlation coefficient test, which measures the strength and direction of the linear relationship between two variables. In this case, the two variables are the level in college (Freshman, Sophomore, Junior, Senior) and annual income in dollars.

Pearson's correlation coefficient ranges from -1 to 1, where a value of -1 indicates a perfect negative correlation (i.e., as one variable increases, the other decreases) and a value of 1 indicates a perfect positive correlation (i.e., as one variable increases, the other also increases). A value of 0 indicates no correlation between the two variables.

Once you have collected data on the subjects' level in college and annual income, you can calculate Pearson's correlation coefficient using statistical software or a calculator. If the correlation coefficient is significantly different from 0 (i.e., there is a correlation between the two variables), you can then interpret the strength and direction of the correlation to determine how the level in college relates to annual income.

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The volume of the cone is determined as 2,463 ft³.

The volume of the cone is calculated as follows;

V = ¹/₃πr²h

where;

From the diagram, the radius of the cone = 14 ft

The height of the cone = 12 ft

The volume of the cone is calculated as follows;

V = ¹/₃π (14 ft)² (12 ft )

V = 2,463 ft³

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Numerous-Choice Exams 14 true-false and two multiple-choice questions make up Professor Easy's final exam. There are 65,536 possible answer sheets.

For each true-false question, there are two possible answers (true or false), so there are [tex]2^{14[/tex] possible answer sheets for the true-false questions.

For each multiple-choice question, there are 4 possible answers. Since there are 2 multiple-choice questions, there are [tex]4^2 = 16[/tex] possible answer sheets for the multiple-choice questions.

To find the total number of possible answer sheets, we need to multiply the number of possible answer sheets for the true-false questions by the number of possible answer sheets for the multiple-choice questions. Therefore, the total number of possible answer sheets is:

[tex]2^{14} \times 16 = 65,536[/tex]

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Linear programming problem using graphical methods. We will follow these steps:

1. Define the objective function
2. Identify the constraints
3. Plot the constraints on a graph
4. Identify the feasible region
5. Find the vertices of the feasible region
6. Calculate the objective function value for each vertex
7. Determine the maximum value

1. Objective function: Maximize f = 6x + 2y

2. Constraints:
- x ≥ 0
- y ≥ 0
- 7x + 3y ≤ 105
- 2x + 5y ≤ 50
- 9x + 7y ≤ 70

3. Plot the constraints on a graph: You can plot each constraint as a line on a graph, using x and y as your axes. For instance, for the constraint 7x + 3y ≤ 105, you can plot the line 7x + 3y = 105 and shade the region below it.

4. Identify the feasible region: The feasible region is the intersection of all the shaded regions, which represents the area where all the constraints are satisfied.

5. Find the vertices of the feasible region: By inspecting the graph, you can determine the corner points of the feasible region. Let's call them A, B, C, and D.

6. Calculate the objective function value for each vertex: Evaluate f = 6x + 2y for each of the vertices A, B, C, and D.

7. Determine the maximum value: Compare the objective function values for all the vertices, and select the vertex with the highest value. That vertex will be the solution to your linear programming problem.

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The probability of finding a sample mean (Y¯) of 1.5 ounces or more when the liquor tax has no impact on consumption (µ = 0). To determine this probability, we would use the Central Limit Theorem and the z-score formula.

Since the population mean (µ) is 0, we'll need to know the population standard deviation (σ) and the sample size (n) to proceed. Without these values, it's impossible to provide an exact probability. However, I can explain the general process.

First, you would calculate the standard error (SE) using the formula SE = σ / √n. Next, you would find the z-score, which is the difference between the sample mean (Y¯) and the population mean (µ) divided by the standard error: z = (Y¯ - µ) / SE.

Once you have the z-score, you can look it up in a standard normal distribution table or use a calculator to find the probability associated with it. In this case, you're looking for the probability of finding a sample mean of 1.5 ounces or more, which corresponds to the area to the right of the z-score in the distribution.

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The cases are explained in the solution.

We know that, the isosceles triangle has two equal side.

Ist case =

perimeter=22 cm

Let's suppose that the known side of 6 cm is one of the two equal sides

perimeter=6+6+x

22=6+6+x

x=22-12

x=10 cm

The possible lengths of the other two sides are

6 cm 

10 cm

IInd case -

Let's suppose that the known side of 6 cm is the side that is not equal

perimeter=22 cm

perimeter=6+x+x

22=6+x+x

2x=22-6

2x=16

x=8 cm

the possible lengths of the other two sides are

8 cm 

8 cm

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Judy bought 25 pens in packages of 5 for $0.80 per package and sold 600 pens in packages of 3 for $0.60 per package.

Judy bought the pens in packages of 5 for $0.80 per package, which means she paid $0.16 per pen (0.80/5=0.16). If she sold them in packages of 3 for $0.60 per package, she received $0.20 per pen (0.60/3=0.20). This means that her profit per pen was $0.20 - $0.16 = $0.04.

If her profit was $8.00, we can use the formula profit = revenue - cost to calculate how many pens she bought and sold. Let's call x the number of packages she bought and y the number of packages she sold.

Judy's cost was:

cost = x * 5 * 0.16 = 0.8x

Judy's revenue was:

revenue = y * 3 * 0.20 = 0.6y

Her profit was:

profit = revenue - cost = 0.6y - 0.8x = 8

We can simplify this equation by dividing both sides by 0.2:

0.3y - 4x = 40

Now we need to find two integers x and y that satisfy this equation. We can use trial and error or substitution to find them. For example, if we try x=5, we get:

0.3y - 4(5) = 40

0.3y = 60

y = 200

This means that Judy bought 5 packages of pens (25 pens) and sold 200 packages of pens (600 pens).

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The box, from 44 to 52  of this box plot includes about 50% of the data.

The box represents the middle 50% of the data, with the bottom and top of the box representing the first and third quartiles, respectively. The line inside the box represents the median.

The whiskers extend from the box to show the range of the data, excluding outliers. Outliers are typically shown as individual points outside the whiskers.

Based on this information, we can see that the box in the box plot represents the middle 50% of the data.

Therefore, The box, from 44 to 52, includes about 50% of the data.

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To construct a 95% confidence interval for the standard deviation of the height of students at UH, we can use the Chi-Square distribution. The answer is a) (2.096, 5.265).

The formula for the confidence interval is:

(sqrt((n-1)*s^2)/chi2(a/2,n-1), sqrt((n-1)*s^2)/chi2(1-a/2,n-1))

where n is the sample size, s is the sample standard deviation, and chi2 is the Chi-Square distribution with degrees of freedom equal to n-1.

Plugging in the values from the problem, we get:

(sqrt((11-1)*3^2)/chi2(0.025,11-1), sqrt((11-1)*3^2)/chi2(0.975,11-1))

Using a Chi-Square table or calculator, we find that chi2(0.025,10) = 3.169 and chi2(0.975,10) = 20.483.

Evaluating the formula, we get:

(sqrt((11-1)*3^2)/3.169, sqrt((11-1)*3^2)/20.483)

Simplifying, we get:

(2.096, 5.265)

Therefore, the answer is a) (2.096, 5.265).

To construct a 95% confidence interval for the standard deviation of the height of students at UH, we will use the chi-square distribution. Here are the steps:

1. Identify the given values: n (sample size) = 11, s (sample standard deviation) = 3.0 feet.

2. Determine the degrees of freedom: df = n - 1 = 11 - 1 = 10.

3. Look up the chi-square values for the 95% confidence interval: For df = 10, the chi-square values are 2.700 (lower) and 19.023 (upper).

4. Calculate the lower and upper bounds for the confidence interval:
- Lower bound: ((n - 1) * s^2) / chi-square upper = (10 * 3^2) / 19.023 ≈ 4.724
- Upper bound: ((n - 1) * s^2) / chi-square lower = (10 * 3^2) / 2.700 ≈ 30.000

5. Take the square root of the lower and upper bounds to get the confidence interval for the standard deviation:
- Lower bound: √4.724 ≈ 2.173
- Upper bound: √30.000 ≈ 5.477

The 95% confidence interval for the standard deviation of the height of students at UH is approximately (2.173, 5.477), which is not among the given options. Therefore, the correct answer is f) None of the above.

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Emily can ride her bike at a speed of 65 mph.

Let's use "x" to represent Emily's speed in mph.
We know that Kristen's speed is 8 mph faster, so her speed would be x + 8 mph.

We also know that Emily takes one hour longer than Kristen to travel 58.5 miles. So we can set up an equation:
[tex]\frac{58.5}{x}  = \frac{58.5}{x+8} +1[/tex]

This equation represents the fact that the time it takes for Emily to travel 58.5 miles is one hour more than the time it takes for Kristen to travel the same distance.

Now, let's solve for x:
Multiplying both sides by x(x+8), we get:
[tex]58.5(x+8) = 58.5x + x(x+8)[/tex]
[tex]58.5x + 468 = 58.5x + x^2 + 8x[/tex]

Simplifying, we get:
[tex]x^2 + 8x - 468 = 0[/tex]

Now we can use the quadratic formula:
[tex]x = \frac{(-8 ± \sqrt{8^{2} - 4(1)(-468) }}{2(1)}[/tex]
[tex]x = \frac{(-8±\sqrt{18976)}}{2}[/tex]
[tex]x = \frac{(-8±132)}{2}[/tex]
x = -73 or x = 65

Since Emily's speed can't be negative, we can discard the negative solution. Therefore, Emily can ride her bike at a speed of 65 mph.

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When the measure being made consists of judgments or ratings of multiple observers, the degree of agreement among observers can be established by using a statistical measure of inter-rater reliability.

Inter-rater reliability is a statistical measure used to assess the degree of agreement among multiple observers or raters who are rating or judging the same thing. It is commonly used in research studies that involve subjective measures such as ratings of behavior, symptoms, or attitudes.

Inter-rater reliability can be estimated using various statistical measures, such as Cohen's kappa, Fleiss' kappa, or intraclass correlation coefficients (ICC). These measures provide a numerical estimate of the degree of agreement among raters, taking into account both the level of agreement and the level of disagreement that would be expected by chance.

A high level of inter-rater reliability indicates that there is a high degree of agreement among raters, whereas a low level of inter-rater reliability indicates that there is a significant amount of disagreement among raters. Inter-rater reliability is important because it helps to establish the validity and reliability of the measure being used and ensures that the results are consistent and replicable.

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To test the hypothesis that the average time to deliver pizza, once the order is placed, is greater than 25 minutes in the population, the appropriate statistical test to use would be a t-test. This test is used to compare the means of two groups, in this case, the actual average time it takes to deliver the pizza and the hypothesized value of 25 minutes.

The t-test is preferred over a z-test because the population standard deviation is unknown, which is a requirement for a z-test. The t-test, on the other hand, uses the sample standard deviation to estimate the population standard deviation.

To conduct a t-test, we need to collect a random sample of delivery times and calculate the sample mean and standard deviation. Then we would use a one-sample t-test to compare the sample mean to the hypothesized value of 25 minutes. If the calculated t-value is greater than the critical value at a chosen level of significance, we can reject the null hypothesis and conclude that the average time to deliver pizza is indeed greater than 25 minutes in the population.

In conclusion, to test the hypothesis that the average time to deliver pizza, once the order is placed, is greater than 25 minutes in the population, we would use a t-test.

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Answer: 1/6

Step-by-step explanation:

Out of 30 students, 5 liked country music.

To find the fraction of students who liked country music, we need to divide the number of students who liked country music by the total number of students in the class.

Fraction of students who liked country music = (Number of students who liked country music) / (Total number of students)

Fraction of students who liked country music = 5/30

We can simplify this fraction to lowest terms by dividing both the numerator and denominator by the greatest common factor, which is 5.

5/30 ÷ 5/5 = 1/6

Therefore, the fraction in lowest terms that shows how many students liked country is 1/6.

Answer: 1/6

Step-by-step explanation:

Out of 30 students, 5 liked country music.

To find the fraction of students who liked country music, we need to divide the number of students who liked country music by the total number of students in the class.

Fraction of students who liked country music = (Number of students who liked country music) / (Total number of students)

Fraction of students who liked country music = 5/30

We can simplify this fraction to lowest terms by dividing both the numerator and denominator by the greatest common factor, which is 5.

5/30 ÷ 5/5 = 1/6

Therefore, the fraction in lowest terms that shows how many students liked country is 1/6.

Answer:

your answer would be 121

Step-by-step explanation:

put the equation as 100(1+10/100)^2

100(11/10)^2

100(121/100)

cancel 100 on both sides

and your final answer would be 121 :)

Situational factors and analysis factors are both sources of error in measurement.

Situational and analysis factors are both important considerations in measurement, and efforts should be made to minimize their effects on the accuracy and reliability of the results. This can be achieved by using clear and standardized measurement instruments, providing clear instructions and definitions, and ensuring consistent and accurate scoring and statistical analysis techniques.

Situational factors refer to circumstances or conditions that can affect the accuracy and consistency of measurement. In the case of lack of clarity of the scale, including the instructions or the items themselves, this can lead to ambiguity and confusion among respondents, resulting in inconsistent or inaccurate responses.

For example, if the scale asks respondents to rate their satisfaction with a product on a scale of 1 to 5, but does not provide clear definitions or examples of what each number means, respondents may interpret the scale differently and provide inconsistent responses.

Analysis factors, on the other hand, refer to the methods and techniques used to analyze and interpret the data collected from the measurement. Differences in scoring and statistical analysis can introduce error and affect the validity and reliability of the results.

For instance, if different analysts use different methods or criteria for scoring responses, this can lead to different outcomes and interpretations of the data. Similarly, if statistical analysis is not done correctly or is based on flawed assumptions, the results may be misleading or inaccurate.

To learn more about Situational factors, refer here:

https://brainly.com/question/15054331#

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