a cohort study on the effectiveness of a treatment for alcoholism will follow 50 people for two years. in this time, it is expected that the number of people who drop out of the study due to relapse will be ten, with standard deviation four. it is also expected that the number of people who drop out of the study because they move out of the study area will be six, with a standard deviation of three. what is the expected number of people who will drop out due to either relapse or moving away?

Answer:

1.5

Step-by-step explanation:

20/20 = 3

30/20 = 1.5

Therefore, based on the inspection of the sample, it can be estimated that 8% of the articles in the original shipment may be defective.

The answer  is that the probable percentage of defective articles in the original shipment can be estimated using the formula:

Probable percentage of defective articles = (Number of defective articles in sample / Sample size) x 100

In this case, the number of defective articles in the sample is 4, and the sample size is 50. Plugging these values into the formula, we get:

Probable percentage of defective articles = (4/50) x 100 = 8%

Therefore, based on the inspection of the sample, it can be estimated that 8% of the articles in the original shipment may be defective.


Sampling is a technique used to estimate the characteristics of a large population by examining a smaller subset of it. In this case, a sample of 50 articles was inspected to estimate the probable percentage of defective articles in the original shipment of 500 articles. The number of defective articles in the sample was found to be 4, which represents 8% of the sample size. This percentage can then be used to estimate the probable percentage of defective articles in the entire shipment. However, it is important to note that the estimate may not be completely accurate, as the sample may not be fully representative of the entire population.

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Answer:

2nd one

6th one

Step-by-step explanation:

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The class width of a frequency distribution is the difference between the upper class limit of a class and the lower class limit of the same class. In other words, it represents the range of data values included in a particular class.

For example, suppose we have a frequency distribution with the following classes: 0-9, 10-19, 20-29, 30-39, and 40-49.

The class width of this frequency distribution would be the same for all classes and would be equal to 10. This is because the upper class limit of each class is 9, 19, 29, 39, and 49, respectively, and the lower class limit of each class is 0, 10, 20, 30, and 40, respectively. Therefore, the difference between the upper class limit and lower class limit for each class is 9, 9, 9, 9, and 9, respectively, giving a class width of 10.

In the given problem, the first class is 10-19 and the second class is 20-29. Therefore, the lower class limit of the first class is 10 and the upper class limit of the second class is 29.

Therefore, the class width is the difference between the upper class limit of the second class (29) and the lower class limit of the first class (10), which is equal to 29 - 10 = 19.

So the correct answer is 19.

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Answer:

21

Step-by-step explanation:

0.3 x 70 = 21

Answer:

21

Step-by-step explanation:

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The answer is that the type of data that is being described is quantitative data. This means that the data consists of numerical values that represent the number of customers served each week.

, quantitative data is numerical data that can be measured and expressed in numerical form. In this case, the number of customers served each week is being recorded, which is a quantitative variable. This data can then be analyzed and used to make decisions about the restaurant's operations, such as staffing levels, inventory management, and marketing strategies. Overall, the recording of customer numbers is an important aspect of running a successful restaurant, and the quantitative data collected can provide valuable insights into customer behavior and preferences.


Quantitative data can be further divided into two categories: discrete and continuous data. Discrete data can only take specific values, usually whole numbers (e.g., the number of customers). Continuous data can take any value within a range (e.g., the weight of a serving). In this scenario, the number of customers is an example of discrete quantitative data.

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The test formula is t = (x₁ - x₂) / √(s₁²/n₁) + (s₂²/n₂))

The rejection region for the test is t < -1.971 or t > 1.971.

The question involves the concepts of hypothesis testing and the two-sample t-test.

Hypothesis testing is a statistical method used to make decisions about a population parameter based on a sample of data. In this case, we are interested in comparing the population mean wearing times for old braces and new bands.

The two-sample t-test is a statistical test used to determine if there is a significant difference between the means of two independent samples. The test involves calculating a test statistic (t-value) that measures the difference between the sample means, relative to the variation within the samples.

Here we have

Two hundred children were randomly assigned, 100 to each group.

A summary of the data is given below.

Old Braces: n₁ = 100, sample mean = 425, s₁ = 45 days;

New Bands: n₂ = 100, sample mean = 385, s₂ = 60 days;

You are interested in conducting a test to determine if the population mean wearing times differ using α = 0.10.

(i) The research hypothesis can be framed as:

H₀ : The population mean wearing times for old braces and new bands are equal.

Hᵃ: The population mean wearing time for old braces and new bands are not equal.

(ii) The test formula for comparing the population mean wearing times of two independent samples can be expressed as:

t = (x₁ - x₂) / √(s₁²/n₁) + (s₂²/n₂))

Where x₁ and x₂ are the sample means, s₁ and s₂ are the sample standard deviations, and n₁ and n₂ are the sample sizes.

(iii) To find the rejection region for the test, we first need to determine the degrees of freedom (df), which can be calculated as:

df = (s₁²/n₁+ s₂²/n₂)² / [(s₁²/n₁)² / (n₁ -1) + (s₂²/n₂)² / (n₂ -1)]

Using the given values, we get:

df = (45²/100 + 60²/100)² / [(45²/100)² / 99 + (60²/100)² / 99] = 185.98

Since we have two tails in the test and an alpha level of 0.10, we need to split the alpha level between the two tails.

Therefore, we can find the critical t-values using a t-distribution table with 185 degrees of freedom and an alpha level of 0.05 (half of 0.10).

For a two-tailed test at an alpha level of 0.05, the critical t-values are approximately ±1.971.

Thus, The rejection region for the test is t < -1.971 or t > 1.971. If the calculated t-value falls outside this range, we reject the null hypothesis in favor of the alternative hypothesis and conclude that the population mean wearing times for old braces and new bands are not equal.

Therefore,

The test formula is t = (x₁ - x₂) / √(s₁²/n₁) + (s₂²/n₂))

The rejection region for the test is t < -1.971 or t > 1.971.

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On a scatter plot, the vertical distance between the dot for the actual score and the regression line represents the residual or the error.

The regression line is a line that is drawn through the scatter plot of two variables (usually denoted as x and y) that shows the average relationship between those variables. It is the line that minimizes the sum of the squared errors between the observed y-values and the predicted y-values for each x-value.

The actual score is the observed y-value for a given x-value, and the predicted score is the value of y predicted by the regression line for that same x-value. The difference between the actual score and the predicted score is the residual, or the error.

The residual can be positive or negative, depending on whether the actual score is above or below the regression line, respectively. The size of the residual represents how far away the actual score is from the predicted score, in units of the y-variable.

Thus, the vertical distance between the dot for the actual score and the regression line represents the residual or the error, which is the difference between the actual score and the predicted score.

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A) The probability percentage that 121 or more Americans in the survey are afraid to fly is 13.57

B) The probability percentage that 165 or more Americans in the survey are afraid to fly is 0

C) What is the probability percentage that 8% or less of the Americans surveyed answered they were afraid to fly is 1.36

Percentage of people afraid to fly = 10%

P = 0.10

Total number of people  = 1100

Standard deviation = p (1 - p) / n

σ = 0.10(1 -0.1)/1100

σ = 0.00905

z is standard normal table

A) p (ρ ≥ [tex]\frac{121}{1100}[/tex]) = p [tex](\frac{z\geq\frac{121}{1100} }{0.00905} )[/tex]

p (ρ ≥ [tex]\frac{121}{1100}[/tex]) = p ( z ≥ 1.10)

p (ρ ≥ [tex]\frac{121}{1100}[/tex]) = 13.57 %

B) p (ρ ≥ [tex]\frac{165}{1100}[/tex] ) = p[tex](\frac{z\geq\frac{165}{1100} - 0.10 }{0.00905} )[/tex]

p (ρ ≥ [tex]\frac{165}{1100}[/tex] ) = p (z ≥ 5.52)

p (ρ ≥ [tex]\frac{165}{1100}[/tex] ) = 0

C) p (ρ ≤ 0.08 ) = p[tex](\frac{z\leq 0.08 - 0.10 }{0.00905} )[/tex]

p (ρ ≤ 0.08 ) = p (z ≤ -2.21)

p (ρ ≤ 0.08 )  = 1.36

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The integral [1,3] f(2x)dx=(1/2) ∫[2,6] f(x) dx.

What is integral?

In calculus, an integral is a mathematical operation that represents the area between a function and the x-axis on a graph. It is a way to calculate the area under a curve or between two curves.

We can use the substitution method to solve the integral. Let u = 2x, which means du/dx = 2 or du = 2dx.

Then we can rewrite the integral as:

∫[1,3] f(2x) dx = (1/2) ∫[2,6] f(u) du (substituting u = 2x and changing the limits of integration)

Since F'(x) = f(x), we can rewrite the right-hand side of the equation as:

(1/2) [F(u)] [2,6]

= F(6)/2 - F(2)/2 (using the definition of the antiderivative)

= (1/2) [F(6) - F(2)]

= (1/2) ∫[2,6] f(x) dx (using the definition of the antiderivative again)

So the final answer is:

∫[1,3] f(2x) dx = (1/2) ∫[2,6] f(x) dx.

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Step-by-step explanation:

A. 1/6 + 1/2 = 4/6 = 2/3 (between 0 and 1)

B. 1/6 + 1/6 = 2/6 = 1/3 (between 0 and 1)

C. 1/6 (between 0 and 1)

D. 1/6 * 1/6 = 1/36 (between 0 and 1)

So, all of the options A, B, C, and D could represent the probability of a simple event as they are between 0 and 1?

The average person in an industrialized country uses hundreds of kilograms of mineral resources in a year, and this number is only set to increase as our demand for products and technology continues to grow.

To determine the exact amount of mineral resources used by an average person in an industrialized country in a year, we need to consider the types of minerals used and their respective quantities. According to the US Geological Survey, the most commonly used minerals in the US include copper, iron, aluminum, and zinc, among others.

In 2020, the US per capita consumption of copper was 2.5 kilograms, iron and steel were 505 kilograms, aluminum was 21.5 kilograms, and zinc was 0.024 kilograms. Therefore, the total mineral consumption per capita in the US in 2020 was approximately 529 kilograms.

It's worth noting that this number only accounts for a few commonly used minerals and doesn't include other minerals such as gold, silver, platinum, and more. Additionally, this number varies across different countries based on their industrialization levels and resource availability.

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To calculate the standard deviation of a set of data, you need to have the individual data values. The individual data values are the numeric values that make up the data set.

To calculate the standard deviation, you need to perform the following steps:

Calculate the mean (average) of the data set.

For each data value, subtract the mean from the data value.

Square each of the differences calculated in step 2.

Sum the squared differences calculated in step 3.

Divide the sum of the squared differences by the number of data values minus 1 (this is called the "sample" standard deviation) or by the total number of data values (this is called the "population" standard deviation).

Take the square root of the result obtained in step 5 to obtain the standard deviation.

When calculating the standard deviation, it's important to use the correct number of decimal places and units of measurement to ensure accuracy.

What is the definition of accuracy?

Accuracy refers to how close a measured or calculated value is to the true or accepted value.

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the probabilities for each event are Red ball: 1/5, White ball: 7/20, Yellow ball: 9/20 by using formula of  probability =possible outcome /total outcomes

To find the probability of a given event, we need to determine the number of successful outcomes and divide that by the total number of possible outcomes.

In this case, the event we want to find the probability for is not specified, so I will provide the probabilities for each color:

1. Probability of drawing a red ball:

Number of successful outcomes: 4 red balls
Total number of outcomes: 4 red + 7 white + 9 yellow = 20 balls

Probability of drawing a red ball = (Number of red balls) / (Total number of balls) = 4/20 = 1/5

2. Probability of drawing a white ball:

Number of successful outcomes: 7 white balls

Probability of drawing a white ball = (Number of white balls) / (Total number of balls) = 7/20

3. Probability of drawing a yellow ball:

Number of successful outcomes: 9 yellow balls

Probability of drawing a yellow ball = (Number of yellow balls) / (Total number of balls) = 9/20

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The integral test can be applied to the series ∑ [tex]e^{-n}[/tex], and the series converges.

To apply the integral test, we need to compare the given series to the integral of a related function. Let's consider the function f(x) = [tex]e^{-x}[/tex].

First, let's find the definite integral of f(x) from 1 to infinity:

∞

∫ [tex]e^{-x}[/tex] dx = lim [ ∫ [tex]e^{-x}[/tex] dx ]

1→∞ 1

= lim [ [tex]-e^{-x}[/tex] ] from 1 to ∞

= lim [ ([tex]-e^{-infinity}[/tex]) - ([tex]-e^{-1}[/tex]) ]   ∞

= 0 - ([tex]-e^{-1}[/tex])

= [tex]e^{-1}[/tex]

Therefore, the integral of f(x) from 1 to infinity is [tex]e^{-1}[/tex].

Next, we need to compare the given series to the integral of f(x) to determine if the series converges or diverges. The integral test states that if the integral converges, then the series converges, and if the integral diverges, then the series diverges.

Let's set up the inequality to compare the series to the integral:

∞

∫ [tex]e^{-x}[/tex]  dx ≤ ∑ [tex]e^{-n}[/tex]

1

Integrating both sides, we get:

∞

[tex]e^{-x}[/tex]  | from 1 to ∞ ≤ ∑ [tex]e^{-n}[/tex]

1

Simplifying the left-hand side, we get:

∞

[tex]-e^{-infinity}[/tex]- [tex]e^{-1}[/tex] ≤ ∑ [tex]e^{-n}[/tex]

1

Since e^−∞ equals zero, we can simplify further:

[tex]e^{-1}[/tex]≤ ∑ [tex]e^{-n}[/tex]

Now, since the integral of f(x) from 1 to infinity converges, [tex]e^{-1}[/tex] is a finite value, and the given series is greater than or equal to [tex]e^{-1}[/tex], the series must also converge.

Therefore, we can confirm that the integral test can be applied to the series ∑ [tex]e^{-n}[/tex], and the series converges.

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Answer:

Step-by-step explanation:

To match each term to the corresponding probability of it snowing tomorrow, we can use the following definitions:

Likely means that the event has a high chance of happening, so the probability is close to 1. For example, P(snow) = 0.9 means that it is very likely to snow tomorrow.

Unlikely means that the event has a low chance of happening, so the probability is close to 0. For example, P(snow) = 0.1 means that it is very unlikely to snow tomorrow.

Certain means that the event will definitely happen, so the probability is exactly 1. For example, P(snow) = 1 means that it will surely snow tomorrow.

Impossible means that the event will never happen, so the probability is exactly 0. For example, P(snow) = 0 means that it will not snow tomorrow at all.

Using these definitions, we can match each term to the corresponding probability as follows:

Likely: P(snow) > 0.5

Unlikely: P(snow) < 0.5

Certain: P(snow) = 1

Impossible: P(snow) = 0

Based on the given information, we know that the arrival rate is 9 customers per hour and the service rate is 14 customers per hour. Since the arrival and service distributions are not known, we cannot use the m/m/1 formulas to calculate the average waiting time and total time spent in the system.

However, we can still use the Little's Law formula to relate the average number of customers in the system to the average waiting time. Little's Law states that the average number of customers in the system (N) is equal to the product of the arrival rate (λ) and the average time spent in the system (T), or N = λT.

Since we want to find the total time spent in the system, we can rearrange the formula to solve for T. Thus, T = N / λ.

We know from the given information that the average waiting time in the line is 18 minutes. Therefore, the average time spent in the system for a customer is T = 18 minutes + (1/14 hour), which is equal to 1.9 hours.

To calculate the total time spent in the system, we need to add the waiting time to the service time. Since the service rate is 14 customers per hour, the service time per customer is 1/14 hour or approximately 4.3 minutes. Thus, the total time spent in the system is approximately 22.3 minutes or 0.37 hours.

In summary, the average time spent in the system for a customer is 1.9 hours, and the total time spent in the system is approximately 22.3 minutes or 0.37 hours.

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Answer: 3.743324

Step-by-step explanation:

The z-score for the value 63.49 is approximately 0.24.

What is statistics?

Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of numerical data.

Yes, it is possible to calculate a z-score for the value 63.49 assuming that the data follows a normal distribution.

The z-score measures the number of standard deviations a data point is away from the mean of the distribution. To calculate the z-score, we first need to calculate the mean and standard deviation of the dataset.

The mean can be calculated by adding up all the values and dividing by the total number of values:

Mean = (59 + 60 + 61 + 62 + 62 + 63 + 63 + 63 + 64 + 64 + 65 + 66 + 67 + 68) / 14 = 63

The standard deviation can be calculated using the following formula:

Standard deviation = sqrt((1/N) * sum((xi - x_mean)²))

where N is the number of values, xi is each value in the dataset, and x_mean is the mean of the dataset.

Using this formula, we can calculate the standard deviation of the dataset:

Standard deviation = sqrt((1/14) * ((59 - 63)² + (60 - 63)² + ... + (68 - 63)²))

Standard deviation ≈ 2.02

Now we can calculate the z-score for the value 63.49 using the following formula:

z-score = (x - x_mean) / standard deviation

where x is the value we want to calculate the z-score for.

z-score = (63.49 - 63) / 2.02 ≈ 0.24

Therefore, the z-score for the value 63.49 is approximately 0.24.

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The left right hand derivative is 3.

The left hand derivative is 2.

The function is not differentiable at x = 1.

When x < 1,

given that the function is, f(x) = 2x² - 2x - 1

Differentiating with respect to 'x' we get, f'(x) = 4x - 2

When x [tex]\geq[/tex] 1,

given the function is, f(x) = 3x - 3

Differentiating with respect to 'x', f'(x) = 3

Now, left hand derivative,

f'(1 -) = (4x - 2) at 1 = 4*1 - 2 = 4 - 2 = 2

f'(1+) = 3

Since, left hand derivative and right hand derivative for given function at x =  1 is not equal, so the function is not differentiable at x = 1.

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To calculate the probability that the club will win at least 1 of the 3 matches, we need to calculate the probability that they will lose all 3 matches and then subtract that from 1.

The probability of losing all 3 matches would be (0.7)^3 = 0.343. So the probability of winning at least 1 of the 3 matches would be 1 - 0.343 = 0.657, or approximately 66 percent.
To find the probability that the club will win at least 1 of the next 3 matches with a 30 percent probability of winning each match, we can use the complementary probability method. This involves finding the probability of the opposite event occurring (i.e., the club losing all 3 matches) and then subtracting that probability from 1.
Step 1: Determine the probability of losing each match. Since the club has a 30 percent probability of winning each match, the probability of losing each match is 1 - 0.30 = 0.70.
Step 2: Find the probability of losing all 3 matches. Since the matches are independent events, you can multiply the probability of losing each match together: 0.70 * 0.70 * 0.70 = 0.343.
Step 3: Calculate the complementary probability. To find the probability of winning at least 1 match, subtract the probability of losing all 3 matches from 1: 1 - 0.343 = 0.657.
So, the probability that the club will win at least 1 of the next 3 matches with a 30 percent probability of winning each match is 0.657 or 65.7%.

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To investigate whether there is a relationship between college GPA and high school GPA, we can use a statistical technique called linear regression. Linear regression can help us determine whether there is a linear relationship between the two variables and whether college GPA can be predicted from high school GPA.

First, we would need to plot the data points to see if there is a clear linear pattern between the two variables. If there is a linear pattern, we can then calculate the correlation coefficient, which measures the strength and direction of the linear relationship between the two variables. A positive correlation coefficient would indicate that higher high school GPAs are associated with higher college GPAs, while a negative correlation coefficient would indicate the opposite.

Once we have established the correlation between the two variables, we can then use linear regression to create a model that can predict college GPA based on high school GPA. The model would involve estimating the slope and intercept of the line that best fits the data points and using that line to predict college GPA for any given high school GPA.

Overall, by using statistical techniques like linear regression, we can investigate the relationship between college GPA and high school GPA and determine whether college GPA can be predicted from high school GPA.

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The range of the number of heads we can expect with 95% confidence is:

Range = 37.5 +/- 8.49

Range = (29.01, 45.99)

The expected number of heads when flipping a fair coin is equal to the probability of getting a heads, which is 0.5, multiplied by the number of flips.

The expected number of heads in 75 flips is:

Expected number of heads = 0.5 × 75 = 37.5

The standard deviation for the number of heads can be calculated using the formula:

Standard deviation = [tex]\sqrt{(n \times p \times (1-p))[/tex]

n is the number of trials (75 in this case) and p is the probability of success (getting a heads, which is 0.5).

The standard deviation for the number of heads in 75 flips is:

Standard deviation = [tex]\sqrt{(75 \times 0.5 \times (1-0.5))[/tex] = [tex]\sqrt{(18.75)[/tex] = 4.33 (rounded to two decimal places)

The range of the number of heads that can be expected with a certain level of confidence can be calculated using the formula:

Range = z × standard deviation

z is the number of standard deviations from the mean that corresponds to the desired level of confidence.

If we want to be 95% confident that the true number of heads falls within the range, we use a z-score of 1.96, which corresponds to the 95% confidence level.

The range of the number of heads we can expect with 95% confidence is:

Range = 1.96 × 4.33 = 8.49 (rounded to two decimal places)

So we can expect to get around 37.5 heads, give or take 8.49.

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The value of the line integral over C is 8/3.

To evaluate the line integral, we need to parameterize each line segment and then evaluate the integral for each segment.

= [tex]\int\limits^a_b {C2 xy dx + (x - y)} \, dx \\\\\int\limits^a_b {0^1 (3 + t)(2t) dt + ((3 + t) - 2t)(2 dt)} \, dx \\\\\int\limits^a_b {0^1 (6t + 2t^2) dt + (6 + 2t - 4t) dt} \, \\\\\int\limits^a_b {0^1 (8 + 2t^2) dt} \, \\\\[/tex]

[tex][8t + \frac{2}{3} t^3]0^1[/tex]

= 8/3

Therefore, the line integral over C is the sum of the line integrals over the two parts:

= 0 + 8/3

= 8/3

Hence, the value of the line integral over C is 8/3.

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we first need to figure out how long the entire 10k race will take the runner.

Since the runner has completed 30% of the race in 20 minutes, we can use that information to estimate the total time it will take the runner to complete the entire race.

To do this, we can use a proportion. If the runner completed 30% of the race in 20 minutes, we can set up the equation: 30/100 = 20/x, Here, x represents the total time it will take the runner to complete the race. To solve for x, we can cross-multiply:

30x = 100 * 20

30x = 2000

x = 2000/30

x ≈ 66.67

So, the runner will complete the entire 10k race in approximately 66.67 minutes. Next, we need to determine whether the runner can maintain the same pace for the entire race. If the runner can maintain the same pace, we can use the information we have to estimate the runner's final time.

If the runner completed 30% of the race in 20 minutes, we can use that to calculate how long it will take the runner to complete the remaining 70% of the race. To do this, we can set up the equation: 30/100 = 20/x, Solving for x, we get: x = 20 * 100 / 30, x ≈ 66.67/3, x ≈ 22.22 .

So, the runner will complete the remaining 70% of the race in approximately 22.22 minutes if she can maintain the same pace. Adding this time to the 20 minutes the runner has already completed, we get: 20 + 22.22 = 42.22,

Therefore, if the runner can maintain the same pace, her final time for the 10k race will be approximately 42.22 minutes.

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Answer:

2 m/s

Step-by-step explanation:

To find the speed of the ball, we can use the formula:

Speed = Distance / Time

Given that the distance the ball moves is 3 m and the time it takes is 1.5 s, we can substitute those values into the formula to get:

Speed = 3 m / 1.5 s

Simplifying the expression by dividing 3 by 1.5, we get:

Speed = 2 m/s

Therefore, the speed of the ball is 2 m/s.

The Moellers had 1,500 miles remaining to reach their destination. On the first day, the Moellers drove 1/2 (or 0.5) of the 3,000 miles, which is 1,500 miles. This means they had 1,500 miles remaining to reach their destination.

On the second day, they drove 1/4 (or 0.25) of the remaining 1,500 miles, which is 375 miles. Therefore, they had 1,125 miles remaining to reach their destination after driving 1/2 on the first day and 1/4 on the second day.

Based on the given information, the Moellers drove 1/3 of the distance on the first day and 1/4 of the remaining distance on the second day. Let's calculate the remaining distance to reach their destination:

Total distance: 3,000 miles

First day: 1/3 of 3,000 miles = 1,000 miles

Remaining distance after the first day: 3,000 - 1,000 = 2,000 miles

Second day: 1/4 of 2,000 miles = 500 miles

Remaining distance after the second day: 2,000 - 500 = 1,500 miles

So, the Moellers had 1,500 miles remaining to reach their destination.

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The boat is either about 2.334 miles or 2.666 miles from lighthouse B, depending on whether it sailed to the left or to the right of the line AB.

Let's draw a diagram to help visualize the problem. Let A and B be the positions of the lighthouses, and let C be the position of the boat after sailing 5 miles from A. Let x be the distance from C to B that we want to find, and let θ be the angle ACB.

Since the bearing of the boat from B is N65°E, we know that the angle ACB is (180° - 65°) = 115°. Also, since ACB is a straight line, we have

cos(115°) = x/8

Solving for x, we get

x = 8 cos(115°) ≈ 2.334 miles

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Statement iii is correct.

The test statistic is significant at the 0.03 level of significance, which is equivalent to a 97% confidence level.

Therefore, we can conclude that a 90% confidence interval for the difference in means will not contain the value 0 (Option I is false). Similarly, a 95% confidence interval for the difference in means will also not contain the value 0 (Option II is false). However, we cannot make any conclusion about a 99% confidence interval for the difference in means.

In general, as the level of confidence increases, the width of the confidence interval also increases. Therefore, it is possible that a 99% confidence interval for the difference in means may include the value 0 even though the 97% level of significance rejects the null hypothesis.

So, the only statement that must be true is III. A 99% confidence interval for the difference in means may or may not contain the value 0.

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