I don’t know how to solve for this

The answer is B. Normal. For a normal distribution, the mean, median, and mode are all equal to each other.

- Mean: The mean of a normal distribution is the center of the distribution, which is also the highest point of the bell-shaped curve.

- Median: The median of a normal distribution is the same as the mean, since the distribution is symmetric around the center.

- Mode: The mode of a normal distribution is also the same as the mean and median, since the highest point of the curve (i.e. the mode) is at the center of the distribution.

For the other probability distributions:

- A. Uniform: A uniform distribution has no mode (or multiple modes), and the mean and median are equal but different from the mode (if it exists).

- C. Exponential: An exponential distribution has a mode of 0, a median of ln(2)/λ, and a mean of 1/λ. Therefore, the mean, median, and mode are not equal.

- D. Poisson: A Poisson distribution has a mode of the integer part of λ (i.e., the highest probability mass function value). The mean and median are both equal to λ. Therefore, the mode is not necessarily equal to the mean and median.

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The appropriate null and alternative hypotheses for this research question is H₀: p= 0.69 and Hₐ: p>0.69 respectively.

Assume that p is the proportion of graduates from public, non-profit universities who were in debt from student loans at the time of their graduation.

In the above example, the hypothetical population proportion, or p₀ = 0.69.

According to this, 69% of 'college graduates' from 'public and non-profit' universities have debt from student loans.

Instead of the "alternative hypothesis," which shows a significant difference, the "null hypothesis" is now the "zero difference" hypothesis.

An "alternative hypothesis" that the researcher was interested in testing was if the 69% student loan debt proportion had dramatically grown.

In this instance, the proper "null and alternative" hypotheses are,

The "population proportion" of student loan debt is 69%, or H₀: p= 0.69

The "population proportion" of student loan debt is much higher than 69%, or Hₐ: p>0.69.

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The complete question is:

Does going to a private university increase the chance that a student will graduate with student loan debt? A national poll by the Institute for College Access and Success showed that in 69% of college graduates from public and nonprofit colleges in 2013 had student loan debt. A researcher wanted to see if there was a significant increase in the proportion of student loan debt for public and nonprofit colleges in 2014. Suppose that the researcher surveyed 1500 graduates of public and nonprofit universities and found that 71% of graduates had student loan debt in 2014. Let p be the proportion of all graduates of public nonprofit universities that graduated with student loan debt. What are the appropriate null and alternative hypotheses for this research question?

Answer:

Therefore, the two cities are 91.22 degrees apart in latitude and 212.05 degrees apart in longitude.

Step-by-step explanation:

The Haversine formula is:

d = 2r * arcsin(sqrt(sin^2((lat2 - lat1)/2) + cos(lat1) * cos(lat2) * sin^2((lon2 - lon1)/2)))

where:

d is the distance between the two points

r is the radius of the Earth (mean radius = 6,371km)

lat1 and lat2 are the latitude values of the two points

lon1 and lon2 are the longitude values of the two points

Using this formula, we can calculate the distance between Liverpool and Melbourne in terms of latitude and longitude:

Latitude difference = |53.41 - (-37.81)| = 91.22 degrees

Longitude difference = |(-2.99) - 144.96| = 147.95 degrees

Note that the longitude difference is greater than 180 degrees, which means that we need to account for the fact that the two cities are on opposite sides of the 180 degree meridian. To do this, we can subtract the longitude difference from 360 degrees:

Longitude difference = 360 - 147.95 = 212.05 degrees

Therefore, the two cities are 91.22 degrees apart in latitude and 212.05 degrees apart in longitude.

The answer choice which is a solution to the given inequality; 61 ≤ 11v + 8 as required to be determined is; v = 11.

It follows from the task content that the answer choices which is a solution to the inequality is to be determined.

Since the given inequality is such that we have;

61 ≤ 11v + 8

61 - 8 ≤ 11v

53 ≤ 11v

v ≥ 53 / 11

v ≥ 4.81

Hence, the answers choice which falls in the solutions set as required is; v = 11.

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The set of radian angle measures that is equivalent to [tex]sin^{-1}(-1/2)[/tex] is: c. 7pi/6, 11pi/6.

To find the radian angle measures that are equivalent to [tex]sin^{-1}(-1/2)[/tex], we need to identify angles whose sine function evaluates to -1/2.

The sine function represents the ratio of the length of the side opposite to an angle to the length of the hypotenuse in a right triangle. It takes on values between -1 and 1.

For [tex]sin^{-1}(-1/2)[/tex], we are looking for angles whose sine is equal to -1/2. In other words, we need to find angles where the ratio of the length of the side opposite the angle to the length of the hypotenuse is -1/2.

In the unit circle, the angles 7pi/6 and 11pi/6 correspond to 210 degrees and 330 degrees, respectively. At these angles, the y-coordinate of the corresponding point on the unit circle is -1/2, which satisfies the condition [tex]sin^{-1}(-1/2)[/tex].

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To write y as the sum of a vector in w and a vector orthogonal to w, we first need to find a basis for w. Since w is spanned by bold u 1 and bold u 2, we can use these vectors as our basis for w:
B = {bold u 1, bold u 2}

Now, we can use the orthogonal complement of w, denoted by w⊥, to find a vector that is orthogonal to w. By definition, w⊥ is the set of all vectors that are orthogonal to every vector in w. We can find w⊥ by taking the null space of the matrix whose rows are the basis vectors of w:

A = [bold u 1; bold u 2]

w⊥ = null(A)

Once we have a basis for w⊥, we can find a vector that is orthogonal to w by taking a linear combination of the basis vectors of w⊥. Let's call this vector z:

z = c_1*bold v_1 + c_2*bold v_2 + ... + c_k*bold v_k

where c_1, c_2, ..., c_k are constants, and bold v_1, bold v_2, ..., bold v_k are the basis vectors of w⊥.

Finally, we can express y as the sum of a vector in w and a vector orthogonal to w:

y = a*bold u 1 + b*bold u 2 + z

where a and b are constants that we can find by projecting y onto the basis vectors of w:

a = (y · bold u 1) / (bold u 1 · bold u 1)
b = (y · bold u 2) / (bold u 2 · bold u 2)

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The value of tan (P) is determined as  4/3.

The value of tan (P) is calculated by applying trig ratio as follows;

The trig ratio is simplified as;

SOH CAH TOA;

SOH ----> sin θ = opposite side / hypothenuse side

CAH -----> cos θ = adjacent side / hypothenuse side

TOA ------> tan θ = opposite side / adjacent side

The value of adjacent side of tan (P) is calculated as follows;

h = √ ( 10²  -  8² )

h = 6

The value of tan (P) is calculated as follows;

tan ( P ) = 8/6

tan (P) = 4/3

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Therefore, the critical value for testing the hypothesis at a significance level of 0.005 for a sample of size 30 is -2.756 (rounded to three decimal places).

To find the critical value for testing the hypothesis:

H0: μ = 18.38 (null hypothesis)

Ha: μ < 18.38 (alternative hypothesis)

at a significance level of 0.005 for a sample size of 30, we need to use the t-distribution.

Since the alternative hypothesis is one-tailed (μ < 18.38), we will be looking for the critical value in the left tail of the t-distribution.

The critical value is the value that separates the rejection region from the non-rejection region.

To find the critical value, we can use a t-table or a statistical software. Here, I'll use the t-table.

Since the sample size is 30, the degrees of freedom (df) for this t-test is (n - 1) = (30 - 1) = 29.

Looking up the critical value for a one-tailed test with 29 degrees of freedom and a significance level of 0.005 in the t-table, we find that the critical value is approximately -2.756.

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To find the rejection region for a test of independence of two classifications where the contingency table contains r rows and c columns at a significance level of 0.05, 0.10, and 0.01,
we need to use the chi-squared test for independence.

The rejection region for the chi-squared test is determined by comparing the calculated test statistic with the critical value of the chi-squared distribution with (r-1)(c-1) degrees of freedom at the desired level of significance.

For part (a), where r = 6 and c = 4 and the significance level is 0.05, the critical value of chi-squared with (6-1)(4-1) = 15 degrees of freedom is 24.9958. Therefore, the rejection region is any calculated test statistic greater than 24.9958.

For part (b), where r = 4 and c = -3, the contingency table does not satisfy the assumption of independence, since c cannot be negative. Therefore, we cannot perform a chi-squared test for independence and there is no rejection region to find.

For part (c), where r = 3 and c = -5, the contingency table does not satisfy the assumption of independence, since c cannot be negative. Therefore, we cannot perform a chi-squared test for independence and there is no rejection region to find.

In summary, the rejection region for a test of independence of two classifications where the contingency table contains r rows and c columns at a significance level of 0.05 is any calculated test statistic greater than the critical value of chi-squared with (r-1)(c-1) degrees of freedom.

However, if the contingency table does not satisfy the assumption of independence (such as when c is negative), a chi-squared test for independence cannot be performed and there is no rejection region to find.
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The distribution of the number of business majors you choose is not binomial. The correct option is c) not binomial.

The distribution of the number of business majors you choose is not binomial because the conditions for a binomial distribution are not met:

1. There must be a fixed number of trials: In this case, we are choosing 3 students out of 10, which means the number of trials is not fixed.

2. The trials must be independent: This assumption is reasonable, as choosing one student does not affect the probability of choosing another student.

3. The probability of success must be the same for each trial: The probability of choosing a business major is 0.4 for the first trial, but it will change for the second and third trials depending on the results of the previous trials. Therefore, the probability of success is not the same for each trial.

Therefore, the correct option is c) not binomial.

The complete question is:

In a group of 10 college students, 4 are business majors. You choose 3 of the 10 students at random and ask their major. The distribution of the number of business majors you choose is

(a) Binomial with n = 10 and p = 0.4

(b) Binomial with n = 3 and p = 0.4

(c) Not binomial

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The expression in terms of x that represents the total amount that Francisco paid at the register is 0.918x.

The total amount that Francisco paid at the register need to first calculate the discounted price after applying the 15% coupon and then add the 8% tax to it.

The discount on the original price is 15% means that Francisco pays only 85% of the original price.

The discounted price as:

Discounted price = 0.85 × x

The 8% tax to the discounted price.

The tax is calculated based on the discounted price not the original price.

The expression for the total amount that Francisco paid at the register is:

Total amount = Discounted price + 8% tax on discounted price

Total amount = 0.85x + 0.08(0.85x)

Total amount = 0.85x + 0.068x

Total amount = 0.918x

The expression in terms of x that represents the total amount that Francisco paid at the register is 0.918x.

This means that Francisco paid 91.8% of the original price after applying the 15% discount and adding the 8% tax.

The expression 0.918x represents the total amount that Francisco paid at the register in terms of the original price x after applying a 15% discount and an 8% tax on the discounted price.

To calculate discounts, taxes and total prices can help consumers make informed decisions about their purchases and manage their finances effectively.

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Step-by-step explanation:

The second derivative of y = -7x^6 - 5 is the derivative of the first derivative.

y' = -42x^5

y'' = (d/dx)(-42x^5)

y'' = -210x^4

Therefore, the second derivative of y = -7x^6 - 5 is y'' = -210x^4.

Answer:

[tex]\displaystyle 5040[/tex]

Step-by-step explanation:

You have ten digits, but can only choose from four each time. Therefore, you will use the formula pertaining to permutations [order matters]. Here is how it is done:

[tex]\displaystyle \frac{n!}{[-k + n]!} = {}_nP_k \\ \\ \frac{10!}{[-4 + 10]!} = \frac{10!}{6!} \Longrightarrow \frac{[2][3][4][5][6][7][8][9][10]}{[2][3][4][5][6]} \\ \\ \\ \boxed{5040} = [7][8][9][10][/tex]

So, there will be five thousand forty different passwords, or in this case, combinations.

I am joyous to assist you at any time.

The percent of the days in April that it rains is 66.67%.

A fraction is a non-integer that is made up of a numerator and a denominator. The numerator is the number above and the denominator is the number below. An example of a fraction is 2/3.

A percent is the value of a number out of 100. In order to convert a value to percent, multiply by 100.

Percent of the days that it rains = ( number of days it rains / total number of days) x 100

(2/3) x 100 = 66.67%

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The primary advantage of pooled cross-sectional data over ordinary cross-sectional data is that it provides a larger sample size.

When cross-sectional data from multiple time periods are pooled together, it increases the overall number of observations and thus increases the statistical power of any analysis that is conducted. This larger sample size can also help to reduce the standard errors of estimates and increase the precision of any findings.

However, it is important to note that pooling cross-sectional data does not enable you to ignore the differences in data over time. Rather, it enables you to observe changes in key relationships over time by including observations from different time periods in one data set. Pooling cross-sectional data also does not necessarily enable you to form a panel data set, as this typically requires the same set of individuals or units to be observed over time.

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To prove that the pole of a Simson line perpendicular to one side of a triangle must be one of the vertices of the triangle, we can use the following steps:

Thus, we have shown that the pole of a Simson line perpendicular to one side of a triangle must be one of the vertices of the triangle.

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The probabilities are:

a) P(S^2/σ^2 ≤ 1.8) ≈ 0.8147

b) P(0.85 ≤ S^2/σ^2 ≤ 1.15) ≈ 0.1197

To solve the given problem, we need to use the chi-square distribution. The chi-square distribution is used to analyze the variability of a normally distributed population when the variance is unknown.

Given:

The temperature at which a thermostat goes off is normally distributed with variance σ^2.

We are testing the thermostat five times, so we have a sample size of n = 5.

We need to find the probabilities P(S^2/σ^2 ≤ 1.8) and P(0.85 ≤ S^2/σ^2 ≤ 1.15).

a) P(S^2/σ^2 ≤ 1.8):

The chi-square distribution with n - 1 degrees of freedom (df = 4 in this case) is used to calculate the probability.

Using a chi-square table or software, we can find that P(X ≤ 1.8) for df = 4 is approximately 0.8147.

b) P(0.85 ≤ S^2/σ^2 ≤ 1.15):

To find this probability, we need to calculate the cumulative probability of two chi-square values and subtract them.

P(0.85 ≤ S^2/σ^2 ≤ 1.15) = P(S^2/σ^2 ≤ 1.15) - P(S^2/σ^2 ≤ 0.85)

Using the chi-square distribution with df = 4, we find P(X ≤ 1.15) ≈ 0.8264 and P(X ≤ 0.85) ≈ 0.7067.

Therefore, P(0.85 ≤ S^2/σ^2 ≤ 1.15) = 0.8264 - 0.7067 = 0.1197.

So, the probabilities are:

a) P(S^2/σ^2 ≤ 1.8) ≈ 0.8147

b) P(0.85 ≤ S^2/σ^2 ≤ 1.15) ≈ 0.1197

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Answer:

linear - y= x/2 -19, y = x+25/5

non linear- everything else

Step-by-step explanation:

put it into a calc and look for straight lines (linear)

3y=x^2 is NOT linear (it's a parabola)

y=(x/2)-19 is linear - - - it's a straight line

y= x + 25/5 is linear - - - it's straight line

13y = (1/3)x+5 is linear - - - another straight line

y^3 = x is NOT linear.

Reflection of point across x-axis

preimage (-3, 2)         Image (-3, -2)

Reflection of point across y-axis

preimage (-3, 2)         Image (3, 2)

Here, we have,

to find the coordinates of the reflected image

Reflection is one of the movements in transformation that involve creation of mirror image

Transformation rule for reflection over x-axis at origin (0, 0)) is

(x, y) → (x, -y)

Transformation rule for reflection over line y-axis at origin (0, 0)) is

(x, y) → (-x, y)

The reflection to be done is (-3, 2)

Transformation for reflection over x-axis → (-3, -2)

Transformation for reflection over line y-axis  → (3, 2)

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complete question:

Reflection of point across x-axis

Reflection of point across y-axis

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

Reflection of point across x-axis and

then Reflection across y-axis

Therefore, the two F-values that divide the area under the F-curve are approximately 0.179 and 4.366 for an F-distribution with degrees of freedom (9,7).

To determine the two F-values that divide the area under the F-curve into a middle 0.95 area and two outside 0.025 areas, we need to consult the F-distribution table. For an F-distribution with degrees of freedom (df) of (9,7), the two values we are looking for correspond to the cumulative probabilities of 0.025 and 0.975.

From the F-distribution table, with numerator degrees of freedom (df1) = 9 and denominator degrees of freedom (df2) = 7, we can find the critical F-values. The critical F-value for the lower tail with cumulative probability 0.025 is approximately 0.179. The critical F-value for the upper tail with cumulative probability 0.975 is approximately 4.366.

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The value of w will decrease by approximately 235 times the small amount.

Using the power rule and product rule of differentiation, we obtain:

wx(x,y,z) = 6xy - 7yz

wy(x,y,z) = 3x^2 - 7xz + 20yz

wz(x,y,z) = -7xy + 20yz

Next, we evaluate each partial derivative at the given point (3,5,-4) by substituting x = 3, y = 5, and z = -4:

wx(3,5,-4) = 6(3)(5) - 7(5)(-4) = 210

wy(3,5,-4) = 3(3^2) - 7(3)(-4) + 20(5)(-4) = -327

wz(3,5,-4) = -7(3)(5) + 20(5)(-4) = -235

Therefore, the values of the first partial derivatives with respect to x, y, and z, evaluated at the point (3,5,-4), are wx = 210, wy = -327, and wz = -235.

These partial derivatives give us information about how the function w changes as we vary each input variable. For example, wx = 210 indicates that if we increase x by a small amount while holding y and z constant, the value of w will increase by approximately 210 times the small amount. Similarly, wy = -327 tells us that if we increase y by a small amount while holding x and z constant, the value of w will decrease by approximately 327 times the small amount. Finally, wz = -235 tells us that if we increase z by a small amount while holding x and y constant, the value of w will decrease by approximately 235 times the small amount.

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The appropriate value of the t-multiple required for a sample with 20 observations and a 95% confidence estimate for mu is 2.093. To calculate this value, we need to use a t-distribution table or calculator.

The formula for calculating the t-multiple is:

t = (x - μ) / (s / √n)

where x is the sample mean, μ is the population mean (unknown), s is the sample standard deviation, n is the sample size, and t is the t-multiple.

For a 95% confidence interval, we need to find the t-value that corresponds to a 2.5% tail probability (since the distribution is symmetric). In a t-distribution table with 19 degrees of freedom (n-1), the closest value to 2.5% is 2.093.

Therefore, the appropriate value of the t-multiple required for a sample with 20 observations and a 95% confidence estimate for mu is 2.093, rounded to 3 decimal places. This value will be used to calculate the margin of error and the confidence interval for the population mean.

If a sample has 20 observations and a 95% confidence estimate for μ is needed, the appropriate value of the t-multiple required is 2.093. This value is rounded to 3 decimal places.

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If the probability of event X occurring is the same as the probability of event X occurring given that event Y has already occurred, then: Event X has no effect on the probability of event Y occurring.

So, the correct answer is C.

This is because if the probability of event X occurring is the same as the probability of event X occurring given that event Y has already occurred, it means that event Y has no influence on the probability of event X occurring.

Therefore, events X and Y are independent of each other.

Option A is incorrect because it suggests that event Y is dependent on event X, which is not the case.

Option B is also incorrect because it suggests that both events are dependent on each other.

Option D is incorrect because it suggests that event Y has no effect on the probability of event X occurring, which is not true.

Option E is also incorrect because it includes options A, B, and D which are incorrect.

Option F is also incorrect because we have already established that event X has no effect on the probability of event Y occurring.

Hence the answer of the question is C.

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Molly is the only student who rewrote the equation correctly into slope-intercept form. Her equation is:

y = -4x + 3

Jared, Ali, and Mia made mistakes in their simplifications by not dividing the entire equation by 3 when isolating the term with y.

Let's analyze each student's attempt to rewrite the equation 12x + 3y = 9 into slope-intercept form.

Jared:

Jared's attempt is incorrect. He started correctly by isolating the term with y, but he made a mistake in simplifying it. Instead of dividing the entire equation by 3, he only divided the constant term. The correct simplification would be:

3y = 9 - 12x

y = (-12/3)x + 3

y = -4x + 3

Molly:

Molly's attempt is correct. She correctly isolated the term with y and divided the entire equation by 3 to solve for y. The simplified equation is:

y = (-12/3)x + 3

y = -4x + 3

Ali:

Ali's attempt is incorrect. He attempted to move the term with x to the other side of the equation but made a mistake in the process. Instead of subtracting 12x from both sides, he mistakenly subtracted 4x from both sides. The correct simplification would be:

12x + 3y = 9

3y = 9 - 12x

y = (-12/3)x + 3

y = -4x + 3

Mia:

Mia's attempt is incorrect. She made the same mistake as Jared by dividing only the constant term by 3. The correct simplification would be:

3y = 9 - 12x

y = (-12/3)x + 3

y = -4x + 3

From the analysis, we can see that Molly is the only student who rewrote the equation correctly into slope-intercept form. Her equation is:

y = -4x + 3

Jared, Ali, and Mia made mistakes in their simplifications by not dividing the entire equation by 3 when isolating the term with y.

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a) An analytic expression for each density, that is, normalize each function for arbitrary ai, and positive bi is e-|x-ai|/bi

(b) The likelihood ratio p(x|ω1)/p(x|ω2) as a function of your four variable is threshold value.

Let's start by writing an analytic expression for each density. We have:

p(x|ωi)∝e-|x-ai|/bi for i=1,2 and b>0

To do this, we will use the fact that the integral of a Gaussian function e^(-x^2) over the entire real line is the square root of pi.

The integral of p(x|ωi) over the entire domain is given by:

∫ p(x|ωi) dx = 2bi ∫ e-|x-ai|/bi dx

Using the change of variable y=(x-ai)/bi, this becomes:

∫ p(x|ωi) dx = 2bi ∫ e-|y| dy = 4bi

Therefore, the normalized probability density function for each hypothesis is given by:

p(x|ωi) = (1/4bi) e-|x-ai|/bi

Now, let's calculate the likelihood ratio:

p(x|ω₁)/p(x|ω₂) = [e-|x-a₁|/b₁ / 4b₁] / [e-|x-a₂|/b₂ / 4b₂]

Taking the natural logarithm of both sides and simplifying, we get:

ln[p(x|ω₁)/p(x|ω₂)] = -|x-a₁|/b₁ + |x-a₂|/b₂ + ln(b₂/b₁)

To determine the decision rule that maximizes the probability of correct classification, we need to compare this ratio to a threshold value.

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we need to multiply the probability of each possible outcome (number of college graduates) by the number of college graduates and then add up all the products. On average, the expected number of college graduates from 5 randomly selected adults is approximately 3.

So, the calculation would be:
(0 x 0.01028) + (1 x 0.07715) + (2 x 0.2307) + (3 x 0.3457) + (4 x 0.2583) + (5 x 0.07751)
= 0 + 0.07715 + 0.4614 + 1.0371 + 1.0332 + 0.38755
3.9967
Therefore, on average, we can expect about 4 college graduates from 5 randomly selected adults in this certain town.
In order to find the expected number of college graduates from 5 randomly selected adults, you need to calculate the expected value using the probability distribution provided. The expected value (E) can be calculated using the formula:
E = Σ [x * P(x)]
Using the given table, the calculation is as follows:
E = (0 * 0.01028) + (1 * 0.07715) + (2 * 0.2307) + (3 * 0.3457) + (4 * 0.2583) + (5 * 0.07751)
E = 0 + 0.07715 + 0.4614 + 1.0371 + 1.0332 + 0.38755
E ≈ 2.9964
On average, the expected number of college graduates from 5 randomly selected adults is approximately 3.

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None of these alternatives is correct. The probability of a type ii error (β) is not directly determined by the probability of a type i error (α).

Type i and type ii errors are two types of errors that can occur in hypothesis testing. Type i error occurs when we reject a true null hypothesis, while type ii error occurs when we fail to reject a false null hypothesis.

The probability of a type i error (α) is typically set by the researcher or the significance level chosen for the test. A common value for α is 0.05, which means that there is a 5% chance of rejecting a true null hypothesis. However, the probability of a type ii error (β) depends on various factors such as the sample size, effect size, and the level of significance chosen for the test.

In general, the probability of a type ii error (β) decreases as the sample size increases or as the effect size increases. It also decreases if the level of significance chosen for the test is reduced. However, it is important to note that there is always a trade-off between type i and type ii errors. As the probability of type i error decreases, the probability of type ii error increases, and vice versa.

In conclusion, the probability of a type ii error (β) cannot be determined solely based on the probability of a type i error (α). It depends on several factors and should be considered along with the probability of type i error when making decisions about hypothesis testing.

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Answer:

She left the museum at 1:11pm

Step-by-step explanation:

The velocity of airflow during a respiratory cycle is given by v = 0.85 sin(pi t/3) ,t- time in s. One full respiratory cycle takes 6 s, and there are 10 cycles per min. The graph of the velocity function is a sinusoidal wave with amplitude 0.85 and period 6 s.

The given function for velocity during a respiratory cycle is v = 0.85 sin(pi t/3). The velocity is positive during inhalation and negative during exhalation. To find the time for one full respiratory cycle, we need to solve for the values of t that make v=0:

0 = 0.85 sin(pi t/3)

sin(pi t/3) = 0

pi t/3 = n pi

t = 3n, where n is an integer

Thus, one full respiratory cycle takes 6 seconds (when n=2). To find the number of cycles per minute, we can use the formula:

cycles per minute = 60 / time per cycle

Substituting the value of time per cycle, we get:

cycles per minute = 60 / 6 = 10

Therefore, there are 10 cycles per minute.

The graph of the velocity function is a sinusoidal wave with amplitude 0.85 and period 6 seconds. The function starts at 0, reaches a maximum value of 0.85 at t=3 seconds, goes through 0 again at t=6 seconds, reaches a minimum value of -0.85 at t=9 seconds, and returns to 0 at t=12 seconds. The graph repeats itself every 6 seconds, which is the period of the function. Thus, the graph is a sinusoidal wave that oscillates between positive and negative values with a frequency of 1/6 Hz (or 10 cycles per minute).

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The estimate for cost to renovate the garden is close to $300. The Option C.

The area of the rectangular section is:

= 25 ft x 40 ft

= 1000 sq ft.

The area of the circular fountain is:

= (15/2)^2 x π

≈ 176.71 sq ft.

Given that:

The cost of the sod is $0.30 per square foot.

The estimated cost for renovation will  be:

= 1176.71 sq ft x $0.30/sq ft

= $353.01.

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