For a sample of 45 observations, you have the following information: Σxi = 153.7, Σyi = 231.2, Σxiyi = 712.5, Σ(xi)2 = 718, Σ(yi)2 = 1775.2. What is the sample correlation coefficient between X and Y?

The 95% confidence interval for the relative sea level trend in Daytona Beach, FL from 1925 to 1983 is 2.32 ± 0.62 mm/year, meaning we are 95% confident that the true sea level trend lies between 1.70 mm/year and 2.94 mm/year for this location and time period.
   
Zero is not within this interval, which provides enough evidence to suggest that sea levels are indeed rising in Daytona Beach during the given period.
   
Here are the relevant terms:

- Center: 2.32 mm/year (the mean sea level trend)
- Standard Deviation: Not provided in the question, but necessary for calculating the confidence interval
- Number of years (N population): 1983 - 1925 = 58 years
- Degrees of freedom (DF): N - 1 = 57
- Confidence: 95% (specified in the question)
- Confidence interval: 2.32 ± 0.62 mm/year
- T CL: Not provided in the question, but it represents the critical value from the t-distribution for a 95% confidence level and the given degrees of freedom.

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The length of the table is [tex]\frac{8}{5} m[/tex].

In order to get the length of the table, we will use the formula for the area of a rectangle which is length x width.

Let L be the length of the table. Then, we have:

=>>> L x 3/4 = 1 1/5 m

Multiplying both sides by 4/3, we get:

L = (4/3) x 1 1/5

L = 4/3 x 6/5

L = 24/15

L = 8/5

Full question "Katya is making a rectangular table that is 3/4 m wide. the table has an area of 1 1/5 m. How long is the table".

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The median first-name length of a person at the meeting is 6.5

To determine the value of the their median first name, we need to know that;

The median of a given set of data is the described as the middle number when the data is arranged in an order from least to the greatest value, that is, in an ascending order of arrangement.

From the information given, we have that;

The lengths of their first names are;

9, 3, 5, 8, 6, 7, 4, and 5 letters.

arrange in ascending order

3, 4, 5, 5, 6, 7, 8, 9

The middle values are;

= 5 + 6/2

Add the values

= 11/2

= 5. 5

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a. Probability, P(X ≤ 10) = 0.4148 (rounded to 4 decimal places).

b. Probability, P(X > 3) = 0.5683 (rounded to 4 decimal places).

c. Probability, P(X < 2) = 0.2829 (rounded to 4 decimal places).

Poisson probability calculations.

Here are the solutions:

(a) P(X ≤ 10), λ = 11.0

We can use the Poisson probability formula:

[tex]P(X = x) = (e^-\lambda * \lambda^x) / x![/tex]

where λ is the mean or expected number of occurrences, and x is the actual number of occurrences.

To find P(X ≤ 10), we need to calculate the sum of probabilities for all values of X less than or equal to 10:

P(X ≤ 10) = Σ P(X = x), for x = 0 to 10

Using λ = 11.0, we get:

P(X ≤ 10) = [tex]\sum [(e^-11.0 * 11.0^x) / x!], for x = 0 to 10[/tex]

[tex]= [e^-11.0 * (11.0^0 / 0!) + e^-11.0 * (11.0^1 / 1!) + ... + e^-11.0 * (11.0^10 / 10!)][/tex]

= 0.4148

Therefore, P(X ≤ 10) = 0.4148 (rounded to 4 decimal places).

(b) P(X > 3), λ = 5.2

To find P(X > 3), we need to calculate the sum of probabilities for all values of X greater than 3:

P(X > 3) = Σ P(X = x), for x = 4 to infinity

Using λ = 5.2, we get:

P(X > 3) [tex]= \sum [(e^-5.2 * 5.2^x) / x!], for x = 4 to infinity[/tex]

[tex]= e^-5.2 * [(5.2^4 / 4!) + (5.2^5 / 5!) + ...][/tex]

= 0.5683.

Therefore, P(X > 3) = 0.5683 (rounded to 4 decimal places).

(c) P(X < 2), λ = 3.7

To find P(X < 2), we need to calculate the sum of probabilities for all values of X less than 2:

P(X < 2) = Σ P(X = x), for x = 0 to 1

Using λ = 3.7, we get:

[tex]P(X < 2) = \sum [(e^-3.7 * 3.7^x) / x!], for x = 0 to 1[/tex]

= [tex]e^-3.7 * [(3.7^0 / 0!) + (3.7^1 / 1!)][/tex]

= 0.2829.

Therefore, P(X < 2) = 0.2829 (rounded to 4 decimal places).

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the F-statistic for the regression is approximately 64.76.

To calculate the F-statistic for the regression, we need to use the following formula:

F = (SSR / p) / (SSE / (n - p - 1))

where SSR is the sum of squares for regression, p is the number of predictors (excluding the intercept), SSE is the sum of squares for error, and n is the total number of observations.

From the ANOVA table provided, we can see that:

SSR = 113

df for regression = p = 3 (since there are three predictors)

MS for regression = SSR / p = 113 / 3 = 37.67

SSE = 178 - 113 = 65

df for error = n - p - 1 = 72 - 3 - 1 = 68

MS for error = SSE / df for error = 65 / 68 = 0.956

Plugging these values into the F-formula, we get:

F = (37.67 / 3) / (0.956 / 68) ≈ 64.76

Therefore, the F-statistic for the regression is approximately 64.76.

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In Friedman's test for a randomized block design, the correct alternative hypothesis is a. ha: not all the sample means are equal

The Friedman's test is a non-parametric statistical test used in randomised block designs, where the same individuals are evaluated under various circumstances or at various times, to compare three or more similar groups or treatments. In Friedman's test, the alternative hypothesis (Ha) argues that certain sample means are not equal to all other sample means,

Whereas in the test, null hypothesis (H0) states that all sample means are equal. In other words, the alternative hypothesis takes into account the likelihood of such differences and Friedman's test is used to assess if there are any statistically significant variations in the mean rankings of the groups or treatments.

Complete Question:

In friedman's test for a randomized block design, what is the correct alternative hypothesis?

a. ha: not all the sample means are equal

b. ha: all the medians are equal

c. ha: not all the medians are equal

d. ha: all sample means are equal

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2. Discrete Variable.
3. The probability that a randomly selected package will weigh less than 18 pounds is 0.67.

4. The probability that a randomly selected package will weigh between 16 and 18 pounds is 0.33.

2. The amount of time it takes students to get to school would best be modeled as a Discrete variable because time can be measured in infinitely many decimal values.


3. The probability that a randomly selected package will weigh less than 18 pounds can be found by calculating the area under the probability density curve to the left of 18 pounds. Since the weights are uniformly distributed, the probability density function is a straight line with a slope of 1/(20-14) = 1/6.

The area of a triangle with base 4 (from 14 to 18 pounds) and height 1/6 is (1/2)(4)(1/6) = 1/12.

Therefore, the probability that a randomly selected package will weigh less than 18 pounds is the area under the curve from 14 to 18 pounds, which is 1/12 divided by the total area under the curve from 14 to 20 pounds, which is 1.

This gives us a probability of 1/12 ÷ 1 = 0.0833, which can be rounded to 0.08 or 0.10. The closest answer choice is c. 0.20, but neither a nor b nor d are correct.


4. The probability that a randomly selected package will weigh between 16 and 18 pounds can be found by calculating the area under the probability density curve between 16 and 18 pounds.

Again, since the weights are uniformly distributed, the probability density function is a straight line with a slope of 1/(20-14) = 1/6.

The area of a triangle with base 2 (from 16 to 18 pounds) and height 1/6 is (1/2)(2)(1/6) = 1/12.

Therefore, the probability that a randomly selected package will weigh between 16 and 18 pounds is the area under the curve from 16 to 18 pounds, which is 1/12 divided by the total area under the curve from 14 to 20 pounds, which is 1.

This gives us a probability of 1/12 ÷ 1 = 0.0833, which can be rounded to 0.08 or 0.10. The closest answer choice is not listed, but the correct probability is between 0.07 and 0.09.

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The five-number summary for the given dataset is: 19.98, 30.585, 73.295, 99.24, and 114.60.

To calculate the five-number summary for the given dataset, we first need to sort the data in ascending order:

19.98, 41.19, 63.08, 83.51, 83.88, 114.60

Now, let's find the five-number summary components:

1. Minimum: The smallest number in the dataset.
Minimum = 19.98

2. First Quartile (Q1): The median of the lower half, not including the overall median if the dataset has an odd number of data points.
Q1 = (19.98 + 41.19) / 2 = 30.585

3. Median: The middle number of the dataset.
Median = (63.08 + 83.51) / 2 = 73.295

4. Third Quartile (Q3): The median of the upper half, not including the overall median if the dataset has an odd number of data points.
Q3 = (83.88 + 114.60) / 2 = 99.24

5. Maximum: The largest number in the dataset.
Maximum = 114.60

The five-number summary for the given dataset is: 19.98, 30.585, 73.295, 99.24, and 114.60.

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Using similar side theorem, the value of x is 4 units and the length of BD is 13 units.

The Similar Side Theorem, also known as the Angle Bisector Theorem, states that if a ray bisects an angle of a triangle, then it divides the opposite side into two segments that are proportional to the other two sides of the triangle.

More formally, let ABC be a triangle with angle bisector AD, where D lies on the side BC. Then, the following proportion holds:

BD/DC = AB/AC

where BD and DC are the two segments into which AD divides the side BC, and AB and AC are the other two sides of the triangle.

In this problem, we have to find the value of x

12 / x = 27 / x + 5

27x = 12(x + 5)

27x = 12x + 60

27x - 12x = 60

15x = 60

x = 4

BD = BC + CD

BD = 4 + (4 + 5)

BD = 4 + 9

BD = 13

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Answer: 10x+8

Step-by-step explanation: 6x+2(4+2x)

Distribute 2 to numbers in parenthesis

2(4) + 2(2x)

8+4x

Don't forget to keep the 6x in the equation

6x+8+4x

Combine like terms (numbers with the same variable (letter))

6x+4x

10x

Keep the 8 because it is a whole solo number

10x+8

Hope this helps!

Since f(5) = 0, we have shown that 5 is one of the zeros of the function f(x).

What is function zero?

A function zero (also known as a root or a solution) is a value of the independent variable in a function that makes the function equal to zero. In other words, a function zero is a value of x for which f(x) = 0.

To prove that 5 is one of the zeros of the function f(x) = x³ - 2x² - 75, we need to show that f(5) = 0.

Substituting x = 5 into the function, we get:

f(5) = 5³ - 2(5)² - 75

= 125 - 50 - 75

= 0

Since f(5) = 0, we have shown that 5 is one of the zeros of the function f(x).

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Answer: delation

Step-by-step explanation:

The three sorts of unbending changes are interpretation, revolution, and reflection. Each of these changes can be utilized to demonstrate that two triangles are compatible, as long as the comparing sides and points are compatible after the change.

Be that as it may, there's one change that cannot be utilized to demonstrate coinciding between two triangles, which may be a enlargement. A expansion may be a change that changes the estimate of an protest, but does not protect separations or points. Subsequently, in case we expand one triangle, we cannot ensure that the comparing sides and points of the two triangles will be compatible.

The number in the sequence closest to 300 is 320.

What is sequence?

In mathematics, a sequence is an ordered list of numbers or other objects that follow a specific pattern or rule. Each element in the sequence is called a term, and the position of a term in the sequence is called its index.

To find the number in the sequence closest to 300, we can subtract 110 from 300 to get 190, and then divide by 30 to find how many additional terms we need to add to the sequence after 110:

(300 - 110) / 30 = 6

So we need to add 6 more terms to the sequence after 110.

The next term in the sequence after 110 is:

110 + 30 = 140

And the 6th term after that is:

140 + 30(6) = 320

So the number in the sequence closest to 300 is 320.

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The Excel function used when calculating the t critical value is the T.INV function.

The TINV function returns the inverse of the Student's t-distribution, which is a probability distribution that is commonly used in hypothesis testing when the sample size is small and the population standard deviation is unknown. The TINV function requires two arguments: the probability value (alpha) and the degrees of freedom.

The degrees of freedom depend on the sample size and the number of parameters estimated in the model. The TINV function returns the value of the t statistic that corresponds to a given probability and degrees of freedom. This t critical value is compared to the calculated t statistic to determine if the null hypothesis should be rejected or not.

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The derivative, implicit differentiation: dhe 8x w+v+wzº+byx = 0 = 8/w - b times y / w. To find the expressions for dy/dx and dz/dx, we'll need additional information or equations relating y, z, and x.

To calculate the derivative using implicit differentiation, we need to differentiate both sides of the equation with respect to the variable we are interested in, which in this case is x.

First, we need to apply the chain rule to each term that contains a function of x. For example, the term 8x becomes 8 times the derivative of x, which is just 8. Similarly, the term byx becomes b times y times the derivative of x, which is just b times y.

Next, we need to apply the product rule to each term that contains two or more variables that are functions of x. For example, the term wzº becomes w times the derivative of zº plus zº times the derivative of w, which simplifies to w times 0 plus zº times the derivative of w.

Putting all of this together, we get:

8 + w + v + w times derivative of zº plus b times y = 0

To solve for the derivative of zº, we just need to isolate it on one side of the equation:

w times derivative of zº = -8 - w - v - b times y

Dividing both sides by w gives us:

derivative of zº = (-8 - w - v - b times y) / w

So the derivative of the original equation with respect to x is:

8 + w + v + (-8 - w - v - b times y) / w

which simplifies to:

8/w - b times y / w.

To calculate the derivative using implicit differentiation for the equation 8x + w + wz + byx = 0, we'll need to differentiate both sides with respect to x:

Derivative of 8x: 8
Derivative of w: 0 (since w is considered a constant with respect to x)
Derivative of wz: w * (dz/dx) (applying product rule)
Derivative of byx: b * (dy/dx) (applying product rule)

Now, differentiate both sides with respect to x:

8 + 0 + w * (dz/dx) + b * (dy/dx) = 0

Now, solve for dy/dx and dz/dx:

b * (dy/dx) = -8 - w * (dz/dx)

(dy/dx) = (-8 - w * (dz/dx)) / b

To find the expressions for dy/dx and dz/dx, we'll need additional information or equations relating y, z, and x.

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The approximate area of the larger pin is 62.04 cm².

The area of an equilateral triangle is given by:

A = (√3/4) * a²

Where a is the side length

Let S and L represent the side length of smaller and larger pin respectively

For the smaller pin:

A = (√3/4) * S²

6.9 = (√3/4) * S²

S = 3.99 cm

Since L = 3 * S

L = 3 * 3.99 = 11.97 cm

For the larger pin:

A = (√3/4) * 11.97²

A = (√3/4) * 143.2809

A = 62.04 cm²

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Answer:

i dont think the full question is here but the correct way to it you would get the anser
5x^3+7x^2+3x-3

Step-by-step explanation:

Answer:

the first one is 3 and 2 the

Step-by-step explanation:

hope this helps

To test the reliability of the coefficients obtained from the Fama-French model, you can use a hypothesis test.

Then are the detailed  way to set up a  thesis test   Step 1 Define the null and indispensable  suppositions   The null  thesis( H0) is that the measure of a particular factor is equal to zero, while the indispensable  thesis( Ha) is that the measure isn't equal to zero.   H0 βi =  0  Ha βi ≠ 0   where βi is the measure of the factor in the Fama- French model.  

Step 2 Determine the significance  position   The significance  position is the probability of rejecting the null  thesis when it's actually true. It's  generally set at0.05 or0.01.   Step 3 Calculate the test statistic   The test statistic is a measure of how far the sample estimate of the measure is from the  hypothecated value of zero, relative to the standard error of the estimate. In the case of the Fama- French model, the t- test can be used to calculate the test statistic as follows   t =  βi/ SE( βi)   where SE( βi) is the standard error of the estimate of the ith measure.

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It will take 8.15 years for the amount $3725 to double at 8.5% if compounded continuously, the correct option is (a).

We have to find the time it takes for the amount $3725 to double at 8.5% compounded continuously,

So, we use the formula for "Continuous-Compounding",

which is : A = P[tex]e^{r\times t}[/tex],

where A = final amount, P = initial amount, r = annual interest-rate (in decimal), t = time in years,

Since we want to find the time it takes for $3725 to double,

the double of $3725 is $7450,

So, the amount "A" is = $7450, and P is = $3725,

the rate is = 8.5% = 0.085,

Substituting the values,

We get,

⇒ $7450 = $3725[tex]e^{0.085\times t}[/tex],

⇒ 2 = [tex]e^{0.085\times t}[/tex],

⇒ ln(2) = 0.085×t,

⇒ t = 8.1546 ≈ 8.15 years.

Therefore, it will take (a) 8.15 years for the amount to double.

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The given question is incomplete, the complete question is

How long does it take for $3725 to double if it is invested at 8.5% compounded continuously, Round your answer to two decimal places

(a) 8.15 years

(b) 0.32 years

(c) 0.01 years

(d) 0.08 years

It would be surprising if a sample mean of $2,940 was obtained from a random sample of 36 Pell grant recipients, under the condition average Pell grant award for 2007-2008 was $2,600.


For this case, the standard deviation of Pell grant awards is $500 hence we are sampling 36 recipients. Then, the standard deviation of the sample mean is $500/√36 = $83.33.

The formula for evaluating the z-score for a sample mean is

z = (x' - μ) / (σ / √n)

Here
x'= sample mean,
μ = population mean,
σ = population standard deviation,
n= sample size.

Now, If we assume that the population mean is $2,600 and we want to test whether a sample mean of $2,940 is significantly different from this value, we can evaluate the z-score

z = (2940 - 2600) / (83.33)
= 4.08

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The correct options are:

They can work to decrease their marginal cost.

They can raise prices to increase marginal revenue.

They can keep marginal costs below marginal revenues.

What is the equivalent expression?

Equivalent expressions are expressions that perform the same function despite their appearance. If two algebraic expressions are equivalent, they have the same value when we use the same variable value.

Producers can make the most profit by:

Working to decrease their marginal cost.

Keeping marginal costs below marginal revenues.

Raising prices to increase marginal revenue, as long as it does not decrease demand for their product.

Therefore, the correct options are:

They can work to decrease their marginal cost.

They can raise prices to increase marginal revenue.

They can keep marginal costs below marginal revenues.

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Therefore the recursive formula to locate the fourth term is [tex]a_n = 3 * (a_{n-1} )^{2} _[/tex]

[tex]a_4 = 3 * (a_{ 3})^{2} = 3 * 108^2= 34992[/tex]

Yes, I can show you how to create a recursive formula for the numbers 4, 12, and 108.

A recursive formula is one that generates the next term by using previous terms in the sequence. To create a recursive formula, we must first find a pattern in the series.

We can see from the given sequence that each phrase is created by multiplying the previous term by a factor of three. 12 is calculated by multiplying 4 by 3, while 108 is calculated by multiplying 12 by 9 (which is 3 increased to the power of 2).

As a result, we may create a recursive formula like this:

[tex]a_1 = 4[/tex] (the sequence's first term is 4)

[tex]a_n = 3 * (a_{n-1} )^{2} _[/tex] each term for n > 1) is calculated by multiplying the previous term by three raised to the power of the present term minus one).

We may use this recursive formula to locate any term in the series by using it again, beginning with the first term. To find the fourth term in the sequence, for example, use the formula: [tex]a_4 = 3 * (a_{ 3})^{2} = 3 * 108^2= 34992[/tex]

As a result, 34992 is the fourth term in the sequence.

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The probability that the second ticket chosen will be blue is P(blue|red) = 8/19.

To answer this question, we need to use conditional probability. We want to find the probability that the second ticket chosen will be blue, given that the first ticket chosen was red. We can denote this as P(blue|red).

We know that there are 12 red tickets and 8 blue tickets, so the total number of tickets is 20. When the first ticket is chosen, there are now only 11 red tickets and 8 blue tickets left.

To find P(blue|red), we need to divide the number of ways that we can choose a blue ticket on the second draw, given that we already chose a red ticket on the first draw, by the total number of ways that we can choose a second ticket.

The number of ways that we can choose a blue ticket on the second draw, given that we already chose a red ticket on the first draw, is 8 (since there are still 8 blue tickets left in the box). The total number of ways that we can choose a second ticket is 19 (since there are now only 19 tickets left in the box).

Therefore, P(blue|red) = 8/19.

In other words, the probability of choosing a blue ticket on the second draw, given that we already chose a red ticket on the first draw, is 8/19.

Overall, it is important to remember that the probability of an event can change based on the outcome of a previous event (as we saw in this case with the number of red and blue tickets left in the box). Conditional probability allows us to take these changing probabilities into account and make more accurate predictions.

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[tex]sin(70^o )=\cfrac{\stackrel{opposite}{12}}{\underset{hypotenuse}{y}}\implies y=\cfrac{12}{\sin(70^o)}\implies y\approx 12.770[/tex]

Make sure your calculator is in Degree mode.

(a) Yes, there is evidence that coefficient of restitution is approximately normally distributed.

(b) The null hypothesis is that there is no difference in the mean coefficient of restitution between the two brands, while the alternative hypothesis is that there is a difference.

(c) The P-value of the test statistic in part (b) is reject the null hypothesis

(d) The power of the statistical test in part (b) to detect a true difference in mean coefficient of restitution of 0.2 is false

(e) The sample size would be required to detect a true difference in mean coefficient of restitution of 0.1 with power of approximately 0.8 is 0.1

(f) The confidence interval will give us a range of plausible values for the true difference in means with 95% confidence.

(a) Before we conduct any statistical tests, we need to check if our data satisfies certain assumptions. One of the assumptions for conducting hypothesis tests is that the data is normally distributed.

(b) To test whether there is a significant difference in the mean coefficient of restitution between the two brands, we can use a two-sample t-test.

(c) The p-value is the probability of obtaining a test statistic as extreme or more extreme than the observed one, assuming that the null hypothesis is true. If the p-value is less than our chosen significance level of 0.05, we reject the null hypothesis and conclude that there is a significant difference in the mean coefficient of restitution between the two brands.

(d) The power of a statistical test is the probability of rejecting the null hypothesis when it is actually false. In this case, we want to detect a true difference in mean coefficient of restitution of 0.2. We can calculate the power of the test using the effect size, the sample size, and the chosen significance level. A higher sample size or a larger effect size will result in a higher power.

(e) To determine the sample size required to detect a true difference in mean coefficient of restitution of 0.1 with a power of approximately 0.8, we can use power analysis. We need to choose a significance level, a desired power level, and an effect size.

(f) To construct a 95% two-sided confidence interval on the mean difference in coefficient of restitution between the two brands, we can use the formula for a confidence interval for the difference in means of two independent samples.

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The answer is (B) 1, which is not a solution to the problem.

We can start by finding the velocity function of the particle by integrating the acceleration function a(t):

[tex]v(t) = ∫ a(t) dt = ∫ (2t - 7) dt = t^2 - 7t + C[/tex]

We know that the initial velocity of the particle is 6, so we can use this information to find the value of the constant C:

[tex]v(0) = 0^2 - 7(0) + C = 6[/tex]

[tex]C = 6[/tex]

Therefore, the velocity function of the particle is:

[tex]v(t) = t^2 - 7t + 6[/tex]

To find the position function of the particle, we integrate the velocity function:

[tex]s(t) = ∫ v(t) dt = ∫ (t^2 - 7t + 6) dt = (1/3)t^3 - (7/2)t^2 + 6t + D[/tex]

We don't know the value of the constant D yet, but we can use the fact that the particle starts at position 0[tex](i.e., s(0) = 0)[/tex] to find it:

[tex]s(0) = (1/3)(0)^3 - (7/2)(0)^2 + 6(0) + D = 0[/tex]

[tex]D = 0[/tex]

Therefore, the position function of the particle is:

[tex]s(t) = (1/3)t^3 - (7/2)t^2 + 6t[/tex]

To find the time when the particle is farthest to the right, we need to find the maximum of the position function. We can do this by finding the critical points of the function and using the second derivative test to determine whether they correspond to a maximum or minimum.

The derivative of the position function is:

[tex]s'(t) = t^2 - 7t + 6[/tex]

Setting this derivative equal to zero and solving for t, we get:

[tex]t^2 - 7t + 6 = 0[/tex]

Using the quadratic formula, we get:

[tex]t = (7 ± sqrt(49 - 4(1)(6))) / 2[/tex]

[tex]t = (7 ± sqrt(37)) / 2[/tex]

We can verify that both of these critical points correspond to a minimum by using the second derivative test:

[tex]s''(t) = 2t - 7[/tex]

At t = (7 + sqrt(37)) / 2, we have:

[tex]s''((7 + sqrt(37)) / 2) = 2(7 + sqrt(37)) / 2 - 7 = sqrt(37) - 5 > 0[/tex]

Therefore, the critical point [tex]t = (7 + sqrt(37)) / 2[/tex] corresponds to a minimum of the position function.

[tex]At t = (7 - sqrt(37)) / 2[/tex], we have:

[tex]s''((7 - sqrt(37)) / 2) = 2(7 - sqrt(37)) / 2 - 7 = -sqrt(37) - 5 < 0[/tex]

Therefore, the critical point [tex]t = (7 - sqrt(37)) / 2[/tex] corresponds to a maximum of the position function.

Therefore, the particle is farthest to the right [tex]at t = (7 - sqrt(37)) / 2[/tex], which is approximately 0.28. The answer is (B) 1, which is not a solution to the problem.

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The answer is (d) 0.2358.

To find P(Z > 0.72) for a standard normal random variable Z, you need to consult the standard normal (Z) table or use a calculator with a normal distribution function. The steps to find the probability are:

1. Identify the given Z value: In this case, Z = 0.72.

2. Look up the cumulative probability of Z = 0.72 in the standard normal table or use a calculator with a normal distribution function. The cumulative probability, P(Z ≤ 0.72), is approximately 0.7642.

3. Since you want to find P(Z > 0.72), subtract the cumulative probability from 1: 1 - P(Z ≤ 0.72) = 1 - 0.7642 = 0.2358.

So, the answer is (d) 0.2358.

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The probability that a male college student from the group chose "housework" as their most likely activity on Saturday mornings is 0.1, or 10% (rounded to the nearest thousandth).

The probability that a male college student from the group chose "housework" as their most likely activity on Saturday mornings depends on the available data or information provided. Without specific data or information on the preferences or behaviors of male college students in the group, it is not possible to determine the exact probability.

To calculate the probability, we would need to know the total number of male college students in the group and the number of male college students who chose "housework" as their most likely activity on Saturday mornings.

Let's assume there are 100 male college students in the group, and out of those, 10 male college students chose "housework" as their most likely activity on Saturday mornings.

The probability can be calculated as the ratio of the number of male college students who chose "housework" to the total number of male college students in the group:

Probability = Number of male college students who chose "housework" / Total number of male college students in the group

Plugging in the values, we get:

Probability = 10 / 100 = 0.1

Therefore, the probability that a male college student from the group chose "housework" as their most likely activity on Saturday mornings is 0.1, or 10% (rounded to the nearest thousandth).

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Answer :

1. radius of circle = 14 cm .

Circumference = 2πr

where,

[tex]:\implies \: \: [/tex] 2 × 22/7 × 14

[tex]:\implies \: \: [/tex] 2 × 22 × 2

[tex]:\implies \: \: [/tex] 44 × 2

[tex]:\implies \: \: [/tex] 88 cm.

Now,

Area of Circle = πr²

[tex]:\implies \: \: [/tex] 22/7 × 14 × 14

[tex]:\implies \: \: [/tex]22 × 2 × 14

[tex]:\implies \: \: [/tex] 44 × 14

[tex]:\implies \: \: [/tex] 616 cm².

2. Radius of circle = 11 in.

Circumference = 2πr

[tex]:\implies \: \: [/tex] 2 × 22/7 × 11

[tex]:\implies \: \: [/tex]44/7 × 11

[tex]:\implies \: \: [/tex]484/7

[tex]:\implies \: \: [/tex]69.14 cm

Area = πr²

[tex]:\implies \: \: [/tex]22/7 × 11 × 11

[tex]:\implies \: \: [/tex]22/7 × 121

[tex]:\implies \: \: [/tex] 2622/7

[tex]:\implies \: \: [/tex]374.5 cm²

3. Radius of circle = 13 in.

Circumference = 2πr

[tex]:\implies \: \: [/tex] 2 × 22/7 × 13

[tex]:\implies \: \: [/tex] 44 /7 × 13

[tex]:\implies \: \: [/tex] 572/7

[tex]:\implies \: \: [/tex] 81.71 mm

Area = πr²

[tex]:\implies \: \: [/tex] 22/7 × 13 × 13

[tex]:\implies \: \: [/tex] 22/7 × 169

[tex]:\implies \: \: [/tex] 3718/7

[tex]:\implies \: \: [/tex] 531.14 mm².

4. Radius of circle = 4.5 m

Circumference = 2πr

[tex]:\implies \: \: [/tex] 2 × 22/7 × 4.5

[tex]:\implies \: \: [/tex] 44/7 × 4.5

[tex]:\implies \: \: [/tex] 198/7

[tex]:\implies \: \: [/tex] 28.28 m

Area = πr²

[tex]:\implies \: \: [/tex] 22/7 × 4.5 × 4.5

[tex]:\implies \: \: [/tex] 22 × 20.25/7

[tex]:\implies \: \: [/tex] 445.5/7

[tex]:\implies \: \: [/tex] 63.64 m²

5. Radius of circle = 9.2 yd.

Circumference = 2πr

[tex]:\implies \: \: [/tex] 2 × 22/7 × 9.2

[tex]:\implies \: \: [/tex]44/7 × 9.2

[tex]:\implies \: \: [/tex] 404.8/7

[tex]:\implies \: \: [/tex] 58.34 yd

Area = πr²

[tex]:\implies \: \: [/tex] 22/7 × 9.2 × 9.2

[tex]:\implies \: \: [/tex] 22/7 × 84.64

[tex]:\implies \: \: [/tex] 1862.08/7

[tex]:\implies \: \: [/tex] 266.01 yd²