Find the exact location of all the relative and absolute extrema of the function.
g(t) = 6e−t2 with domain (−[infinity], +[infinity])
g has an absolute maximum at (t,y):

2482 divided by 7 gives a quotient of 354 and a remainder of 7, with no decimals.

In mathematics, the dividend is the number being divided in a division operation. In other words, it is the number that is being split into equal parts or groups. The dividend is typically written on top of the division symbol, with the divisor  written below it, and the quotient written to the right of the symbol. For example, in the division problem 24 ÷ 6 = 4, the dividend is 24, the divisor is 6, and the quotient is 4.

The quotient of 2482 and 7, the ratio of 2482 and 7, as well as the fraction of 2482 and 7 all mean (almost) the same:

2482 divided by 7, often written as 2482/7.

Therefore, 2482 divided by 7 gives a quotient of 354 and a remainder of 7, with no decimals.

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Answer:

9

Step-by-step explanation:

1. Just input the x value with -180:

24 (+) -180 / 12 = answer

2. Follow PEMDAS to solve, which in this case division comes before addition:

24 + (-180 / 12)

24 + (-15)

3. Addition

24 + (-15) or 24 - 15

= 9

Therefore, 24 (+) -180 / 12 equals 9.

Answer:

891p

Step-by-step explanation:

1bag=99p

9bags=99x9

=891p

The value of ∠EGF in the triangle is 78°

An angle is formed from the intersection of two lines. Types of angles are acute, obtuse and right angled.

The sum of all angles in a triangle is 180 degrees.

For the triangle shown:

∠EGF + ∠EFG + ∠FEG = 180° (sum of angles in a triangle)

Substituting:

∠EGF + 64 + 38 = 180

∠EGF = 78°

The value of ∠EGF in the triangle is 78°

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Answer:

4, 12, 36, 108, 324

Step-by-step explanation:

The rule multiple by any number is the sequence of number that are arranged in such a form that the next number to an integer is times the particular given number of the multiple.

From the question,

4 × 3 = 12

12 × 3 = 36

36 × 3 = 108

108× 3 = 324

Therefore, the number sequence follows the rule multiply by 3, starting from 4 are 4, 12, 36, 108, 324.

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The vector perpendicular to the level curve of g that passes through the point (2.4, 3) is (-0.2083, -0.1667).

To find a vector perpendicular to the level curve of g that passes through the point (2.4, 3), we first need to determine the equation of the level curve of g at point (2.4, 3).

Let's assume that g(x,y) = z. Then, the level curve of g at point (2.4, 3) is given by the equation g(x,y) = g(2.4, 3) = z_0, where z_0 is a constant.

Next, we need to find the gradient of g at point (2.4, 3), which is a vector that points in the direction of the greatest increase of g at that point. The gradient of g is given by the partial derivatives of g with respect to x and y, i.e.,

grad(g) = (dg/dx, dg/dy)

We can use the gradient vector to find a vector perpendicular to the level curve of g at point (2.4, 3). The vector we want is the negative reciprocal of the gradient vector, which is given by

v = (-1/dg/dx, -1/dg/dy)

So, all we need to do now is to evaluate the gradient of g and plug in the values at point (2.4, 3).

Let's assume that g(x,y) = x^2 + y^2. Then,

dg/dx = 2x

dg/dy = 2y

At point (2.4, 3), we have

dg/dx = 2(2.4) = 4.8

dg/dy = 2(3) = 6

So, the gradient of g at point (2.4, 3) is

grad(g) = (4.8, 6)

The vector perpendicular to the level curve of g at point (2.4, 3) is then

v = (-1/4.8, -1/6) = (-0.2083, -0.1667)

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the total amount of money in the account after 6 years is approximately $3,617.08.

What is simple interest?

A quick and simple way to figure out interest on money is to use the simple interest technique, which adds interest at the same rate for each time cycle and always to the initial principal amount. Any bank where we deposit our funds will pay us interest on our investment. One of the different types of interest charged by banks is simple interest. Now, before exploring the idea of basic curiosity in further detail,

a. The formula for the amount A after t years, with an initial principal P, an annual interest rate r, and n times compounded per year is given by:

[tex]A = P(1 + r/n)^nt[/tex]

In this case, P = $3000, r = 0.025 (2.5% expressed as a decimal), n = 2 (compounded semiannually), and t is the number of years. So the function A(t) can be written as:

[tex]A(t) = 3000(1 + 0.025/2)^2t[/tex]

b. To find the total amount of money in the account after 6 years, we need to evaluate A(6):

[tex]A(6) = 3000(1 + 0.025/2)^{(16)[/tex]

≈ $3,617.08

Therefore, the total amount of money in the account after 6 years is approximately $3,617.08.

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a) Null hypothesis (H0): The average age at time of is 30 years (μ = 30). Alternative hypothesis (H1): The average age at time of is more than 30 years (μ > 30). This is a right-tailed test.

b) The random variable represents the sample mean age at the time of for the 40 row inmates surveyed.(c) We will use the t-distribution for this test, as the population standard deviation is unknown. (d) Test statistic formula: t = (X - μ) / (s / √n), t = (32 - 30) / (4 / √40) = 2 / (4 / 6.32) = 2 / 0.632 = 3.164. (e) To find the p-value, we use the t-distribution CDF (tcdf) function: tcdf(lower bound, upper bound, degrees of freedom p-value = tcdf(3.164, 1E99, 39) ≈ 0.0017.

f) Decision: Since the p-value (0.0017) is less than the significance level (0.01), we reject the null hypothesis.
Conclusion: There is significant evidence to suggest that the average age at the time of for death row inmates is greater than 30 years.

(g) Type 1 error: We reject the null hypothesis when it is true, i.e., we conclude that the average age at the time of is more than 30 years when it is actually 30 years.

Type 2 error: We fail to reject the null hypothesis when it is false, i.e., we conclude that the average age at the time of is not significantly more than 30 years when it actually is more than 30 years.

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The geometric series is convergent and the sum of the series is given by the term as [tex]S_n=\frac{a}{1-r} = \frac{8}{\pi -1}[/tex].

Measures of central tendencies can be used in mathematics and statistics to quickly convey the summary of values for the entire data collection. The mean, median, mode, and range are the most crucial measurements of central trends.

The data set's mean will provide you a general notion of the data among these. The average of numbers is determined by the mean. Arithmetic Mean (AM), Geometric Mean (GM), and Harmonic Mean are the many forms of means (HM).

The geometric series is,

[tex]\sum_{n=1} \frac{8}{\pi ^n}[/tex] = [tex]\frac{8}{\pi} +\frac{8}{\pi ^2} +\frac{8}{\pi ^3} +...[/tex]

Here a = 8/π

Common ratio r = [tex]\frac{1}{\pi}[/tex] which is numerically less than 1.

By geometric series test the given series is convergent,

Now, [tex]S_n=\frac{a}{1-r} = \frac{8}{\pi -1}[/tex].

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Complete question:

Determine whether the geometric series is convergent or divergent. sigma_n = 1^infinity 8/pi^n convergent divergent If it is convergent, find its sum. (If the quantity diverges, enter DIVERGES.)

Answer: a) -8/9

b) The series is convergent

c) 1/17

Step-by-step explanation:

The series a+ar+ar²+ar³⋯ =∑ar^(n−1) is called a geometric series, and r is called the common ratio.

If −1<r<1, the geometric series is convergent and its sum is expressed as ∑ar^(n−1) = a/1-r

a is the first tern of the series.

a) Rewriting the series ∑(-8)^(n−1)/9^n given in the form ∑ar^(n−1) we have;

∑(-8)^(n−1)/9^n

= ∑(-8)^(n−1)/9•(9)^n-1

= ∑1/9 • (-8/9)^(n−1)

From the series gotten, it can be seen in comparison that a = 1/9 and r = -8/9

The common ratio r = -8/9

b) Remember that for the series to be convergent, -1<r<1 i.e. r must be less than 1 and since our common ratio of -8/9 is less than 1, the series is convergent.

c) Since the sun of the series tends to infinity, we will use the formula for finding the sum to infinity of a geometric series.

S∞ = a/1-r

Given a = 1/9 and r = -8/9

S∞ = (1/9)/1-(-8/9)

S∞ = (1/9)/1+8/9

S∞ = (1/9)/17/9

S∞ = 1/9×9/17

S∞ = 1/17

The sum of the geometric series is 1/17.

The given statement is true. Rank A = rank M

To prove this, we can use formula which states that rank of a matrix A is equal to the dimension of its row space or column space.

Let's consider the matrix M of TA with respect to bases B of Rm and B of Rn. Since M is the matrix of a linear transformation, its row space and column space are the same as the range of TA.

Now, according to the hint, we can use formula for both matrices A and M. We have rank A = dimension of row space of A and rank M = dimension of row space of M = dimension of column space of M (since row space and column space of M are the same).

Since M is the matrix of TA, its column space is a subspace of the range of TA. Therefore, dimension of column space of M ≤ dimension of range of TA. But we know that rank A = dimension of range of TA.

Hence, we have rank M ≤ rank A.

On the other hand, we can also consider the matrix A as the matrix of a linear transformation from Rn to Rm. Then, by the same argument, we can show that rank A ≤ rank M.

Therefore, we have rank A = rank M.

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The proportion of the population that is less than 39 is 72.57%. The probability that a randomly chosen value will be greater than 36 is approximately 12.10%.

7) First, we have to find the proportion of the population with a value less than 39 in a normal distribution with a mean of 33 and a standard deviation of 10.

Calculate the z-score for the value 39.
z = (X - mean) / standard deviation
z = (39 - 33) / 10
z = 6 / 10
z = 0.6

Look up the z-score (0.6) in a standard normal distribution table or use a calculator to find the proportion.
The proportion for a z-score of 0.6 is approximately 0.7257.

So, approximately 72.57% of the population is less than 39.

8) Now, we have to find the probability that a randomly chosen value will be greater than 36 in a normal distribution with a mean of 29 and a standard deviation of 6.

Calculate the z-score for the value 36.
z = (X - mean) / standard deviation
z = (36 - 29) / 6
z = 7 / 6
z ≈ 1.17

Look up the z-score (1.17) in a standard normal distribution table or use a calculator to find the proportion.
The proportion for a z-score of 1.17 is approximately 0.8790.

Since we want the probability that a randomly chosen value will be greater than 36, we need to find the proportion of values above the z-score of 1.17.
Probability = 1 - proportion
Probability = 1 - 0.8790
Probability ≈ 0.1210

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The function dependency A -> B for relation schema R(A,B,C,D) implies that:
d. Any two tuples in R that have the same value for A must have the same value for B

The correct answer is option d. This is because the function dependency A -> B means that the value of B is functionally dependent on A.  The productivity function X → Y is called trivial if Y is part of X. In other words, the FD:X → Y dependency means that the value of Y is determined by the value of X. Two bunches of X values ​​that share the same thing must have the same Y value where Z = U - XY is the residue. In simple terms, if the values ​​of the X attributes are known (assuming they are x), then the values ​​of the Y attributes corresponding to x can be determined by looking at them in an R tuple containing x.

Therefore, any two tuples in R that have the same value for A must have the same value for B. This ensures that the relationship is functional and follows the rules of normalization. Tuples are individual rows in a relation and value refers to a specific entry in a tuple for a particular attribute.

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Check the picture below.

so the parabola looks more or less like so, since the directrix is above the focus point, the parabola is opening downwards, that means the "p" distance from the focus point or directrix to the vertex is negative.

hmm from the focus point to the directrix is only 1 unit up, and the vertex is half-way between both, so that puts the vertex at (-4 , 1) as you see there, just 1/2  unit above the focus point or 1/2 below the directrix, anyhow

[tex]\textit{vertical parabola vertex form with focus point distance} \\\\ 4p(y- k)=(x- h)^2 \qquad \begin{cases} \stackrel{vertex}{(h,k)}\qquad \stackrel{focus~point}{(h,k+p)}\qquad \stackrel{directrix}{y=k-p}\\\\ p=\textit{distance from vertex to }\\ \qquad \textit{ focus or directrix}\\\\ \stackrel{p~is~negative}{op ens~\cap}\qquad \stackrel{p~is~positive}{op ens~\cup} \end{cases} \\\\[-0.35em] ~\dotfill[/tex]

[tex]\begin{cases} h=-4\\ k=1\\ p=-\frac{1}{2} \end{cases}\implies 4(-\frac{1}{2})(~~y-1~~) = (~~x-(-4)~~)^2 \\\\\\ -2(y-1)=(x+4)^2\implies y-1=-\cfrac{1}{2}(x+4)^2\implies \boxed{y=-\cfrac{1}{2}(x+4)^2+1}[/tex]

A college president is interested in student satisfaction with recreational facilities on campus. A questionnaire is sent to all students and asks them to rate their satisfaction on a scale of 1 to 5.

The instrument of measurement is:

=> the questionnaire

The common types of measuring tools include speedometers, measuring tape, thermometers, compasses, digital angle gauges, levels, laser levels, macrometer, measuring squares, odometers, pressure gauges, protractors, rulers, angle locators, bubble inclinometers, and calipers.

The measuring instruments in mechanical engineering are dimensional control instruments used to measure the exact size of object. These are adjustable devices and can measure with an accuracy of 0.00 l mm or better. The gauges are fixed dimension instruments and are not graduated.

The correct option is (d).

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Given the information u · u = 9, u · v = 6, and v · v = 8, we can find the dot product of (3u - 2v) and (2u - 3v).
(3u - 2v) · (2u - 3v) = (3u · 2u) - (3u · 3v) - (2v · 2u) + (2v · 3v)

Using the given information, we can substitute the values:
= (3 * 9 * 2) - (3 * 6 * 3) - (2 * 6 * 2) + (2 * 8 * 3)
= (54) - (54) - (24) + (48)
= 0 - 24 + 48
= 24
So, (3u - 2v) · (2u - 3v) = 24.

To find (3u − 2v) · (2u − 3v), we can use the distributive property of the dot product:
(3u − 2v) · (2u − 3v) = 3u · 2u - 3u · 3v - 2v · 2u + 2v · 3v

Now we can substitute the given values:
= 3(9) - 3(6) - 2(6) + 2(8)

Simplifying:
= 27 - 18 - 12 + 16
= 13

Therefore, (3u − 2v) · (2u − 3v) = 13, given that u · u = 9, u · v = 6, and v · v = 8.

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-$14,469 is the Overapplied Manufacturing Overhead. We can calculate it in the following manner.

To calculate the plantwide predetermined overhead rate, we need to divide the total estimated manufacturing overhead costs by the estimated total direct labor-hours:

Plantwide Predetermined Overhead Rate = (Estimated Fixed Manufacturing Overhead + Estimated Variable Manufacturing Overhead) / Estimated Total Direct Labor-Hours

Plantwide Predetermined Overhead Rate = ($558,000 + ($3.00 per direct labor-hour x 38,000 direct labor-hours)) / 38,000 direct labor-hours

Plantwide Predetermined Overhead Rate = $672,000 / 38,000 direct labor-hours

Plantwide Predetermined Overhead Rate = $17.68 per direct labor-hour

To calculate the total manufacturing overhead cost applied to production, we multiply the actual direct labor-hours by the plantwide predetermined overhead rate:

Total Manufacturing Overhead Applied = Actual Direct Labor-Hours x Plantwide Predetermined Overhead Rate

Total Manufacturing Overhead Applied = 44,500 direct labor-hours x $17.68 per direct labor-hour

Total Manufacturing Overhead Applied = $787,960

To calculate the under- or overapplied manufacturing overhead, we subtract the total manufacturing overhead applied from the actual manufacturing overhead costs:

Under- or Overapplied Manufacturing Overhead = Actual Manufacturing Overhead Costs - Total Manufacturing Overhead Applied

Under- or Overapplied Manufacturing Overhead = $773,491 - $787,960

Under- or Overapplied Manufacturing Overhead = -$14,469

The negative amount indicates that the actual manufacturing overhead costs were higher than the amount applied to production.

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The domain of the function is (-∞, -2) ∪ (-2, 2) ∪ (2, ∞).

In mathematics, the domain of a function is the set of all possible input values (also known as the independent variable) for which the function is defined. It is the set of values that can be substituted into the function to obtain a valid output value.

In this case, we have:

f(x) = 4/|x| - 2

The absolute value of x is always non-negative, so |x| > 0. Thus, we can rewrite the function as:

f(x) = 4/(|x| - 2)

To find the domain of this function, we need to identify any values of x that make the denominator zero, since division by zero is undefined. In this case, we have:

|x| - 2 = 0

|x| = 2

So, the function is undefined for x = ±2. This means that the domain of the function is all real numbers except x = ±2. In interval notation, we can write:

Domain: (-∞, -2) ∪ (-2, 2) ∪ (2, ∞).

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The x-intercept of the curve is (5, 0) and the y-intercept is (0, 0). The curve should have an asymptote at x = 5 and a horizontal asymptote at y = 0.

To sketch the curve y = x/x − 5, we need to locate the x-intercept and y-intercept of the curve.

1. Making the point's y-coordinate equal to zero will help us locate the x-intercept. As a result, we must find x such that 0 = x/x- 5.

This equation can be rewritten as 0 = (x-5)/x, and then both sides of the equation can be multiplied by x to produce 0x = x-5. By simplifying, we arrive at x = 5, and the curve's x-intercept is (5, 0).

2. Making the point's x-coordinate equal to zero will help us discover the y-intercept. Thus, we must find y by solving 0 = x/x- 5.

This equation can be written as 0 = (x-5)/x, and we can then multiply both sides of the equation by x-5 to get the result 0(x-5) = x.

By simplifying and getting x = 5, we may get y = 0/5, or y = 0, by substituting this into the original equation. Hence, the curve's y-intercept is (0, 0).

3. Once the x-intercept and y-intercept of the curve have been established, they can now be plotted on a graph.

Then, a straight line that cuts through both locations is drawn. The curve should approach, but never touch, the x-axis as x approaches 5 from either side since it should have an asymptote at x = 5.

The curve should approach, but never touch, the y-axis as y approaches 0 from either side because it will also have a horizontal asymptote at y = 0.

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The surface area of the triangular pyramid with lateral height 6 inches and an equilateral base with base edge 9 inches is 116.0 square inches that is, 116 square inches.

The lateral height of a pyramid is the perpendicular distance between the apex (top) and the base edge along the lateral face. In other words, it is the height of each of the lateral triangles that make up the pyramid. The lateral height is also known as the slant height.

A triangle is called an equilateral triangle if all of its sides have the same length. In other words, an equilateral triangle is a special case of a triangle where all three sides are equal. Since it has three congruent sides, each of its angles also measures 60 degrees.

Surface area of the triangular pyramid = Base area + Lateral surface area

Here the base is an equilateral triangle with side 9 inches.

Therefore, Base area = Area of the equilateral triangle =  [tex]\frac{\sqrt{3} }{4} a^{2}[/tex]

= [tex]\frac{\sqrt{3} }{4}[/tex] × [tex]9^{2}[/tex]

= [tex]\frac{\sqrt{3} }{4}[/tex] × 81 = [tex]\frac{1.73}{4}[/tex] × 81

=35.0325 square inches

Lateral surface is a triangle with base 9 inches and height 6 inches

Therefore, Lateral surface area = 3 × [tex]\frac{1}{2}[/tex] bh

= 3 × [tex]\frac{1}{2}[/tex]  ×9×6

= 81 square inches

Hence, Surface area = 35.0325 + 81 = 116.0325 square inches

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The manufacturers need to test at least 725 light bulbs to estimate the percentage of defective light bulbs with a 97% confidence interval and a margin of error of 3.6%.

To determine the sample size needed to estimate the percentage of defective light bulbs with a 97% confidence interval and a margin of error of 3.6%, we can use the formula:

n = (z^2 * p * q) /[tex]E^2[/tex]

where:

n = sample size

z = z-score for the desired confidence level (97% confidence level corresponds to a z-score of 1.8808)

p = estimated proportion of defective light bulbs (unknown)

q = 1 - p

E = margin of error as a proportion (0.036)

Since the proportion of defective light bulbs is unknown, we can assume a conservative estimate of 0.5, which gives the maximum possible sample size. Thus, we have:

n = ([tex]1.8808^2[/tex] * 0.5 * 0.5) / [tex]0.036^2[/tex] = 724.75

Rounding up to the nearest whole number, we get:

n = 725

Therefore, the manufacturers need to test at least 725 light bulbs to estimate the percentage of defective light bulbs with a 97% confidence interval and a margin of error of 3.6%.

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The final answer is as followed:

In this case, a research chemist is interested in understanding how multiple predictors (formaldehyde concentration, catalyst ratio, temperature, and curing time) are associated with the wrinkle resistance of cotton cloth. To do this, we can use a multiple regression analysis, which is a statistical technique that allows us to examine the relationship between one dependent variable (wrinkle resistance rating) and several independent variables (predictors).

1. Import the data and create variable labels as instructed.

2. The general multiple regression model can be written as:

  Rating = β0 + β1(Conc) + β2(Ratio) + β3(Temp) + β4(Time) + ε

3. Create a scatterplot matrix to visually examine the relationships between the variables.

4. Conduct the multiple regression analysis using the Forward method of selection.

5. After the analysis, you will get the final model equation, which may look like:

  Rating = β0 + β1(Conc) + β2(Ratio) + ε (assuming that only Conc and Ratio were significant predictors in the final model)

6. Calculate the effect size using R2 adjusted and Cohen's equation. SPSS may provide this information automatically.

7. Perform a residual analysis to check for any deviations from the assumptions of the regression model.

8. In the write-up, include the following information:
  - The purpose of the study.
  - The multiple regression model used.
  - The final model equation.
  - The effect size and its interpretation.
  - Results of the residual analysis and any potential issues with the model's assumptions.

Keep in mind that the specific values of the coefficients (β) and the R2 adjusted will be obtained from the SPSS analysis.

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The estimated mean of the time for completion of race is 220 minutes where as the MAD to the mean ratio is 22.272%.

What is mean?

In statistics, the mean is one of the measures of central tendency. Another two are median and mode. Mean is  the average of the given set of data.

You interviewed a random sample of 25 marathon runners and compiled the following statistics.

Mean time to complete the race = 220 minutes and MAD = 50 minutes.

So from given data it can be concluded that

mean = 220             MAD= 50

Now we find ,

(MAD/Mean ) × 100%

= (50/220)× 100%

= (5×100)/22 %

≈ 22.272%

Hence , the estimated mean of the time for completion of race is 220 minutes where as the MAD to the mean ratio is 22.272%.

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the equation of the tangent line is: y = -25x - 16TTo find the slope (m) of the tangent line to the curve y = √(6 - 75x) at the point (-1, 9), we first need to find the derivative of the curve with respect to x.

Let's differentiate y with respect to x using the chain rule:

dy/dx = d(√(6 - 75x))/dx = (1/2)(6 - 75x)^(-1/2) * (-75)

Now, we can find the slope of the tangent line at the point (-1, 9) by evaluating the derivative at x = -1:

m = (1/2)(6 - 75(-1))^(-1/2) * (-75) = (1/2)(81)^(-1/2) * (-75)

m = -25

Now we have the slope of the tangent line, m = -25. To find the equation of the tangent line, we can use the point-slope form of a linear equation: y - y1 = m(x - x1). We have the point (-1, 9) and the slope -25, so:

y - 9 = -25(x - (-1))

Simplify the equation:

y - 9 = -25(x + 1)

y = -25x - 25 + 9

Therefore, the equation of the tangent line is:

y = -25x - 16

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The probability of a sample mean being greater than 216 would depend on several factors, such as the population mean, standard deviation, and the distribution of the population.

However, if you have the population standard deviation (denoted as σ) and assuming the population distribution is normal, you can use the formula for the standard error of the mean (SEM) to estimate the probability of the sample mean being greater than 216.

The formula for SEM is:

SEM = σ / sqrt(n)

where n is the sample size.

Once you have the SEM, you can use the standard normal distribution to find the probability of a sample mean being greater than 216.

For example, if σ = 20, then:

SEM = 20 / sqrt(75) = 2.31

To find the probability of a sample mean being greater than 216, you need to calculate the z-score corresponding to 216 using the formula:

z = (x - μ) / SEM

where x is the sample mean, μ is the population mean (which we don't know), and SEM is the standard error of the mean.

Assuming μ = 200 (for example), then:

z = (216 - 200) / 2.31 = 6.93

Using a standard normal distribution table (or calculator), you can find that the probability of a z-score being greater than 6.93 is essentially 0. Therefore, the probability of a sample mean being greater than 216, assuming a population standard deviation of 20 and a normal distribution, would be extremely small.

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The weight of the nails he used is: (2/3) * (7/12) = 14/36 = 7/18 kg.

What is fraction?

A fraction is a way of representing a part of a whole or a part of a group. It is a numerical quantity that is expressed as the ratio of two integers, one written above the other and separated by a horizontal line called the fraction bar or the vinculum.

Let's start by finding the weight of the nails that Thomas used.

If he took out a box of nails weighing 7/12 kg, and he used 2/3 of the nails, then the weight of the nails he used is:

(2/3) * (7/12) = 14/36 = 7/18 kg

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Answer:

[tex]30\pi \: in[/tex]

Step-by-step explanation:

Circumference formula: 2pir=2×15pi=30pi in

The expected value E(X) is 5.7 (rounded to one decimal place).

A random variable with a constrained and countable range of possible values is referred to as a discrete random variable. It can have a countable variety of different values. A discrete random variable is, for instance, the result of rolling a dice, as there are only six possible outcomes. A discrete random variable's weighted average equals its mean. On the other hand, a continuous random variable can have any value within a specified range.

To find the expected value E(X) of a discrete random variable, you need to multiply each value of x with its corresponding probability p(x), and then sum up the results. Here's the calculation for your data:

E(X) = (4 * 0.3) + (5 * 0.2) + (6 * 0.2) + (7 * 0.1) + (8 * 0.2) = 1.2 + 1 + 1.2 + 0.7 + 1.6 = 5.7

The expected value E(X) is 5.7 (rounded to one decimal place).

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Using 6 rectangles, L6 estimate is 35.5, R6 estimate is 51.5, and M6 estimate is 43.5. L6 is an underestimate of the true area, while R6 and M6 are overestimates. M6 gives the best estimate as it approximates the shape of the curve better than L6 or R6.

To estimate the area under a graph of a function from x=0 to x=12 using six rectangles, we can use different methods such as the left endpoint (L6), right endpoint (R6), and midpoint (M6) rules.

These methods use different sample points to calculate the area and give different estimates. The L6 rule will underestimate the area, while the R6 rule will overestimate it. The M6 rule may give a better estimate, as it uses the midpoint of each subinterval.

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The  probability is 0.1241. The appropriately labeled sketch of the Normal curve and shaded regions are not provided, but they can be drawn using a graphing calculator or software.

To find the probabilities using technology, we can use a standard Normal distribution table or a calculator with a Normal distribution function. The standard Normal curve is a bell-shaped curve with mean 0 and standard deviation 1. a. To find the probability that a z-score will be 1.22 or less, we need to shade the area to the left of 1.22 on the Normal curve. Using a calculator, we can use the NormalCDF function with the parameters -1000 (a very small number) and 1.22 to find the area under the curve. The result is 0.8888. So the probability is 0.8888.

b. To find the probability that a z-score will be 1.22 or more, we need to shade the area to the right of 1.22 on the Normal curve. Using the same calculator function but with the parameters 1.22 and 1000 (a very large number), we find the area to be 0.1112. So the probability is 0.1112. c. To find the probability that a z-score will be between 1.4 and 1.04, we need to shade the area between these two values on the Normal curve. Using the same calculator function but with the parameters 1.04 and 1.4, we find the area to be 0.1241. So the probability is 0.1241. The appropriately labeled sketch of the Normal curve and shaded regions are not provided, but they can be drawn using a graphing calculator or software.

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Twenty two points per game are projected to be scored. A simplified version of the given equation is y = 20.2355.

A scatter plot is a graph that presents the values for two variables for a set of data using Cartesian coordinates. A series of points is used to depict the data, with each point's position on the horizontal axis being determined by the value of one variable and its position on the vertical axis by the value of the other.

A player who attempts 4.5 free throws every game can forecast how many points they will score using the model y = 4.413x + 0.377.

Start by changing x in the equation to 4.5 to compute this. As a result, we get y = 4.413(4.5) + 0.377.

Upon simplification, we obtain y = 20.2355.

The expected number of points per game, when rounded to the nearest tenth, is 20.2.

The scatter plot and the model demonstrate that the number of free throws attempted per game and the number of points scored per game are positively correlated.

The number of free throws made every game increases along with the number of points scored. Since athletes who attempt more free throws per game are probably also scoring more points per game, this makes intuitive sense.

With the help of the model, it is possible to forecast how many points will be scored for a particular amount of free throw attempts every game.

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The complete question is,

Question:

This scatter plot shows points per game and free throw attempts for basketball players in a tournament.

The model y = 4.413x + 0.377 is also graphed.

x represents free throw attempts per game.

y represents points per game.

Use the model to predict the number of points per game for a player who attempts 4.5 free throws per game.

Round your answer to the nearest tenth.