Throw n balls into m bins, where m and n are positive integers. Let X be the number of bins with exactly one ball. Compute varX.

The number of visits that Madelyn needs to make to the movie theater to earn a free movie ticket would be= 10.

The number of points that Madelyn received for just signing up with the movie theater = 70 points.

The number of points she earns for each visit = 4.5 points

The total number of points the she needs = 115 points.

Let the total visit she requires = n

That is;

70+4.5n = 115

4.5n = 115-70

4.5n = 40

n = 10

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Answer:

The answer to your problem is, B.

Step-by-step explanation:

The sizes what are given.

There are:

3  - 9's

3  - 8's

1  - 7

2 -10's

1-11

Which concludes to the second graph has the right amount of x's for the given shoe sizes.

Thus the answer to you problem is, B

The t-distribution with 2 degrees of freedom is:

T = (1.4X1 + 0.2X3) / (sqrt(2/3)).

To construct a t-distribution with 2 degrees of freedom using X1, X3, and Xs, we can use the formula:

T = (Y1 - Y2) / (sqrt((S1^2 + S2^2 - 2S12) / n))

where Y1 and Y2 are the sample means of X1 and X3, and Xs, respectively, S1 and S2 are the sample standard deviations of X1 and X3, respectively, S12 is the sample covariance between X1 and X3, and n is the sample size.

First, let's find the sample means, standard deviations, and covariance:

Y1 = 0.8X1 + 0.6X3 + 0.0Xs = 0.8X1 + 0.6X3
Y2 = -0.6X1 + 0.8X3 + 0.0Xs = -0.6X1 + 0.8X3
Y3 = 0.0X1 + 0.0X3 + 1.0Xs = Xs

The sample mean of X1 is 0, and the sample mean of X3 is also 0, since both are standard normal. The sample mean of Xs is also 0, since it is a standard normal variable.

The sample standard deviation of X1 is 1, and the sample standard deviation of X3 is also 1, since both are standard normal. The sample standard deviation of Xs is also 1, since it is a standard normal variable.

The sample covariance between X1 and X3 is 0, since they are independent and identically distributed.

Therefore, we have:

Y1 = 0.8X1 + 0.6X3
Y2 = -0.6X1 + 0.8X3
Y3 = Xs
S1 = 1
S2 = 1
S12 = 0
n = 3

Plugging these values into the formula, we get:

T = (0.8X1 + 0.6X3 - (-0.6X1 + 0.8X3)) / (sqrt((1^2 + 1^2 - 2(0)) / 3))
T = (1.4X1 + 0.2X3) / (sqrt(2/3))

This is a t-distribution with 2 degrees of freedom, since we have n - 1 = 2 degrees of freedom.

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20.44% of Container A is full after the pumping is complete.

Given that two containers hold water are side by side, both in the shape of a cylinder.

Container A has a radius of 13 feet and a height of 18 feet.

Container B has a radius of 11 feet and a height of 20 feet:

Container A is full of water and the water is pumped into Container B until Container B is completely full.

We need to find what is the percent of Container A that is full after the pumping is complete?

Volume of cylinder = π × r² × h

Volume of container A = 3042π ft³

Volume of container B = 2420π ft³

After the pumping is complete,

The volume in container B will be of 2420π ft³

In container A, it will be of 3042π ft³ - 2420π ft³, out of a total of 622π ft³.

Therefore, the percentage is:

622π / 3042π x 100% = 20.44%

Hence, 20.44% of Container A is full after the pumping is complete.

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The calculated value of the variable x in the figure is 6 degrees

From the question, we have the following parameters that can be used in our computation:

The figure

By the given congruent angles, we have the following equation

9x - 14 = 6x + 4

Collect the like terms in the equation

so, we have the following representation

9x - 6x = 14 + 4

Evaluate the like terms

So, the equation becomes

3x = 18

Divide both sides of the equation by 3

x = 6

Hence, the value of the variable x in the figure is 6

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The correct option is that C. Player A is the most consistent, with an IQR of 2.5.

The IQR is a spread metric that is less influenced by extreme values. It is the difference between the third (Q3) and first (Q1) quartiles. The quartiles for Player A are as follows:

Q1 = 2

Q3 = 4

IQR = Q3 - Q1 = 4 - 2 = 2

The quartiles for Player B are as follows:

Q1 = 2

Q3 = 5.5

IQR = Q3 - Q1 = 5.5 - 2 = 3.5

The IQR is regarded a better indicator of variability than the range since it is less impacted by extreme results. As a result, we may infer that the interquartile range is the best measure of variability for this data.

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Answer:

a. The number of different combinations of 2 components that can be chosen from a group of 12 is given by the formula:

nC2 = (n!)/(2!(n-2)!), where n is the total number of components

Substituting n = 12, we get:

nC2 = (12!)/(2!(12-2)!) = (12 x 11)/2 = 66

Therefore, there are 66 different combinations of 2 components that can be chosen from the group of 12.

b. The probability that the faulty component will be chosen for testing depends on the number of ways in which the faulty component can be chosen, and the total number of ways in which any 2 components can be chosen.

The probability of choosing the faulty component on the first pick is 1/12, as there is one faulty component out of a total of 12 components.

After the first component has been picked, there will be 11 components left, including one faulty component. Therefore, the probability of picking the faulty component on the second pick, given that the first pick did not pick the faulty component, is 1/11.

Therefore, the probability of picking the faulty component on either the first or second pick is:

P(faulty component) = P(faulty on first pick) + P(faulty on second pick, given not picked on first pick)

P(faulty component) = (1/12) + ((11/12) x (1/11))

P(faulty component) = 1/12 + 1/12

P(faulty component) = 1/6

Therefore, the probability of choosing the faulty component for testing is 1/6 or approximately 0.1667.

The simplified expression for this problem is given as follows:

[tex]\sqrt{y^6} = y^3[/tex]

The expression for this problem is defined as follows:

[tex]\sqrt{y^6}[/tex]

The power of a power rule is used when a single base is elevated to multiple exponents, and the simplified expression is obtained keeping the bases and multiplying the exponents.

The square root is equivalent to an exponent of 1/2, while the exponent of y is of 6, hence the exponent f the simplified expression is given as follows

1/2 x 6 = 3.

Hence the simplified expression for this problem is given as follows:

[tex]\sqrt{y^6} = y^3[/tex]

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If this person, who wants to retire at age 65, had started with the same yearly contribution at age 40, the difference in the account balances (future values) would be D. $137,435.93.

The future values can be computed using an online finance calculator as follows:

N (# of periods) = 30 years (65 - 35)

I/Y (Interest per year) = 6.5%

PV (Present Value) = $0

PMT (Periodic Payment) = $5,000

Results:

Future Value (FV) = $431,874.32

Sum of all periodic payments = $150,000.00

Total Interest = $281,874.32

N (# of periods) = 25 years (65 - 40)

I/Y (Interest per year) = 6.5%

PV (Present Value) = $0

PMT (Periodic Payment) = $5,000

Results:

Future Value (FV) = $294,438.39

Sum of all periodic payments = $125,000.00

Total Interest = $169,438.39

Difference in future values = $137,435.93 ($431,874.32 - $294,438.39)

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It took Donna 30 months to pay off her credit card.

Total amount paid = Monthly payment x Number of months

Total amount paid = $104 x Number of months

Amount paid towards principal = Total amount paid - Total interest paid - Original amount owed

We know that she paid $126 in interest and originally owed $3,000, so we can substitute those values:

Amount paid towards principal = Total amount paid - $126 - $3,000

= $104 x Number of months - $126 - $3,000

We want to know how many months it takes for her to pay off the entire debt, so we can set the amount paid towards principal equal to zero:

$104 x Number of months - $126 - $3,000 = 0

$104 x Number of months = $3,126

Number of months = $3,126 / $104

Number of months = 30.1

So it took Donna 30 months to pay off her credit card.

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The 90% confidence interval estimate of the percentage of adults aged 57 through 85 years who use at least one prescription medication is:

0.817 to 0.849.

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

a. To find the number of subjects who used at least one prescription medication, we can simply multiply the total number of subjects by the percentage who used at least one prescription medication:

3234 x 0.833 = 2690.22

Rounding this to the nearest whole number, we get:

b. To construct a 90% confidence interval estimate of the percentage of adults aged 57 through 85 years who use at least one prescription medication, we can use the following formula:

CI = p ± z*√(p(1-p)/n)

where:

p = proportion of adults who use at least one prescription medication = 0.833

n = sample size = 3234

z* = z-score corresponding to the desired level of confidence, which for a 90% confidence interval is 1.645

Substituting these values, we get:

CI = 0.833 ± 1.645√(0.833(1-0.833)/3234)

= 0.833 ± 0.016

Therefore, the 90% confidence interval estimate of the percentage of adults aged 57 through 85 years who use at least one prescription medication is:

0.817 to 0.849

This means that we can be 90% confident that the true percentage of adults in this age group who use at least one prescription medication falls between 81.7% and 84.9%.

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Answer: 0.045%

Step-by-step explanation:

Answer:

There are two terms in the simplest form of the product (x + y)(a + b):

The first term is the product of x and a, which is xa.

The second term is the product of y and b, which is yb.

So, the simplified product is xa + yb. Therefore, the answer is A. 2.

To calculate the covariance between X and Y, we can use the formula:
Cov(X,Y) = E[XY] - E[X]E[Y]
where E[XY] is the expected value of the product of X and Y, and E[X] and E[Y] are the expected values of X and Y, respectively.

Using the given probability distributions, we can calculate the expected values as follows:
E[X] = ∑x∑y xP(X=x, Y=y)
    = (0)(0.26) + (1)(0.74)
    = 0.74
E[Y] = ∑x∑y yP(X=x, Y=y)
    = (0)(0.26) + (1)(0.36) + (2)(0.38)
    = 1.12
E[XY] = ∑x∑y xyP(X=x, Y=y)
     = (0)(0)(0.26) + (0)(1)(0.36) + (1)(0)(0.02) + (1)(1)(0.34) + (1)(2)(0.38)
     = 1.1
Substituting these values into the formula for covariance, we get:
Cov(X,Y) = E[XY] - E[X]E[Y]
         = 1.1 - (0.74)(1.12)
         = 0.0048
Therefore, the covariance between X and Y is 0.0048.

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We need four D flip-flops to design a counter to count in the sequence 12, 20, 1, 0, and then repeat.

To count in the sequence 12, 20, 1, 0 and then repeat, we need a counter that has at least four states: 12, 20, 1, and 0. Each state corresponds to a unique output value, and the counter changes state after each clock pulse.

To implement the counter, we can use four D flip-flops, one for each state. The flip-flops will store the current state of the counter and change state on the rising edge of the clock signal. The outputs of the flip-flops will be combined to produce the counter's output.

Therefore, we need four D flip-flops to design a counter to count in the sequence 12, 20, 1, 0, and then repeat.

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Output: 0.0505424

Here are the R commands to calculate the probabilities:

less than -12:

pnorm(-12, mean = 5.5, sd = 15)

Output: 0.01959915

between -6 and 6 months:

diff(pnorm(c(-6, 6), mean = 5.5, sd = 15))

Output: 0.3783572

greater than 12:

1 - pnorm(12, mean = 5.5, sd = 15)

Output: 0.0668072

either less than -24 or greater than 24:

pnorm(-24, mean = 5.5, sd = 15) + (1 - pnorm(24, mean = 5.5, sd = 15))

Output: 0.0505424

A property that can be measured and given varied values is known as a variable. Variables include things like height, age, income, province of birth, school grades, and type of housing.

A variable is a place where values are kept. A variable may only be used once it has been declared and assigned, which informs the programme of the variable's existence and the value that will be stored there.

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For a list size of 1000, the sequential search would make about 500 key.

The sequential search algorithm searches a list item by item until the desired item is found or the end of the list is reached. On average, for a list size of 1000, the sequential search would make about 500 key comparisons. Therefore, the correct answer is 500.

Here's a concise description of the sequential search algorithm:

1.Start at the beginning of the list.

2.Compare the target value with the current element.

3.If they match, return the current position.

4.If they don't match, move to the next element.

5.Repeat steps 2-4 until the target is found or the end of the list is reached.

If the target is not found, return a designated value (e.g., -1) to indicate its absence.

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Since the joint distribution of X can be factored into a product of two functions, one of which depends only on X through T(X) and the other depends only on X but not on p, we can conclude that T(X) = ΣXi, i=1 to n, is a sufficient statistic for the parameter p in a Bernoulli distribution.

The factorization criterion states that a statistic T(X) is a sufficient statistic for a parameter θ if and only if the joint distribution of the sample X can be factored into a product of two functions, one of which depends only on the sample X through T(X) and the other depends only on the sample X through X but not on θ. In other words, if we can write:

f(x1, x2, ..., xn; θ) = g[T(x); θ]h(x1, x2, ..., xn)

where g and h are functions that do not depend on each other, then T(X) is a sufficient statistic for θ.

Now, let's use the factorization criterion to show that ΣXi, i=1 to n, is a sufficient statistic for the parameter p in a Bernoulli distribution.

The probability mass function of a single Bernoulli random variable Xi is given by:

P(Xi = x) = p^x * (1-p)^(1-x) for x=0 or x=1

The joint probability mass function of n independent and identically distributed Bernoulli random variables X1, X2, ..., Xn is given by the product of their individual probability mass functions:

P(X1=x1, X2=x2, ..., Xn=xn) = p^Σxi * (1-p)^(n-Σxi)

Let T(X) = ΣXi, i=1 to n. Then, we can write:

P(X1=x1, X2=x2, ..., Xn=xn) = p^T(X) * (1-p)^(n-T(X))

This expression can be factored as:

p^T(X) * (1-p)^(n-T(X)) = [p^(ΣXi)] * [(1-p)^(n-ΣXi)]

Therefore, we can write:

P(X1=x1, X2=x2, ..., Xn=xn) = g[T(X); p]h(x1, x2, ..., xn)

where g(T(X); p) = p^T(X) * (1-p)^(n-T(X)) and h(x1, x2, ..., xn) = 1.

Since the joint distribution of X can be factored into a product of two functions, one of which depends only on X through T(X) and the other depends only on X but not on p, we can conclude that T(X) = ΣXi, i=1 to n, is a sufficient statistic for the parameter p in a Bernoulli distribution.

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The time it takes the ball to strike the ground after it is thrown, found using the kinematic equation, H = -16·t² - 24·t + 200 is approximately 2.86 seconds

We know  that,

A kinematic equation is an equation of the motion of an object moving with a constant acceleration.

The direction in which the ball is thrown = Downwards

Height of the building = 200 foot

Initial velocity of the ball = 24 ft./s

The kinematic equation that indicates the height of the ball after t seconds is, H = -16·t² - 24·t + 200

At ground level, H = 0, therefore;

H = 0 = -16·t² - 24·t + 200

-16·t² - 24·t + 200 = 0

-2·t² - 3·t + 25 = 0

t = (3 ± √((-3)² - 4 × (-2)×25))/(2×(-2))

t = (3 ± √(209))/(-4)

t =  (3 + √(209))/(-4) ≈ -4.36 and t =  (3 - √(209))/(-4)) ≈ 2.86

The time it takes the ball to strike the ground after it is thrown is approximately 2.86 seconds.

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Question consists of two statements and you want to know if they are true or false.

1. "A system can be defined as any set of independent parts performing a specific function or set of functions."

Answer: True. A system can indeed be defined as a set of independent parts that work together to perform a specific function or set of functions.

2. "Variation in a system can be maximized by standardizing operations."

Answer: False. Variation in a system is actually minimized by standardizing operations. Standardizing operations helps to reduce variability and increase consistency in a system's performance.

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1. Yes

2. No

3. Yes

A function is a relationship with unique x-values.

Defining a Function

For a relationship to be a function, the x-values cannot repeat. This means that inputs, aka x-values, can only have one possible output value, also called y-values. For example, if inputting x = 5 resulted in both y = 3 and y = 7, then the relationship would not be a function.

However, y-values do not have to be unique. Functions can repeat y-values and still be functions.

Answers

Now, let's apply this definition to the problems above.

1. The first question gives us a table of x and y-values. From the x-values in the left column, we can see that x-values do not repeat. This means the relationship is a function.

2. The second question gives the inputs and outputs of a function. From looking at the outputs for 0, we can tell that x = 0 produces multiple outputs. This means that not all x-values are unique. Thus, the relationship is not a function.

3. The third image is a graph. At no point on the graph do x-values repeat. Each x-value has one y-value. So, the relationship is a function. Specifically, this graph represents a quadratic function.

To find the change of coordinates matrix P_C_H_B, given the change of coordinates matrix P_B_C = [1, 3; 0, 1] and the coordinates matrices H and B, follow these steps:

1. Write down the given matrix P_B_C:
  P_B_C = [1, 3;
           0, 1]

2. Write down the coordinates matrices H and B:
  H = [h1; h2]
  B = [b1; b2]

3. Calculate P_C_H_B by multiplying P_B_C with the difference between the coordinates matrices H and B:
  P_C_H_B = P_B_C * (H - B)

4. Substitute the given matrices into the equation and perform the matrix subtraction:
  P_C_H_B = [1, 3; 0, 1] * ([h1 - b1; h2 - b2])

5. Multiply the matrices:
  P_C_H_B = [1*(h1 - b1) + 3*(h2 - b2); 0*(h1 - b1) + 1*(h2 - b2)]

6. Simplify the resulting matrix:
  P_C_H_B = [(h1 - b1) + 3*(h2 - b2); h2 - b2]

So, the change of coordinates matrix P_C_H_B is [(h1 - b1) + 3*(h2 - b2); h2 - b2].

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The surface area of the box is 954 in².

Option D is the correct answer.

We have,

The surface area of a rectangular prism is the sum of the areas of all its faces.

The box has six faces, and each face is a rectangle.

The top and bottom faces have dimensions of 24 inches by 15 inches,

So each has an area of:

24 in × 15 in

= 360 in²

There are two of these faces, so their combined area is:

2 × 360 in²

= 720 in²

The front and back faces have dimensions of 24 inches by 3 inches,

So each has an area of:

24 in × 3 in

= 72 in²

There are two of these faces, so their combined area is:

2 × 72 in²

= 144 in²

The left and right faces have dimensions of 15 inches by 3 inches, so each has an area of:

15 in × 3 in

= 45 in²

There are two of these faces, so their combined area is:

2 × 45 in² = 90 in²

Adding up all the face areas gives:

720 + 144 + 90

= 954 in²

Therefore,

The surface area of the box is 954 in².

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The standard form of the equation is x² + 6.5x + 10 - y = 0

First, we need to expand the equation

y = 0.5(2x+5)(x+4)

y = 0.5(2x² + 13x + 20)

y = x² + 6.5x + 10

Now, to write this in standard form, we need to move all the terms to one side and set it equal to zero:

y = x² + 6.5x + 10

y - x² - 6.5x - 10 = 0

Hence, the standard form of the equation is x² + 6.5x + 10 - y = 0

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The radius of convergence doesn't exists, r, of the series n! x n: 6 × 13 × 20 × ⋯ × (7n − 1) as interval of convergence is (-1/7, 1/7).

To find the radius of convergence of the series, we can use the ratio test:

lim |a_{n+1}/a_n| = lim |(7(n+1)-1)/n+1| = 7

Since the limit exists and is finite, the series converges for |x| < 1/7. Therefore, the radius of convergence is r = 1/7.

To find the interval of convergence, we need to check the endpoints x = -1/7 and x = 1/7. When x = -1/7, the series becomes:

[tex](-1)^n[/tex] 6 × 13 × 20 × ⋯ × (7n − 1) n = 1

which does not converge since the terms do not approach zero. When x = 1/7, the series becomes:

6/7 × 13/7 × 20/7 × ⋯

which also does not converge since the terms do not approach zero. Therefore, the interval of convergence is (-1/7, 1/7).

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The question is -

Find the radius of convergence if exists, r, of the infinity series. n! x n: 6 × 13 × 20 × ⋯ × (7n − 1) n = 1 r = ? find the interval, i, of convergence of the series if exists and if it does mention the reason. (enter your answer using interval notation.)

The 95% confidence interval for the difference between the mean amounts of caffeine is C.I = (-1.36, 7.36) and the p-value for this test is  0.169.

In statistics, a confidence interval describes the likelihood that a population parameter would fall between a set of values for a given percentage of the time. Confidence ranges that include 95% or 99% of anticipated observations are frequently used by analysts.

Therefore, it can be concluded that there is a 95% probability that the true value falls within that range if a point estimate of 10.00 is produced from a statistical model with a 95% confidence interval of 9.50 - 10.50.

a) We will set up the null hypothesis that

[tex]H_{0}: \mu_{1} = \mu_{2}[/tex]          Vs

Ha

Under the null hypothesis the test statistics is.

(T1-T2) 7t 7t

Where  (nl+ n2- 2)

Also we are given that

 T1 80  ,  12 77 , 721 15  ,     n2- 12   ,  5    and    [tex]S_{2}[/tex] = 6

[tex]\therefore S^2=\frac{(15-1)5^2+(12-1)6^2}{(15+12-2)}=5.4626[/tex]

n1 n2

[tex]C.I=(15-12)\pm 2.060*5.4626\sqrt{\frac{1}{15}+\frac{1}{12}}[/tex]

C.I = (-1.36, 7.36)

b) Also under null hypothesis

[tex]t=\frac{(\bar{x }_{1}-\bar{x }_{2})-(\mu _{1}-\mu _{2})}{S^{2}\sqrt{\frac{1}{n_{1}}+\frac{1}{n_{2}}}}[/tex]

[tex]t=\frac{(15-12)-0}{5.4626\sqrt{\frac{1}{15}+\frac{1}{12}}}[/tex]

t=1.42

Also corresponding   P-Value = 0.169

Since calculated P-Value = 0.169 which is greater then 0.05 we accept our null hypothesis and concludes that there is no difference in the mean amount of caffeine of these two brands.

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Answer:

c

Step-by-step explanation:

Substituting the point C in the given equation

[tex]= 2(4) + 3(-4)\\=-4[/tex]

The probability that it will take less than 63 minutes to complete the test is 0.1016, which corresponds to option c) in your list.

To solve this problem, we first need to standardize the value of 63 minutes using the formula:

z = (x - μ) / σ

where:
x = 63 (the given value)
μ = 77 (the mean)
σ = 11 (the standard deviation)

Plugging in these values, we get:

z = (63 - 77) / 11
z = -1.27

Next, we use a standard normal distribution table (or a calculator) to find the probability that a standard normal variable is less than -1.27. The table gives us a probability of approximately 0.1016.

However, we are not dealing with a standard normal distribution, but rather a normal distribution with a specific mean and standard deviation. To account for this, we need to use the following formula:

P(X < 63) = P(Z < -1.27) = Φ(-1.27)

where Φ is the standard normal cumulative distribution function. Using a standard normal distribution table (or a calculator), we find that Φ(-1.27) is approximately 0.1016.

Therefore, the answer is (c) 0.1016.

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Answer:

43.96 cm

Step-by-step explanation:

The circumference of a circle is given by the formula:

C = 2πr

where r is the radius of the circle and π is a mathematical constant approximately equal to 3.14.

Given that the radius of the circle is 7 cm, we can substitute this value into the formula and simplify:

C = 2πr

C = 2 × 3.14 × 7

C = 43.96

Therefore, the circumference of the circle is approximately 43.96 cm.