0 .08 1 .17 2 .26 3 .21 4 .18 5 .10 a. If we define the experiment as observing the number of computers sold tomorrow, determine the sample space. b. Use set notation to define the event, sell more than three computers. c. Whatistheprobabilityofsellingfivecomputers

To find the spherical coordinate limits for the integral, we first need to determine the bounds for ρ, θ, and ϕ.

Since the sphere and hemisphere intersect at ρ=4cos(ϕ), we can set these two equations equal to each other to find the limits for ϕ:

4cos(ϕ) = 6

ϕ = arccos(3/2)

For the limits of θ, we note that the solid is symmetric about the z-axis, so we can integrate from 0 to 2π.

Finally, for the limits of ρ, we need to find the limits for z. Since the hemisphere has equation ρ=6 and z≥0, we know that the top of the solid is at z=6. To find the bottom of the solid, we need to solve for z in the equation for the sphere:

ρ = 4cos(ϕ)

z = 4cos(ϕ)cos(θ)sin(ϕ)

Substituting ρ=4cos(ϕ) and simplifying, we get:

z = 2cos^2(ϕ)sin(θ)

Since z≥0, we have:

0 ≤ 2cos^2(ϕ)sin(θ) ≤ 6

0 ≤ sin(θ) ≤ 3/(2cos^2(ϕ))

So the limits for ρ are 4cos(ϕ) ≤ ρ ≤ 6, the limits for θ are 0 ≤ θ ≤ 2π, and the limits for ϕ are arccos(3/2) ≤ ϕ ≤ π/2.

To evaluate the integral, we use the formula for a volume in spherical coordinates:

V = ∫∫∫ ρ^2sin(ϕ) dρdθdϕ

Applying the limits we found above, we get:

V = ∫ from arccos(3/2) to π/2 ∫ from 0 to 2π ∫ from 4cos(ϕ) to 6 (ρ^2sin(ϕ)) dρdθdϕ

Evaluating the integral, we get:

V = 256π/15 - 8/3

Therefore, the volume of the solid is 256π/15 - 8/3 cubic units.

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The sampling technique is called "simple random sampling."

In the field of statistics, the process of sampling is used to select a subset of individuals or units from a larger population to study and analyze.

The goal of sampling is to gather data that can be used to make accurate and reliable inferences about the characteristics of the entire population.

One of the most common and straightforward methods of sampling is simple random sampling.

In this technique, each member of the population has an equal chance of being selected to be a part of the sample.

The process of selecting individuals for the sample is usually done through a randomization process, which ensures that each member of the population has an equal probability of being chosen.

Simple random sampling is considered to be an unbiased method of sampling because it ensures that all members of the population have an equal chance of being selected.

This helps to minimize the potential for sampling bias, which is a type of error that can occur when the sample selected is not representative of the entire population.

To implement simple random sampling, researchers can use various methods, including a random number generator or drawing names from a hat.

The sample size required for simple random sampling will depend on the size of the population and the level of precision required for the study.

Overall, simple random sampling is a powerful tool for gathering data that can be used to make accurate and reliable inferences about the characteristics of a larger population.

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The hourly wage in 10 years = $12.37,  In this scenario, automobile plant workers' hourly wages increased from $6.10 to $8.58 over a period of 7 years, with the wages growing exponentially.

To calculate their hourly wage in 10 more years, we will use the exponential growth formula:

Final Amount = Initial Amount * (1 + Growth Rate)^Years

First, we need to find the annual growth rate. To do this, we can rearrange the formula as follows:

Growth Rate = [(Final Amount / Initial Amount)^(1 / Years)] - 1

Plugging in the given values:

Growth Rate = [(8.58 / 6.10)^(1 / 7)] - 1
Growth Rate ≈ 0.0476

Now that we have the annual growth rate, we can calculate their hourly wage in 10 more years:

Hourly Wage in 10 Years = 8.58 * (1 + 0.0476)^10
Hourly Wage in 10 Years ≈ $12.79

Therefore, the automobile plant workers' hourly wage will be approximately $12.79 in 10 more years, assuming their wages continue to grow exponentially at the same rate.

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Therefore, a p-value less than 0.05 is considered statistically significant, which means that the observed result is unlikely to have occurred by chance and supports the rejection of the null hypothesis.

If your findings yield a 1 in 100 chance that differences were not due to the hypothesized reason, then the corresponding p-value would be 0.01. The p-value represents the probability of obtaining a result as extreme or more extreme than the observed result, assuming that the null hypothesis is true. In other words, a p-value of 0.01 indicates that there is a 1% chance of observing the data if the null hypothesis (the hypothesized reason) is true. Generally, a p-value less than 0.05 is considered statistically significant, which means that the observed result is unlikely to have occurred by chance and supports the rejection of the null hypothesis.

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The interquartile range of the data is IQR = 6 and the median is M = 6.5

Given data ,

Let the data be represented as A

Now , A = { 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 }

Median = (n + 1) / 2

where n is the number of data points.

Median = (12 + 1) / 2 = 6.5

Since 6.5 is not a data point in the given data set, we take the average of the two middle values:

Median = (6 + 7) / 2 = 6.5

Let the first quartile be Q1

Now ,

Q1 = Median of the lower half of the data set.

Since we have an even number of data points, the lower half would be the first six values:

Q1 = (6 + 1) / 2 = 3.5

Since 3.5 is not a data point in the given data set, we take the average of the two values closest to it:

Q1 = (3 + 4) / 2 = 3.5

Let the third quartile be Q3

Now ,

Q3 = Median of the upper half of the data set.

Again, since we have an even number of data points, the upper half would be the last six values:

Q3 = (12 + 7) / 2 = 9.5

Since 9.5 is not a data point in the given data set, we take the average of the two values closest to it:

Q3 = (9 + 10) / 2 = 9.5

And , IQR is given by

IQR = Q3 - Q1

IQR = 9.5 - 3.5 = 6

Hence , the third quartile is 9.5, the interquartile range (IQR) is 6, and the median is 6.5 for the given data set { 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 }

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The domain of the composite function fog = f(g(x) = {1, 4, 5, 9} and its range is {4, 2}

We know that the domain of a function is the valid number of input values to the function whicle its range is the valid number of output values to the function.

Now, we have the functions f(x) and g(x) and we require the composite function fog = f(g(x))

From the figure the domain of g(x) is {4,5,6,7,9} and its range is {1,4,5,6,9}

So, x maps to g(x) as

4 → 6, 5 → 1, 6 → 4, 7 → 9, 9 → 5

From the figure the domain of f(x) is {1,4,5,7,9} and its range is {2,4,7}

So, x maps to f(x) as

1 → 4, 4 → 4, 5 → 2, 7 → 7, 9 → 2

Now, the ouput of g(x) is the input of f(g(x)). So, we have that

g(x) maps to f(g(x)) as

1 → 4, 4 → 4, 5 → 2, 9 → 2

So, the domain of f(g(x) = {1, 4, 5, 9} and its range is {4, 2}

So,  the domain of the function f(g(x) = {1, 4, 5, 9} and its range is {4, 2}

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In family dice game, if a player rolls five dice at a time, then  7776 outcomes can be possible in one roll.

When a single die is rolled, there are six possible outcomes: 1, 2, 3, 4, 5, or 6.

Since there are five dice being rolled in the family game, the total number of possible outcomes is simply the product of the number of outcomes for each die.

Each of the five dice being rolled has six possible outcomes. Each die is independent of the others, meaning that the outcome of one die does not affect the outcome of the others.

Therefore, to find the total number of possible outcomes for all five dice, we multiply the number of outcomes for each die, which gives

6 x 6 x 6 x 6 x 6 = 7776

So, there are 7776 possible outcomes in one roll of five dice in the family game.

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We would need 453 more observations to increase your confidence level to 95% certainty.

To determine the required number of observations to be 90% sure of being within ±2.5% of the population mean for an activity occurring 30% of the time, we'll use the sample size formula for proportions:

n = (Z^2 * p * (1-p)) / E^2

Here, n is the sample size, Z is the z-score corresponding to the desired confidence level, p is the proportion (30% or 0.30), and E is the margin of error (±2.5% or 0.025).

For a 90% confidence level, the z-score (Z) is 1.645. Plugging the values into the formula:

n = (1.645^2 * 0.30 * (1-0.30)) / 0.025^2
n ≈ 1023.44

So, you would need approximately 1024 observations to be 90% sure of being within ±2.5% of the population mean.

To increase the confidence level to 95%, the z-score (Z) changes to 1.96. Using the same formula:

n = (1.96^2 * 0.30 * (1-0.30)) / 0.025^2
n ≈ 1476.07

So, you would need approximately 1477 observations for a 95% confidence level.

To find the additional number of observations needed, subtract the initial sample size from the new sample size:

1477 - 1024 = 453

Therefore, you would need 453 more observations to increase your confidence level to 95% certainty.

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Answer: Katie worked for 13.5 hours.

Step-by-step explanation:

If Katie makes $12 an hour and made $162, we can use a simple formula to find how many hours she worked:

Total pay = Hourly rate × Number of hours worked

$162 = $12/hour × Number of hours worked

Number of hours worked = $162 ÷ $12/hour

Number of hours worked = 13.5

Therefore, Katie worked for 13.5 hours to earn $162.

The vertical distance between the max in the window and the maximum height of the ball is about 55.16 feet.

To determine the vertical distance between the maximum height of the ball and the window in the building, we need to know the maximum height the ball reaches.

A projectile is an object that moves in a parabolic path under the influence of gravity. The maximum height of a projectile is reached when its vertical velocity is zero. The vertical velocity of a projectile depends on its initial velocity and the angle of launch.

According to the web search results, the formula for the maximum height of a projectile is:

H=2gu2sin2θ​

where H is the maximum height, u is the initial velocity, θ is the angle of launch, and g is the acceleration due to gravity.

In this question, we are given that the window in the building is 55 feet above the ground, and the ball is thrown vertically from the window. This means that the angle of launch is 90 degrees, and the initial velocity is unknown. We can use the formula to find the initial velocity:

H=2gu2sin290​

55=2(32)u2​

u2=55×64

u=55×64​

u≈59.16 feet per second

Now that we have the initial velocity, we can use it to find the maximum height of the ball above the ground. We can use the same formula, but this time we need to add 55 feet to the result, since that is the height of the window from the ground:

H=2gu2sin290​+55

H=2(32)(59.16)2​+55

H≈110.16 feet

Therefore, the maximum height of the ball above the ground is about 110.16 feet.

The vertical distance between the max in the window and the maximum height of the ball is simply the difference between these two heights:

D=H−55

D=110.16−55

D≈55.16 feet

Therefore, the vertical distance between the max in the window and the maximum height of the ball is about 55.16 feet.

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The process capability in sigmas for the given lab is approximately 0.148. This indicates that the process is not very capable and there is significant room for improvement.

To calculate the process capability in sigmas, we first need to calculate the process capability index (Cpk). Cpk measures how well the process is able to produce parts within specifications, relative to the variability of the process.

Cpk is calculated using the following formula:

Cpk = min(USL - mean, mean - LSL) / (3 × standard deviation)

where USL is the upper specification limit (25 minutes in this case), LSL is the lower specification limit (10 minutes in this case), and the mean and standard deviation are as given (mean = 19.0 minutes, standard deviation = 4.5 minutes).

Substituting these values in the formula, we get:

Cpk = min(25 - 19.0, 19.0 - 10) / (3 × 4.5)

= min(6.0, 9.0) / 13.5

= 0.444

Now, the process capability in sigmas can be calculated using the following formula:

Process capability in sigmas = Cpk / 3

Substituting the value of Cpk, we get:

Process capability in sigmas = 0.444 / 3

= 0.148

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There are 65,535 ways a computer can randomly generate one or more integers from 1 through 16

To determine the number of ways a computer can randomly generate one or more integers from 1 through 16, we need to use the concept of permutations and combinations.

If we want to randomly select only one integer from 1 through 16, then there are 16 possible choices. This can be represented as 16P1 or 16C1, which both equal 16.

However, if we want to randomly select more than one integer from 1 through 16, we need to use combinations. For example, if we want to randomly select 2 integers from 1 through 16, there are 16C2 or 120 possible combinations.

In general, the number of ways a computer can randomly generate one or more integers from 1 through 16 is equal to the sum of the number of ways to select 1 integer, 2 integers, 3 integers, and so on up to 16 integers. This can be represented as:

16C1 + 16C2 + 16C3 + ... + 16C16

Using the formula for the sum of combinations, we can simplify this to:

2^16 - 1

Therefore, there are 65,535 possible ways a computer can randomly generate one or more integers from 1 through 16.

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The homothet coefficients and the value of x are

The homothet coefficient by definition and in this context, is the scale factor of dilation

Using the above as a guide, we have the following:

Figure (a)

If the image of point P is P′, then

Homothet coefficient = 18/9

Homothet coefficient = 2

Also, we have

x/9 = 35/18

x = 9 * 35/18

x = 17.5

Figure (b)

If the image of point P is P′, then

Homothet coefficient = 15/9

Homothet coefficient = 5/3

Also, we have

x/10 = 15/9

x = 10 * 15/9

x = 50/3

Figure (c)

Here, we have

Homothet coefficient = 15/6

Homothet coefficient = 5/2

Also, we have

x/2 = 15/6

x = 2 * 15/6

x = 5

Hence, the value of x is 5

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A variable is standardized in the sample (d) by subtracting off its mean and dividing by its standard deviation. The correct answer is (d) by subtracting off its mean and dividing by its standard deviation.

Standardizing a variable means transforming it to have a mean of 0 and a standard deviation of 1. This is done to make it easier to compare variables that have different scales and units.

To standardize a variable in a sample, you need to subtract its mean from each observation to center it around 0, and then divide by its standard deviation to scale it to have a standard deviation of 1.

So, the formula for standardizing a variable in a sample is:

z = (x - μ) / σ

where z is the standardized value, x is the original value, μ is the mean, and σ is the standard deviation.

Option (d) is the only choice that correctly describes this process. Options (a) and (c) only involve multiplication, and do not involve centering the variable around its mean. Option (b) involves centering the variable around its mean, but does not scale it to have a standard deviation of 1.

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When conducting a hypothesis test, the p-value represents the probability of obtaining a result as extreme as the one observed or more extreme, assuming the null hypothesis is true. In this case, the null hypothesis would be that there is no difference in the average price of 3-bedroom, 2-bath homes in Chicago and Denver.

Given a z-value of 0.42, we need to determine the corresponding area under the standard normal distribution curve to find the p-value. Using a standard normal distribution table or calculator, we can find that the area to the right of a z-score of 0.42 is approximately 0.3336. However, since we are testing for a one-tailed hypothesis (i.e. Chicago having a higher average price than Denver), we need to find the area to the right of 0.42 and then multiply it by 2.

Therefore, the p-value would be approximately 2(0.3336) = 0.6672. This means that if the null hypothesis were true (i.e. no difference in average price between Chicago and Denver), we would expect to observe a result as extreme as or more extreme than the one observed (a z-score of 0.42) approximately 66.72% of the time. Since this p-value is larger than the commonly used alpha level of 0.05, we would fail to reject the null hypothesis and conclude that there is not enough evidence to prove that Chicago has a higher average price than Denver.

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The wall cloud is approximately 3 miles away.

An angle that is formed when an object is viewed above the horizontal is said to be an angle of elevation.

From the details of the question, we can determine the distance of the wall cloud by;

let the distance of the wall cloud be represented by x, applying the trigonometric function;

Sin θ = opposite/ hypotenuse

Sin 11 = 3000/ x

x = 3000/ 0.1908

  = 15722.53

The wall cloud is 15722.53 feet away.

But 1 mile = 5,280 feet. so that;

x = 15722.53/ 5280

  = 2.9778

x = 3 miles

Therefore, the wall cloud is 3 miles away.

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The value of the integral is 2.383.

To evaluate the integral taking ω as the region bounded between y=x3 and y=x2, we first need to set up the limits of integration. We can see that the region ω is bounded by the curves y=x3 and y=x2. Thus, the limits of integration for y are y=x3 to y=x2.

Next, we need to determine the limits of integration for x. To do this, we can solve for x in terms of y for each curve:

y=x3
⇒ x=y^(1/3)

y=x2
⇒ x=y^(1/2)

Thus, the limits of integration for x are x=y^(1/3) to x=y^(1/2).

Now we can write the integral as:

∫∫(7x^4*2y^2) dxdy = ∫ from y=x3 to y=x2 ∫ from x=y^(1/3) to x=y^(1/2) (7x^4*2y^2) dxdy

We can now integrate with respect to x:

∫ from y=x3 to y=x2 [(7/5)x^5*2y^2] evaluated from x=y^(1/3) to x=y^(1/2)] dy

= ∫ from y=x3 to y=x2 [(7/5)(y^(5/2)-y^(5/3))*2y^2] dy

= (14/5) ∫ from y=x3 to y=x2 (y^(9/2) - y^(11/3)) dy

= (14/5) [ (2/11)y^(11/2) - (3/14)y^(14/3) ] evaluated from y=x3 to y=x2

= (14/5) [ (2/11)(x2)^(11/2) - (3/14)(x2)^(14/3) - (2/11)(x3)^(11/2) + (3/14)(x3)^(14/3) ]

= (14/5) [ (2/11)(sqrt(2) - sqrt(3)) - (3/14)(2sqrt(2) - 3sqrt(3)) ]

= 2.383

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The likelihood that a randomly selected bag of cement weighs more than 50 is approximately 93.32%

When dealing with normally distributed data, we use the mean and standard deviation to determine the likelihood of certain events occurring. In this case, the mean weight of bags of cement is 53 with a standard deviation of 2.

To find the likelihood that a randomly selected bag has a weight greater than 50, we need to calculate the z-score for 50. The z-score tells us how many standard deviations away a particular value is from the mean.

z = (X - μ) / σ

where X is the value we're interested in (50), μ is the mean (53), and σ is the standard deviation (2).

z = (50 - 53) / 2 = -1.5

A z-score of -1.5 means that a weight of 50 is 1.5 standard deviations below the mean. To find the likelihood of a bag weighing more than 50, we can use a z-table or a calculator to find the area to the right of this z-score.

Looking up a z-score of -1.5, we find that the area to the left is approximately 0.0668, which means the area to the right (the likelihood of a bag weighing more than 50) is:

1 - 0.0668 = 0.9332

Thus, the likelihood that a randomly selected bag of cement weighs more than 50 is approximately 93.32%.

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Step he could have used instead to simplify the expression cubed is   [tex]5^{(4*3)}[/tex] = [tex]5^{12}[/tex].

Raj's step of multiplying ([tex]5^{4}[/tex]) three times to simplify [tex](5^{4}) ^{3}[/tex] is correct, but there is an error in the resulting expression.

When we multiply the same base raised to an exponent, we add the exponents. So, the correct simplification of  [tex](5^{4}) ^{3}[/tex]  would be:

[tex](5^{4}) ^{3}[/tex]  = [tex]5^{(4*3)}[/tex] = [tex]5^{12}[/tex]

Therefore, the step that Raj could have used instead to simplify the expression  [tex](5^{4}) ^{3}[/tex]   correctly is:

[tex](5^{4}) ^{3}[/tex]   =  [tex]5^{(4*3)}[/tex] = [tex]5^{12}[/tex]

Option A,  [tex]5^{4}[/tex]  times 3, is also correct, but it's not the most efficient method as it involves multiplication of a large number.

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The equation also holds true for k+1. By mathematical induction, we have proved that the equation is true for all positive integers n.

To prove that 12−22+32−…+(−1)n−1n2=(−1)n−1n(n+1)2 whenever n is a positive integer using mathematical induction, we must first establish the base case.

When n=1, we have 1^2 = 1 and (-1)^(1-1) * 1 * (1+1) / 2 = 1. Therefore, the equation holds true for n=1.

Next, we assume that the equation holds true for some arbitrary positive integer k, meaning:

1^2 - 2^2 + 3^2 - ... + (-1)^(k-1) * k^2 = (-1)^(k-1) * k * (k+1) / 2

Now, we must prove that the equation also holds true for k+1:

1^2 - 2^2 + 3^2 - ... + (-1)^(k-1) * k^2 + (-1)^k * (k+1)^2 = (-1)^k * (k+1) * (k+2) / 2

Starting with the left side of the equation, we can substitute in the assumed equation for k:

(-1)^(k-1) * k * (k+1) / 2 + (-1)^k * (k+1)^2

Simplifying this expression:

(-1)^(k-1) * k * (k+1) / 2 - (k+1)^2 * (-1)^k

= (k+1) * [(-1)^(k-1) * k / 2 - (k+1) * (-1)^k]

= (k+1) * [(-1)^(k-1) * k / 2 + (k+1) * (-1)^{k+1}]

= (k+1) * [(-1)^(k-1) * k / 2 + (-1)^k * (k+1)]

= (k+1) * [(-1)^k * (k+1) / 2]

= (-1)^k * (k+1) * (k+2) / 2

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25463 grams radioactive kind of americium will be left after 2160 years.

We know that Half Life Formula will be,

[tex]N=I(\frac{1}{2})^{\frac{t}{T}}[/tex]

where N is the quantity left after time 't'; 'T' is the half life of the substance and 'I' is the initial quantity of the substance.

Given that the initial quantity of the substance (I) = 814816 grams

Half life of the radioactive kind of americium is (T) = 432 years

The time elapsed (t) = 2160 years

Now we have to find the quantity left that is the value of N for the given values.

N = [tex]814816\times(\frac{1}{2})^{\frac{2160}{432}}=814816\times(\frac{1}{2})^5[/tex] = 814816/32 = 25463 grams.

Hence 25463 grams radioactive kind of americium will be left after 2160 years.

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The conclusion is our that : ∠B and ∠C are congruent

i.e., ∠B ≅ ∠C

We have the following:

A △ABC with AB ≅ AC

Since AB ≅ AC implies AB=AC.

The lengths of sides A B and A C are congruent.

Our assumption is ∠B and ∠C are not congruent.

Then the measure of one angle is greater than the other and

It is also given that:

m∠B > m∠C, then AC > AB because of the triangle parts relationship theorem.

and if m∠B < m∠C, then AC < AB by the same reason.  

As we know if in a triangle two sides are equal then the triangle becomes an isosceles triangle.

Since triangle is isosceles then the angles opposite to equal sides are equal i.e.,

if AB=AC then ∠B = ∠C in △ABC

which is contradiction to the assumption that ∠B and ∠C are not congruent.

Therefore, it can be concluded that

∠B and ∠C are congruent.

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a) the probability of a randomly chosen person scoring more than 125 is approximately 4.75%. b) the probability of a randomly chosen person scoring between 90 and 110 is approximately 49.72%.

(a) To find the probability that a randomly chosen IQ test taker obtains a score more than 125, we need to calculate the area under the normal curve to the right of 125. We can use the standard normal distribution to find the z-score of 125:

z = (125 - 100) / 15 = 1.67

Using a standard normal distribution table or calculator, we find that the area to the right of z = 1.67 is approximately 0.0475. Therefore, the probability that a randomly chosen IQ test taker obtains a score more than 125 is approximately 0.0475 or 4.75%.

(b) To find the probability that a randomly chosen IQ test taker obtains a score between 90 and 110, we need to calculate the area under the normal curve between 90 and 110. We can use the standard normal distribution to find the z-scores of 90 and 110:

z1 = (90 - 100) / 15 = -0.67
z2 = (110 - 100) / 15 = 0.67

Using a standard normal distribution table or calculator, we find that the area to the left of z = -0.67 is approximately 0.2514 and the area to the left of z = 0.67 is approximately 0.7486. Therefore, the area between z = -0.67 and z = 0.67 is:

0.7486 - 0.2514 = 0.4972

This means that the probability that a randomly chosen IQ test taker obtains a score between 90 and 110 is approximately 0.4972 or 49.72%.

To find the probabilities for the given scenarios, we'll use the standard normal distribution (Z-distribution) and a Z-score formula:

Z = (X - μ) / σ

where X is the IQ score, μ is the mean (100), and σ is the standard deviation (15).

(a) Probability of a score more than 125:

1. Calculate the Z-score for 125:
Z = (125 - 100) / 15 = 25 / 15 = 1.67

2. Use a Z-table or calculator to find the probability for Z > 1.67:
P(Z > 1.67) ≈ 0.0475

So, the probability of a randomly chosen person scoring more than 125 is approximately 4.75%.

(b) Probability of a score between 90 and 110:

1. Calculate the Z-scores for 90 and 110:
Z_90 = (90 - 100) / 15 = -10 / 15 = -0.67
Z_110 = (110 - 100) / 15 = 10 / 15 = 0.67

2. Use a Z-table or calculator to find the probability between Z_90 and Z_110:
P(-0.67 < Z < 0.67) ≈ 0.7486 - 0.2514 = 0.4972

So, the probability of a randomly chosen person scoring between 90 and 110 is approximately 49.72%.

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Answer: divide

Step-by-step explanation:when the number in the question is divided to the number next to it the answer can be found when you multiply it after you add it to the nearest tenth.

Answer:

20°

Step-by-step explanation:

A right triangle equals 90°.

So, you can subtract the angle that you already know.

90°

-50°

-----------

40°

Since the other two angles are congruent you can divide 40° into two parts.

40°

÷2

-----------

20°

So, each unknown measurement of the triangle is 20°.

There are 10 ways for Maria to distribute the fruits among her four friends if she doesn't give Jacky any oranges.

If Maria doesn't give any oranges to Jacky, she must give him all three apples. Then she is left with three oranges to distribute among the remaining three friends.

We can think of this as placing the oranges into three boxes (one for each friend), with the restriction that each box must contain at least one orange (since we cannot leave any oranges for Jacky).

This problem can be solved using the stars and bars method. We can think of the oranges as "stars" and the boxes as "bars" separating them. We need to place two bars to create three boxes. The number of ways to do this is:

(3 + 2) choose 2 = 5 choose 2 = 10

Therefore, there are 10 ways for Maria to distribute the fruits among her four friends if she doesn't give Jacky any oranges.

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N is greater than N-2, we know that P' is greater than P. In other words, removing two outcomes increases the probability of the remaining favorable outcome.

What is probability?

Probability is a measure of the likelihood of an event occurring.

If we remove two outcomes, then the total number of possible outcomes will be reduced by two.

Therefore, the probability of the remaining favorable outcome will increase.

Suppose the original probability of the favorable outcome was P, and there were a total of N possible outcomes, including the favorable outcome. Then, the original probability can be expressed as P = 1/N.

If we remove two outcomes, the total number of possible outcomes will decrease to N-2. However, the number of favorable outcomes will remain the same, as only the unfavorable outcomes are being removed. Therefore, the new probability can be expressed as P' = 1/(N-2).

Since N is greater than N-2, we know that P' is greater than P. In other words, removing two outcomes increases the probability of the remaining favorable outcome.

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The side lengths mentioned in option E are the sides of the right angled triangle.

Three given side lengths of a triangle a, b and c are said to be the sides of the right triangled triangle if -

a² = b² + c²

We can write for the given set of numbers in option 5 as -

(13)² = (12)² + (5)²

169 = 144 + 25

169 = 169

LHS = RHS

So, the side lengths mentioned in option E are the sides of the right angled triangle.

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The simple linear regression model is given by yi = β0 + β1xi + εi, where β0 is the intercept, β1 is the slope, xi is the predictor variable, yi is the response variable, and εi is the error term. These are the least squares coefficient estimates for the simple linear regression model.

The goal of least squares regression is to find the values of β0 and β1 that minimize the sum of the squared residuals.
To find the least squares coefficient estimates, we need to minimize the sum of the squared residuals. The residual is the difference between the observed value of yi and the predicted value of yi. The predicted value of yi is given by β0 + β1xi. Therefore, the residual can be written as yi - (β0 + β1xi).
The sum of squared residuals is given by:
Σi=1n (yi - β0 - β1xi)²
To find the values of β0 and β1 that minimize this sum, we take the partial derivatives with respect to β0 and β1 and set them equal to zero:
∂/∂β0 Σi=1n (yi - β0 - β1xi)² = 0
∂/∂β1 Σi=1n (yi - β0 - β1xi)² = 0
Solving these equations yields:
βˆ 0 = ¯y − βˆ 1x
and
βˆ 1 = Pn i=1(xi − x¯)(yi − y¯) / Pn i=1(xi − x¯)²
where y¯ = 1 n Pn i=1 yi and x¯ = 1 n Pn i=1 xi. These are the least squares coefficient estimates for the simple linear regression model.

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