2. *
2 points
Which set of ordered pairs represents a function?
1) {(0,4),(2,4),(2,5)}
2) {(6,0),(5,0),(4,0)}
3) {(4,1),(6,2), (6,3),(5,0)}
4) {(0,4),(1,4),(0,5),(1,5)}
Option 1

Answer:

Kindly check explanation

Step-by-step explanation:

Given the following :

Number of booths = 29

Food booth = 12

Cost of food booth = 175 + $4 per day

Game booth = (29 - 12) = 17

Cost of game booth = $50 + $10 per day

Number of days for which fair lasts (d) = 40 days

Total cost of food booth:

Number of booths * cost per booth

12($175 + 4d) ----(1)

Total cost of game booth:

Number of booths * cost per booth

17($50 + 10d) - - - (2)

(1) + (2)

12($175 + 4d) + 17($50 + 10d)

2100 + 48d + 850 + 170d

2950 + 218d

d = 40

2950 + 218(40)

2950 + 8720

= 11,670

Answer:

50 kg

Step-by-step explanation:

Given that:

SMALL HELICOPTERS:

Weight of small helicopters = 3kg

Weight of shipping container = 20kg

LARGE HELICOPTERS:

Weight of large helicopters = 4kg

Weight of shipping container = 10kg

Number of helicopters each shipping container can hold = s ; all of the packed containers will have the same shipping weight

Shipping weight :

(Weight per helicopter * number of helicopter) + weight of shipping container

Shipping weight of Small helicopters :

(3kg * s) + 20

Shipping weight Large helicopters :

(4kg * S) + 10

Shipping weight of Small helicopters = shipping weight of large helicopters

3s + 20 = 4s + 10

20 - 10 = 4s - 3s

10 = s

Hence, member of shipped helicopters = 10

Total shipping weight :

(4 * S) + 10

(4*10) + 10

40 + 10 = 50kg

Answer:

300

Step-by-step explanation:

multiply 25 by 12

The false statement about statistical analysis is: 3) Observational studies can determine cause and effect relationships. Option 3

Observational studies are a type of study in which researchers observe and collect data on individuals or groups without any intervention or manipulation of variables. These studies are often used to explore relationships and associations between variables but cannot establish cause and effect relationships.

In observational studies, researchers do not have control over the variables being studied, which means that they cannot manipulate or assign treatments to participants.

On the other hand, experiments are designed to investigate cause and effect relationships. In an experiment, researchers actively manipulate independent variables and control other variables to determine the effects on the dependent variable.

While observational studies are valuable in identifying patterns and associations, they cannot establish causality on their own. To establish cause and effect relationships, controlled experiments with random assignment are needed.

option 3

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Answer:

The area is 77

Step-by-step explanation:

7*11=77

Answer:

area = 77

Step-by-step explanation:

if 7 is one side, and 11 is the other, and we know that length times width is the area of a rectangle or a square, we can set up a simple equation to solve for the area:

7 x 11 = ?

when looking at multiplying by 11s, the pattern is 2 of the number your multiplying it by until you exceed 9 (ex. 11 x 5 = 55, 11 x 4 = 44, 11 x 9 = 99, etc.) so:

7 x 11 = 77. which is your answer.

i hope this helped :))

Answer:

no

Step-by-step explanation:

.25 is first

[tex]f(x)=\frac{2x^2-7x+6}{x^2-9x+20}\\ f(x)=\frac{(2x-3)(x-2)}{(x-5)(x-4)}\\\\[/tex]

Therefore, the value of x must not be either 5 or 4 which will give the undefined answer.

Therefore, the domain is x<4, x>5 or R - {4,5}

All of the above factors - confidence level, required precision (margin of error), and the degree of variation in the population as measured by its standard deviation - are used to determine sample size.

When determining the sample size for a study or survey, several factors need to be considered: confidence level, required precision (margin of error), and the degree of variation in the population as measured by its standard deviation. Let's break down each factor and understand how they contribute to determining the sample size:

Confidence Level: The confidence level represents the degree of certainty or confidence desired in the results. It is typically expressed as a percentage (e.g., 95% or 99%). The higher the confidence level, the more confident we can be that the estimated result falls within a certain range. For example, a 95% confidence level means that if the study were repeated 100 times, we would expect the true population parameter to fall within the estimated range in 95 out of those 100 times.

Required Precision (Margin of Error): The required precision, also known as the margin of error, defines the acceptable amount of uncertainty or error allowed in the estimation. It represents the maximum difference between the estimated result and the true population parameter that is considered acceptable. A smaller margin of error indicates higher precision and vice versa. For instance, if you want to estimate the proportion of people supporting a particular political candidate with a margin of error of ±3%, it means that the estimated proportion should be within 3% of the true proportion.

Degree of Variation (Standard Deviation): The degree of variation in the population, quantified by its standard deviation, reflects how much the values within the population differ from the population mean. If the population has a high standard deviation, it suggests a larger spread of values, indicating greater heterogeneity. Conversely, a low standard deviation implies less variability and more homogeneity in the population. The standard deviation helps determine how much information is needed from the sample to make accurate estimates of the population parameters.

These factors are interrelated in determining the sample size. As the confidence level increases, the margin of error becomes smaller, requiring a larger sample size to achieve the desired precision. Similarly, a higher degree of variation in the population (larger standard deviation) necessitates a larger sample size to capture that variability accurately.

In summary, the confidence level, required precision (margin of error), and degree of variation (standard deviation) are essential factors used in determining the appropriate sample size for a study or survey. The balance between these factors ensures that the sample is representative enough to provide reliable estimates of the population parameters with the desired level of confidence and precision.

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To determine which location yields the higher profit per year, we need the specific data for the annual profit in each location. Please provide the annual profit values for the locations in question, and I will be able to calculate and compare the profit per year.

Without the necessary information regarding the annual profits of the locations, it is not possible to determine which location yields the higher profit per year. To make a meaningful comparison, we need the specific profit figures for each location. Once the annual profit values are provided, we can calculate the profit per year by dividing the total profit by the number of years.

Once the profit per year is calculated for each location, we can compare the values to determine which location has the higher profit per year. However, as the specific data regarding the annual profit in each location is missing, it is not feasible to provide a conclusive answer at this time. Please provide the annual profit figures for the locations in question, and I will be able to assist you further in determining which location yields the higher profit per year.

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Answer:

1. 678.58 in^2

2. 59.08 m^2

Step-by-step explanation:

Answer:

The answer is - 8

......

Answer:

0.6, 2/3, 67%, 1.6

Step-by-step explanation:

Let's put everything in decimal form:

2/3= 0.666666666666666666 and repeating

67% is 0.67 exactly

So 0.6 is the smallest. Then is 2/3. After that is 67%. The biggest is 1.6.

Note: Don't get confused by 2/3 and 67%. 2/3 is sometimes rounded to 0.67. But in this case, it is EXACTLY 0.6666 repeating, so 67% is larger.

Answer:

You can break 24 things

Step-by-step explanation:

One minute is 60 seconds, so all you have to do is double how many things you can break in 30 seconds

Answer:

x = 24

Step-by-step explanation:

We can use a ratio to solve

30 seconds          60 seconds

------------------ = --------------

12 things           x things

Using cross products

30x = 60 * 12

Divide by 30

30x/30 = 60 * 12/30

x = 24

Answer:

40

Step-by-step explanation:

Answer:

-24

Step-by-step explanation:

(-4)(6)

-24

Answer:

-24

Step-by-step explanation:

(-4)(6)

I'm pretty sure you just multiply these two numbers

So -4(6)

  =-24

Hope this helps :)

The value of x within the given range is x = 45°.

We have the equation 2sin^2(x) = 1. To find the value of x within the given range, we can solve for sin(x) first and then find the corresponding angle.

Starting with 2sin^2(x) = 1, we divide both sides by 2 to get sin^2(x) = 1/2. Taking the square root of both sides, we have sin(x) = √(1/2). Since 0° < x < 90°, we know that sin(x) is positive in this range.

The value of √(1/2) is approximately 0.707. Therefore, sin(x) = 0.707.

To find the corresponding angle x, we can use the inverse sine function (sin^(-1)). Taking the inverse sine of both sides, we have x =[tex]sin^(^-^1^)^(^0^.^7^0^7^)[/tex].

Evaluating sin^(-1)(0.707) using a calculator, we find that x is approximately 45°.

Note that sine is a periodic function, and there are other angles that satisfy the equation 2sin^2(x) = 1. However, within the specified range of 0° < x < 90°, the value of x is 45°.

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Standard Error for the sample mean ≈ 0.037

The standard error for the sample mean measures the average amount of variability or uncertainty expected in the sample mean compared to the population mean.

It quantifies how much the sample mean is likely to deviate from the true population mean.

In this case, the actual size of computer chips is normally distributed with a mean of 2 centimeters and a standard deviation of 0.2 centimeters.

By taking a random sample of 30 computer chips, we can estimate the mean size of the population.

The standard error for the sample mean can be calculated using the formula:

Standard Error = Standard Deviation / √(Sample Size)

Standard Error = 0.2 / √(30)

Calculating the square root of 30, we find:

Standard Error ≈ 0.037

This indicates that the sample mean is expected to deviate from the true population mean by around 0.037 centimeters on average.

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A can is a cylinder shape.

Volume of a cylinder = PI x r^2 X h

r is the radius and h is the height.

The diameter is given as 3.8, the radius is half that: 3.8 /2 =  1.9

Height is given as 6.5

Volume = 3.14 x 1.9^2 x 6.

Volume = 73.6801 cubic inches.

Rounded to the nearest tenth = 73.7 cubic inches.

Answer:

144

Step-by-step explanation:

Using the Method Of LCM

Find and list multiples of each number until the first common multiple is found. This is the lowest common multiple.

Multiples of 16:

16, 32, 48, 64, 80, 96, 112, 128, 144, 160, 176

Multiples of 18:

18, 36, 54, 72, 90, 108, 126, 144, 162, 180

Multiples of 24:

24, 48, 72, 96, 120, 144, 168, 192

Therefore,

LCM(16, 18, 24) = 144

Answer:

x=3

Step-by-step explanation:

Covert to decimals

5x+.25=15.25

5x=15.25-.25

5x= 15

x= 3

Answer:

5 hours

Step-by-step explanation:

(53-13)÷8

53-13=40

40÷8=5

She worked 5 hours I hope this helped. :)

Using the information provided in the table, The total holding cost is  $547.50, the total setup cost is $600 and the total cost is  $1,147.50.

To develop a POQ (Periodic Order Quantity) solution use a lot-for-lot solution, which means that we will order exactly what we need for each period.

The missing values can be found on the attached table.

From the table, the total holding cost which is the sum of the holding costs for all periods is $547.50 while the total setup cost which is the sum of the setup costs for all periods is $600.

Therefore, the total cost is the sum of the holding cost and the setup cost and it is calculated as $1,147.50.

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Answer:

variable is ( x )

∆product of 8 :

8 x

∆the number increased by 2 :

8 x + 2

And we're done ♥️

Answer:

The sentence can be interpreted in two ways: 8x+2 or 8(x+2).

The first one would be the product of 8 and a number, increased by 2.

The second one would be the product of 8, and a number increased by 2.

Hope that helps.

Answer:

3)

[tex](-4^{6})^{-2} \\=-4^{-12} \\=\frac{1}{-4^{12} }[/tex]

4)

[tex](-12^{3})^{4} \\= -12^{12}[/tex]

6)

[tex](-x^{-13})^{2} \\= -x^{-26}\\= \frac{1}{-x^{26} }[/tex]

Answer:

2d

Step-by-step explanation:

Since Pete has double what Leo has, we need to multiply Leo's number of stamps by 2. But because we don't know how many he actually has, and the amount is represented by "d", we multiple "d" by 2

Answer:

Step-by-step explanation:

The difference is

Pete had d more stamps than Leo

Answer: it’s 10/15 and 14/21 ;)

Step-by-step explanation:

Answer: N=20

Step-by-step explanation:

30^20=3n since the bases are the same, set the exponents equal

20=n

swap

n=20

answer is n=20

Answer:

n=7,776

Step-by-step explanation:

3 to the power of 2 equals 6.

6 to the power of 10 equals 60,466,176

Square root of 3 to the power of n is n

square root of 60,466,176 is 7,776

9514 1404 393

Answer:

  $126

Step-by-step explanation:

110% more than 100% is 210% of his June earnings:

  july = 2.10×$60 = $126

Silva is projected to make $126 in July.

_____

As always, you can think of % and /100 as being interchangeable.

  210% = 210/100 = 2.10

Answer:

126

Step-by-step explanation:

1. When the population mean shifts to 7.8, the probability of detecting the change is approximately 0.0495 (or 4.95%).

2.  The probability of detecting the change is 0.0495 or 4.95%.

To solve this problem, we'll use the concept of control limits and the sampling distribution of the sample means.

When the population mean shifts to 7.8: First, let's calculate the standard deviation of the sampling distribution, also known as the standard error (SE). The formula for SE is given by SE = σ / sqrt(n), where σ is the standard deviation of the population (1.0 ounce) and n is the sample size (47).

SE = 1.0 / sqrt(47) ≈ 0.145

Next, we need to determine the z-score corresponding to the lower and upper tails of the sampling distribution that capture 5% each. Since the total probability in both tails is 10%, each tail will have a probability of 5%. We can find the z-scores using a standard normal distribution table or calculator.

The z-score corresponding to the lower tail of 5% is approximately -1.645.

The z-score corresponding to the upper tail of 5% is approximately 1.645.

Now, let's calculate the lower and upper control limits:

Lower Control Limit (LCL) = Population Mean - (z * SE)

Upper Control Limit (UCL) = Population Mean + (z * SE)

LCL = 7.8 - (-1.645 * 0.145) ≈ 8.026

UCL = 7.8 + (1.645 * 0.145) ≈ 9.574

To find the probability of detecting the change, we need to calculate the area under the sampling distribution curve that falls beyond the control limits. In this case, we're interested in the area above the upper control limit.

Since the distribution is assumed to be normal, we can use the standard normal distribution's cumulative distribution function (CDF) to calculate this probability.

Probability of detecting the change = 1 - CDF(z-score for UCL)

Using the z-score for the upper control limit (UCL), we can calculate the probability.

Probability of detecting the change ≈ 1 - CDF(1.645) ≈ 0.0495

Therefore, when the population mean shifts to 7.8, the probability of detecting the change is approximately 0.0495 (or 4.95%).

When the population mean shifts to 8.6: We'll follow the same steps as before.

SE = 1.0 / sqrt(47) ≈ 0.145

The z-score corresponding to the lower tail of 5% is still approximately -1.645.

The z-score corresponding to the upper tail of 5% is still approximately 1.645.

LCL = 8.6 - (-1.645 * 0.145) ≈ 8.926

UCL = 8.6 + (1.645 * 0.145) ≈ 10.274

Probability of detecting the change = 1 - CDF(z-score for UCL)

Probability of detecting the change ≈ 1 - CDF(1.645) ≈ 0.0495

Therefore, when the population mean shifts to 8.6, the probability of detecting the change is also approximately 0.0495 (or 4.95%).

In both cases, the probability of detecting the change is 0.0495 or 4.95%.

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