Anton surveys 80 students at his school and finds that 55% of them have mobile phones. He also finds that 98% of 50 adults have mobile phones. How many more adults than students have mobile phones?

we can reject the null hypothesis at the 5% significance level since the p-value is less than 0.05.

To calculate the p-value, we need to perform a hypothesis test.

Let's assume the null hypothesis is that the proportion of defective systems produced by x-game is equal to or less than the industry standard of 0.02, while the alternative hypothesis is that the proportion is greater than 0.02.

We can use the normal approximation to the binomial distribution since we have a large sample size (n=120) and np and n(1-p) are both greater than or equal to 10.

The test statistic can be calculated as:

[tex]z = (p_hat - p) / \sqrt{(p*(1-p)/n)}[/tex]

where [tex]p_hat[/tex]  is the sample proportion of defective systems, p is the hypothesized proportion under the null hypothesis, and n is the sample size.

Using the values given in the question, we have:

[tex]p_hat = 6/120 = 0.05[/tex]

p = 0.02

n = 120

Plugging these values into the formula, we get:

[tex]z = (0.05 - 0.02) / \sqrt{(0.02*(1-0.02)/120) = 2.041}[/tex]

The p-value can be calculated as the probability of getting a test statistic as extreme or more extreme than 2.041 under the null hypothesis.

Since this is a one-tailed test (the alternative hypothesis is one-sided), we look up the area in the right tail of the standard normal distribution table.

Using a standard normal distribution table, we find that the area to the right of 2.041 is 0.0207.

Therefore, the p-value is 0.0207.

Therefore, we can reject the null hypothesis at the 5% significance level since the p-value is less than 0.05.

This means that there is strong evidence that the proportion of defective systems produced by x-game exceeds the industry standard of 0.02.

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Answer:x= -67 over 24

Step-by-step explanation:

The size and nature of the dataset, the choice of independent variables, and the assumptions underlying the model, should also be considered when interpreting [tex]R^2[/tex] values.

The percent of the total variance that can be explained by the regression equation is known as the coefficient of determination, denoted as[tex]R^2.[/tex]

It represents the proportion of the total variation in the dependent variable (Y) that is accounted for by the independent variable(s) (X) included in the regression model.
To calculate[tex]R^2[/tex], you can follow these steps:
Obtain the sum of the squared differences between the actual and predicted values of the dependent variable (also known as the residual sum of squares, or RSS).
Obtain the sum of the squared differences between the actual values of the dependent variable and their mean (also known as the total sum of squares, or TSS).
Divide the RSS by the TSS: [tex]R^2 = 1 - (RSS/TSS).[/tex]
[tex]R^2[/tex] ranges between 0 and 1, with higher values indicating a better fit of the regression model.

An[tex]R^2[/tex] of 1 indicates that the regression equation perfectly explains the total variance, while an [tex]R^2[/tex] of 0 indicates that the regression equation does not explain any of the total variance.
Keep in mind that while[tex]R^2[/tex] is a useful measure of model fit, it should not be the only criterion used to evaluate the quality of a regression model.

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The percentage of the original area of wetlands currently left in the United States is approximately 50%. So, the correct

option is 50% (option 3).

Percentages are a way of expressing a proportion or a fraction as a part of 100. It is denoted by the symbol "%".

According to the United States Environmental Protection Agency (EPA), it is estimated that about 50% of the original

wetlands in the contiguous United States have been lost since the 1600s due to human activities such as agriculture,

development, and urbanization. Therefore, the correct answer to the question is 50%.

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The percentage of the original area of wetlands currently left in the United States is approximately 50%.

Wetlands are regions of land where the soil is continually or intermittently soaked with water. Wetlands have a particular hydrology, soil, and vegetation mix that results in specialised ecosystems that offer a variety of ecological functions.

There are many different types of habitats where wetlands can be found, such as coastal locations, interior regions, and high-altitude mountain regions. They come in a variety of shapes, such as marshes, swamps, bogs, fens, and estuaries, and can be freshwater, brackish, or saline.

Wetlands are significant for several reasons. For species, such as migrating birds, amphibians, and fish, they offer crucial habitats. They also aid in removing contaminants from water, lessen the effects of flooding, and give people access to recreational activities. Wetlands are crucial for carbon sequestration as well.
Based on the information provided, the question is asking for the approximate percentage of the original area of wetlands currently left in the United States. The answer is approximately 50%.

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The second derivative is negative, the point (x, y) = (3, 7) corresponds to a local maximum on the curve.

To find the leftmost point on the curve defined by the parametric equations x = t² + 2t and y = t² - 2t + 3, we need to find the value of t that corresponds to this point. We can do this by analyzing the derivative of the curve with respect to t.

The leftmost point on the curve corresponds to the point where the slope of the curve is zero or undefined. This occurs when the derivative of y with respect to x is zero or undefined.

We can express y as a function of x by eliminating t from the given parametric equations. Solving for t in terms of x, we get:

t = -1 ± √(x + 1)

Substituting this value of t in the equation for y, we get:

y = (x + 1) ± 4√(x + 1) + 3

y = ±4√(x + 1) + x + 4

To find the leftmost point on the curve, we need to find the value of x that corresponds to this point. We can do this by finding the minimum value of x for which y is defined.

Differentiating y with respect to x, we get:

dy/dx = 1 + 2/√(x + 1)

Setting dy/dx = 0, we get:

1 + 2/√(x + 1) = 0

2/√(x + 1) = -1

Solving for x, we get:

x = 3

Note that this value of x is within the given range of -2 ≤ t ≤ 3. Therefore, the leftmost point on the curve is the point corresponding to t = 1.

To justify that we have found the requested point, we can analyze the second derivative of y with respect to x. The second derivative will tell us whether the point corresponds to a local minimum, local maximum, or inflection point.

Differentiating dy/dx with respect to x, we get:

d²y/dx² = -2/[tex](x + 1)^{(3/2)}[/tex]

Substituting x = 3, we get:

d²y/dx² = -2/64

Since the second derivative is negative, the point (x, y) = (3, 7) corresponds to a local maximum on the curve. Therefore, we have found the leftmost point on the curve as requested.

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The question is -

Identify the requested point and justify that you have found the requested point by analyzing an appropriate derivative. x = t² + 2t, y = t² - 2t + 3, - 2 ≤ t ≤ 3 Leftmost point

The dependent variable must be continuous in order to conduct an ANOVA (Analysis of Variance) test.

Let me briefly explain the key terms and reasoning:
Dependent variable:

This is the outcome variable that you are measuring in an experiment or study.

It depends on the independent variable(s), which are the factors you manipulate or observe to determine their effect on the dependent variable.
Continuous variable:

A continuous variable is a numerical variable that can take on an infinite number of values within a given range. Examples include weight, height, and temperature.
ANOVA:

ANOVA is a statistical method used to compare the means of two or more groups to determine if there are significant differences between them.

It evaluates the effect of one or more independent variables on a continuous dependent variable.
To conduct an ANOVA, the dependent variable must be continuous because the test relies on calculating the variance within and between groups.

The variance is a measure of dispersion or spread of the continuous data.

If the dependent variable were not continuous, it would be difficult to quantify the differences between group means, making it impossible to determine if the independent variable(s) had a significant effect.
ANOVA test, the dependent variable must be continuous, allowing for the calculation of variances and comparison of group means to determine any significant differences based on the effect of the independent variable(s).

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Answer:

4. Mean: 89

5. Median: 90

6. Mode: None.

Step-by-step explanation:

To find the mean, add all of the numbers, then divide by the total.

There are 7 numbers in this set.

First, add all of the numbers. Then, divide the sum by 7.

[tex]85+95+88+93+94+78+90= 623\\623/7 =89[/tex]

The mean of this data set is 89!

------------------------------------------------

Now, let's find the median.

The median is the "middle number" of the data set.

First, put all of the numbers in order from least to greatest.

85, 95, 88, 93, 94, 78, 90

↓

78, 85, 88, 90, 93, 94, 95,

The number in the middle of the data set is 90!

Therefore, the median is 90.

------------------------------------------------

Now, let's find the mode.

The mode is the number that appears most frequently.

85, 95, 88, 93, 94, 78, 90

Since each number appears once, then there is no mode.

Let me know if you have any questions.

The length of CD is 6 inches.

We can start by finding the area of triangle EDC

Area of triangle EDC = Area of triangle EDB - Area of triangle EBC

= 60.5 - 27.5

= 33

Using the formula for the area of a triangle, we can write

Area of triangle EDC = (1/2) * CD * ED

Substituting the given values, we get

33 = (1/2) * CD * 11

Multiplying both sides by 2 and then dividing by 11, we get

CD = 6

Therefore, CD is 6 inches.

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The total number of non-empty subsets s of[tex]{1, 2, 3, . . . , 8}[/tex] such that the product of the elements of s is at most 200 is:
[tex]255 - (127 + 63 + 31) + 2 = 36.[/tex]
So, there are 36 such subsets.

Number of non-empty subsets s of[tex]{1, 2, 3, . . . , 8}[/tex] such that the product of the elements of s is at most 200, we can use a method called inclusion-exclusion principle.
First, we need to count the total number of non-empty subsets of the given set.

Since each element can either be included or excluded, there are [tex]2^8 - 1 = 255[/tex] non-empty subsets.
Next, we need to count the number of subsets whose product is greater than 200.

We can start by considering the subsets that contain 8, since 8 is the largest element in the set.

There are only two such subsets: {8} and {1, 8}.

Both of these subsets have a product greater than 200. Similarly, we can consider subsets that contain 7, and so on. We find that there are[tex]2^7 - 1 = 127[/tex] subsets that contain 7, and each of these subsets has a product greater than 200. Similarly, there are [tex]2^6 - 1 = 63[/tex] subsets that contain 6, and each of these subsets has a product greater than 200.
Double-counted the subsets that contain both 6 and 7, as well as those that contain both 6 and 8, and those that contain both 7 and 8.

Subtract the number of subsets that contain both 6 and 7, both 6 and 8, and both 7 and 8.

There are [tex]2^5 - 1 = 31[/tex] subsets that contain both 6 and 7, and each of these subsets has a product greater than 200.

Similarly, there are[tex]2^5 - 1 = 31[/tex] subsets that contain both 6 and 8, and each of these subsets has a product greater than 200.

Finally, there are [tex]2^5 - 1 = 31[/tex] subsets that contain both 7 and 8, and each of these subsets has a product greater than 200.
However, we have subtracted too much, since we have now excluded subsets that contain all three of 6, 7, and 8. There are only two such subsets: {6, 7, 8} and {1, 6, 7, 8}. Both of these subsets have a product greater than 200.

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To find the number of non-empty subsets s of {1, 2, 3, . . . , 8} such that the product of the elements of s is at most 200, we can use the concept of power set and combinatorics. By analyzing the pattern, we can determine that there are a total of 120 subsets whose product is at most 200.

To find the number of non-empty subsets s of the set {1, 2, 3, . . . , 8} such that the product of the elements of s is at most 200, we can use the concept of power set and combinatorics. The power set of a set is the set of all its subsets. We know that the number of elements in the power set of a set with n elements is 2n. In this case, we have 8 elements in the set, so the power set will have 28 = 256 subsets. However, we need to find the number of subsets with a product at most 200.

We can analyze the products of all subsets to determine the count.

By analyzing the pattern, we can determine that there are a total of 120 subsets whose product is at most 200. This can be calculated by summing the total number of subsets for each number of elements (1-element subsets + 2-element subsets + 3-element subsets + ... + 8-element subsets).

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Therefore, the solution is equation (x, y) = (52/7, -10/7).

To solve the system of equations by elimination, we need to eliminate one of the variables. We can do this by multiplying one or both equations by a constant to create opposite coefficients for one of the variables. Then, we can add or subtract the equations to eliminate that variable and solve for the other variable. Here's how to solve the given system of equations:

Multiply the first equation by 3 and the second equation by 2 to create opposite coefficients for y:

[tex]3(x - 2y = 12) - > 3x - 6y = 36[/tex]

[tex]2(-5x + 3y = -44) - > -10x + 6y = -88[/tex]

Add the equations to eliminate y:

[tex]3x - 6y + (-10x + 6y) = 36 + (-88)[/tex]

[tex]-7x = -52[/tex]

Solve for x by dividing both sides by -7:

[tex]x = 52/7[/tex]

Substitute x = 52/7 into either equation to solve for y. Using the first equation:

[tex]52/7 - 2y = 12[/tex]

[tex]-2y = 12 - 52/7[/tex]

[tex]-2y = 72/7 - 52/7[/tex]

[tex]-2y = 20/7[/tex]

[tex]y = -(10/7)[/tex]

Check the solution by substituting the values of x and y into both equations:

[tex]x - 2y = 12 - > 52/7 - 2(-10/7) = 12 (true)[/tex]

[tex]-5x + 3y = -44 - > -5(52/7) + 3(-10/7) = -44 (true)[/tex]

Therefore, the solution is (x, y) = (52/7, -10/7).

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The length created by a central angle is 0.04 feet.

What is radius of circle?

The radius of a circle is the distance from the center of the circle to any point on the circle's edge. It is typically denoted by the letter "r" and is one of the fundamental measurements used to describe the geometry of a circle.

First, we need to convert the central angle from degrees to radians. Since there are 60 minutes in a degree, we can divide 18 by 60 to get the angle in degrees as a decimal,

18/60 = 0.3 degrees

Next, we convert this to radians by multiplying by π/180,

0.3 × π/180 ≈ 0.00524 radians

To find the length of the arc created by this central angle, we use the formula,

arc length = radius × central angle

So, for a circle of radius 8 feet and a central angle of 0.00524 radians, the arc length is arc length = 8 × 0.00524 ≈ 0.04192 feet

Rounded to the nearest hundredth, the length of the arc is approximately 0.04 feet.

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The bigger engine is larger than the smaller one by 235.4724 cubic centimeters.

We know that 1 inch=2.54 centimeters

Then 1 cubic inches-(2.54)^3 cubic centimeters

We have that:

⇒ 1 cubic inches=(2.54)3 = 16.3870 cubic centimeters

⇒350 cubic inches= 350 x 16.3870 = 5735.4724 cubic centimeters

Since, the other displaces 5,500 cubic centimeters  and 5735.4724< 5500. The difference between them is:

= 5735.4724 - 5500

= 235.4724

Hence, the bigger engine larger than the smaller one by 235.4724 cubic centimeters.

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Answer:

f(x) is the blue graph, g(x) is the red graph.

x^2 - 3x + 17 - (2x^2 - 3x + 1) = 16 - x^2

16 - x^2 = 0 when x = -4, 4

So the area between these two graphs is (using the TI-83 graphing calculator):

fnInt (16 - x^2, x, -4, 4) = 85 1/3

These investments do indeed produce a net return of $133.

What is algebra?

Algebra is a branch of mathematics that deals with mathematical operations and symbols used to represent numbers and quantities in equations and formulas.

Let's call the amount Devon invested in the real estate fund "x". Then, we know that the amount invested in the large cap stock fund is twice that, or "2x". The total amount invested is $9500, so we can write:

x + 2x + y = 9500

where "y" is the amount invested in the bond fund.

We also know the returns of each fund, so we can calculate the total return on the investments:

0.047(2x) - 0.122x + 0.054y = 133

Simplifying this equation, we get:

0.998x + 0.054y = 133

We have two equations and two unknowns (x and y), so we can solve for them. Let's start by solving the first equation for y:

y = 9500 - 3x

Now we can substitute this expression for y into the second equation:

0.998x + 0.054(9500 - 3x) = 133

Simplifying and solving for x, we get:

0.888x = 459.8

x = 517.57

So Devon invested $517.57 in the real estate fund. The amount invested in the large cap stock fund is twice that, or $1035.14. The amount invested in the bond fund is:

y = 9500 - 3x = 8464.29

To check that these investments produce a net return of $133, we can calculate the total return on each investment and add them up:

0.047(2x) - 0.122x + 0.054y = 0.047(2517.57) - 0.122517.57 + 0.054*8464.29 = 133.00

So these investments do indeed produce a net return of $133.

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Sampled 200 delinquent credit card accounts instead of 100, the margin of error would decrease.

Sampled 200 delinquent credit card accounts, the margin of error for the sample mean at a 95% confidence level would be $80.164.

Smaller than the margin of error we found earlier for a sample size of 100.

The margin of error for the sample means at a 95% confidence level, we need to use the formula:
[tex]Margin of error = z\times (standard deviation / square root of sample size)[/tex]
[tex]z\times[/tex] is the z-score for the 95% confidence level, which is 1.96.
So, plugging in the given values, we get:
[tex]Margin of error = 1.96 \times  (578 / \sqrt 100)[/tex]
[tex]Margin of error = 1.96 \times 57.8[/tex]
Margin of error = 113.008
Therefore, the margin of error for the sample mean at a 95% confidence level is $113.008.
Repeated samples of 100 delinquent credit card accounts and computed the sample mean each time, we would expect the true population mean to be within $113.008 of our sample mean about 95% of the time.
The margin of error is inversely proportional to the square root of the sample size. So, as the sample size increases, the margin of error decreases.
To see this, let's plug in the new sample size into the margin of error formula:
[tex]Margin of error = 1.96 \times (578 / \sqrt 200)[/tex]
[tex]Margin of error = 1.96 \times 40.9[/tex]
Margin of error = 80.164

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Answer:

The amplitude is 1/2, the period is 2π/4 = π/2, and the phase shift is π/2.

Step-by-step explanation:

The given function is:

f(t) = -1/2(4t - 2π)

We can rewrite this function in the form:

f(t) = A cos(B(t - C)) + D

where A is the amplitude, B is the period, C is the phase shift, and D is the vertical shift.

Comparing this with the given function, we can see that:

A = 1/2

B = 4

C = π/2

D = 0

Therefore, the amplitude is 1/2, the period is 2π/4 = π/2, and the phase shift is π/2.

Note that the negative sign in front of the function does not affect the amplitude, period, or phase shift. It simply reflects the function across the x-axis.

The probability that the point is in the smaller, shaded square is approximately 14.06%.

Probability is the measure of the likelihood or chance of an event occurring. It is a number between 0 and 1, where 0 indicates that the event will not occur and 1 indicates that the event will certainly occur. The probability of an event is calculated as the number of favorable outcomes divided by the total number of possible outcomes. Probability is widely used in various fields, including mathematics, statistics, science, finance, and engineering.

The area of the large square is (8 cm)² = 64 cm², and the area of the shaded square is (3 cm)² = 9 cm². The probability of choosing a point at random that is in the shaded square is equal to the ratio of the area of the shaded square to the area of the large square:

Probability = (area of shaded square) / (area of large square)

Probability = 9 cm² / 64 cm²

Probability = 0.140625 or 14.06% (rounded to two decimal places)

Therefore, the probability that the point is in the smaller, shaded square is approximately 14.06%.

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The best center of the speed of the last 10 pitches thrown by a pitcher is the mean

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

{90, 92, 85, 88, 94, 86, 93, 90, 88, 95}

The possible measure of centers are

In this case, we use the mean

This is because the data values do not have any outlier present in them

Hence, the center is the mean

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Answer:

Step-by-step explanation:

The circumference of a circle is found by the formula 2πr. r is defined as the radius, which is the distance from the middle of the circle to any point on its outer curve. In this case, we are given the radius from the line that is drawn halfway through the circle. We can plug in this value (11) and solve it.

C = 2πr

C = 2π(11)

C = 22π cm

Hope this helps!

Answer:  the answer is A. 14.

Step-by-step explanation: In each trial of the reenactment, Scott chooses one card from the stack and records its digit. Based on the given data, a digit of or 1 speaks to a objective scored, and a digit of 2 through 9 speaks to a missed endeavor.

Out of the 5 endeavors per amusement, on the off chance that Scott scores precisely 2 objectives, it implies he missed 3 endeavors. Subsequently, the likelihood of this occasion can be calculated as:

P(exactly 2 objectives) = (0.2)²(0.8)³ = 0.008192

This likelihood can be utilized to discover the anticipated number of diversions in which Scott scores precisely 2 objectives, by duplicating it by the overall number of diversions reenacted:

Anticipated number of recreations = P(exactly 2 objectives) × Add up to number of recreations = 0.008192 × 84 ≈ 0.68

Adjusting to the closest entire number, we get that Scott is anticipated to score precisely 2 objectives in 1 diversion out of the 84 recreated diversions.

The estimated regression equation for the given data is y = -30.7 + 3.409x

To develop an estimated regression equation for the given data, we need to use the method of least squares.

The formula for the slope of the regression line is given by:

b = ∑(xi - x)(yi - y) / ∑(xi - x)²

where xi and yi are the individual values of the two variables, x and y are their respective means.

The formula for the intercept of the regression line is given by:

a = y - b × x

where a is the intercept and b is the slope.

Using the given data, we can calculate the values of x, y, b, and a as follows

x = (6 + 11 + 15 + 18 + 20) / 5 = 14

y = (7 + 9 + 12 + 21 + 30) / 5 = 15.8

∑(xi - x)(yi - y) = (6 - 14)(7 - 15.8) + (11 - 14)(9 - 15.8) + (15 - 14)(12 - 15.8) + (18 - 14)(21 - 15.8) + (20 - 14)(30 - 15.8) = 306.8

∑(xi - x)² = (6 - 14)² + (11 - 14)² + (15 - 14)² + (18 - 14)² + (20 - 14)² = 90

b = ∑(xi - x)(yi - y) / ∑(xi - x)² = 306.8 / 90 = 3.409

a = y - b × x = 15.8 - 3.409 × 14 = -30.7

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The given question is incomplete, the complete question is:

Given are data for two variables, x and y. Develop an estimated regression equation for these data.

When the width of the rectangle is 4 in, the length is 6 in.

Let's call the length of the rectangle "L" and the width "W". The problem states that the area of the rectangle is constant, so we can write:

LW = A

where A is some constant. We are also told that the length varies inversely with the width, which means that if the width increases, the length must decrease proportionally to keep the area constant. Mathematically, this means

L ∝ 1/W

or

L = k/W

where k is some constant of proportionality. We can find k by using the information given in the problem. We are told that when the width is 3 in, the length is 8 in

8 = k/3

Multiplying both sides by 3 gives:

24 = k

Now we can use this value of k to find the length when the width is 4 in:

L = k/W = 24/4 = 6 inches

When the width of the rectangle is 4 in, the length is 6 in, where the length and width of the rectangle are related by the inverse proportion L = k/W, with k = 24.

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The coin makes approximately 16.53 turns when rolled on its edge for 1.34 m.

The circumference of the coin can be calculated as follows to determine the number of turns it makes:

C = πd

where C is the circumference, d is the diameter, and π is the mathematical constant pi (approximately equal to 3.14159).

So, for the given $2 coin with a diameter of 25.75 mm, the circumference is:

C = πd = 3.14159 x 25.75 mm ≈ 80.926 mm

Divide the distance traveled by the coin's circumference to determine the number of turns it makes when rolled on its edge for 1.34 meter:

Number of turns = distance traveled / circumference of the coin

Number of turns = 1.34 m / 0.080926 m

Number of turns ≈ 16.53

Therefore, the coin makes approximately 16.53 turns when rolled on its edge for 1.34 m.

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Step-by-step explanation:

rotate the triangle in your mind (or as actual picture on your phone or computer), so that the right angle is the bottom right or bottom left, and C being the opposite bottom angle.

then we see

28 = cos(C) × 35

21 = sin(C) × 35

and so,

sin(C) = 21/35 = 3/5

cos(C) = 28/35 = 4/5

tan(C) = sin(C)/cos(C) = 3/5 / 4/5 = 3/4

True.  

The decision that would result from a Type I error is:

Closing the park when the lead levels are within the allowed limit. C

In this scenario, the null hypothesis is that the lead levels are okay, and the alternative hypothesis (Ha) is that lead levels exceed the limit.

The decisions that would result from the Type I error are:

Closing the park when the lead levels are in excess of the allowed limit.

This decision would be a false positive, as the park would be closed even though the lead levels are actually within the allowed limit.

This is a Type I error.

Closing the park when the lead levels are within the allowed limit.

This decision would be a correct decision as the park should be closed if the lead levels are not within the allowed limit.

This is not a Type I error.

Keeping the park open when the lead levels are in excess of the allowed limit.

This decision would be a false negative, as the park would remain open even though the lead levels are actually above the allowed limit.

This is a Type II error.

Closing the park because of the increased noise level in the neighborhood.

This decision is not related to the hypothesis testing for lead levels in the park, and therefore, it is not a Type I error.

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The probability that more than 7 of the invoices will be paid within ten working days is 0.8281

This is a binomial distribution problem where:

n = 10 (sample size)

p = 0.5 (probability of success, i.e., an invoice being paid within ten working days)

We need to find the probability of getting more than 7 invoices paid within ten working days, i.e., P(X > 7), where X is the number of invoices paid within ten working days.

Using the binomial probability formula, we get:

[tex]P(X > 7) = 1 - P(X ≤ 7)[/tex]

=[tex]1 - ∑(i=0 to 7) [nCi * p^i * (1-p)^{(n-i)][/tex]

where nCi is the number of ways to choose i items from n items, given by [tex]nCi = n! / (i! * (n-i)!).[/tex]

Calculating the above expression, we get:

[tex]P(X > 7) = 1 - [10C0 * 0.5^0 * 0.5^10 + 10C1 * 0.5^1 * 0.5^9 + ... + 10C7 * 0.5^7 * 0.5^3][/tex]

[tex]= 1 - 0.1719[/tex]

[tex]= 0.8281[/tex]

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a) Any value outside of Q1 - 1.5(IQR) and Q3 + 1.5(IQR) can be considered a potential outlier.

b) This will give us a visual representation of the distribution of income among the full-time students.

c) It is important to analyze outliers carefully to ensure that they are not artificially skewing the results of our analysis.

(a) To check for outliers in the data set, we can use the box-and-whisker plot or the z-score method. However, since we do not have the exact data, we cannot use these methods. One way to identify potential outliers is to calculate the quartiles (Q1, Q2, and Q3) and the interquartile range (IQR).

(b) To draw a histogram of the data, we can use the frequency distribution table given in the question. The x-axis should represent the income ranges (e.g. $0-$100, $100-$200, etc.) and the y-axis should represent the frequency (i.e. the number of students who earned income within each range).

(c) If there are any outliers in the data set, we need to investigate them further to determine the reason for their unusual values. Possible reasons for outliers could be data entry errors, extreme values due to high- or low-income jobs, or unique situations such as unexpected windfalls or emergencies.

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The distance between the two points can be found to be, and the number that goes beneath the radical symbol is 80.

To find the distance between two points in a plane, you can use the distance formula derived from the Pythagorean theorem. The distance formula is:

d = √[(x₂ - x₁)² + (y₂ - y₁)²]

where (x₁, y₁) and (x₂, y₂) are the coordinates of the two points.

In this case, the coordinates of the two points are (-4, 1) and (4, 5). So, x₁ = -4, y₁ = 1, x₂ = 4, and y₂ = 5.

Now, apply the distance formula:

d = √[(4 - (-4))² + (5 - 1)²]

d = √[(8)² + (4)²]

d = √(64 + 16)

d = √80

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The monthly interest rate is the annual interest rate divided by 12

To calculate the monthly interest rate, we need to divide the annual interest rate by 12 (since there are 12 months in a year).

So if the annual interest rate is 8%, the monthly interest rate can be calculated as:

Monthly interest rate = Annual interest rate / 12

Monthly interest rate = 8% / 12

Monthly interest rate = 0.6667%

Therefore, the monthly interest rate would be 0.6667%.

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The model have 4 degrees of freedom if a regression model has a sample of 30 observations using a dependent variable, 3 independent variables, and a constant.

In a linear regression model, the degrees of freedom for the model are calculated as the number of independent variables plus one (for the constant or intercept term). Therefore, in this case, since the model has 3 independent variables and a constant, the degrees of freedom for the model would be 3 + 1 = 4.

The degrees of freedom for the model represent the number of parameters estimated from the data to build the model. These parameters are the coefficients or weights of the independent variables and the constant term.

The degrees of freedom for the model are used to calculate the F-statistic, which is a measure of the overall significance of the regression model. The F-statistic is calculated as the ratio of the explained variance to the unexplained variance of the model, and it is compared to the F-distribution with degrees of freedom (k, n-k-1), where k is the number of independent variables and n is the sample size.

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