Find the indicated area under the standard normal curve. To the left of z= - 2.75 and to the right of z=2.75

Therefore, we would use a chi-squared distribution with 12 degrees of freedom to conduct the hypothesis test of the sample.

The degrees of freedom for a chi-squared test of independence with a contingency table with r rows and c columns can be calculated as (r-1)(c-1). In this case, we have a contingency table with 3 rows and 7 columns, so the degrees of freedom would be (3-1)(7-1) = 12. A chi-squared test of independence is a statistical test used to determine if there is a relationship between two categorical variables. It is commonly used to analyze contingency tables, which are tables that display the frequency distribution of two or more categorical variables.

The degrees of freedom (df) for a chi-squared test of independence with a contingency table are calculated using the formula (r-1)(c-1), where r is the number of rows in the table and c is the number of columns.

In this case, we have a contingency table with 3 rows and 7 columns. Therefore, r = 3 and c = 7.

Substituting these values into the formula, we get:

df = (r-1)(c-1)

= (3-1)(7-1)

= 2 x 6

= 12

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A marginal distribution refers to the distribution of one variable in a dataset without taking into account the other variables. It is the probability distribution of a single random variable. On the other hand, a conditional distribution refers to the distribution of one variable in a dataset given that another variable has a specific value. It is the probability distribution of a random variable, given that another random variable has a specific value. Marginal distributions are often used to calculate overall probabilities, while conditional distributions are used to calculate probabilities under specific conditions. Marginal and conditional distributions are important concepts in statistics and are used to analyze data and make predictions. In general, marginal distributions are useful when considering the overall distribution of a dataset, while conditional distributions are useful when considering the distribution of a dataset under specific conditions or circumstances.
Hi! A "marginal distribution" is meant to describe the probability distribution of a single variable in a multivariate setting, without considering the relationships with other variables. To find the marginal distribution, you sum or integrate the joint distribution over all possible values of the other variables.

A "conditional distribution," on the other hand, is meant to describe the probability distribution of a variable given the values of one or more other variables. In this case, you focus on a specific subset of the data where the given condition(s) hold true, and then calculate the probability distribution for the variable of interest within that subset.

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Answer:

14/13

Step-by-step explanation:

40/3

Answer: 10

Step-by-step explanation:

| –10 | = 10

Because that's the number of spaces it's from 0. :-)

Answer:

290 degrees

Step-by-step explanation:

To find the length of the major arc subtracts 360-70.

You do this because angle LKM is a central angle, so the arc of LKM will be the same as the angle which is 70. If LKM was an inscribed angle the arc angles would be twice the degrees as the inscribed angle. And because arc LNM is a major arc and because a circle is 360 degrees all you have to do is subtract 70 from 360.

The image below shows an inscribed angle and a central angle.

The diameter of the hemisphere with a volume of 557 m³ is approximately 12.8 m, to the nearest tenth of a meter.

The volume of a hemisphere can be calculated using the formula V = (2/3)πr³, where V is the volume and r is the radius. Given that the volume of the hemisphere is 557 m³, we can find the radius by solving for r:

557 = (2/3)πr³

To find the radius, first, we need to isolate r³ by multiplying both sides by 3/(2π):

r³ = (3 * 557) / (2 * π)

r³ ≈ 265.18

Now, take the cube root of both sides to find the radius:

r ≈ 6.4 m

To find the diameter, simply multiply the radius by 2:

d ≈ 2 * 6.4
d ≈ 12.8 m

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The error between the exponential function e^x and its third degree Taylor polynomial P3(x) is given by e^x - P3(x).

Let's compute this error for various values of x:

For x = 0, we have e^0 - P3(0) = 1 - 1 = 0.

For x = 1, we have e^1 - P3(1) = e - (1 + 1 + 1/2 + 1/6) = e - 2.16667 ≈ -0.0803.

For x = -1, we have e^-1 - P3(-1) = 1/e - (1 - 1 + 1/2 - 1/6) = 0.581976 ≈ 0.582.

For x = 2, we have e^2 - P3(2) = e^2 - (1 + 2 + 4/2 + 8/6) = e^2 - 5.33333 ≈ 2.995.

For x = -2, we have e^-2 - P3(-2) = 1/e^2 - (1 - 2 + 4/2 - 8/6) = 0.133151 ≈ 0.133.

Overall, we can see that the error between the exponential function and its third degree Taylor polynomial decreases as x gets closer to 0. This is because the Taylor polynomial is centered at x = 0 and becomes a better approximation as x gets closer to the center. However, for larger values of x, the error can become quite significant.

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The given expression, (sec A x sin C - tan A x tan C)/sin B, simplifies to (x/40) x AB, where AB is the length of the side opposite the right angle in triangle ABC.

Since triangle ABC is right-angled at B, we can use the following trigonometric ratios:

sin A = opposite/hypotenuse = AC/BC

cos A = adjacent/hypotenuse = AB/BC

tan A = opposite/adjacent = AC/AB

sin C = opposite/hypotenuse = AB/BC

cos C = adjacent/hypotenuse = AC/BC

tan C = opposite/adjacent = AB/AC

Using these ratios, we can simplify the given expression as follows:

(sec A x sin C - tan A x tan C)/sin B

= [(1/cos A) x sin C - tan A x tan C]/sin B (using sec A = 1/cos A)

= [(1/cos A) x (AB/BC) - (AC/AB) x (AB/AC)]/sin B (substituting sin C and tan C)

= [(AB/BCcos A) - (AC/BC)]/sin B (simplifying)

= [(AB/BC) - (ACcos A/BCcos A)]/sin B (getting a common denominator)

= [(AB - ACcos A)/BC]/sin B (simplifying)

= [(AB - ABsin A)/BC]/sin B (substituting)

Since triangle ABC is right-angled at B, we can use the following trigonometric ratios:

sin A = opposite/hypotenuse = AC/BC

cos A = adjacent/hypotenuse = AB/BC

tan A = opposite/adjacent = AC/AB

sin C = opposite/hypotenuse = AB/BC

cos C = adjacent/hypotenuse = AC/BC

tan C = opposite/adjacent = AB/AC

Using these ratios, we can simplify the given expression as follows:

(sec A x sin C - tan A x tan C)/sin B

= [(1/cos A) x sin C - tan A x tan C]/sin B (using sec A = 1/cos A)

= [(1/cos A) x (AB/BC) - (AC/AB) x (AB/AC)]/sin B (substituting sin C and tan C)

= [(AB/BCcos A) - (AC/BC)]/sin B (simplifying)

= [(AB/BC) - (ACcos A/BCcos A)]/sin B (getting a common denominator)

= [(AB - ACcos A)/BC]/sin B (simplifying)

= [(AB - ABsin A)/BC]/sin B (substituting sin A = AC/BC)

= [AB(1 - sin A)/BC]/sin B (factoring out AB)

= [(AB/BC) x (cos A/sin A)]/sin B (using sin A = opposite/hypotenuse and cos A = adjacent/hypotenuse)

= [cot A x (AB/BC)]/sin B (using cot A = cos A/sin A)

= (AB/BC) x (cos A/sin A) x (1/sin B) (multiplying fractions)

= (AB/BC) x (cos A/sin A) x csc B (using csc B = 1/sin B)

= (AB/BC) x cot A x csc B (using cot A = cos A/sin A)

= (BC/AB) x tan A x sin B (taking the reciprocal of both sides)

= (2BC/2AB) x (sin B/cos B) x (sin A/cos A) (multiplying and dividing by cos B and cos A)

= (BC/AB) x (2sin Bcos A)/(2sin A cos B) (simplifying)

= (BC/AB) x (sin (B + A)/sin (A + B)) (using sum and difference formulae)

= (BC/AB) x (sin C/sin 90) (since A + B + C = 90 degrees in a right-angled triangle)

= (BC/AB) x sin C

Substituting the given values, we get:

= [(2x + 20)/80] x AB

= (x/40) x AB

Therefore, the final answer is (x/40) x AB.

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Therefore, it is approximately 0.8911, or 89.11% (rounded to four decimal places), that a household watches television for between 4 and 12 hours per day.

z = (x - μ) / σ

For part (a), we want to find the probability that a household views television between 4 and 12 hours a day. We can translate this into finding the probability that a random variable X with mean μ = 8.35 and standard deviation σ = 2.5 falls between 4 and 12:

P(4 ≤ X ≤ 12)

To find this probability, we first standardize the values 4 and 12:

z1 = (4 - 8.35) / 2.5

= -1.74

z2 = (12 - 8.35) / 2.5 = 1.46

Now that we have the area under the curve between these two z-scores, we can use a normal distribution table or calculator to determine it:

P(-1.74 ≤ Z ≤ 1.46) ≈ 0.8911

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The correct set of image points for trapezoid W’X’Y’Z’ for 180 degrees rotation is (a) W’(4, –2), X’(3, –4), Y’(1, –4), Z’(0, –2)

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

W(-4, 2), X(-3, 4), Y(-1, 4), Z(0, 2)

Rule: 180 degrees rotation

The rule of 180 degrees rotation is

(x, y) = (-x, -y)

Substitute the known values in the above equation, so, we have the following representation

W’(4, –2), X’(3, –4), Y’(1, –4), Z’(0, –2)

Hence, the image = W’(4, –2), X’(3, –4), Y’(1, –4), Z’(0, –2)

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Answer:

53.93 cm

Step-by-step explanation:

The given arc is 38.95 and the angle is 260 degrees.

A full angle would be 360 degrees, so we can use a ratio to find what the full circle would be:  

260/360 = 38.95/x

This equation is saying that 38.95 is equal to 260 degrees of the circle, while x is equal to 360 degrees.

Solving for x:

260x = 14022

x = 53.9306792

Round and it is:

53.93 cm

So the order from left to right would be: Mode, Median, Mean.

In a positively skewed distribution, the mean is typically larger than the median, and the median is larger than the mode.

This can be illustrated in the following way:

Mean: The mean is affected by extreme values in the tail of the distribution, and will be pulled in the direction of the skew. Therefore, in a positively skewed distribution, the mean will be to the right of the median.

Median: The median is the value that separates the lower 50% of the data from the upper 50% of the data. In a positively skewed distribution, the tail of the distribution is on the right-hand side, which means that the median will be closer to the left-hand side than the mean.

Mode: The mode is the most frequent value in the distribution. In a positively skewed distribution, the mode will be the smallest value, located at the left-hand side of the distribution, while the mean and median will be to the right of it.

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The given function is C(x) = 936 + 80x + 0.12x^2, where x represents the number of mountain bikes produced daily. The average cost function is also given by C(x).
To make the mountain bike as affordable as possible, the company should target a production level of 333.33 mountain bikes daily, where the average cost function is at its minimum. Rounded to 2 decimal places, the answer is

c = 333.33.

First, let's correct the given cost function: C(x) = 936x + 80x^2 + 0.12x^3. Now, we'll use the terms "function," "average," and "increases."

To find the average cost function, we need to divide the total cost function, C(x), by the number of mountain bikes produced daily, x. Let A(x) represent the average cost function:

A(x) = C(x) / x

Now, let's substitute C(x) into this equation:

A(x) = (936x + 80x^2 + 0.12x^3) / x

Simplify by canceling out an x term:

A(x) = 936 + 80x + 0.12x^2

To find the production level c where the average cost function decreases and then increases, we need to find the minimum point on the A(x) curve. To do this, we'll differentiate A(x) with respect to x to obtain the first derivative:

A'(x) = 80 + 0.24x

Now, set the first derivative equal to zero and solve for x:

80 + 0.24x = 0

0.24x = -80

x = c = -80 / 0.24

Round to 2 decimal places:

c ≈ 333.33

So, to make the mountain bike as affordable as possible, the company should target a production level of approximately 333.33 mountain bikes daily.

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The critical value for this problem would be 1.729, as this corresponds to a 5% error level and a two-tailed test.
To explain further, we can use hypothesis testing to determine whether the claim made by the local professor is supported by the sample data.

H0: The true mean salary for college graduates in the program is equal to or less than $53,500.

And our alternative hypothesis as:

Ha: The true mean salary for college graduates in the program is greater than $53,500.

We can then use the sample mean and standard deviation to calculate the test statistic:

t = (sample mean - hypothesized mean) / (standard deviation / sqrt(sample size))
t = (50994 - 53500) / (521 / sqrt(20))
t = -2.01

Using a t-distribution table with 19 degrees of freedom (sample size minus 1), and a significance level of 0.05 (corresponding to a 5% error), we can find the critical value for a two-tailed test:

t_critical = +/- 2.093

Since our calculated test statistic falls outside of the critical value range, we can reject the null hypothesis and conclude that there is evidence to suggest that the true mean salary for college graduates in the program is less than $53,500. Therefore, the rival university's faculty may have a valid point in disputing the local professor's claim.
To find the critical value for this problem, we will use a one-tailed t-test since we want to test if the actual value is less than $53,500.

Given:
- Sample size (n) = 20 graduates
- Significance level (α) = 5% or 0.05
- Degrees of freedom (df) = n - 1 = 20 - 1 = 19

Now, let's find the critical value (t) using the t-distribution table. For a one-tailed test at a 5% significance level and 19 degrees of freedom, the critical value is -1.729.

So, the critical value for this problem is -1.729.

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The answer is: one pair of opposite parallel sides, congruent diagonals, and congruent base angles.

An isosceles trapezoid has the following properties:

- One pair of opposite parallel sides

- Diagonals are congruent

- Congruent base angles

As a result, the right options are:

- One set of opposite parallel sides

- Congruent diagonals

- Congruent base angles

Hence, answer is: one pair of opposite parallel sides, congruent diagonals, and congruent base angles.

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1. The interval contains the fewest data values is 4-5.

2. The total number of students are 20.

3. The percent of the students read fewer than six magazines is 85%.

We have histogram that shows the numbers of magazines read last month by the students in a class.

1. The interval contains the fewest data values is 4-5 as it has 0 data.

2. The total number of students are

= 2 + 15 + 0 + 3

=20

3. The percent of the students read fewer than six magazines

= 17/20 x 100

= 85%

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The least number of socks you need to pick is 10 red socks, 10 blue socks, and 10 white socks to ensure you have a matching pair. The least number of socks that can be taken from the drawer is one.

Follow these steps:
1. Pick one sock from the drawer (it could be any color, let's say red).
2. Pick a second sock from the drawer (if it's red, you have a matching pair; if not, let's say it's blue).
3. If you don't have a matching pair yet, pick a third sock from the drawer (now, it's either red, blue, or white, and you'll have a matching pair for sure).
So, the least number of socks you need to pick to ensure a matching pair is 3.

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Type I Error: Concluding that the percentage of adults with jobs is greater than 88% when it is actually equal to 88%, Type II Error: Concluding that the percentage of adults with jobs is equal to 88% when it is actually greater than 88%

In a hypothesis test, a Type I error occurs when we reject a true null hypothesis. In the case of the indicated claim, this would mean rejecting the hypothesis that the percentage of adults who have a job is not greater than 88%, when in fact it is true. This would be a serious mistake as we would be making a false claim.

On the other hand, a Type II error occurs when we fail to reject a false null hypothesis. In this case, it would mean failing to reject the hypothesis that the percentage of adults who have a job is not greater than 88%, when in fact it is false. This error would lead us to miss the true claim that the percentage of adults who have a job is greater than 88%, which could have important implications for policy and decision-making.

Therefore, in the hypothesis test of the indicated claim, the Type I error would be to falsely claim that the percentage of adults who have a job is greater than 88%, while the Type II error would be to miss the true claim that it is indeed greater than 88%.

First, let's set up our null hypothesis (H0) and alternative hypothesis (H1):
- Null hypothesis (H0): The percentage of adults who have a job is equal to 88% (P = 0.88)
- Alternative hypothesis (H1): The percentage of adults who have a job is greater than 88% (P > 0.88)

Now, let's identify the Type I and Type II errors for this hypothesis test:

1. Type I Error: This occurs when we reject the null hypothesis (H0) when it is actually true. In this context, a Type I error would be concluding that the percentage of adults who have a job is greater than 88% (P > 0.88) when, in reality, it is equal to 88% (P = 0.88).

2. Type II Error: This occurs when we fail to reject the null hypothesis (H0) when it is actually false. In this context, a Type II error would be concluding that the percentage of adults who have a job is equal to 88% (P = 0.88) when, in reality, it is greater than 88% (P > 0.88).

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In hypothesis testing, converting a test statistic to a probability is called p-value. The p-value is the probability of obtaining a test statistic as extreme as the observed one or more extreme, assuming the null hypothesis is true.

If the p-value is less than the chosen level of significance, typically 0.05 or 0.01, the banker would reject the null hypothesis and conclude that there is evidence to support the alternative hypothesis, which is that the proportion of banking customers with a money market savings account has decreased. If the p-value is greater than the chosen level of significance, the banker would fail to reject the null hypothesis and conclude that there is not enough evidence to suggest that the proportion has decreased.

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Looking at the graph, the best time to make a purchase is in month March 2013.

Exchange rate refers to the value ascribed to a particular currency in a foreign currency.

We have to find on which month the  American dollar was the strongest

Usually, currency exchange rates are not static but depend on many factors.

As such, it is always advisable to carry out trading activities when the exchange rate between currencies is favorable.

Looking at the graph, the best time to make a purchase is in March 2013.

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According to the information the volume of this figure would be 16cm³ and the surface area of this figure would be 24cm²

To calculate the surface area and volume of this figure we must perform the following procedure:

Volume:

Multiply height, width and length

2cm * 2cm *2cm = 16cm³

Surface area:

Multiply height by width and multiply the result by the number of faces the figure has.

2cm * 2cm = 4cm²

4cm² * 6 = 24cm²

According to the above, the volume of this figure is 16cm³ and the surface area is 24cm².

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The equations that models a growing line include the following:

B. 3x − 2y + 3 = 0

D. −5x + 3y + 9 = 0

In Mathematics, a steeper slope simply means that the slope of a line is bigger than the slope of another line. This ultimately implies that, a graph with a steeper slope has a greater (faster) rate of change in comparison with another graph.

In order to determine an equation with a growing line, we would have to determine the slope of each line graphically and then taking note of the line with a positive slope because it indicates an increasing function.

In this context, we can reasonably infer and logically deduce that both 3x − 2y + 3 = 0 and −5x + 3y + 9 = 0 are equations that models a growing line.

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Complete Question:

Which of the following equations models a growing line?

A. 2x+4y+12=0

B. 3x−2y+3=0

C. −x−3y−12=0

D. −5x+3y+9=0

Approximately 187.97 Joules of energy is required to raise the temperature of 12.2 grams of gaseous nitrogen from 24.5 °C to 39.3 °C.

To calculate the energy required to raise the temperature of 12.2 grams of gaseous nitrogen from 24.5°C to 39.3°C, we need to use the following formula:

Q = m x c x ΔT

Where Q is the energy required (in joules), m is the mass of the substance (in grams), c is the specific heat capacity of the substance (in J/g°C), and ΔT is the change in temperature (in °C).

The specific heat capacity of nitrogen gas at constant pressure is approximately 1.04 J/g°C. Therefore, we can calculate the energy required as follows:

Q = 12.2 g x 1.04 J/g°C x (39.3°C - 24.5°C)
Q = 163.52 J

Therefore, it would require 163.52 joules of energy to raise the temperature of 12.2 grams of gaseous nitrogen from 24.5°C to 39.3°C.

To calculate the energy required to raise the temperature of 12.2 grams of gaseous nitrogen from 24.5 °C to 39.3 °C, you can use the formula:

q = mcΔT

where:
- q is the energy required (in Joules)
- m is the mass of the substance (in grams)
- c is the specific heat capacity of the substance (in J/g°C)
- ΔT is the change in temperature (in °C)

For gaseous nitrogen, the specific heat capacity (c) is approximately 1.04 J/g°C.

Now, let's calculate the energy required:

1. Calculate the mass (m): 12.2 grams
2. Calculate the change in temperature (ΔT): 39.3°C - 24.5°C = 14.8°C
3. Use the formula q = mcΔT:
  q = (12.2 g) * (1.04 J/g°C) * (14.8 °C)
  q ≈ 187.97 Joules

So, approximately 187.97 Joules of energy is required to raise the temperature of 12.2 grams of gaseous nitrogen from 24.5 °C to 39.3 °C.

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The zeros of the function h(x) = 6x² + x − 1 are x = 1/3 and x = -1/2.

Given the function in the question:

h(x) = 6x² + x − 1

To determine the zeros of the function, we need to solve for x when h(x) equals zero.

Plug in h(x) = 0

0 = 6x² + x − 1

6x² + x − 1 = 0

We can use the quadratic formula to solve for x:

x = [-b ± √(b² - 4ac)] / 2a

where a, b, and c are the coefficients of the quadratic equation.

In the function, a = 6, b = 1, and c = -1.

Plug these values into the above formula

x = [-1 ± √(1² - 4(6)(-1))] / 2(6)

x = [-1 ± √(1 + 24)] / 12

x = [-1 ± √(25)] / 12

x = [-1 ± 5] / 12

Hence, the zeros of the function are:

x = (-1 + 5) / 12 = 4/12 = 1/3

and

x = (-1 - 5) / 12 = -6/12 = -1/2

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[tex]\cfrac{q}{10}=\cfrac{98}{49}\implies \cfrac{q}{10}=2\implies q=20[/tex]

Using a t-distribution with 14 degrees of freedom (n-1) and a 98% confidence level (α = 0.02/2 = 0.01 for each tail), we have:

sample mean (x) = (2051+2061+2162+2167+2169+2171+2180+2183+2186+2195+2196+2198+2205+2210+2211)/15 = 2180.6

sample standard deviation (s) = 39

standard error of the mean (SEM) = s/√n = 39/√15 ≈ 10.077

t-score for a 98% confidence level and 14 degrees of freedom (from t-distribution table or calculator) = 2.977

Margin of error (ME) = t-score × SEM = 2.977 × 10.077 ≈ 30.05

Therefore, the 98% confidence interval for the mean hours worked per year in this state is:

(x- ME, x+ ME) = (2180.6 - 30.05, 2180.6 + 30.05) = (2150, 2211)

Rounding to the nearest integer and putting the limits in ascending order, we get:

(2150, 2211)

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The probability that the next car to drive by Pablo's house will be red or blue is 12 / 25.

For given data probabilities are,

color no. of cars probability

blue 70 0.28

green 30 0.12

red 50 0.2

white 80 0.32

yellow 20 0.08

total 250 1

Hence, the probability that the next car to drive by Pablo's house will be red or blue is,

= P(car drived will be red) + P(car drived will be blue)

= (50 / 250) + (70 / 250)

= 12/25

The probability is 12/25

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