It takes approximately 4 medium apples to make 3 servings of homemade
apple sauce. The cafeteria purchased a bushel of apples (approximately 126
medium apples) at a discount. How many whole servings of applesauce can they
make with the bushel? (Round to the nearest whole number.)

Answer:

53

Step-by-step explanation:

47+6=53

Answer:

53

Step-by-step explanation:

Answer:

X is a discrete random variable.

X can take values from 0 to 12:

[tex]X\in[0,1,2,3,4,5,6,7,8,9,10,11,12][/tex]

Step-by-step explanation:

(a) X = the number of unbroken eggs in a randomly chosen standard egg carton

X is a discrete random variable.

The minimum amount of eggs broken is 0 and the maximum amount of eggs broken is 12 (assuming a dozen egg carton).

Then, X can take values from 0 to 12:

[tex]X\in[0,1,2,3,4,5,6,7,8,9,10,11,12][/tex]

About the probability ditribution nothing can be said, because there is no information about it (it can be a binomial, uniform or non-standard distribution).

The set of possible values for the random variable X, representing the number of unbroken eggs in an egg carton, is {0, 1, 2, 3, ..., 12}. X is a discrete random variable because it takes on distinct, countable values with no gaps in between.

The random variable X represents the number of unbroken eggs in a randomly chosen standard egg carton. To describe the set of possible values for this variable, we can consider the number of eggs in a standard carton, which is typically 12. Therefore, the set of possible values for X would be {0, 1, 2, 3, ..., 12}, as it encompasses all possible outcomes, ranging from having no unbroken eggs (0) to having all 12 unbroken eggs.

Now, let's determine whether the variable X is discrete or not. A discrete random variable is one that can take on a countable number of distinct values with gaps in between.

In this case, X is indeed a discrete random variable because it can only take on integer values from 0 to 12, and there are no intermediate values between these whole numbers. Each value represents a distinct and countable outcome based on the number of unbroken eggs, making it a discrete random variable.

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Answer:

(-2, 6)

Step-by-step explanation:

f(x) = x² + 4x + 10

f(x) = x² + 4x + 4 + 6

f(x) = (x + 2)² + 6

The vertex is at (-2, 6).

The question is wrong since it is not possible to have 111 defective graphite rackets when the total number of graphite racket is 100.

Question:

A sample of 100 wood and 100 graphite tennis rackets are taken from the warehouse. Assuming that If 88 wood and 90 graphite are defective and one racket is randomly selected from the​ sample, find the probability that the racket is wood or defective.

Given Information:  

Total wood = 100

Total graphite = 100

Defective wood = 88

Non-defective wood = 12

Defective graphite = 90

Non-defective graphite = 10

Required Information:  

Probability of racket being selected is wood or defective = ?

Answer:

P(wood or defective) = 0.95

Step-by-step explanation:

The probability of selecting a wood racket is

P(wood) = number of wood rackets/total number of rackets

P(wood) = 100/200 = 1/2

The probability of selecting a defective racket is

P(defective) = number of defective rackets/total number of rackets

P(defective) = 88+90/200 = 178/200 = 89/100

There is double counting of wood so we have to subtract the probability of wood and defective

P(wood and defective) = 88/200 = 11/25

P(wood or defective) = P(wood)  + P(defective) - P(wood and defective)

P(wood or defective) = 1/2 + 89/100 - 11/25

P(wood or defective) = 0.95

Alternatively:

P(defective) = number of defective rackets/total number of rackets

P(defective) = 88+90/200 = 178/200 = 89/100

P(wood and non-defective) = 12/200 = 3/50

There is no double counting here so we dont have to subtract anything

P(wood or defective) = P(wood)  + P(wood and non-defective)

P(wood or defective) = 89/100 + 3/50

P(wood or defective) = 0.95

The equation for the least-squares regression line can be found by calculating the slope and y-intercept using the given data on knee height and overall height. The regression line is used for predicting the height from knee height.

To find the equation of the least-squares regression line, you first need to calculate the slope (b1) and y-intercept (b0) using the given data. The least-squares regression line is essentially a line of best fit that minimizes the sum of the squared residuals.

The formula for the slope (b1) of the regression line is: b1 = (∑xy - n * mean_x * mean_y) / (∑x^2 - n * mean_x^2) And the y-intercept (b0) is calculated as: b0 = mean_y - b1 * mean_x

Following these formulas and plugging in the given data (for knee height x and height y), we can find b1 and b0. Once we've done that, we can write the equation for the least-squares regression line in the form y = b0 + b1*x.

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The least-squares regression line equation is estimated by determining the slope (b1) and y-intercept (b0) of the line. These values are calculated from the given data sets for height and knee height using the formulas: b1 = [N(Σxy) - (Σx)(Σy)] / [N(Σx²) - (Σx)²] and b0 = (Σy - b1(Σx)) / N

The subject matter of this question is statistics, specifically, it's about finding the equation of a least-squares regression line. The least-squares regression line is a tool used in statistics to show the best possible mathematical relationship between two variables. In this case, the variables are height and knee height.

To calculate the least-squares regression line, we need to calculate the slope (b1) and y-intercept (b0) of the line. The formulas to calculate these are:

Where:

N = number of observations (5 in this case)

Σxy = sum of the product of x and y

Σx = sum of x

Σy = sum of y

Σx² = sum of squares of x

After calculating the values for b0 and b1, the equation for the least-squares regression line would be: y = b0 + b1*x. You would need to calculate these values using the provided datasets for height (x) and knee height (y).

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Answer: 5.5hours

Step-by-step explanation:

Total distance = 226miles

Total time = 7 hours

Let b represent total time spent while walking.

Distance (walking) = 4b

Distance ( riding) = 40(7-b)

Total distance 226 = 4b + 40(7-b)

226 = 4b + 280 - 40b

226 = 280-36b

b = 54/36

b = 1.5hours

Amount of time spent walking = 1.5hours

Amount of time spent riding = 7-1.5 = 5.5hours

Amount of time spent on bicycle = 5.5hours

Answer:

The probability that a randomly selected student is female or a physics major is 0.575.

Step-by-step explanation:

A probability is the number of desired outcomes divided by the number of total outcomes.

Desired outcomes:

Students that are female or physics majors.

17 female

10 physics majors, of which 4 are female.

This means that there are 10 total physics majors and 17-4 = 13 non physics majors female. So

[tex]D = 13 + 10 = 23[/tex]

Total outcomes:

The class has 40 students, so [tex]T = 40[/tex]

Probability

[tex]P = \frac{23}{40} = 0.575[/tex]

The probability that a randomly selected student is female or a physics major is 0.575.

You will bike 30.2 miles in the seventh day according to the prediction.

Here we have the following data, You bike

So we can know some facts:

[tex]5.4-5=0.4mi[/tex]

[tex]6.2-5.4=0.8mi[/tex]

[tex]7.8-6.2=1.6mi[/tex]

By taking a look at the pattern, we can see that each day you increases the number of miles by a factor of 2 compared to the previous day. So:

[tex]x_{5}-7.8=3.2mi \\ \\ x_{5}=7.8+3.2=11mi, \ \text{Day 5}[/tex]

[tex]x_{6}-11=6.4mi \\ \\ x_{6}=6.4+11=17.4mi, \ \text{Day 6}[/tex]

Finally:

[tex]x_{7}-17.4=12.8mi \\ \\ x_{7}=12.8+17.4=30.2mi, \ \text{Day 7}[/tex]

By noting the pattern that the daily increase doubles each time, we can predict that the total distance biked on the seventh day would be 30.2 miles.

To predict the number of miles biked on the seventh day, we need to first determine the pattern in the increase of biking distances over the days given. The distances biked in the consecutive days are: 5, 5.4, 6.2, and 7.8 miles. We can see that each day, the distance increases at varying amounts:

The increase pattern appears to be that each day the distance increases by double the amount of the previous day. Therefore, we can predict the increase and the total distance for the next days:

If the pattern continues, the student would bike 30.2 miles on the seventh day.

Answer:

-1

Step-by-step explanation:

We evaluate [tex]\sin(2\pi+3\pi/2)[/tex]

In [tex]2\pi+3\pi/2[/tex], [tex]2\pi[/tex] is a complete revolution and is the same as 0. So we have

[tex]\sin3\pi/2 = \sin(\pi+\pi/2)[/tex]

One [tex]\pi[/tex] is a half revolution, putting the point at (-1, 0). [tex]\pi/2[/tex] is a quarter of a revolution. A quarter circle from (-1, 0) anticlockwise is (0, -1).

The sine is the y-coordinate of a point along a unit circle.

Hence, [tex]\sin(2\pi+3\pi/2)=-1[/tex]

Using the cyclical nature of the unit circle and the sine function, the mathematical expression sin (2π + 3π/2) can be simplified to sin (3π/2). travelling 3π/2 around the unit circle takes us to the point where sin is -1.

The question requires the evaluation of the mathematical expression sin (2π + 3π/2). This can be solved by utilizing the cyclical nature of the unit circle and the sine function. Since the distance around the unit circle is 2π, adding or subtracting multiples of 2π from the angle doesn't change the result of the sin function.

So, we can simplify the function sin (2π + 3π/2) to sin (3π/2). Because every π/2 around the unit circle the sine function repeats, sin (π/2) is 1, sin (2π/2) or sin (π) is 0, sin (3π/2) is -1, and sin (4π/2) or sin (2π) is 0. So sin (3π/2) equals -1.

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Answer:

12.0 (3 sf)

Step-by-step explanation:

E(X) = 0.08(1)+0.2(3)+0.32(5)+0.4(10)

E(X) = 6.28

E(X²) = .08(1²)+.2(3²)+.32(5²)+.4(10²)

E(X²) = 49.88

Var(X) = E(X²) - [E(X)]²

= 49.88 - 6.28² = 10.4416

Var(1.07X) = 1.07² Var(X)

= 1.1449×10.4416 = 11.95458784

12.0 (3 sf)

The variance of the amount paid on the bill (Y) is $10.62.

To find the variance of Y, we need to calculate the expected value of Y first, and then use that to compute the variance.

Step 1: Calculate the expected value of Y (E(Y)).

E(Y) = Σ [P(X) * Y]

where P(X) is the probability of each price category.

E(Y) = (0.08 * $1) + (0.20 * $3) + (0.32 * $5) + (0.40 * $10)

E(Y) = $0.08 + $0.60 + $1.60 + $4.00

E(Y) = $6.28

Step 2: Find the variance of Y.

Variance of Y (Var(Y)) = Σ [P(X) * (Y - E(Y))²]

Var(Y) = (0.08 * ($1 - $6.28)²) + (0.20 * ($3 - $6.28)²) + (0.32 * ($5 - $6.28)²) + (0.40 * ($10 - $6.28)²)

Var(Y) = (0.08 * $27.92) + (0.20 * $10.22) + (0.32 * $1.58) + (0.40 * $14.58)

Var(Y) = $2.24 + $2.04 + $0.51 + $5.83

Var(Y) = $10.62

The variance of Y is $10.62.

The variance measures the spread or dispersion of the values around the expected value, which, in this case, is $6.28.

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Answer:

$1166.08 is the monthly payment for the mortgage per month.

Step-by-step explanation:

The meaning of this stated formula on the statement is the present annuity formula because we will have future monthly payments on the mortgage of the house in which they pay off the present value of the house which is $240000 x 80% = $ 192000 as this amount will excludes the down payment of 20% that is made.

We are given Pv the present value which excludes the down payment  $192000.

We have the interest rate i which is 1.2%/12 as it is compounded monthly.

n is the number of payments made over a period which is 12 x 15 years= 180 payments as it is compounded monthly.

no we substitute the above mentioned information to the present value annuity formula stated to calculate R the monthly payment:

Pv = R[(1-(1+i)^-n)/i]

$192000 = R[(1-(1+(1.2%/12))^-180)/ (1.2%/12)] divide both sides by the coefficient of R

$192000/[(1-(1+(1.2%/12))^-180)/(1.2%/12)] = R

$1166.08 =R which this is the amount that will be paid for the mortgage every month for 15 years.

The area of the circular fire pit is 4096 square inches.

Explanation:

Given that the rectangular fire pit is 7 feet by 6 feet.

We need to determine the area of the largest circular fire that can be made in this fire pit.

The diameter of the circular fire is 6 feet

The radius is given by

[tex]r=\frac{6}{2} =3[/tex]

Radius is 3 feet.

The area of the largest circular fire pit can be determined using the formula,

[tex]Area=\pi r^2[/tex]

Substituting the values in the formula, we have,

[tex]Area = (3.14)(3)^2[/tex]

        [tex]=(3.14)(9)[/tex]

[tex]Area= 28.26 \ ft^2[/tex]

We need to convert feet to inches by multiplying by 12, we get,

[tex]Area = 28.26\times (12)^2[/tex]

[tex]Area = 4096.44 \ in^2[/tex]

Rounding off to the nearest square inch, we get,

[tex]Area= 4096 \ in^2[/tex]

Thus, the area of the circular fire pit is 4096 square inches.

Answer:

Linear Relationship

Step-by-step explanation:

The size is steadily progressing and so is the height.

Answer:

Step-by-step explanation:

linear relationship

Answer:

[tex]t=\frac{201-190}{\frac{10}{\sqrt{25}}}=5.5[/tex]    

[tex]p_v =P(t_{(24)}>5.5)=0.00000589[/tex]  

If we compare the p value and the significance level given [tex]\alpha=0.05[/tex] we see that [tex]p_v<\alpha[/tex] so we can conclude that we have enough evidence to reject the null hypothesis, and we can conclude that the true mean is higher than 190

Step-by-step explanation:

Data given and notation  

[tex]\bar X=201[/tex] represent the mean

[tex]s=10[/tex] represent the sample standard deviation for the sample  

[tex]n=25[/tex] sample size  

[tex]\mu_o =190[/tex] represent the value that we want to test

[tex]\alpha=0.05[/tex] represent the significance level for the hypothesis test.  

t would represent the statistic (variable of interest)  

[tex]p_v[/tex] represent the p value for the test (variable of interest)  

State the null and alternative hypotheses.  

We need to conduct a hypothesis in order to check if the mean is higher than 190, the system of hypothesis would be:  

Null hypothesis:[tex]\mu \leq 190[/tex]  

Alternative hypothesis:[tex]\mu > 190[/tex]  

If we analyze the size for the sample is < 30 and we don't know the population deviation so is better apply a t test to compare the actual mean to the reference value, and the statistic is given by:  

[tex]t=\frac{\bar X-\mu_o}{\frac{s}{\sqrt{n}}}[/tex]  (1)  

t-test: "Is used to compare group means. Is one of the most common tests and is used to determine if the mean is (higher, less or not equal) to an specified value".  

Calculate the statistic

We can replace in formula (1) the info given like this:  

[tex]t=\frac{201-190}{\frac{10}{\sqrt{25}}}=5.5[/tex]    

P-value

The first step is calculate the degrees of freedom, on this case:  

[tex]df=n-1=25-1=24[/tex]  

Since is a one side test the p value would be:  

[tex]p_v =P(t_{(24)}>5.5)=0.00000589[/tex]  

Conclusion  

If we compare the p value and the significance level given [tex]\alpha=0.05[/tex] we see that [tex]p_v<\alpha[/tex] so we can conclude that we have enough evidence to reject the null hypothesis, and we can conclude that the true mean is higher than 190

12x-5y=-20

y=x+4

12x-5(x+4)=-20

12x-5x-20=-20

7x=0,

So, we get: x=0 and y=4

Complete Question

"We might think that a ball that is dropped from a height of 15 feet and rebounds to a height 7/8 of its previous height at each bounce keeps bouncing forever since it takes infinitely many bounces. This is not true! We examine this idea in this problem.

Show that a ball dropped from a height h feet reaches the floor in 1/4√h seconds. Then use this result to find the time, in seconds, the ball has been bouncing when it hits the floor for the first, second, third and fourth times:

Answer:

t = ¼√h seconds

Step-by-step explanation:

Given

Height = 15 feet

Show that a ball dropped from a height h feet reaches the floor in 14h−−√ seconds. Then use this result to find the time, in seconds, the ball has been bouncing when it hits the floor for the first, second, third and fourth times:

From this, we understand that

u = Initial Velocity = 0

a = g = acceleration due to gravity = 9.8m/s² = 32ft/s²

h = initial height = 15

Using Newton equation of motion

h = ut + ½at²

Substitute the values

15 = 0 * t + ½ * 32 t²

15 = 16t² ---- make t² the subject of formula

t² = 15/16 ----- square root both sides

t = √15/√16

t = ¼√15

But h = 15

So, t = ¼√h seconds

Or t = 0.25√h seconds

-- Proved

Final answer:

A ball dropped from a height h feet reaches the floor in 14h−−√ seconds. To find the time the ball bounces when it hits the floor for the first, second, third, and fourth times, we can use this result. For example, if h = 1.5 meters, the time it takes for the ball to bounce for the first, second, third, and fourth times would be approximately 6.93 seconds, 7.95 seconds, 8.96 seconds, and 9.98 seconds, respectively.

Explanation:

Given that a ball dropped from a height h feet reaches the floor in 14h√ seconds, we can use this result to find the time the ball bounces when it hits the floor for the first, second, third, and fourth times.

Let's say the time it takes for the ball to reach the floor for the first time is t1. Using the equation 14h√ = t1, we can solve for t1 by squaring both sides of the equation and solving for t1. Similarly, we can find the time for the second, third, and fourth bounces.

For example, if h = 1.5 meters, the time it takes for the ball to bounce for the first, second, third, and fourth times would be approximately 6.93 seconds, 7.95 seconds, 8.96 seconds, and 9.98 seconds, respectively.

Answer:

45,697,600 license plates can be formed of 4 letters followed by 2 numbers

Step-by-step explanation:

There are 4 letters in the plate. In the alphabet, there are 26 letters. So each of the four letters in the plate can have 26 outcomes.

There are 2 digits in the place. There are 10 possible digits.

How many possible plates?

26*26*26*26*10*10 = 45,697,600

45,697,600 license plates can be formed of 4 letters followed by 2 numbers

Answer:

a.) w = 103 * 1.08^t

b.) 3.5weeks

Step-by-step explanation:

If Her current weight is 103 pounds and she hopes to multiply her her weight each week by 1.08, then

her weight after 1 week = 103 * 1.08 = 103 * 1.08¹

Her weight after 2 weeks = [weight of week 1] * 1.08 = [103* 1.08] * 1.08 = 103 * 1.08²

Weight after 3 weeks= [weight of week 2] * 1.08 = [103 * 1.08 * 1.08] * 1.08 = 103 * 1.08³

Hence weight (W) after t weeks = 103 * 1.08^t

b.) If W = 135, Then

103 * 1.08^t = 135

1.08^t = 135/103

1.08^t = 1.31

Taking log of both sides,

log 1.08^t = log 1.31

t log 1.08 = log 1.32

t = log 1.32/log 1.08

t = 3.5 weeks.

Hence, it will take her 3½ weeks to get to 135pounds weight.

Answer:

Percentage of students who scored greater than 700 = 97.72%

Step-by-step explanation:

We are given that the College Board originally scaled SAT scores so that the scores for each section were approximately normally distributed with a mean of 500 and a standard deviation of 100.

Let X = percentage of students who scored greater than 700.

Since, X ~ N([tex]\mu, \sigma^{2}[/tex])

The z probability is given by;

          Z = [tex]\frac{X-\mu}{\sigma}[/tex] ~ N(0,1)    where, [tex]\mu[/tex] = 500  and  [tex]\sigma[/tex] = 100

So, P(percentage of students who scored greater than 700) = P(X > 700)

   P(X > 700) = P( [tex]\frac{X-\mu}{\sigma}[/tex] < [tex]\frac{700-500}{100}[/tex] ) = P(Z < 2) = 0.97725 or 97.72% Therefore, percentage of students who scored greater than 700 is 97.72%.

Answer: it is cheaper to buy for sweatshirts at $9.69 each.

Step-by-step explanation:

The regular price for one sweatshirt is $12.95. if a customer bought three shirts at the regular price of $12.95, a fourth sweatshirt was free

It means that the cost of buying 4 shirts is

12.95 × 3 = $38.85

Due to a special, the price of one sweatshirt was $9.69. It means that the cost of buying 4 shirts at this price is

9.69 × 4 = $38.76

Therefore, it is cheaper to buy for sweatshirts at $9.69 each than to buy at $12.95 each and get a free shirt

Answer: it is cheaper to buy for sweatshirts at $9.69 each.

Answer:

D) $110 and $190

Step-by-step explanation:

The Empirical Rule states that, for a normally distributed random variable:

68% of the measures are within 1 standard deviation of the mean.

95% of the measures are within 2 standard deviation of the mean.

99.7% of the measures are within 3 standard deviations of the mean.

In this problem, we have that:

Mean = 150

Standard deviation = 20

95% of the monthly food expenditures are between what two amounts?

By the Empirical Rule, within 2 standard deviations of the mean

150 - 2*20 = $110

150 + 2*20 = $190

So the correct answer is:

D) $110 and $190

Answer: D) $110 and $190

Step-by-step explanation:

The Empirical rule states that for a normal distribution, nearly all of the data will fall within three standard deviations of the mean . The empirical rule is further illustrated below

68% of data falls within the first standard deviation from the mean.

95% fall within two standard deviations.

99.7% fall within three standard deviations.

From the information given, the mean is $150 and the standard deviation is $20.

2 standard deviations = 2 × 20 = 40

150 - 40 = $110

150 + 40 = 190

Therefore, about 95% of the monthly food expenditures are between $110 and $190

Answer:

The probability that at most 10 of the next 150 items produced are unacceptable is 0.8315.

Step-by-step explanation:

Let X = number of items with unacceptable quality.

The probability of an item being unacceptable is, P (X) = p = 0.05.

The sample of items selected is of size, n = 150.

The random variable X follows a Binomial distribution with parameters n = 150 and p = 0.05.

According to the Central limit theorem, if a sample of large size (n > 30) is selected from an unknown population then the sampling distribution of sample mean can be approximated by the Normal distribution.

The mean of this sampling distribution is: [tex]\mu_{\hat p}= p=0.05[/tex]

The standard deviation of this sampling distribution is: [tex]\sigma_{\hat p}=\sqrt{\frac{ p(1-p)}{n}}=\sqrt{\frac{0.05(1-.0.05)}{150} }=0.0178[/tex]

If 10 of the 150 items produced are unacceptable then the probability of this event is:

[tex]\hat p=\frac{10}{150}=0.067[/tex]

Compute the value of [tex]P(\hat p\leq 0.067)[/tex] as follows:

[tex]P(\hat p\leq 0.067)=P(\frac{\hat p-\mu_{p}}{\sigma_{p}} \leq\frac{0.067-0.05}{0.0178})=P(Z\leq 0.96)=0.8315[/tex]

*Use a z-table for the probability.

Thus, the probability that at most 10 of the next 150 items produced are unacceptable is 0.8315.

Final answer:

Using the normal approximation to the binomial distribution, the probability that at most 10 of the next 150 items produced are unacceptable is approximately 86.43%.

Explanation:

Approximating the Probability of Defective Items:

To approximate the probability that at most 10 of the next 150 items produced are unacceptable when each item is of acceptable quality independently with probability 0.95, we use the binomial probability formula or normal approximation. However, since the number of trials is large (n = 150), we can use the normal approximation to the binomial distribution to simplify the calculation.

First, we find the mean (μ) and standard deviation (σ) of the binomial distribution:

Next, we convert the binomial problem to a normal distribution problem by finding the z-score for 10.5 (since we are looking for "at most" 10, we use 10 + 0.5 for continuity correction).

The z-score is calculated as follows:

Finally, we look up the z-score in a standard normal distribution table, or use a calculator to find the cumulative probability for Z ≤ 1.10, which is approximately 0.8643. Therefore, the probability that at most 10 of the next 150 items are unacceptable is roughly 86.43%.

Answer:

Step-by-step explanation:

The total number of lines, n(U) = 18

Let the number of lins with verb be n(V) = 11

Let the number of lines with adjectives be n(A) = 13

n(V n A) = 8

Find the number of lines that have a verb but no adjective, that is, n(V n A')

Mathematically, according to sets theory,

n(V) = n(V n A) + n(V n A')

So,

n(V n A') = n(V) - n(V n A) = 11 - 8 = 3.

Hence, only 3 lines have a verb but no adjectives.

Answer:

(a)23 (b)90 (c)3

Step-by-step explanation:

The equation for the line of best fit for this situation is given as

[tex]y=\frac{3}{10}x+8[/tex]

where x=average temperature in degrees

y=average number of hot dogs she sold,

(a) The expected number of hot dogs sold when the temperature is 50° would be___hot dogs.

When x=50°

[tex]y=\frac{3}{10}X50+8=15+8=23[/tex]

When the temperature is 50°, the expected number of hot dogs sold would be 23.

(b)If the vendor sold 35 hot dogs, the temperature is expected to be ___degrees.  

If y=35

[tex]35=\frac{3}{10}x+8\\35-8=\frac{3}{10}x\\27=\frac{3}{10}x[/tex]

Multiply both sides by 10/3

[tex]27 X \frac{10}{3}= \frac{3}{10}x X \frac{10}{3}\\x=90^{0}[/tex]

If the vendor sold 35 hot dogs, the temperature is expected to be 90 degrees.  

(c) Based on the line of best fit, for every 10-degree increase in temperature, she should sell 3 more hot dogs.

Answer:

a)

[tex] P(X=k) = {15 \choose k} * 0.75^{k}*0.25^{15-k} [/tex]

For any integer k between 0 and 15, and 0 for other values of k.

b)

[tex]P(X>10) = 0.2252+ 0.2252+ 0.1559+0.0668+0.0134 = 0.6865[/tex]

c) P(6 ≤ X ≤ 10) = 0.2737

d)  μ = 15*0.75 = 11.25. σ² = 11.25*0.25 = 2.8125

Step-by-step explanation:

X is a binomial random variable with parameters n = 15, p = 0.75. Therefore

a)

[tex] P(X=k) = {15 \choose k} * 0.75^{k}*0.25^{15-k} [/tex]

For any integer k between 0 and 15, and 0 for other values of k.

b)

P(X>10) = P(X=11) + P(X=12)+ P(X=13)+P(X=14)+P(x=15)

[tex]P(X=11) = {15 \choose 11} * 0.75^{11} * 0.25^4 = 0.2252[/tex]

[tex]P(X=12) = {15 \choose 12} * 0.75^{12} * 0.25^3 = 0.2252[/tex]

[tex]P(X=13) = {15 \choose 13} * 0.75^{13} * 0.25^2 = 0.1559[/tex]

[tex]P(X=14) = {15 \choose 14} * 0.75^{14} * 0.25 = 0.0668[/tex]

[tex]P(X=15) = {15 \choose 15} * 0.75^{15} = 0.0134[/tex]

Thus,

[tex]P(X>10) = 0.2252+ 0.2252+ 0.1559+0.0668+0.0134 = 0.6865[/tex]

c) P(6 ≤ X ≤ 10) = P(X = 6) + P(X = 7) + P(X = 8) + P(X=9) + P(X=10)

[tex]P(X=6) = {15 \choose 6} * 0.75^{6} * 0.25^9 = 0.0034[/tex]

[tex]P(X=7) = {15 \choose 7} * 0.75^{7} * 0.25^8 = 0.0131[/tex]

[tex]P(X=8) = {15 \choose 8} * 0.75^{8} * 0.25^7 = 0.0393[/tex]

[tex]P(X=9) = {15 \choose 9} * 0.75^{9} * 0.25^6 = 0.0918[/tex]

[tex]P(X=10) = {15 \choose 10} * 0.75^{10} * 0.25^{5} = 0.1652[/tex]

Thereofre,

[tex]P(6 \leq X \leq 10) = 0.0034 + 0.0134 + 0.0393 + 0.0918 + 0.1652 = 0.2737[/tex]

d)  μ = n*p =  15*0.75 = 11.25

σ² = np(1-p) = 11.25*0.25 = 2.8125

Answer: The addition of the new point alters the previous standard deviation greatly

Step-by-step explanation:

Let the initial five points be : 2 3 4 5 and 6. In order to calculate the standard deviation for this data, we will need to calculate the mean first.

Mean = summation of scores/number of scores.

The mean is therefore: (2+3+4+5+6)/5 = 20/5 = 4.

We'll also need the sum of the squares of the deviations of the mean from all the scores.

Since mean = 4, deviation of the mean from the score "2" = score(2) - mean (4)

For score 3, it is -1

For 4, it's 0

For 5 it's 1

For 6 it's 2.

The squares for -2, -1, 0, 1, and 2 respectively will be 4, 1 , 0, 1, 4. Summing them up we have 10 i.e (4+1+0+1+4=10).

Calculating the standard deviation, we apply the formula:

√(summation of (x - deviation of mean)^2)/N

Where N means the number of scores.

The standard deviation = √(10/5) = 1.4142

If we add another score or point that is far away from the original points, say 40, what happens to the standard deviation. Let's calculate to find out.

i.e we now have scores: 2, 3, 4, 5, 6 and 40

We calculate by undergoing same steps.

Firstly mean. The new mean = (2+3+4+5+6+40)/6 = 60/6 = 10.

The mean deviations for the scores : 2, 3, 4, 5, 6 and 40 are -8, -7, -6, -5, -4 and 30 respectively. The squares of these deviations are also 64, 49, 36, 25, 16 and 900 respectively as well. Their sum will then be 1090. i.e. (64+49+36+25+16+900 = 1090).

The new standard deviation is then=

√(1090/6)

= √181.67

= 13.478.

It's clear that the addition of a point that's far away from the original points greatly alters the size of the standard deviation as seen /witnessed in this particular instance where the standard deviation rises from 1.412 to 13.478

Answer:

We need to check the conditions in order to use the normal approximation.

[tex]np=13*0.4=5.2 \geq 5[/tex]

[tex]n(1-p)=13*(1-0.4)=7.8 \geq 5[/tex]

Assuming that each trial is independent and we have a sample obtained from a random sampling method.

Then we can conclude that we can use the normal approximation since all the conditions are satisfied.

Step-by-step explanation:

The binomial distribution is a "DISCRETE probability distribution that summarizes the probability that a value will take one of two independent values under a given set of parameters. The assumptions for the binomial distribution are that there is only one outcome for each trial, each trial has the same probability of success, and each trial is mutually exclusive, or independent of each other".

Let X the random variable of interest, on this case we now that:

[tex]X \sim Binom(n=13, p=0.4)[/tex]

The probability mass function for the Binomial distribution is given as:

[tex]P(X)=(nCx)(p)^x (1-p)^{n-x}[/tex]

Where (nCx) means combinatory and it's given by this formula:

[tex]nCx=\frac{n!}{(n-x)! x!}[/tex]

We need to check the conditions in order to use the normal approximation.

[tex]np=13*0.4=5.2 \geq 5[/tex]

[tex]n(1-p)=13*(1-0.4)=7.8 \geq 5[/tex]

Assuming that each trial is independent and we have a sample obtained from a random sampling method.

Then we can conclude that we can use the normal approximation since all the conditions are satisfied.

Answer:

121+11y+y^2 not perfect square trinomial because the second member should be twice the value of the products of the first and second monomers.

Step-by-step explanation:

(A+B) ^2=A^2 +2*A*B+B^2

Answer:

(a) No

(b) 0.21 or 21%

Step-by-step explanation:

(a) Since the outcome of the first game influences in the probability of winning the second game, the two games are not independent.

(b) The probability of losing both games is given by the product of the probability of losing the first game and the probability of losing the second game given that you have lost the first:

[tex]P = (1-0.7)*(1-0.3)\\P=0.21=21\%[/tex]

The probability you lose both games is 21%

Given Information:

Probability of wining 1st game = p₁ = 0.7

Probability of wining 2nd game given 1st game won = p₂|p₁ = 0.5

Probability of wining 2nd game given 1st game lost = p₂|q₁ = 0.3

Required Information:

(a) Are the two games independent = ?

(b) Probability of losing both games = ?

Answer:

(a) Are the two games independent = No

(b) Probability of losing both games = 0.21

Step-by-step explanation:

(a) Independent Events:

Two events are said to be independent when the success of one event is not affected by the success or failure of another event.

In this case, the probability of 2nd game depends on the success or failure of the 1st game, therefore, the two games are not independent.

(b) Probability of losing both games

The probability of losing the both games is the product of the probabilities of losing each game.

Probability of losing 1st game = 1 - Probability of wining 1st game

Probability of losing 1st game = 1 - 0.7 = 0.30

Probability of losing 2nd game = 1 - Probability of wining 2nd game given 1st game lost

Probability of losing 2nd game = 1 - 0.3 = 0.70

Please note that since we are finding the probability of losing both games that's why we used the condition of 1st game lost

Probability of losing both games = Probability of losing 1st game*Probability of losing 2nd game

Probability of losing both games = 0.30*0.70

Probability of losing both games = 0.21

Answer: it cost $50.94 to fill an 18 gallon tank.

Step-by-step explanation:

Julio filled his gas tank with 6 gallons of premium unleaded gas for $16.98. This means that amount it cost to will fill his gas tank with 1 gallon of premium unleaded gas would be

16.98/6 = $2.83 per gallon

Therefore, the amount of will cost to an 18 gallon tank with premium unleaded gas would be

18 × 2.83 = $50.94