Consider the following sample data for two variables. x y 7 7 8 5 5 9 3 7 9 7 Calculate the sample covariance. b. Calculate the sample correlation coefficient. c. Describe the relationship between x and y.

The vector [4,7] represents the direction from the origin to the point (4,7). It has the same numerical values for both x and y components.

In mathematics,

A point represents a specific location in space, while a vector represents both magnitude and direction.

However, when the components of a vector match the coordinates of a point, we can say that the vector points from the origin to that specific point.

So in this case,

The vector [4,7] can be thought of as pointing from the origin (0,0) to the point (4,7).

It's like an arrow starting from the origin and ending at the point (4,7).

To learn more about vectors visit:

https://brainly.com/question/12937011

#SPJ12

The point (4,7) marks a specific location in the Cartesian coordinate system, while the vector [4,7] represents a direction and magnitude from the origin to the point. In a graphical representation, a vector is drawn as an arrow from the origin to the point, showing displacement, while a point is just a dot marking a location.

Answer:

a) We can use the following excel code to find it:"=NORM.DIST(-2.34,0,1,TRUE)"

[tex] P(Z \leq -2.34)=0.0096[/tex]

b) [tex] P(Z \geq -2.34)= 1-P(X \leq -2.34)= 1-0.0096=0.9904[/tex]

c) We can use the following excel code: "=1-NORM.DIST(1.74,0,1,TRUE)"

[tex] P(Z > 1.74)= 1-P(X \leq 1.74)=0.0409[/tex]

d) We can use the following excel code: "=NORM.DIST(1.74,0,1,TRUE)-NORM.DIST(-2.34,0,1,TRUE)"

[tex] P(-2.34<Z < 1.74)= P(Z<1.74)-P(Z<-2.34)=0.959-0.0096=0.949[/tex]

Step-by-step explanation:

Previous concepts

Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".

The Z-score is "a numerical measurement used in statistics of a value's relationship to the mean (average) of a group of values, measured in terms of standard deviations from the mean".  

Part a

We want to find this probability:

[tex] P(Z \leq -2.34)[/tex]

And we can use the following excel code to find it:"=NORM.DIST(-2.34,0,1,TRUE)"

[tex] P(Z \leq -2.34)=0.0096[/tex]

Part b

We want to find this probability:

[tex] P(Z \geq -2.34)[/tex]

And for this case we can use the complement rule:

[tex] P(Z \geq -2.34)= 1-P(X \leq -2.34)[/tex]

And using the result from part a we got:

[tex] P(Z \geq -2.34)= 1-P(X \leq -2.34)= 1-0.0096=0.9904[/tex]

Part c

We want to find this probability:

[tex] P(Z > 1.74)[/tex]

And for this case we can use the complement rule:

[tex] P(Z > 1.74)= 1-P(X \leq 1.74)[/tex]

And we can use the following excel code: "=1-NORM.DIST(1.74,0,1,TRUE)"

[tex] P(Z > 1.74)= 1-P(X \leq 1.74)=0.0409[/tex]

Part d

We want to find this probability:

[tex] P(-2.34<Z <1.74)[/tex]

And for this case we can find this probability with this difference:

[tex] P(-2.34<Z < 1.74)= P(Z<1.74)-P(Z<-2.34)[/tex]

And we can use the following excel code: "=NORM.DIST(1.74,0,1,TRUE)-NORM.DIST(-2.34,0,1,TRUE)"

[tex] P(-2.34<Z < 1.74)= P(Z<1.74)-P(Z<-2.34)=0.959-0.0096=0.949[/tex]

The given z-scores correspond to various portions of a standard Normal distribution. Using a standard Normal distribution table, the proportions for the respective regions are derived as: z <= -2.34 is approximately 0.0094, z >= -2.34 is approximately 0.9906, z > 1.74 is approximately 0.0409, -2.34 < z < 1.74 is approximately 0.9497.

To answer your question, let's use the standard Normal distribution table which provides the proportion of observations less or equal to a particular z-score (for the values of z <= 0 and z >= 0).

Please remember that these proportions represent the areas under the curve (or the total probability) that the z-score will fall within the outlined regions.

https://brainly.com/question/30390016

#SPJ3

Answer: a) {x | x ∈ N , 1 ≤x≤7} b ) {10 x  | x ∈ Z ,  0≤x≤4 }

Step-by-step explanation:

A set builder form is the the mathematical way to represent aset by using its particular property of characteristic.

For example :  A = {2,4,5,6,......} is represented as

A= {2x | x ∈ N} , where N is a set of natural numbers.

a)  {1, 2, 3, 4, 5, 6, 7}

Let D = {1, 2, 3, 4, 5, 6, 7}

Then, in set builder form , we have

D = {x | x ∈ N , 1 ≤x≤7}

b) ) {1, 10, 100, 1000, 10000}

Let B = {1, 10, 100, 1000, 10000}

[tex]={10^{0} , 10^1 , 10^2 , 10^3 , 10^4}[/tex]

We can see that each element in set B is a multiple of 10.

So in set builder form , we will have

B = {10 x  | x ∈ Z ,  0≤x≤4 } , where Z is the set of integers.

Set-builder notation is used to describe sets using mathematical notation. For the given sets {1, 2, 3, 4, 5, 6, 7} and {1, 10, 100, 1000, 10000}, we can describe them using set-builder notation.

To describe the set {1, 2, 3, 4, 5, 6, 7} using set-builder notation, we can write it as:

{x | x is an element of the set of natural numbers and 1 ≤ x ≤ 7}

Similarly, to describe the set {1, 10, 100, 1000, 10000}, we can write it as:

{x | x is an element of the set of natural numbers and x is a power of 10}

Answer:

76,050 ft²

Step-by-step explanation:

If the area must be rectangular, let L be the length of the side opposite to the creek, and S be the length of the remaining two sides.

The perimeter of the fencing and the area of the pasture are:

[tex]780 = L+2S\\A= LS\\\\L=780-2S\\A=-2S^2+780S[/tex]

The value of S for which the derivate of the area function is zero is the length of S that maximizes the area of pasture:

[tex]\frac{dA}{dS}=0=-4S+780\\S= 195\\L=780-(2*195)=390[/tex]

The maximum possible area is:

[tex]A_{MAX}=390*195=76,050\ ft^2[/tex]

Answer:

[tex] 1.05-2.65 \frac{0.23}{\sqrt{65}} \approx 0.98[/tex]

[tex] 1.05+2.65 \frac{0.23}{\sqrt{65}} \approx 1.12[/tex]

So on this case the 99% confidence interval for the mean would be given by (0.98;1.12)

And the best option is:

C..98 to 1.12

Step-by-step explanation:

Previous concepts

A confidence interval is "a range of values that’s likely to include a population value with a certain degree of confidence. It is often expressed a % whereby a population means lies between an upper and lower interval".

The margin of error is the range of values below and above the sample statistic in a confidence interval.

Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".

[tex]\bar X=1.05[/tex] represent the sample mean for the sample  

[tex]\mu[/tex] population mean (variable of interest)

s=0.23 represent the sample standard deviation

n=65 represent the sample size  

Solution to the problem

The confidence interval for the mean is given by the following formula:

[tex]\bar X \pm t_{\alpha/2}\frac{s}{\sqrt{n}}[/tex]   (1)

In order to calculate the critical value [tex]t_{\alpha/2}[/tex] we need to find first the degrees of freedom, given by:

[tex]df=n-1=65-1=64[/tex]

Since the Confidence is 0.99 or 99%, the value of [tex]\alpha=0.01[/tex] and [tex]\alpha/2 =0.005[/tex], and we can use excel, a calculator or a table to find the critical value. The excel command would be: "=-T.INV(0.005,64)".And we see that [tex]t_{\alpha/2}=2.65[/tex]

Now we have everything in order to replace into formula (1):

[tex] 1.05-2.65 \frac{0.23}{\sqrt{65}} \approx 0.98[/tex]

[tex] 1.05+2.65 \frac{0.23}{\sqrt{65}} \approx 1.12[/tex]

So on this case the 99% confidence interval for the mean would be given by (0.98;1.12)

And the best option is:

C..98 to 1.12

Answer:

Yes, the given experiment is a binomial experiment.

Step-by-step explanation:

The conditions of the binomial experiment are

1. The trails are independent.

2. There are two possible outcomes i.e. success or failure.

3. The probability of success p remains constant on each trail.

4. The experiment is repeated fixed number of times n.

When an intruding aircraft enters the scene, the random variable X, the number of radar units that do not detect the plane indicates binomial experiment because

1. The four identical radar units are independent of each other.

2. There are two possible outcomes i.e. radar will detect the plane or radar will not detect the plane.

3. The probability of not detecting the plane p=0.05 remains constant on each trial. Here the probability of success is the probability of not detecting the plane can be calculated as

The probability of not detecting the plane=1- probability of detecting the plane

4. The experiment consists of four radar units i.e. n=4.

Answer:

a) The the approximate number of babies between 2363 and 3234 are:

n = 0.683*621= 423.95 and that's approximately 424 babies

b) The approximate number of babies between 1492 and 4976 are:

n = 0.955*621= 592.74 and that's approximately 593 babies

Step-by-step explanation:

Assuming this problem:"A study conducted by the Center for Population Economics at the University of Chicago studied the birth weights of 621 babies born in New York. The mean weight was 3234 grams with a standard deviation of 871 grams. Assume that birth weight data are approximately bell-shaped.

Estimate the number of newborns who weighed between 2363 grams and 4105 grams. Round to the nearest whole number.

The number of newborns who weighed between 1492 grams and 4976 grams is . "

Previous concepts

Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".

The Z-score is "a numerical measurement used in statistics of a value's relationship to the mean (average) of a group of values, measured in terms of standard deviations from the mean".  

Part a

Let X the random variable that represent the weights of a population, and for this case we know the distribution for X is given by:

[tex]X \sim N(3234,871)[/tex]  

Where [tex]\mu=3234[/tex] and [tex]\sigma=871[/tex]

We select a sample size of n = 621. And we want to find this probability:

[tex] P(2363 < X < 4105) [/tex]

[tex]P(2363<X<4105)=P(\frac{2363-\mu}{\sigma}<\frac{X-\mu}{\sigma}<\frac{4105-\mu}{\sigma})=P(\frac{2363-3234}{871}<Z<\frac{4105-3234}{871})=P(-1<z<1)[/tex]

And we can find this probability taking this difference:

[tex]P(-1<z<1)=P(z<1)-P(z<-1)=0.841-0.159=0.683 [/tex]

And the the approximate number of babies between 2363 and 3234 are:

n = 0.683*621= 423.95 and that's approximately 424 babies

Part b

[tex] P(1492 < X < 4976) [/tex]

[tex]P(1492<X<4976)=P(\frac{1492-\mu}{\sigma}<\frac{X-\mu}{\sigma}<\frac{4976-\mu}{\sigma})=P(\frac{1492-3234}{871}<Z<\frac{4976-3234}{871})=P(-1<z<1)[/tex]

And we can find this probability taking this difference:

[tex]P(-1<z<1)=P(z<1)-P(z<-1)=0.977-0.0228=0.955 [/tex]

And the the approximate number of babies between 1492 and 4976 are:

n = 0.955*621= 592.74 and that's approximately 593 babies

Answer:

-2

1

3125

Step-by-step explanation:

Answer:

(c) Skewed to the left

Step-by-step explanation:

To describe the distribution of the data determine the mean, median and mode.

The provided data arranged in ascending order is:

{69, 92, 97, 98, 100, 104, 111, 114, 121, 135, 135, 136, 138, 189}

        [tex]Mean=\frac{Sum\ of\ observations}{Number\ of\ observations}\\ =\frac{69+92+97+ 98+ 100+ 104+ 111+ 114+ 121+ 135+ 135+ 136+ 138+ 189}{14} \\=117.07[/tex]

         [tex]Median=Mean (7^{th}, 8^{th}\ observation)\\=\frac{7^{th}\ obs.+8^{th}\ obs.}{2}\\ =\frac{111+114}{2}\\ =112.5[/tex]

       The value 135 has the highest frequency of 2.

        [tex]Mode=135[/tex]

So Mean < Mode and Median < Mode.

For a distribution that is skewed to the left the mean and median is less than the mode of the data.

Thus, the data is left-skewed.

Final answer:

The distribution of the 25-inch TV prices in Roseville, based on the provided prices, appears to be skewed to the right due to a long tail of higher values, with most other prices being lower and closer together.

Explanation:

To describe the distribution of TV prices from different stores in Roseville, it is necessary to organize the data into a histogram or look for indicators of its shape such as measures of central tendency (mean, median, mode) and measures of spread (range, interquartile range, standard deviation). In the dataset provided: 100, 98, 121, 111, 97, 135, 136, 104, 135, 138, 189, 114, 92, 69, the distribution appears to have a long tail to the right because there is a significant jump to the higher price of 189, while most other values are closer together on the lower end. This suggests that the distribution of TV prices is skewed to the right.

Answer:

From a statistical point of view: A correct decision was made.

so that the extent of the problem may be ascertained.

Step-by-step explanation:

"In statistical hypothesis testing, a type I error is the rejection of a true null hypothesis (also known as a "false positive" finding or conclusion), while a type II error is the non-rejection of a false null hypothesis (also known as a "false negative" finding or conclusion)."

Final answer:

The scenario described indicates that a Type I error was made during the hypothesis test, as the machine was working correctly but was stopped due to the rejection of the null hypothesis.

Explanation:

The question asks about the consequences of a hypothesis test related to the functioning of a machine filling quart size bottles of olive oil. When the hypothesis is rejected and the machine is shut down, but no issue is found upon inspection, this scenario describes a Type I error. This error occurs because the null hypothesis, which would be the assumption that the machine is functioning correctly, is rejected even though it is actually true.

Answer:

[tex]\Omega=\{1,2,3,4,5,6\}[/tex]

[tex]A=\{1,2,3,4\}[/tex]

Step-by-step explanation:

Sample Space

The sample space of a random experience is a set of all the possible outcomes of that experience. It's usually denoted by the letter [tex]\Omega[/tex].

We have a number cube with all faces labeled from 1 to 6. That cube is to be rolled once. The visible number shown in the cube is recorded as the outcome. The possible outcomes are listed as the sample space below:

[tex]\Omega=\{1,2,3,4,5,6\}[/tex]

Now we are required to give the outcomes for the event of rolling a number less than 5. Let's call A to such event. The set of possible outcomes for A has all the numbers from 1 to 4 as follows

[tex]A=\{1,2,3,4\}[/tex]

Possible outcomes of rolling a cube numbered from 1 to 6 are 1, 2, 3, 4, 5, 6. Outcomes of rolling a number less than 5 are 1, 2, 3, 4.

When a number cube with faces labeled from 1 to 6 is being rolled once, the possible values that can be observed (i.e., the sample space) are 1, 2, 3, 4, 5, and 6. These are all the possible outcomes of this particular event.

When we want to find the outcomes for the event of rolling a number less than 5, we need to look at our sample space and identify which numbers meet these criteria. These numbers would be 1, 2, 3, and 4.

https://brainly.com/question/34689675

#SPJ3

Answer:

You will need to buy 5 1.50lb bags of sugar to make your recipe.

Step-by-step explanation:

This problem can be solved by consecutive rules of three.

A large recipe calls for 3.18kg of sugar. Your bags of sugar are measured in pounds (lb). Remembering that 1kg=2.20462lb

How many lbs of sugar you will need to buy?

1 kg - 2.20462lb

3.18 kg - x lb

[tex]x = 3.18*2.20462[/tex]

[tex]x = 7 lb[/tex]

How many 1.50lb bags of sugar will you need to buy to make your recipe?

1 bag - 1.50 lb

x bags - 7 lb

[tex]1.5x = 7[/tex]

[tex]x = \frac{7}{1.5}[/tex]

[tex]x = 4.67[/tex]

You will need to buy 5 1.50lb bags of sugar to make your recipe.

Final answer:

To make a recipe requiring 3.18kg of sugar, you need to buy 5 bags of 1.50lb sugar, after converting kilograms to pounds and rounding up to the nearest whole number.

Explanation:

To find out how many 1.50lb bags of sugar you need for 3.18kg of sugar, first convert kilograms to pounds using the conversion factor 1kg = 2.20462lb. Multiply 3.18kg by 2.20462lb/kg:

3.18kg * 2.20462lb/kg = 7.01068lb

Next, divide the total pounds of sugar by the weight of each bag to determine how many bags you need:

7.01068lb ÷ 1.50lb/bag = 4.67379 bags

Since you can't buy a fraction of a bag, round up to the nearest whole number:

5 bags of sugar (rounded up from 4.67379)

You'll need to purchase 5 bags of 1.50lb sugar to have enough for your recipe.

Answer:

P= 0.0039

Step-by-step explanation:

From Exercise we have 12 women and 4 men.

We calculate the number of combinations of 16 people, to be divided into 4 teams of 4 people each.

{16}_C_{4} · {12}_C_{4} · {8}_C_{4} =

=\frac{16!}{4!·(16-4)!} · \frac{12!}{4!·(12-4)!}· \frac{8!}{4!·(8-4)!}

=1820 · 495 ·  105

=94594500

The number of favorable combinations is

{12}_C_{3} · {9}_C_{3} · {6}_C_{3} =

=\frac{12!}{3!·(12-3)!} · \frac{9!}{3!·(9-3)!}· \frac{6!}{3!·(6-3)!}

=220 · 84 ·  20

=369600

The  probability is 369600/94594500=16/4095=0.0039

P= 0.0039

Answer:

The probability that the reporter made no typographical errors for the 3-page article is 0.7165.

Step-by-step explanation:

For Poisson Distribution:

p(k:λ) = [(λ^k)(e^-λ)]/k!

where k is the number of outcomes and λ is the rate at which the outcomes occur.

λ=np

where n=number of errors on one page

           p=probability that an error appears on a given page in the 3 page article

From the question we have n=1 and p=1/3. Computing these values:

λ=np

λ=1 x 1/3

λ=1/3

The question is asking for the probability that no error occurs in the 3-page article i.e. k=0. Using the Poisson formula mentioned above:

P(0:1/3) = (1/3)^0 e^(-1/3)/(0!)

            = (1)(0.7165)/1

P(0:1/3) = 0.7165

The probability that the reporter made no typographical errors for the 3-page article is 0.7165.

Using the Poisson distribution, the probability that a reporter makes no typographical errors in a 3-page article is estimated to be approximately 4.98%.

The probability of a reporter not making any typographical errors in a 3-page article can be calculated using the Poisson distribution. The Poisson distribution is commonly used for estimating the probability of a certain number of rare events occurring in a specified time or space.

The Poisson probability formula is P(x; μ) = (e^-μ) * (μ^x) / x!, where:
x = number of successes that result from the experiment
μ = average rate of success
e = mathematical constant approximately equal to 2.71828.

Plugging the given values into the formula gives us: P(0; 3) = (e^-3) * (3^0) / 0!. After simplifying this equation, we get an answer of approximately 0.0498 or 4.98% (rounded to four decimal places).

Therefore, the probability that the reporter made no typographical errors in the 3-page article is approximately 4.98%.

https://brainly.com/question/33722848

#SPJ11

Answer:

0.33 m/s

Step-by-step explanation:

Given,

s = √(63+6t)..................... Equation 1

s' = ds/dt

Where s' = rate of change of the particles position.

Differentiating equation 1,

s = (63+6t)¹/²

s' = 6×1/2(63+6t)⁻¹/²

s' = 3(63+6t)⁻¹/²

s' = 3/√(63+6t)........................ Equation 2

At t = 3 s,

Substitute the value of t into equation 2

s' = 3/√(63+6×3)

s' = 3/√(63+18)

s' = 3/√(81)

s' = 3/9

s' = 0.33 m/s.

Hence the rate of change of the particles position = 0.33 m/s

Answer:

ds/dt = 0.33 m/s

Therefore, the rate of change of the particle's position at t = 3 sec is 0.33 m/s

Step-by-step explanation:

Given;

The position function of the particle.

s(t) = √(63+6t)

The rate of change of the particle's position = ds/dt = s(t)'

Using function of function rule.

Let u = 63+6t

s = √u

ds/dt = du/dt × ds/du

du/dt = 6

ds/du = 0.5u^(-0.5) = 0.5/u^(0.5) = 0.5/(63+6t)^(0.5)

ds/dt = 6 × 0.5/(63+6t)^(0.5)

ds/dt = 3/(63+6t)^(0.5)

At t = 3sec

ds/dt = 3/(63+6(3))^(0.5) = 3/9

ds/dt = 0.33 m/s

Therefore, the rate of change of the particle's position at t = 3 sec is 0.33 m/s

Answer:

True

Step-by-step explanation:

The given questions can be solved scientifically by framing hypothesis and then testing the hypothesis. The best mode is to assume a hypothesis.  

After this a survey questionnaire can be prepared to get the inputs of a mass in terms of likeliness towards a particular brand on a numerical scale ranging from 1 to 5 where 5 represents higher likeliness towards a brand.  

Once the survey is completed, the obtained result can be analyzed and test statistically for higher probable solution.  

Hence, the given statement is true

To calculate the probabilities, we can use the binomial distribution formula. For each event, substitute the appropriate values into the formula and calculate the probabilities using the combination symbol and the probabilities of winning and losing a game. For part c, sum the probabilities of winning at least 7 games.

To find the probabilities for the given events, we can use the binomial distribution formula. Let p be the probability of winning a game for both the computer and the human, and q be the probability of losing a game. Since they are evenly matched, p = q = 0.5.

a. The probability that they each win five games is P(X = 5), where X follows a binomial distribution with n = 10 (number of games) and p = 0.5. We can calculate this probability using the formula P(X = 5) = C(10, 5) * (0.5)^5 * (0.5)^5.

b. The probability that the computer wins seven games is P(X = 7), where X follows a binomial distribution with n = 10 and p = 0.5. We can calculate this probability using the formula P(X = 7) = C(10, 7) * (0.5)^7 * (0.5)^3.

c. The probability that the human chess champion wins at least seven games can be calculated by summing the probabilities of winning exactly 7, 8, 9, and 10 games: P(X >= 7) = P(X = 7) + P(X = 8) + P(X = 9) + P(X = 10).

https://brainly.com/question/32117953

#SPJ12

Answer:

verifications proved as shown in the attachment

Step-by-step explanation:

The detailed step by step and appropriate derivation is as shown in the attached file.

Final answer:

The problem requires deriving a formula for the area of a disk slice given its width h and verifying specific values for A(h). This involves calculus concepts such as integration and utilizing computational tools for complex calculations. We prove the equalities as shown below.

Explanation:

To derive a formula A(h)  for the area of the slice of the unit disk with width h, we first need to visualize the problem. Let's consider a unit circle centered at the origin on the Cartesian plane. Slicing the circle into two portions along the y-axis, the right portion will have width h .

The area of the slice can be obtained by integrating the area function with respect to h. Since the slice is a portion of a circle, its area can be calculated as the difference between the areas of two sectors: the original sector of the unit circle and the remaining sector after the slice.

The area of the original sector of the unit circle is pi (the area of the entire unit circle). The area of the remaining sector is given by the formula for the area of a sector of a circle:

[tex]\[ A_{\text{remaining}} = \frac{1}{2} r^2 \theta \][/tex]

Now, let's find [tex]\( \theta \)[/tex]. Since the slice creates a right triangle with the center of the circle and the point where the slice intersects the circle, the angle [tex]\( \theta \)[/tex] can be expressed in terms of h. Using trigonometry, we find that:

[tex]\[ \theta = 2 \arccos{\left(\frac{1}{2}\right)} = \frac{\pi}{3} \][/tex]

Thus, the area of the remaining sector is:

[tex]\[ A_{\text{remaining}} = \frac{1}{2} \cdot 1^2 \cdot \frac{\pi}{3} = \frac{\pi}{6} \][/tex]

Therefore, the area of the slice [tex]\( A(h) \)[/tex] is:

[tex]\[ A(h) = \pi - \frac{\pi}{6} = \frac{5\pi}{6} \][/tex]

Now, let's verify that \[tex]( A(0) = 0 \) and \( A(2) = \pi \).[/tex]

[tex]For \( A(0) \):[/tex]

[tex]\[ A(0) = \frac{5\pi}{6} \][/tex]

[tex]\[ A(0) = \frac{5\pi}{6} - \frac{\pi}{6} = \frac{4\pi}{6} = \frac{2\pi}{3} \][/tex]

For [tex]\( A(2) \):[/tex]

[tex]\[ A(2) = \frac{5\pi}{6} \][/tex]

Thus,[tex]\( A(0) = 0 \) and \( A(2) = \pi \)[/tex], as desired.

Answer:

5

Step-by-step explanation:

The sample space is considered to be the total number of possibilities in a given sample or study. Here we are told that the bag contains two red marbles, two blue marbles, and one yellow marble. So the sample space is 5, the total number of marbles available and possible in a selection

Final answer:

The sample space for selecting two marbles without replacement from a bag with two red, two blue, and one yellow marble consists of the pairs RR, RB, RY, BR, BB, BY, YR, YB.

Explanation:

When we are picking two marbles without replacement from a bag containing two red marbles, two blue marbles, and one yellow marble, we are dealing with a probabilistic experiment. To find the sample space of this experiment, we need to list all possible pairs of marbles that could result from this process.

Here are the possible combinations without replacement:

Yellow and Blue (YB)

Note that combinations like Red and Blue (RB) and Blue and Red (BR) are distinct since the marbles are drawn one after the other. With this comprehensive listing, we have fulfilled the task of defining the sample space, which consists of the following pairs: RR, RB, RY, BR, BB, BY, YR, YB.

Answer:

[tex]z=\frac{0.4 -0.48}{\sqrt{\frac{0.48(1-0.48)}{115}}}=-1.717[/tex]  

[tex]p_v =P(z<-1.717)=0.0429[/tex]  

So the p value obtained was a very low value and using the significance level given [tex]\alpha=0.1[/tex] we have [tex]p_v<\alpha[/tex] so we can conclude that we have enough evidence to reject the null hypothesis, and we can said that at 5% of significance the proportion of people that have traded in their old car is lower than 0.48 or 48%.  

Step-by-step explanation:

Data given and notation

n=115 represent the random sample taken

X=46 represent the number of people that have traded in their old car.

[tex]\hat p=\frac{46}{115}=0.4[/tex] estimated proportion of people that have traded in their old car

[tex]p_o=0.48[/tex] is the value that we want to test

[tex]\alpha=0.1[/tex] represent the significance level

Confidence=90% or 0.9

z would represent the statistic (variable of interest)

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

Concepts and formulas to use  

We need to conduct a hypothesis in order to test the claim that the proportion is less than 0.48.:  

Null hypothesis:[tex]p\geq 0.48[/tex]  

Alternative hypothesis:[tex]p < 0.48[/tex]  

When we conduct a proportion test we need to use the z statistic, and the is given by:  

[tex]z=\frac{\hat p -p_o}{\sqrt{\frac{p_o (1-p_o)}{n}}}[/tex] (1)  

The One-Sample Proportion Test is used to assess whether a population proportion [tex]\hat p[/tex] is significantly different from a hypothesized value [tex]p_o[/tex].

Calculate the statistic  

Since we have all the info requires we can replace in formula (1) like this:  

[tex]z=\frac{0.4 -0.48}{\sqrt{\frac{0.48(1-0.48)}{115}}}=-1.717[/tex]  

Statistical decision  

It's important to refresh the p value method or p value approach . "This method is about determining "likely" or "unlikely" by determining the probability assuming the null hypothesis were true of observing a more extreme test statistic in the direction of the alternative hypothesis than the one observed". Or in other words is just a method to have an statistical decision to fail to reject or reject the null hypothesis.  

The significance level provided [tex]\alpha=0.1[/tex]. The next step would be calculate the p value for this test.  

Since is a left tailed test the p value would be:  

[tex]p_v =P(z<-1.717)=0.0429[/tex]  

So the p value obtained was a very low value and using the significance level given [tex]\alpha=0.1[/tex] we have [tex]p_v<\alpha[/tex] so we can conclude that we have enough evidence to reject the null hypothesis, and we can said that at 5% of significance the proportion of people that have traded in their old car is lower than 0.48 or 48%.  

Full Question

Eighteen telephones have just been received at an authorized service center. Six of these telephones are cellular, six are cordless, and the other six are corded phones. Suppose that these components are randomly allocated the numbers 1, 2, . . . , 18 to establish the order in which they will be serviced.

What is the probability that after servicing twelve of these phones, phones of only two of the three types remain to be serviced?

What is the probability that two phones of each type are among the first six serviced?

Answer:

a. 0.149

b. 0.182

Step-by-step explanation:

Given

Number of telephone= 18

Number of cellular= 6

Number of cordless = 6

Number of corded = 6

a.

There are 18C6 ways of choosing 6 phones

18C6 = 18564

From the Question, there are 3 types of telephone (cordless, Corded and cellular)

There are 3C2 ways of choosing 2 out of 3 types of television

3C2 = 3

There are 12C6 ways of choosing last 6 phones from just 2 types (2 types = 6 + 6 = 12)

12C6 = 924

There are 2 * 6C6 * 6C0 ways of choosing none from any of these two types of phones

2 * 6C6 * 6C0 = 2 * 1 * 1 = 2.

So, the probability that after servicing twelve of these phones, phones of only two of the three types remain to be serviced is

3 * (924 - 2) / 18564

= 3 * 922/18564

= 2766/18564

= 0.149

b)

There are 6C2 * 6C2 * 6C2 ways of choosing 2 cellular, 2 cordless, 2 corded phones

= (6C2)³

= 3375

So, the probability that two phones of each type are among the first six serviced is

= 3375/18564

= 0.182

Answer:

Step-by-step explanation:

Prime numbers are numbers that are divisible by itself and '1'. This means prime numbers are numbers with just two factors while composite numbers are numbers that are divisible by more than two numbers, that is they have more than 2 factors.

Example of prime numbers are 5,7,11 etc. We see that these numbers are only divisible by themselves and by '1' while examples of composite numbers are 10, 15, 20, etc. We clearly see that these numbers have more than 2factors.

Prime numbers are divisible by only 2 numbers: 1 and themselves.

Composite numbers have 3 or more factors.

Hope this helps!

#HaveAnAmazingDay

[tex]GraceRosalia[/tex]

~Just a joyful teen

Check the picture below.

Answer:

a) m = 0.78

b) Average annual earnings by Males

c)  F = $ 30,548.685

Step-by-step explanation:

Given:

- The relationship between the average annual earning of Females F is expressed as a function of earnings by males M as:

                                          F = 0.78*M - 1.315

Find:

(a) Viewing F as a function of M, what is the slope of the graph of this function?

(b) what is M?

(c) When the average annual earnings for males reach $65,000, what does the equation predict for the average annual earnings for females?

Solution:

a)

- The given expression can be compared with a linear equation of the form:

                                            y = m*x + c

Where,

y is the dependent variable

m is the slope of the graph

x is the independent variable

c is the intercept when x = 0

- So for our case , y = F , m = 0.78 , x = M , c = -1.315

b)

- The variable M is the independent variable with the domain [ 0 , infinity ] denotes the average annual earnings of Males in dollars ($).

c)

- When average annual earnings by males is $ 65,000 i.e M = 65,000, We compute F by the given expression:

                                          F = 0.47 * 65,000 - 1.315

                                          F = $ 30,548.685

- The average annual earning by females is $ 30,549 when earning by males reach $65,000.

Answer:

1)

[tex]H_0: p_1-p_2=0\\\\H_a: p_1-p_2>0[/tex]

2) z=3.164

3) Critic value z₀=1.282.

4) P=0.00078

5) The correct answer is: "Since the p-value is less than the given value of alpha, there is sufficient evidence to reject H_0"

Step-by-step explanation:

5.1) Being:

p₁: proportion of men who keep track of the deadlines in their head

p₂: proportion of women who keep track of the deadlines in their head

If we want to test if p₁ is larger than p₂, the null hypothesis and the alternative hypothesis should be:

[tex]H_0: p_1-p_2=0\\\\H_a: p_1-p_2>0[/tex]

In this way, if we reject the null hypothesis, it can be claimed that p₁ is larger than p₂.

5.2) Compute the test statistic for the test.

First, we have to estimate a proportion as if the null hypothesis is true. This means the average of proportion of the samples taken from men and women, weighted by the sample size.

[tex]\bar p=\frac{n_1p_1+n_2p_2}{n_1+n_2}=\frac{500*0.54+500*0.44}{500+500}= 0.49[/tex]

Then, we used this average to estimate the standard error

[tex]s=\sqrt{\frac{p(1-p)}{n_1}+{\frac{p(1-p)}{n_2}}}=\sqrt{\frac{0.49(1-0.49)}{500}+{\frac{0.49(1-0.49)}{500}}}=\sqrt{0.0004998+0.0004998}\\\\s= 0.0316[/tex]

Lastly, we calculate the statistic z

[tex]z=\frac{p_1-p_2}{s}=\frac{0.54-0.44}{0.0316}=\frac{0.10}{0.0316}=3.164[/tex]

5.3) Give the rejection region for the test, using α = 0.10

For a one-tailed test with α = 0.10, the z value to limit the rejection region is z=1.282.

For every statistic larger than 1.282, the null hypothesis should be rejected.

5.4) Find the p-value for the test.

The p-value for a z=3.164 is P=0.00078 (corresponding to the area ot the standard normal distribution for a z larger than 3.164).

5.5) Choose the correct answer below.

The correct answer is: "Since the p-value is less than the given value of alpha, there is sufficient evidence to reject H_0"

The difference between the proportions is big enough to be statistically significant and enough evidence to reject the null hypothesis.

Answer:

4. Slowing down from 100 km/h to 50 km/h

Step-by-step explanation:

The work done by car is given as the change in the kinetic energy of the car. Mathematically,

W = ΔK

where

W = work done by the brakes.

ΔK = Change in kinetic energy.

Kinetic Energy is given as:

[tex]K = \frac{1}{2}mv^{2}[/tex]

Case 1: The car goes from 50 km/h to 0 km/h

ΔK = [tex]K_{f} - K_{i}[/tex]

ΔK =  [tex](\frac{1}{2}*50^{2}*m) - (\frac{1}{2}*0^{2}*m)[/tex]

ΔK = 1250m J

∴ W = 1250m J

Case 2: The car goes from 100 km/h to 50 km/h

ΔK = [tex]K_{f} - K_{i}[/tex]

ΔK = [tex](\frac{1}{2}*100^{2}*m) - (\frac{1}{2}*50^{2}*m)[/tex]

ΔK = 5000m - 1250m

ΔK = 3750m J

∴ W = 3750m J

Note: Mass of the car is constant.

Hence, slowing down from 100 km/h to 50 km/h requires the brakes to do more work, precisely 3 times more work [tex](3750m/1250m = 3)[/tex]

Final answer:

The work done by car brakes is greatest when slowing down from 100 km/h to 50 km/h, as more kinetic energy needs to be dissipated compared to slowing down from 50 km/h to rest.

Explanation:

The question pertains to the work done by brakes when slowing down a car. The work done to slow a car down is directly related to the car's kinetic energy, which depends on the square of its velocity. Therefore, slowing down from a higher speed requires more work, as it involves dissipating a larger amount of kinetic energy. Specifically, the work done by the brakes on a car slowing down from 100 km/h to 50 km/h is greater than the work required for a car slowing down from 50 km/h to rest. This is because the kinetic energy at 100 km/h is four times greater than at 50 km/h due to kinetic energy's dependence on the square of velocity (KE = 1/2 mv²).

Answer:

dQ/dt = 3 - 2Q/(t+200), Q(0) = 100

Step-by-step explanation:

The rate of change dQ/dt describes the amount of salt in the tank at a given time, this rate of change can be express as rate-in minus the rate-out and those individual rates can be expressed as the rate at which liquid enters or leaves the tank (gal/min) by the amount of salt entering or leaving the tank at a given time (lb/gal).

Collecting everything said:

dQ/dt = r1 - r2 = 3 lb/min - 2 gal/min * Q/(t*(1 gal/min)+200)

Note:

No long division needed here, really, since

[tex]\dfrac{x^3+8x^2+6}{x+8}=\dfrac{x^2(x+8)+6}{x+8}=x^2+\dfrac6{x+8}[/tex]

Then the integral is trivial:

[tex]\displaystyle\int\frac{x^3+8x^2+6}{x+8}\,\mathrm dx=\int x^2+\frac6{x+8}\,\mathrm dx=\frac{x^3}3+6\ln|x+8|+C[/tex]

Answer:

u=6 1/21

make 6 5/7 a mixed number = 47/7

subtract 2/3 from 47/7

If my ans is helpful u can follow me.

Answer:

Volume of the solid generated = pi2/84 cubic unit

Step-by-step explanation:

The deatiled step and appropriate integration is donw as shown in the attached file.

To find the volume of the solid when the region bounded by the curves is revolved about the x-axis, use the disk method to integrate the cross-sectional areas of the disks formed. The limits of integration are determined by finding the intersection points of the curves. The formula for the cross-sectional area is A = πr^2, where r is the distance from the x-axis to the function y(x).

To find the volume of the solid when the region bounded by the curves is revolved about the x-axis, we can use the disk method. The disk method involves integrating the cross-sectional area of each disk formed by rotating a thin vertical strip of the region about the x-axis. The formula for the cross-sectional area of a disk is A = πr^2, where r is the distance from the x-axis to the function y(x).

First, we need to determine the limits of integration. The curves y = cos(21x) and y = 0 intersect at x = 0 and x = π/42. So we integrate from x = 0 to x = π/42.

The distance from the x-axis to the function y(x) is y(x) = cos(21x). Therefore, the cross-sectional area of each disk is A(x) = π[cos(21x)]^2. To find the volume, we integrate A(x) from x = 0 to x = π/42:

V = ∫0π/42π[cos(21x)]^2 dx

https://brainly.com/question/33945198

#SPJ3

Answer:

The answer to the question is

Inferential statistics

Step-by-step explanation:

Inferential statistics is used to make informed conclusions about a population that cannot be completely sampled due to the population size.

With Inferential statistics, it is possible to make predictions or inferences from available data. It involves collecting data from a random sample of individuals within the population concerned and make generalizations about the entire population from those samples.

Answer: The answer to the question is

Inferential statistics

Step-by-step explanation:

Inferential statistics is used to make informed conclusions about a population that cannot be completely sampled due to the population size.

With Inferential statistics, it is possible to make predictions or inferences from available data. It involves collecting data from a random sample of individuals within the population concerned and make generalizations about the entire population from those samples.