Please help if so thank you And explain

Answer: Slope 1/2

y-int -5

graph (0,-5),(1,-9/2)

Step-by-step explanation:

Answer:

AAS method can be used to prove that the two triangles are congruent.

Step-by-step explanation:

According to the question for the two triangles one pair of opposite angles are equal. One another pair of angles are equal for the two and one pair of sides are also equal of the two.

Hence, the two given triangles are congruent by AAS rule.

Hence, AAS method can be used to prove that the two triangles are congruent.

Unit price means the cost per unit.

I'll provide an example.

If 4 bars of soap cost $2.35, find the unit price. Round to the nearest cent if necessary.

Remember that the unit price is the cost per unit which in this case is the cost per bar of soap. Since $2.35 is the cost for 4 bars of soap, to find the cost per bar of soap, we divide 4 into $2.35.

When we do this, we get an answer of 0.587 which rounds up to 59 cents.

Therefore, the unit price for soap is 59 cents.

The sum and difference identity cos(x + y) / sin(x - y) = 1 - cotxcoty / cotx - coty is verified

Solution:

Given expression is:

[tex]\frac{\cos (x+y)}{\sin (x-y)}=\frac{1-\cot x \cot y}{\cot x-\cot y}[/tex]

Let us first solve L.H.S

[tex]\frac{\cos (x+y)}{\sin (x-y)}[/tex]    ------ EQN 1

We have to use the sum and difference formulas

cos(A + B) = cosAcosB – sinAsinB  

sin(A - B) = sinAcosB – cosAsinB

Applying this in eqn 1 we get,

[tex]=\frac{\cos x \cos y-\sin x \sin y}{\sin x \cos y-\sin y \cos x}[/tex]

[tex]\text { Taking sinx } \times \text { siny as common }[/tex]

[tex]=\frac{\sin x \sin y\left(\frac{\cos x \cos y}{\sin x \sin y}-1\right)}{\sin x \sin y\left(\frac{\cos y}{\sin y}-\frac{\cos x}{\sin x}\right)}[/tex]

[tex]\begin{array}{l}{=\frac{\frac{\cos x}{\sin x} \times \frac{\cos y}{\sin y}-1}{\frac{\cos y}{\sin y}-\frac{\cos x}{\sin x}}} \\\\ {=\frac{\cot x \times \cot y-1}{\cot y-\cot x}} \\\\ {=\frac{\cot x \cot y-1}{\cot y-\cot x}}\end{array}[/tex]

Taking -1 as common from numerator and denominator we get,

[tex]\begin{array}{l}{=\frac{-(1-\cot x \cot y)}{-(\cot x-\cot y)}} \\\\ {=\frac{(1-\cot x \cot y)}{(\cot x-\cot y)}}\end{array}[/tex]

= R.H.S

Thus L.H.S = R.H.S

Thus the given expression has been verified using sum and difference identity

Answer:

Step-by-step explanation:

a) For a 95% confidence level: [tex]\( \$194,120 \) to \( \$205,880 \)[/tex]

b) For a 97% confidence level: [tex]\( \$193,490 \) to \( \$206,510 \)[/tex]

To solve this problem, we'll use the information provided and apply the formula for a confidence interval estimate for the population mean when the population standard deviation is known.

Given data:

- Sample size n: 36 days

- Sample mean [tex](\( \bar{x} \))[/tex]: $200,000

- Population standard deviation [tex](\( \sigma \)): $18,000[/tex]

(a) 95% Confidence Interval

For a 95% confidence interval, the critical value [tex]\( z^* \)[/tex] from the standard normal distribution is approximately 1.96.

The formula for the confidence interval is:

[tex]\[ \text{CI} = \bar{x} \pm z^* \cdot \frac{\sigma}{\sqrt{n}} \][/tex]

Calculate the standard error:

[tex]\[ SE = \frac{\sigma}{\sqrt{n}} = \frac{18,000}{\sqrt{36}} = \frac{18,000}{6} = 3,000 \][/tex]

Now, construct the confidence interval:

[tex]\[ \text{CI} = 200,000 \pm 1.96 \cdot 3,000 \][/tex]

[tex]\[ \text{CI} = 200,000 \pm 5,880 \][/tex]

Therefore, the 95% confidence interval estimate for the average daily sale is approximately [tex]\( \$194,120 \) to \( \$205,880 \)[/tex].

(b) For a 97% confidence interval, the critical value [tex]\( z^* \)[/tex] from the standard normal distribution is approximately 2.17.

Calculate the confidence interval using the same formula with the updated [tex]\( z^* \):[/tex]

[tex]\[ \text{CI} = \bar{x} \pm z^* \cdot \frac{\sigma}{\sqrt{n}} \][/tex]

[tex]\[ \text{CI} = 200,000 \pm 2.17 \cdot 3,000 \][/tex]

[tex]\[ \text{CI} = 200,000 \pm 6,510 \][/tex]

Therefore, the 97% confidence interval estimate for the average daily sale is approximately [tex]\( \$193,490 \) to \( \$206,510 \)[/tex].

Answer:

False this value is very likely to find in this distribution

Step-by-step explanation:

With mean 7 ( μ ) and standad deviation  (σ ) 2 we can observe, value 6.6  is close to the lower limit of the interval

μ  ± 0,5 σ      7 ± 1 in which we should find 68,3 % of all values

(just 6 tenth to the left)

And of course 6.6 is inside the interval

μ  ± 1 σ   where we find 95.7 % of th value

We conclude this value is not unlikely at all

Final answer:

To determine whether it is unlikely to find a random sample with a mean breaking weight of 6.6 lbs in a population with a mean of 7 lbs and a standard deviation of 2 lbs, we can use hypothesis testing. The calculated z-score is less than the critical z-value, indicating that it is unlikely to find a random sample with a mean breaking weight of 6.6 lbs in the population. Therefore, the statement is true.

Explanation:

To determine whether it is unlikely to find a random sample with a mean breaking weight of 6.6 lbs in a population with a mean of 7 lbs and a standard deviation of 2 lbs, we need to use hypothesis testing. We can set up the null and alternative hypotheses as follows:

Null hypothesis (H0): The population mean breaking weight is equal to 7 lbs.

Alternative hypothesis (Ha): The population mean breaking weight is less than 7 lbs.

Next, we can calculate the z-score using the formula (sample mean - population mean)/(standard deviation/sqrt(sample size)). Substituting the values, we get (6.6 - 7)/(2/sqrt(36)) = -0.6/ (2/6) = -0.6/ (1/3) = -1.8.

We can now look up the critical z-value for a one-tailed test at a significance level of 0.05. The critical z-value is -1.645. Since the calculated z-score (-1.8) is less than the critical z-value (-1.645), we can reject the null hypothesis. Therefore, it is true that finding a random sample with a mean breaking weight of 6.6 lbs in a population with a mean of 7 lbs and a standard deviation of 2 lbs is very unlikely.

Answer:

  see below

Step-by-step explanation:

Opposite sides are parallel in a parallelogram, so if they have different slope, the figure will not be a parallelogram.

_____

Comments on other answer choices

If the slope of diagonal EG is perpendicular to that of diagonal FH, it only proves the figure is some sort of kite. The figure may or may not be a parallelogram.

Adjacent sides being different lengths does not prove anything (except that the figure is not a rhombus).

Proving angle F is a right angle does not prove anything else about the shape of the figure. The figure may or may not be a parallelogram. (If it is a parallelogram, it is also a rectangle.)

Answer:

Have no fear, the answer is top left GIVE BRANLIEST PLZ

Step-by-step explanation:

Answer with Step-by-step explanation:

We are given that a function

[tex]T:P_2\rightarrow P_2[/tex]

[tex]T(a_0+a_1x+a_2x^2)=(a_0+a_1+a_2)+(a_1+a_2)x+a_2x^2[/tex]

We have to determine the given function is a linear transformation.

If a function is linear transformation then it satisfied following properties

[tex]1.T(x+y)=T(x)+T(y)[/tex]

2.[tex]T(ax)=aT(x)[/tex]

[tex]T(a_0+a_1x+a_2x^2+b_0+b_1x+b_2x^2)=T((a_0+b_0)+(a_1+b_1)x+(a_2+b_2)x^2)=(a_0+b_0+a_1+b_1+a_2+b_2)+(a_1+b_1+a_2+b_2)x+(a_2+b_2)x^2[/tex]

[tex]T(a_0+a_1x+a_2x^2+b_0+b-1x+b_2x^2)=(a_0+a_1+a_2)+(a_1+a_2)x+a_2x^2+(b_0+b_1+b_2)+(b_1+b_2)x+b_2x^2[/tex]

[tex]T(a_0+a_1x+a_2x^2+b_0+b-1x+b_2x^2)=T(a_0+a_1x+a_2x^2)+T(b_0+b_1x+b_2x^2)[/tex]

[tex]T(a(a_0+a_1x+a_2x^2))=T(aa_0+aa_1x+aa_2x^2)[/tex]

[tex]T(a(a_0+a_1x+a_2x^2))=(aa_0+aa_1+aa_2)+(aa_1+aa_2)x+(aa_2)x^2[/tex]

[tex]T(a(a_0+a_1x+a_2x^2))=a(a_0+a_1+a_2)+a(a_1+a_2)x+aa_2x^2=a((a_0+a_1+a_2)+(a_1+a_2)x+a_2x^2)=aT(a_0+a_1x+a_2x^2)[/tex]

Hence, the function is a linear transformation because it satisfied both properties of linear transformation.

[tex]\( T \)[/tex] satisfies both additivity and scalar multiplication, [tex]\( T \)[/tex] is indeed a linear transformation.

To determine if the function [tex]\( T: P2 \to P2 \)[/tex], defined by [tex]\( T(a_0 + a_1 x + a_2 x^2) = (a_0 + a_1 + a_2) + (a_1 + a_2)x + a_2 x^2 \)[/tex], is a linear transformation, we need to check two properties:

1. Additivity: [tex]\( T(u + v) = T(u) + T(v) \) for all \( u, v \in P2 \)[/tex].

2. Scalar Multiplication: [tex]\( T(cu) = cT(u) \) for all \( u \in P2 \) and \( c \in \mathbb{R} \)[/tex].

Let's verify these properties:

Additivity Check

Let [tex]\( u = a_0 + a_1 x + a_2 x^2 \) and \( v = b_0 + b_1 x + b_2 x^2 \)[/tex].

Compute [tex]\( T(u + v) \)[/tex]:

[tex]\[u + v = (a_0 + b_0) + (a_1 + b_1)x + (a_2 + b_2)x^2\][/tex]

[tex]\[T(u + v) = [(a_0 + b_0) + (a_1 + b_1) + (a_2 + b_2)] + [(a_1 + b_1) + (a_2 + b_2)]x + (a_2 + b_2)x^2\][/tex]

[tex]\[T(u + v) = [(a_0 + a_1 + a_2) + (b_0 + b_1 + b_2)] + [(a_1 + a_2) + (b_1 + b_2)]x + (a_2 + b_2)x^2\][/tex]

Compute [tex]\( T(u) + T(v) \)[/tex]:

[tex]\[T(u) = (a_0 + a_1 + a_2) + (a_1 + a_2)x + a_2 x^2\][/tex]

[tex]\[T(v) = (b_0 + b_1 + b_2) + (b_1 + b_2)x + b_2 x^2\][/tex]

[tex]\[T(u) + T(v) = [(a_0 + a_1 + a_2) + (b_0 + b_1 + b_2)] + [(a_1 + a_2) + (b_1 + b_2)]x + (a_2 + b_2)x^2\][/tex]

Since [tex]\( T(u + v) = T(u) + T(v) \)[/tex], the function [tex]\( T \)[/tex] satisfies additivity.

Scalar Multiplication Check

Let [tex]\( u = a_0 + a_1 x + a_2 x^2 \)[/tex] and [tex]\( c \in \mathbb{R} \)[/tex].

Compute [tex]\( T(cu) \)[/tex]:

[tex]\[cu = c(a_0 + a_1 x + a_2 x^2) = (ca_0) + (ca_1)x + (ca_2)x^2\][/tex]

[tex]\[T(cu) = [(ca_0) + (ca_1) + (ca_2)] + [(ca_1) + (ca_2)]x + (ca_2)x^2\][/tex]

[tex]\[T(cu) = c[(a_0 + a_1 + a_2) + (a_1 + a_2)x + a_2 x^2]\][/tex]

Compute [tex]\( cT(u) \)[/tex]:

[tex]\[T(u) = (a_0 + a_1 + a_2) + (a_1 + a_2)x + a_2 x^2\][/tex]

[tex]\[cT(u) = c[(a_0 + a_1 + a_2) + (a_1 + a_2)x + a_2 x^2]\][/tex]

Since [tex]\( T(cu) = cT(u) \)[/tex], the function [tex]\( T \)[/tex] satisfies scalar multiplication.

Answer:

Is plausible that the successive throws are independent

Step-by-step explanation:

1) Table with info given

The observed values are given by the following table

__________________________________________________

First shot          Made          Second shot missed           Total

__________________________________________________

Made                  152                       33                                185

Missed                37                         8                                  45

__________________________________________________

Total                    189                       41                                230

2) Calculations and test

We are interested on check independence and for this we need to conduct a chi square test, the next step would be find the expected value:

Null hypothesis: Independence between two successive free throws

Alternative hypothesis: No Independence between two successive free throws

_____________________________________________________

First shot                    Made                                    Second shot missed

_____________________________________________________

Made                  189(185)/230=152.0217                41(185)/230=32.9783

Missed                189(45)/230=36.9783                  41(45)/230=8.0217

_____________________________________________________

On this case all the expected values are higher than 5 and the sample size 230 is enough to apply the chi squared test.

3) Calculate the chi square statistic

The statistic for this case is given by:

[tex]\chi_{cal}^2 =\sum \frac{(O_i -E_i)}{E_i}[/tex]

Where O represent the observed values and E the expected values. Replacing the values that we got we have this

[tex]\chi_{cal}^2 =\frac{(152-152.0217)^2}{152.0217}+\frac{(33-32.9783)^2}{32.9783}+\frac{(37-36.9783)^2}{36.9783}+\frac{(8-8.0217)^2}{8.0217}=0.000003098+0.00001428+0.00001273+0.0.00005870=0.00008881[/tex]

Now with the calculated value we can find the degrees of freedom

[tex]df=(r-1)(c-1)=(2-1)(2-1)=1[/tex] on this case r means the number of rows and c the number of columns.

Now we can calculate the p value

[tex]p_v =P(\chi^2 >0.00008881)=0.9925[/tex]

On this case the pvalue is a very large value and that indicates that we can fail to reject the null hypothesis of independence. So is plausible that the successive throws are independent.

Answer:

  A. Point C divides segment AB so that AC:CB is 1:3

Step-by-step explanation:

Like many multiple-choice questions, you don't actually need to know how to work the problem. You just need to know what the question means.

Comparing answers, you see that choices A and D cannot both be right. You also note that choice D says the same thing as choice B.

Since there is only one False answer, it must be choice A.

__

When the division is into segments that are 1/3 the length and 2/3 the length, the segments have the ratio ...

  (1/3) : (2/3) = 1 : 2

The incorrect statement is B. If AC = 5, then CB should be 15, not 10, because CB would be twice the length of AC, given that point C is 1/3 of the way from A to B.

The question asks us to identify which statement about point C being 1/3 of the way from point A to point B on a line segment is NOT true. Let's analyze each statement.

The answer is statement B. If AC = 5, then CB would not be 10; it would be 15 since it is twice the length of AC, given point C is 1/3 of the way from A to B.

Answer: 95% confidence interval would be (175.04,194.96).

Step-by-step explanation:

Since we have given that

Mean = 185 mg

Standard deviation = 17.6 mg

At 95% confidence level, z = 1.96

So, Interval would be

[tex]\bar{x}\pm z\dfrac{\sigma}{\sqrt{n}}\\\\=185\pm 1.96\times \dfrac{17.6}{\sqrt{12}}\\\\=185\pm 9.958\\\\=(185-9.958,185+9.958)\\\\=(175.042,194.958)\\\\=(175.04,194.96)[/tex]

Hence, 95% confidence interval would be (175.04,194.96).

Using the provided values and steps for constructing a 95% confidence interval, we estimate, with 95% confidence, that the true mean cholesterol content of all such eggs lies between 146.5 and 223.5 milligrams.

To construct a 95% confidence interval for the mean cholesterol content of all such eggs, we will use the provided sample mean (185 milligrams), standard error (17.6 milligrams), and the fact that the sample size (12 eggs) is relatively small, so we use a t-distribution.

Firstly, we need to find the t-score for a 95% confidence level. Since degrees of freedom (df) is n-1 -> 12-1=11, and at a 95% confidence level, the t-score (from t-distribution table) is approximately 2.201 for a two-tailed test.

Next, we use the formula for confidence interval:

Calculating these gives:

So, we estimate with 95 percent confidence that the true mean cholesterol content of all such eggs is between 146.5 and 223.5 milligrams.

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Answer:

Part A)  Not collide

Part B)  k = 4

Part C)  Particle B is moving fast.

Step-by-step explanation:

Two particles move in the xy-plane. At time, t

Position of particle A:-

[tex]x(t)=5t-5[/tex]

[tex]y(t)=2t-k[/tex]

Position of particles B:-

[tex]x(t)=4t[/tex]

[tex]y(t)=t^2-2t+1[/tex]

Part A)  For k = -6

Position particle A, (5t-5,2t+6)

Position of particle B, [tex](4t,t^2-2t-1)[/tex]

If both collides then x and y coordinate must be same

Therefore,

5t - 5 = 4t    

       t = 5

[tex]2t+6=t^2-2t-1[/tex]

[tex]t^2-4t-7=0[/tex]

[tex]t=-1.3,5.3[/tex]

The value of t is not same. So, k = -6 A and B will not collide.

Part B) If both collides then x and y coordinate must be same

5t - 5 = 4t    

       t = 5

[tex]2t-k=t^2-2t-1[/tex]

Put t = 5

[tex]10-k=25-10-1[/tex]

[tex]k=4[/tex]

Hence, if k = 4 then A and B collide.

Part C)

Speed of particle A, [tex]\dfrac{dA}{dt}[/tex]

[tex]\dfrac{dA}{dt}=\dfrac{dy}{dt}\cdot \dfrac{dt}{dx}[/tex]

[tex]\dfrac{dA}{dt}=2\cdot \dfrac{1}{5}\approx 0.4[/tex]

Speed of particle B, [tex]\dfrac{dB}{dt}[/tex]

[tex]\dfrac{dB}{dt}=\dfrac{dy}{dt}\cdot \dfrac{dt}{dx}[/tex]

[tex]\dfrac{dB}{dt}=2t-2\cdot \dfrac{1}{4}[/tex]

At t = 5

[tex]\dfrac{dB}{dt}=10-2\cdot \dfrac{1}{4}=2[/tex]

Hence, Particle B moves faster than particle A

To determine if the particles collide, we set up equations with their x-coordinates and y-coordinates. Part (a) asks if they collide when k = -6. Part (b) asks for the value of k that guarantees a collision, and part (c) compares the speeds at the time of collision.

To determine if the particles collide, we need to find out if their x-coordinates and y-coordinates are equal at any given time. Given the positions of particles A and B, we can set up two equations by equating their x-coordinates and y-coordinates. For part (a), when k = -6 we can solve the equations to find if they intersect. For part (b), we need to find the value of k that makes the two particles collide. Finally, for part (c), we compare the speeds of particles A and B when they collide.

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Answer:

The length of the person’s shadow is 5.7ft

Explanation:

Length of the flagpole =a= 35ft

Length of the shadow of the flagpole= b=50ft

Length of the person=c= 4ft

Suppose the length of the person’s shadow is=d

According to the  rules of trigonometry

[tex]\frac{\text { Length of the flagpole }}{\text { Length of the shadow of the flagpole }}=\frac{\text { Length of the person }}{\text { Length of the person's shadow }}[/tex]

[tex]\frac{a}{b}=\frac{c}{d}[/tex]

[tex]\frac{35}{50}=\frac{4}{d}[/tex]

35d=200

d=[tex]\frac{200}{35}[/tex]

d=5.7ft  

Hence, The length of the person’s shadow is 5.7ft.

Answer:

  3

Step-by-step explanation:

The slope (m) is the ratio of the difference in y-values to the corresponding difference in x-values:

  m = (y2 -y1)/(x2 -x1)

  m = (9 -0)/(4 -1) = 9/3

  m = 3

The slope of the line is 3.

Answer:

565 robots

Step-by-step explanation:

We can do this numerically, month by month:

At t = 0, sale is 10 units. Growth rate is 5 robots per month.

At t = 1, sale is 15 units. Growth rate is 7 robots per month.

At t = 2, sale is 22 units. Growth rate is 9 robots per month.

At t = 3, sale is 31 units. Growth rate is 11 robots per month.

At t = 4, sale is 42 units. Growth rate is 13 robots per month.

At t = 5, sale is 55 units. Growth rate is 15 robots per month.

At t = 6, sale is 70 units. Growth rate is 17 robots per month.

At t = 7, sale is 87 units. Growth rate is 19 robots per month.

At t = 8, sale is 106 units. Growth rate is 21 robots per month.

At t = 9, sale is 127 units. Growth rate is 23 robots per month.

So the total robots we can expect to sell by the end of tenth month is

565 robots.

Answer:20 hoseholds, what is the probability that more than

Step-by-step explanation:

Answer:

We fail to reject the null hypothesis at the significance level of 0.05.

Step-by-step explanation:

We have a larga sample size of n = 80, [tex]\bar{x} = 4.39[/tex] and s = 2.29. We want to test

[tex]H_{0}: \mu = 4.50[/tex] vs [tex]H_{1}: \mu \neq 4.50[/tex] (two-tailed alternative)  

Because we have a large sample, our test statistic is

[tex]Z = \frac{\bar{X}-4.50}{s/\sqrt{n}}[/tex] which is normal standard approximately. We have the observed value

[tex]z_{0} = \frac{4.39-4.50}{2.29/\sqrt{80}} = -0.4296[/tex].

The p-value is given by 2P(Z < -0.4296) = (2)(0.3337) = 0.6674 (because of the simmetry of the normal density)

With the significance level [tex]\alpha = 0.05[/tex], we fail to reject the null hypothesis because the p-value is greater than 0.05.

We are going to test if the mean is equals to 4.50, thus, the null hypothesis is:

[tex]H_0: \mu = 4.5[/tex]

At the alternative hypothesis, we test if the mean is different to 4.50, that is:

[tex]H_1: \mu \neq 4.50[/tex]

Since we have the standard deviation for the sample, the t-distribution is used. The value of the test statistic is:

[tex]t = \frac{\overline{x} - \mu}{\frac{s}{\sqrt{n}}}[/tex]

For this problem:

[tex]t = \frac{4.39 - 4.5}{\frac{2.29}{\sqrt{80}}}[/tex]

[tex]t = -0.43[/tex]

We are testing if the mean is different from a value, thus, the p-value of test is found using a two-tailed test, with [tex]t = -0.43[/tex] and 80 - 1 = 79 df.

Using a t-distribution calculator, the p-value is of 0.6684.

The p-value is of 0.6684 > 0.05, which means that we can conclude that the population of student course evaluations has a mean equal to 4.50.

A similar problem is given at https://brainly.com/question/24989605

Answer:

a) 0.9292

b)0.1515

Step-by-step explanation:

Explained in attachment.

Answer:

mwah, thank you so much for posting this question! you just saved my life!! :,)

Step-by-step explanation:

thank you thank you.

Mwah!!!!

Answer:

Both are binomials.

Step-by-step explanation:

Given that

a) X is the number of dots on the top face of fair die that is rolled.

When a fair die is rolled, there will be 1 to 6 numbers on each side with dots in that.  Each time a die is rolled the events are independent.  Hence probability of getting a particular number in the die is 1/6. There will be two outcomes either the number or not the number.  Hence X no of times we get a particular number of dots on the top face of fair die that is rolled is binomial with n = no of rolls, and p = 1/6

b) X is the number of defective parts in a sample of ten randomly selected parts coming from a manufacturing process in which 0.02% of all parts are defective.

Here X has two outcomes whether defective or non defective.  EAch part is independent of the other in the sense that the probability for each trial is constant with 0.02% =p and no of trials = n = 10.

Answer:

a)The sample size

Step-by-step explanation:

t - student distribution should be used whe we are facing a Normal Distribution population andwhen a sample size n is below 30.

t- student distribution is also a associated to a bell shape but is more flat and the values are more spread, tails are much more wide.

t- student use the concept of degree of fredom ( each degree of fredom correspont to an specific curve). As degree of fredom increase the curves become more close to a bell shape. For  n = 30 t-student curve is  a bell shape one

Answer:

a) 1.29

b) 1.29

c) 1.28

d) 1.23

Step-by-step explanation:

We are given the following in the question:

Sample size, n = 60

Population standard Deviation = 10

a) Standard error with infinite population

[tex]\text{Standard error} = \displaystyle\frac{\sigma}{\sqrt{n}} = \frac{10}{\sqrt{60}} = 1.29[/tex]

For finite population with size N,

[tex]\text{Standard error} = \sqrt{\displaystyle\frac{N-n}{N-1}}\times \displaystyle\frac{\sigma}{\sqrt{n}}[/tex]

b) N = 60,000

[tex]\text{Standard error} = \sqrt{\displaystyle\frac{60000-50}{60000-1}}\times \displaystyle\frac{10}{\sqrt{60}} = 1.29[/tex]

c) N = 6,000

[tex]\text{Standard error} = \sqrt{\displaystyle\frac{6000-50}{6000-1}}\times \displaystyle\frac{10}{\sqrt{60}} = 1.28[/tex]

d) N = 600

[tex]\text{Standard error} = \sqrt{\displaystyle\frac{600-50}{600-1}}\times \displaystyle\frac{10}{\sqrt{60}} = 1.23[/tex]

To find the standard error of the mean, use the formula σx/√n for infinite population size and σx/√n * √((N - n)/(N - 1)) for finite population size. Use the finite population correction factor when the sample size is not negligible relative to the population size.

To find the value of the standard error of the mean in each case, we need to use the formula for the standard error of the mean, which is σx/sqrt(n), where σ is the population standard deviation and n is the sample size.

If the population size is infinite, we don't need to use the finite population correction factor. So, the standard error of the mean would be σ/√n.

In this case, the population size is finite, but the sample size is less than 5% of the population size. Therefore, we can still consider the population as effectively infinite. So, the standard error of the mean would be σ/√n.

In this case, the population size is finite and the sample size is not negligible relative to the population size. Therefore, we need to use the finite population correction factor, which is √((N - n)/(N - 1)). So, the standard error of the mean would be σ/√n * √((N - n)/(N - 1)).

In this case, the population size is finite and the sample size is not negligible relative to the population size. Therefore, we need to use the finite population correction factor. So, the standard error of the mean would be σ/√n * √((N - n)/(N - 1)).

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Answer:

[tex]\mu_{\hat{p}}=0.50\\\\\sigma_{\hat{p}}=0.0158[/tex]

Step-by-step explanation:

The probability distribution of sampling distribution [tex]\hat{p}[/tex] is known as it sampling distribution.

The mean and standard deviation of the proportion is given by :-

[tex]\mu_{\hat{p}}=p\\\\\sigma_{\hat{p}}=\sqrt{\dfrac{p(1-p)}{n}}[/tex]

, where p =population proportion and  n= sample size.

Given : According to a survey, 50% of Americans were in 2005 satisfied with their job.

i.e. p = 50%=0.50

Now, for sample size n= 1000 , the mean and standard deviation of the proportion will be :-

[tex]\mu_{\hat{p}}=0.50\\\\\sigma_{\hat{p}}=\sqrt{\dfrac{0.50(1-0.50)}{1000}}=\sqrt{0.00025}\\\\=0.0158113883008\approx0.0158[/tex]

Hence, the mean and standard deviation of the proportion for a sample of 1000:

[tex]\mu_{\hat{p}}=0.50\\\\\sigma_{\hat{p}}=0.0158[/tex]

Answer:

Step-by-step explanation:

To estimate the mean age at which Clemson students would like to get married to within 1.5 years with 90% confidence, we need to survey at least 92 Clemson students.

To estimate the mean age at which Clemson students would like to get married to within 1.5 years with 90% confidence, we need to calculate the sample size needed for this level of precision. The formula to determine the sample size is:

n = (z * σ / E)^2

where n is the required sample size, z is the z-score corresponding to the desired confidence level (in this case 90% confidence level which corresponds to a z-score of 1.645), σ is the standard deviation, and E is the desired margin of error (1.5 years).

Plugging in the values, we get:

n = (1.645 * 6 / 1.5)^2

n = 91.154

Rounding up to the nearest whole number, we need to survey at least 92 Clemson students in order to estimate the mean age at which Clemson students would like to get married to within 1.5 years with 90% confidence.

Answer:

0.212 is the probability that a randomly selected tire will have a depth less than 0.70 mm.

Step-by-step explanation:

We are given the following information in the question:

Mean, μ =  0.98 mm

Standard Deviation, σ =  0.35 mm

We are given that the distribution of tire tread is a bell shaped distribution that is a normal distribution.

Formula:

[tex]z_{score} = \displaystyle\frac{x-\mu}{\sigma}[/tex]

a) P(depth less than 0.70 mm)

P(x < 0.70)

[tex]P( x < 0.70) = P( z < \displaystyle\frac{0.70 - 0.98}{0.35}) = P(z < -0.8)[/tex]

Calculating from normal z table, we have:

[tex]P(z<-0.8) = 0.212[/tex]

[tex]P(x < 0.70) = 0.212 = 21.2\%[/tex]

Thus, this event is not unusual and will  not warrant a refund.

One sample t-test

Step-by-step explanation:

In this statistical test, you will be able to test if a sample mean, significantly differs from a hypothesized value.Here you can test if the average swimming velocity differs significantly from an identified value in the hypothesis.Then you can conclude whether the group of 18 fish or that of 21 fish has a significantly higher or lower  mean velocity than the one in the hypothesis.

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Keywords: population, velocity, statistical test,average

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Answer:

The number of ways is equal to [tex]12!8!20![/tex]

Step-by-step explanation:

The multiplication principle states that If a first experiment can happen in n1 ways, then a second experiment can happen in n2 ways ... and finally a i-experiment can happen in ni ways therefore the total ways in which the whole experiment can occur are

n1 x n2 x ... x ni

Also, given n-elements in which we want to put them in a row, the total ways to do this are n! that is n-factorial.

For example : We want to put 4 different objects in a row.

The total ways to do this are [tex]4!=4.3.2.1=24[/tex] ways.

Using the multiplication principle and the n-factorial number :

The number of ways to put all 40 in a row for a picture, with all 12 sophomores on the left,all 8 juniors in the middle, and all 20 seniors on the right are : The total ways to put all 12 sophomores in a row multiply by the ways to put the 8 juniors in a row and finally multiply by the total ways to put all 20 senior in a row ⇒ [tex]12!8!20![/tex]

There are 1.1657416 × 10^30 ways to arrange the students in a row with all the sophomores on the left, juniors in the middle, and seniors on the right.

To find the number of ways to arrange the students in a row with all the sophomores on the left, juniors in the middle, and seniors on the right, we need to calculate the permutations of each group and then multiply them together.

The number of ways to arrange the 12 sophomores is 12!, which is 479,001,600.

The number of ways to arrange the 8 juniors is 8!, which is 40,320.

The number of ways to arrange the 20 seniors is 20!, which is 2,432,902,008,176,640,000.

Multiplying these three numbers together, we get a total of 1.1657416 × 10^30 ways to arrange all the students in a row for a picture.

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Answer:

Lowest acceptable score = 121.3

Step-by-step explanation:

Mean test score (μ) = 115

Standard deviation (σ) = 12

The z-score for any given test score 'X' is defined as:  

[tex]z=\frac{X-\mu}{\sigma}[/tex]  

In this situation, the organization is looking for people who scored in the upper 30% range, that is, people at or above the 70-th percentile of the normally distributed scores. At the 70-th percentile, the corresponding z-score is 0.525 (obtained from a z-score table). The minimum score, X, that would enable a candidate to apply for membership is:

[tex]0.525=\frac{X-115}{12}\\X=121.3[/tex]

Answer:

6x^2 + 18x + 19.

Step-by-step explanation:

5x^2 + 6x - 17 + (x + 6)^2

= 5x^2 + 6x - 17 + x^2 + 12x + 36

=  6x^2 + 18x + 19.

Answer:

6x^2 + 18x + 19

Step-by-step explanation:

The standard form of a polynomial depends on the degree of the polynomial. The polynomial is in the standard form when it is arranged such that the first term contains the highest degree, and it decreases with the consecutive terms.

The square of the binomial is (x+6)^2

(x+6)^2 = (x+6)(x+6) = x^2 + 6x + 6x + 36 = x^2 + 12x + 36

The sum of the two polynomials will be 5x^2+6x−17 + x^2 + 12x + 36

Collecting like terms,

5x^2 + x^2 + 6x + 12x + 36 -17

= 6x^2 + 18x + 19

Answer: 0.0026

Step-by-step explanation:

Let p denotes the proportion of students plan to go into general practice.

As per given , we have

Alternative hypothesis : [tex] H_a: p>0.28[/tex]

Since the alternative hypothesis [tex](H_a)[/tex] is right-tailed so the test is  a right-tailed test.

Also , it is given that ,

i.e. sample size : = 130

x= 490

[tex]\hat{p}=0.39[/tex]

Test statistic(z) for population proportion :

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

, where p=population proportion.

[tex]\hat{p}[/tex]= sample proportion

n= sample size.

[tex]z=\dfrac{0.39-0.28}{\sqrt{\dfrac{0.28(1-0.28)}{130}}}\\\\=\dfrac{0.11}{0.0393798073988}=2.79330975101\approx2.79[/tex]

P-value for right-tailed test = P(z>2.79)=1-P(z≤ 2.79)  [∵P(Z>z)=1-P(Z≤z)]

=1- 0.9974=0.0026  [using z-value table]

Hence, the  P-Value for a test of the school's claim = 0.0026

To find the p-value for a test of the school's claim, we can use a hypothesis test. The null hypothesis (H0) is that the proportion of students planning to go into general practice is equal to or less than 28%. The alternative hypothesis (Ha) is that the proportion of students planning to go into general practice is greater than 28%. Given that 39% of the sample of 130 students plan to go into general practice, we calculated the test statistic and the p-value to determine that the p-value is 0.3078.

To find the p-value for a test of the school's claim, we can use a hypothesis test. The null hypothesis (H0) is that the proportion of students planning to go into general practice is equal to or less than 28%. The alternative hypothesis (Ha) is that the proportion of students planning to go into general practice is greater than 28%.

Given that 39% of the sample of 130 students plan to go into general practice, we can calculate the test statistic and the p-value. Using a chi-square test, we calculate the test statistic to be approximately 1.307. The degrees of freedom for this test is 1.

Looking up the critical value for a one-tailed test with an alpha level of 0.05 and 1 degree of freedom, the critical value is approximately 3.841. Since the test statistic is less than the critical value, we fail to reject the null hypothesis. Therefore, the p-value is greater than 0.05.

Therefore, the correct answer for the P-value is 0.3078.

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Answer:

[tex]SE=\frac{1.5}{\sqrt{120}}=0.137[/tex]

The 95% confidence interval would be given by (4.729;5.271)    

Step-by-step explanation:

1) 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".

The standard error of a statistic is "the standard deviation of its sampling distribution or an estimate of that standard deviation"

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[/tex] represent the sample mean for the sample  

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

s represent the sample standard deviation

n represent the sample size  

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)

We use the t distirbution for this case since we don't know the population standard deviation [tex]\sigma[/tex].

Where the standard error is given by: [tex]SE=\frac{s}{\sqrt{n}}[/tex]

And the margin of error would be given by: [tex]ME=t_{\alpha/2}\frac{s}{\sqrt{n}}[/tex]

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=120-1=119[/tex]

Since the Confidence is 0.95 or 95%, the value of [tex]\alpha=0.05[/tex] and [tex]\alpha/2 =0.025[/tex], and we can use excel, a calculator or a table to find the critical value. And we see that [tex]t_{\alpha/2}=1.98[/tex]

The standard error would be given by:

[tex]SE=\frac{1.5}{\sqrt{120}}=0.137[/tex]

Now we have everything in order to replace into formula (1) and calculate the interval:

[tex]5-1.98\frac{1.5}{\sqrt{120}}=4.729[/tex]    

[tex]5+1.98\frac{1.5}{\sqrt{120}}=5.271[/tex]

So on this case the 95% confidence interval would be given by (4.729;5.271)    

Final answer:

The standard error of the mean (SE) is calculated to be 0.137, allowing the resort to expect the true average number of nights per guest to fall within a range of approximately 4.73 to 5.27 nights with 95% confidence.

Explanation:

The standard error of the mean (SE) is calculated by dividing the sample standard deviation by the square root of the sample size. In this case, the standard error is 1.5 / sqrt(120) = 0.137. With a confidence level of 95%, the resort can expect the true average number of nights to fall within approximately 5 - 1.96 * 0.137 nights to 5 + 1.96 * 0.137 nights, which is roughly between 4.73 and 5.27 nights.