Let Y_1.... Y_n be a random sample from a distribution with the density function

f_θ(y) = 3θ^3/y^4 y≥θ≥0
Is there a UMP test at level a for testing H_o: θ ≤ θ_o vs. H_1 : θ> θ_o? If so, what is the test?

Answer:

-2 , -10 & -99

Step-by-step explanation:

1)

⇒ x + 6 = 4

⇒ x = 4-6

⇒ x = (-2)

2)

⇒x - (-4) = -6

⇒ x + 4 = -6

⇒ x = -6-4

⇒ x = -10

3)

⇒2(x-1) = -200

⇒ x - 1 = -200/2

⇒ x - 1 = -100

⇒ x = 1 - 100

⇒ x = (-99)

Answer:

1. x= -2 2. ×= -10 3. x= -99

Step-by-step explanation:

1. x+6 = 4

x = 4-6

x = -2

2. x-(-4) = -6

x+4 = -6

x = -6 - 4

x = -10

3. 2(x-1) = -200

2x - 2 = -200

2x = -200 +2

2x = -198

x = -99

Answer:

B.) Yes; it can be rotated 180° or less and match the original figure.

Step-by-step explanation:

You have to trust me its b I did the quiz on Pearson Conexus. I would show a picture but I'm on pc and don't know how to .

My answer will help.

Anyways, have a great day/night !! <3

Step-by-step explanation:

Does the figure below have rotational symmetry? Explain.

A.)Yes; it can be rotated 360° or less and match the original figure.

B.)Yes; it can be rotated 180° or less and match the original figure.

C.)No; it does not match the original figure after any rotation of 180° or less.

D.)No; it does not match the original figure after any rotation of 360° or less.

Step-by-step explanation:

[tex]16 {x}^{2} - 25 = 0 \\ 16 {x}^{2} = 0 + 25 \\ 16 {x}^{2} = 25 \\ {x}^{2} = \frac{25}{16} \\ x = \sqrt{ \frac{25}{16} } \\ = 1.25 \: or \frac{5}{4} \: or1 \frac{1}{4} [/tex]

Do note that this question might have two answers as Square roots will actually give you a positive and negative solution. Clarify with your teacher about this.

Answer:

x=0.09765625 or rounded up as 0.1

Step-by-step explanation:

first you have to add 25 to the 0 and subtract it from the sid it is on. making it look like this

16x²=25

256x=25

now divide both sides by 256 and you get

x=0.09765625 or rounded up as 0.1

Answer:

The horizontal distance is 46.1 miles.

Step-by-step explanation:

Here, the given question can be explained by the sides of a right angled triangle. Let x represent the horizontal miles, then applying an appropriate trigonometric function;

Tan θ = [tex]\frac{opposite}{adjacent}[/tex]

Tan [tex]7.9^{o}[/tex] = [tex]\frac{6.4}{x}[/tex]

0.1388 = [tex]\frac{6.4}{x}[/tex]

x = [tex]\frac{6.4}{0.1388}[/tex]

  = 46.1095

x = 46.1 miles

The plane needs to travel a horizontal distance of 46.1 miles.

The two true statements are: b.) The value for the degrees of freedom for Mary's sample population is six, and e.) Mary would use the sample standard deviation to calculate a t-statistic.

a.) The t-distribution that Mary uses does not have skinnier tails than a standard distribution. In fact, the t-distribution has fatter tails, which accounts for the increased variability when working with small sample sizes.

b.) The degrees of freedom for Mary's sample population can be calculated as the number of subjects minus one, which in this case is 7 - 1 = 6. So statement b is true.

c.) The t-distribution that Mary uses is not taller than a standard distribution. The shape of the t-distribution is similar to the standard normal distribution, but it is slightly flatter.

d.) Mary would not use the population standard deviation to calculate a t-distribution. Instead, she would use the sample standard deviation, which provides an estimate of the population standard deviation.

e.) Mary would use the sample standard deviation to calculate a t-statistic. The t-statistic measures the difference between the sample mean and the hypothesized population mean, relative to the variability in the sample.

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Answer:

2.268 tons

Step-by-step explanation:

Multiply 840 to 2.7 to figure out the weight of 840 bricks.

840 x 2.7 = 2268 kilograms

Convert 2268 kilograms into tons which will be 2.268 tons.

How did I convert? Divide 2268 by 1000 because 1 ton = 1000 kilograms.  

Answer:

it's the first one

Step-by-step explanation:

you'd get each output (y) when you subtract 4 from whatever x is

The minimum red blood cell count that can be in the top 26% of counts is approximately 5.37 million cells per microliter. The maximum red blood cell count that can be in the bottom 16% of counts is approximately 4.602 million cells per microliter.

(a) The minimum red blood cell count that can be in the top 26% of counts can be found by calculating the z-score corresponding to the cumulative probability of 0.26.

Using the standard normal distribution table or a calculator, we find that the z-score is approximately 0.675.

We can then use the z-score formula to find the corresponding value in the original distribution: minimum red blood cell count = mean + (z-score * standard deviation) = 5.1 + (0.675 * 0.4) = 5.37 million cells per microliter.

(b) The maximum red blood cell count that can be in the bottom 16% of counts can be found by calculating the z-score corresponding to the cumulative probability of 0.16.

Using the standard normal distribution table or a calculator, we find that the z-score is approximately -0.994.

Using the z-score formula, we can find the corresponding value in the original distribution: maximum red blood cell count = mean + (z-score * standard deviation) = 5.1 + (-0.994 * 0.4) = 4.602 million cells per microliter.

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Answer:

1 min : 12 min

Step-by-step explanation:

To simplify the given ratio, convert both to the same unit of time.

1 hour = 60 minutes

3 hours = 3 x 60 minutes

             = 180 minutes

So that,

15 min : 3 hours = 15 min : 180 min

divide through by 15 to have;

15 min : 180 min = 1 min : 12 min

Thus the simplified form of the number give is 1 min : 12 min.

The value that is small enough to rule out chance as the most likely explanation is called the p-value. Typical p-values considered as statistically significant are 0.05 or lower.

The p-value is a measure of the strength of evidence against the null hypothesis in statistical hypothesis testing. It represents the probability of obtaining the observed data or more extreme results, assuming that the null hypothesis is true. If the p-value is below a predetermined significance level (often 0.05), it is considered statistically significant.

This means that the observed data is unlikely to have occurred by chance alone, providing evidence to reject the null hypothesis in favor of an alternative hypothesis. Lower p-values indicate stronger evidence against the null hypothesis.

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Answer:

∠PSR=90°

m∠QSR= 90-m∠QSP

x= 90-58=32°

Step-by-step explanation:

The actual value of the x is 32°.

Quadrilaterals are four sided polygons which also have four vertices and four angles.

Sum of all the interior angles of a quadrilateral is 360 degrees.

Given quadrilateral is a rectangle.

All the four interior angles of a rectangle is perpendicular angle or is 90 degrees.

So measure of ∠PSR = 90°

m ∠QSR + m ∠QSP = 90°

Given that m ∠QSP = 58°

Substituting,

m ∠QSR + 58° = 90°

m ∠QSR = 90° - 58°

              = 32°

So x° = 32°

Hence the error is that both the angles are made to be equal instead of making the sum of the angles equal to 90°.

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Answer:

100feet

Step-by-step explanation:

Given the height reached by a dolphin expressed by the equation is h = -16t^2 + 80t. At maximum height, the velocity is zero, hence;

v(t) = dh/dt = -32t + 80

0 = -32t + 80

32t = 80

t = 80/32

t = 2.5secs

Substitute t = 2.5 into the height formula h = -16t^2 + 80t

h = -16(2.5)^2 + 80(2.5)

h = -100+200

h = 100feet

Hence  the maximum height the dolphin jumps is 100feet

In discrete mathematics, the expression "L(C) = L(R) ∩ L(S)" means that the language generated by the grammar or automaton represented by C is equal to the intersection of the languages generated by the grammars or automata represented by R and S.

The term "L(C)" refers to the language generated by C, which represents the set of all valid strings or sequences that can be produced by the grammar or automaton C. Similarly, "L(R)" and "L(S)" represent the languages generated by R and S, respectively.

The intersection (∩) symbol signifies the common elements or strings that are present in both L(R) and L(S). In other words, it represents the set of strings that can be generated by both R and S.

As for the underline symbol in the expression "L(S)__L(S)", it is commonly used to denote the closure or complement of a language. However, without additional context or information, it is difficult to determine the exact meaning in this case. It could indicate a specific operation or property related to the language L(S), which would require further clarification.

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Answer:

The Answer is lime 2 if not line 4

Step-by-step explanation:

YEp:)

Answer:

Step-by-step explanation:

multiply all three values

Answer:

√53

Step-by-step explanation:

Use the distance formula to find the distance between the two coordinates

Answer:

why shoulds i answer them?

Step-by-step explanation:

Okay okay fine i will answer them just add me first!

a. The scatter plot of the data shows the incidence of diagnosed cases of celiac disease since 1950.

b. A linear regression model would best fit this data.

c. The equation for the curve of best fit is y = 0.603x + 8.821, where y represents the number of diagnosed cases of celiac disease (per 1000) and x represents the years since 1950.

d. Using the regression model, approximately 10.835 cases of celiac disease may be diagnosed in 2020.

a. To create a scatter plot of the data, we plot the number of diagnosed cases of celiac disease (y-axis) against the years since 1950 (x-axis). Each data point represents a pair of (x, y) values. For example, the first data point would be (0, 10), the second (8, 11), and so on.

b. In this case, a linear regression model would best fit the data because the scatter plot suggests a roughly linear relationship between the years since 1950 and the number of diagnosed cases of celiac disease.

c. To find the equation for the curve of best fit, we can use linear regression. We calculate the slope and intercept of the line that minimizes the sum of squared distances between the data points and the line. The equation of a line is y = mx + b, where m is the slope and b is the intercept. By performing linear regression on the given data points, we obtain the equation y = 0.603x + 8.821.

d. To estimate the number of diagnosed cases of celiac disease in 2020, we substitute x = 70 (since 2020 is 70 years since 1950) into the equation y = 0.603x + 8.821. Solving the equation, we find that y is approximately equal to 10.835. Therefore, using the regression model, around 10.835 cases of celiac disease may be diagnosed in 2020.

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we expect around 1464 Lagrangian Toads to live between 9 years and 19 years.

To solve this problem, we need to calculate the probability that a Lagrangian Toad's life expectancy falls between 9 years and 19 years, given a normal distribution with a mean of 15 years and a standard deviation of 2 years.

First, we calculate the z-scores for the lower and upper bounds of the desired range:

Lower z-score = (9 - 15) / 2 = -3

Upper z-score = (19 - 15) / 2 = 2

Next, we use the z-scores to find the cumulative probability using a standard normal distribution table or calculator.

P(Z < -3) ≈ 0.0013

P(Z < 2) ≈ 0.9772

To find the probability between these two z-scores, we subtract the lower probability from the upper probability:

P(-3 < Z < 2) ≈ 0.9772 - 0.0013 = 0.9759

This means that approximately 97.59% of the Lagrangian Toads in the population are expected to live between 9 years and 19 years.

To find the number of Lagrangian Toads in this range, we multiply the probability by the total population size:

Number of Lagrangian Toads = 0.9759 * 1500 ≈ 1463.85

Rounding to the nearest whole number, we expect around 1464 Lagrangian Toads to live between 9 years and 19 years.

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sorry im on my school computer so the picture is blocked :(

Answer:

A.) 1/7^8

Step-by-step explanation:

guy in the comments said it and i got it write, so this is for the people that cant see the comments.

Answer:

Surface Area varies by object/shape.

Ask your teacher/learning helper for those formulas of surface area

Step-by-step explanation:

Surface area is the sum of all faces (or surfaces) on a 3D shape. A cuboid has 6 rectangular faces. To find the surface area of a cuboid, add the areas of all 6 faces. We can also label the length (l), width (w), and height (h) of the prism and use the formula, SA=2lw+2lh+2hw, to find the surface area.

Answer:

y=-1/3x+5

Step-by-step explanation:

I know the equation would be y=-1/3x+5 because of slope intercept form.

y=mx+b

y= the y coordinate of a point on the graph

m= slope of the graph

x= the x coordinate of a point on the graph

b= y intercept

We know the slope of the graph is -1/3, and we know the y- intercept of the graph is 5.

So, the equation is y=-1/3x+5.

The equation of the following line is y= -1/5x.

Option F is correct.

A line's slope is determined by how its y coordinate changes in relation to how its x coordinate changes. y and x are the net changes in the y and x coordinates, respectively. Therefore, it is possible to write the

change in y coordinate with respect to the change in x coordinate as,

m = Δy/Δx where, m is the slope

From the graph we have the coordinates as (0, 0) and (10, -2)

So, the slope of the line is

= (-2 - 0) / (10- 0)

= -2/ 10

= 1-5

and, using the slope intercept is

y= mx+ b

y= -1/5x + b

Now, put x= 0 and y= 0 in above equation

0 = 1/5(0) + b

b= 0

So, the Equation of line is

y= -1/5x

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The null hypothesis of a sign test is "the population median equals 0." This hypothesis assumes that the median of the population, from which the sample is drawn, is equal to zero.

The sign test is a non-parametric statistical test used when the data is measured on an ordinal scale or when the assumptions for parametric tests are not met.

In a sign test, we compare the observed data to a specified value, typically denoted as "M0" or "m0." This value represents the hypothesized population median.

The null hypothesis assumes that the population median is equal to the specified value. In this case, the specified value is zero.

The alternative hypothesis, in contrast, would state that the population median is not equal to the specified value.

During the sign test, we focus on the signs of the differences between the observed values and the specified value. We count the number of positive and negative differences.

The test statistic is the smaller of the two counts, and its probability distribution is approximated by the binomial distribution.

By comparing the test statistic to the critical values from the binomial distribution, we determine whether there is enough evidence to reject the null hypothesis.

In summary, the null hypothesis for a sign test is that the population median is equal to the specified value, which in this case is zero.

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The carrying capacity for the yeast population, estimated from the plot, is 680 cells. The initial relative growth rate can be determined using the first two points of the data.

(a) From the plot of the data, we observe that the yeast population levels off or stabilizes around the value of 680 cells. This value represents the carrying capacity of the yeast population in the laboratory culture. (b) To estimate the initial relative growth rate, we use the first two points of the data: (0, 18) and (2, 39). The relative growth rate can be calculated by dividing the change in the number of yeast cells by the change in time. In this case, the change in cells is 39 - 18 = 21, and the change in time is 2 - 0 = 2. Therefore, the initial relative growth rate is 21 / 2 = 10.5 cells per hour.

(c) An exponential model for the data can be determined by fitting an exponential function to the data points. Using the initial point (0, 18) as the initial condition, we can write the exponential model as P(t) = P₀e^(kt), where P(t) represents the number of yeast cells at time t, P₀ is the initial number of cells, k is the growth rate constant, and e is the base of the natural logarithm. Substituting the given data, we can solve for the values of P₀ and k. The exponential model for these data is P(t) = 17.08e^(0.189t).

(d) A logistic model accounts for the carrying capacity of the yeast population. It is given by the formula P(t) = K / (1 + ae^(-kt)), where K represents the carrying capacity, a is a constant related to the initial condition, and k is the growth rate constant. Using the given data, we can solve for the values of K, a, and k. The logistic model for these data is P(t) = 680 / (1 + 4.126e^(-0.189t)).(e) Using the logistic model obtained in part (d), we can estimate the number of yeast cells after 5 hours by substituting t = 5 into the equation. Thus, P(5) = 680 / (1 + 4.126e^(-0.189(5))). Calculating this expression yields approximately 231 yeast cells after 5 hours.

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Answer:

Group A, IQR = 700

GROUP B, IQR =.450

Group A's IQR is greater

The values in the middle 50%.of Group A are more spread out Than those of Group B.

Step-by-step explanation:

Data for.each group :

The IQR = Q3 - Q1

From the boxplot :

Group A:

Q1 = 1100 ; Q3 = 1800

IQR = 1800 - 1100 = 700

Group B:

Q1 = 1400; Q3 = 1850

IQR = 1850 - 1400 = 450

Greater IQR = Group A

The values in the middle 50%.of Group A are more spread out Than those of Group B.

Answer:

x=  -16/19

Step-by-step explanation:

x−14x+4+12=6x+32

−13x+16=6x+32

16=6x+32+13x

16=19x+32

16−32=19x

−16=19x

-16/19=x

Answer:

639.8

7000

2520

525

450

..........