A new beta-blocker medication is being tested to treat high blood pressure. Subjects with high blood pressure volunteered to take part in the experiment. 180 subjects were randomly assigned to receive a placebo and 200 received the medicine. High blood pressure disappeared in 100 of the controls and in 107 of the treatment group. Test the claim that the new beta-blocker medicine is effective at a significance level of �
α = 0.01.

Answer: #3 = 22/50 (reduced version is 11/25

Step-by-step explanation:

Answer:

Step-by-step explanation:

Predicted probabilities are different then experimental probabilities.  Experimental probabilities use the actual data.

P(red)= red/(total)= 22/(12+15+22)  = 22/50 = .44

P(hot cocoa = hot cocoa/total =5/(7+5+8) = 1/4 = .25

T3 = S(a, 1) = {x ∈ M : d(x,a) < 1} = {a} U (M{a}), using the same argument as for S3 in part (a).

In a discrete metric space, any subset of the space is an open set, since every point has a neighborhood of radius 1 that contains only that point. Therefore, for any point a in the discrete metric space, we have:

(a) Si = B(a, j) = {x ∈ M : d(x,a) < j} = {a}, since the only point within a distance of j from a is a itself.

S2 = B(a, g) = {x ∈ M : d(x,a) < g} = {a}, since the only point within a distance of g from a is a itself.

S3 = B(a, 1) = {x ∈ M : d(x,a) < 1} = {a} U (M{a}), since every point in M except a is within a distance of 1 from a, so the open ball of radius 1 centered at a contains all points in M except a, as well as a itself.

(b) Ti = S(0,7) = {x ∈ M : d(x,0) < 7} = M, since every point in the discrete metric space is within a distance of 7 from 0.

T2 = S(a, 1) = {x ∈ M : d(x,a) < 1} = {a} U (M{a}), using the same argument as for S3 in part (a).

T3 = S(a, 1) = {x ∈ M : d(x,a) < 1} = {a} U (M{a}), using the same argument as for S3 in part (a).

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Answer:

Step-by-step explanation:

im pretty sure it is 5:2

The angle of elevation of Elea's line of sight to the top of the tree is 12.4°

The angle of elevation is an angle that is formed between the horizontal line and the line of sight.

The vertical distance or height from the line of sight to the top of the tree is

23.5 - 1.6

= 21.9

This means the height will be the opposite to the angle of elevation and the distance between Elea and the tree is the adjascent.

Using trigonometry;

If tetha is the angle of elevation

tan(tetha) = opp/adj

tan(tetha) = 21.9/100

tan(tetha) = 0.219

tetha = tan^-1( 0.219)

= 12.4°

therefore the angle of elevation is 12.4°

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The frog's probability of being on lily pad B after an hour (or in the long run) is around 0.2975 or 29.75%.

To calculate the probability that the frog will be on lily pad B after an hour, we can start by calculating the long-run distribution or steady-state probabilities of the frog's movement between the two lily pads.

Let p represent the probability that the frog is on lily pad B at any given time. We may develop two equations based on the probabilities given in the problem:

p = 0.85(1-p) (the frog moves from point A to point B with a probability of 0.85 and stays on point B with a probability of 1-p).

1-p = 0.65p (the frog moves from B to A with the probability of 0.65 and stays on A with the probability of 1-p).

Simplifying the equation, we get:

p = 0.85 - 0.85p + 0.65p

p = 0.85(1 - 0.65) = 0.2975

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The angle measure to the nearest degree of tan B = .5543 is 29°.

Given that tan B = 0 .5543, we need to find the measure to the nearest degree of tan B,

Since, we need to find the measurement of the angle, so we will use the concept of inverse of trigonometric functions,

tan B = 0 .5543

B = tan⁻¹ (0.5543)

B = 28.99 ≈ 29°

Hence, the angle measure to the nearest degree of tan B = .5543 is 29°.

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The surface area of the regular hexagonal pyramid would be =550.28.

To calculate the surface area of a hexagonal pyramid, the formula that should be used would be given as follows;

S.A. = P×h/2 + B

P = Perimeter of base = 8×6 = 48

h = Slant height = 16

B = area of base = (3√3/2)a²

= 3√3/2)8²

= 3√3/2)64

= 166.28

S.A. = 48×16/2 + 166.28

= 384+166.28

= 550.28

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Therefore, the area of the trapezoid is 1338 3/4 square units.

A quadrilateral with at least one set of parallel sides is known in geometry as a trapezium, or trapezium in British and other versions of English. In Euclidean geometry, a trapezium is invariably a convex quadrilateral. The trapezoid's parallel sides are referred to as its bases.

To find the area of a trapezoid, we use the formula:

Area = (1/2) × (sum of parallel sides) × (height)

In this case, we have the following information:

The two parallel sides are 12 1/4 and 24 1/4.

The height is 73 1/2.

First, we need to add the two parallel sides to find the sum:

12 1/4 + 24 1/4 = 36 1/2

Next, we can plug these values into the formula:

Area = (1/2) × 36 1/2 × 73 1/2

Area = 1338.75

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Correct Question:

Given sides are 12(1/4), 24 (1/4), 73 (1/2) and 134(1/2), then What is the area of this trapezoid?

Given that the function:

[tex]f(x)=x^3+8x^2+x-42[/tex]

The function has zeros located at -7, 2, -3

First, let check f(2) = 0

[tex]2^3+8(2)+2-42=0[/tex]

 [tex]8+16+2-42=0[/tex]

        [tex]42-42=0[/tex]

Therefore, f(2) has a zero, that is (x - 2) is a factor of the polynomial function..

So, divide the given function by (x-2) to get the quadratic function.

[tex]\dfrac{x^3+8x^2+x-42}{x-2} =x^2+10x+21[/tex]

Now, solve the quadratic function.

    [tex]x^2+10x+21=0[/tex]

 [tex]x^2+7x+3x+21=0[/tex]

[tex]x(x+7)+3(x+7)=0[/tex]

[tex]x+7=0 \ \text{and} \ x+3=0[/tex]

       [tex]x=-7 \ \text{or} -3[/tex]

From the explanation above, it shows that -7, 2, and -3 are the roots (zeros) of the given polynomial function.

Thus, the behavior of the function can be described by using the degree and the leading coefficient.

The leading degree is 3 and the leading coefficient is 1.

So since the leading degree is odd (3), the end of the function will point in the opposite direction.

And since the leading coefficient is positive (+1), the graph rises to the right.

Hence, the behavior of the function falls to the left and rises to the right.

The standard deviation of this sample of counts is approximately 1.83.

To find the standard deviation of the sample of red blood cell counts, we can use the formula:

s = sqrt [ Σ(x - X)2 / (n - 1) ]

where Σ(x - X)2 is the sum of the squared deviations from the mean, n is the sample size, and X is the sample mean.

First, we need to find the sample mean:

X = (53 + 49 + 54 + 51 + 48 + 51) / 6 = 51

Next, we can calculate the squared deviations from the mean for each observation:

(53 - 51)2 = 4

(49 - 51)2 = 4

(54 - 51)2 = 9

(51 - 51)2 = 0

(48 - 51)2 = 9

(51 - 51)2 = 0

Then we can sum these squared deviations:

Σ(x - X)2 = 4 + 4 + 9 + 0 + 9 + 0 = 26

Finally, we can plug in these values into the formula for the standard deviation:

s = sqrt [ Σ(x - X)2 / (n - 1) ] = sqrt [ 26 / (6 - 1) ] ≈ 1.83

Therefore, the standard deviation of this sample of counts is approximately 1.83.

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The appropriate test to use in this scenario would be the t-test in Statcrunch.

This is because the sample size is less than 30 (n=16), and the population standard deviation is unknown.

Since the population is normally distributed, a t-test would be appropriate to estimate the confidence interval on the average levels of COVID antigens in the sample.

The t-test is used when the population standard deviation is unknown and must be estimated from the sample. The t-distribution is used to estimate the population means, with the degrees of freedom determined by the sample size. Therefore, in this scenario, a t-test would be the most appropriate test to use.

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Among the given numbers, -0.52 is closer to -1/2, 0.35 is closer to 0, and 0.25 is closer to both 0 and 1/2.

To determine which number is closer to -1/2, 0, and 1/2, we need to find the absolute value of the difference between each number and the three given values, and then compare the results.

- For 0.35: The absolute value of the difference between 0.35 and -1/2 is approximately 0.85, the absolute value of the difference between 0.35 and 0 is 0.35, and the absolute value of the difference between 0.35 and 1/2 is approximately 0.15. Therefore, 0.35 is closer to 0 than to -1/2 or 1/2.

- For -3/5: The absolute value of the difference between -3/5 and -1/2 is approximately 0.05, the absolute value of the difference between -3/5 and 0 is approximately 0.6, and the absolute value of the difference between -3/5 and 1/2 is approximately 0.95. Therefore, -3/5 is closer to 0 than to -1/2 or 1/2.

- For -0.52: The absolute value of the difference between -0.52 and -1/2 is approximately 0.02, the absolute value of the difference between -0.52 and 0 is approximately 0.52, and the absolute value of the difference between -0.52 and 1/2 is approximately 1.02. Therefore, -0.52 is closer to -1/2 than to 0 or 1/2.

- For 0.25: The absolute value of the difference between 0.25 and -1/2 is approximately 0.75, the absolute value of the difference between 0.25 and 0 is 0.25, and the absolute value of the difference between 0.25 and 1/2 is approximately 0.25. Therefore, 0.25 is closer to 0 and 1/2 than to -1/2.

- For 3/5: The absolute value of the difference between 3/5 and -1/2 is approximately 1.15, the absolute value of the difference between 3/5 and 0 is approximately 0.6, and the absolute value of the difference between 3/5 and 1/2 is approximately 0.05. Therefore, 3/5 is closer to 0 than to -1/2 or 1/2.

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Answer:

[tex] {9}^{x} \div {81}^{ \frac{2}{x} } = 1[/tex]

[tex]{9}^{x} \times {81}^{ \frac{ - 2}{x} } = 1[/tex]

[tex]{9}^{x} \times { ({9}^{2}) }^{ \frac{ - 2}{x} } = 1[/tex]

[tex]{9}^{x} \times {9}^{ \frac{ - 4}{x} } = 1[/tex]

[tex] {9}^{(x - \frac{4}{x}) } = 1[/tex]

[tex]{9}^{0 } = 1 \: \: \: \: \: (known)[/tex]

Equating the exponents

[tex]x - \frac{4}{x} = 0[/tex]

[tex] \frac{ {x}^{2} - 4 }{x} = 0[/tex]

[tex] {x}^{2} = 4 \\ x = + 2 \: or \: 2[/tex]

Therefore, the standard deviation of the sampling distribution of the sample proportion is approximately 0.024, rounded to three decimal places.  Therefore, the statement "The sampling distribution is normal or approximately normal" is true.

a) The mean of the sampling distribution of the sample proportion is equal to the population proportion, which is given as 0.07:

μp = p = 0.07

b) The standard deviation of the sampling distribution of the sample proportion is given by the formula:

σp = √[(p*(1-p))/n]

where n is the sample size. Substituting the given values, we get:

σp = √[(0.07*(1-0.07))/200]

≈ 0.024

c) To determine whether the sampling distribution is normal or approximately normal, we need to check two conditions: the sample size and the shape of the population distribution.

The sample size is given as n = 200, which is large enough for the Central Limit Theorem to apply.

The shape of the population distribution is not given, but since the sample size is large, we can assume that the distribution of the sample proportion will be approximately normal by the Central Limit Theorem.

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To obtain the following tableau, we pivot around the element at the intersection of the x1 column and the x6 row:

| BV | x1 | x2 | x3 | x4 | x5 | x6 | x7

A variable (from the Latin variabilis, "changeable") is a mathematical symbol. A variable can be a number, a vector, a matrix, a function, its argument, a set, or an element of a set.

To solve the given linear programming problem using the Big M method, we need to convert the problem into standard form by adding slack, surplus, and artificial variables as needed. Then, we use the simplex algorithm to iteratively improve the solution until we reach an optimal solution.

Let's first write the problem in standard form by introducing slack and artificial variables as follows:

Maximize Z = -2x1 + x2 - 4x3 + 3x4

Subject to:

x1 + x2 + 3x3 + 2x4 + x5 = 4

x1 - x3 + x4 - x6 = -1

2x1 + x2 + x7 = 2

x1 + 2x2 + x3 + 2x4 = 2

where x5, x6, x7 are slack and artificial variables.

We can see that the problem is infeasible because the last equation is inconsistent with the second equation. To make the problem feasible, we need to introduce artificial variables for the second equation and modify the objective function to penalize their use. This leads us to the following modified problem:

Maximize Z = -2x1 + x2 - 4x3 + 3x4 - M(x6 + x8)

Subject to:

x1 + x2 + 3x3 + 2x4 + x5 = 4

x1 - x3 + x4 + x6 - x8 = -1

2x1 + x2 + x7 = 2

x1 + 2x2 + x3 + 2x4 = 2

where x5, x6, x7, x8 are slack and artificial variables, and M is a large positive constant.

Now, we can construct the initial simplex tableau as follows:

| BV | x1 | x2 | x3 | x4 | x5 | x6 | x7 | x8 | RHS |

|----|----|----|----|----|----|----|----|----|-----|

| x5 | 1  | 1  | 3  | 2  | 1  | 0  | 0  | 0  |  4  |

| x6 | 1  | 0  | -1 | 1  | 0  | 1  | 0  | -1 | -1  |

| x7 | 2  | 1  | 1  | 0  | 0  | 0  | 1  | 0  |  2  |

| x8 | 1  | 1  | 2  | 1  | 0  | 0  | 0  | -1 |  2  |

| Z  | -2 | 1  | -4 | 3  | 0  | M  | 0  | -M |  0  |

The column for the objective function includes the coefficients of the original variables and the artificial variables, with the artificial variables having a coefficient of M in the objective function.

To perform the simplex algorithm, we select the most negative coefficient in the bottom row, which corresponds to x1, as the entering variable. We then select the row with the smallest nonnegative ratio of the RHS to the coefficient of the entering variable, which corresponds to x6, as the leaving variable. We pivot around the element in the intersection of the x1 column and the x6 row to obtain the next tableau:

| BV | x1 | x2 | x3 | x4 | x5 | x6 | x7

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Question: "Identify the correct description for the formula g'(x) ≈ g(x)/h – g(x – h)/h from the following options: FFD1: forward finite difference with stepsize h for the first derivative of g at a BFD1: backward finite difference with stepsize h for the first derivative of g at a CFD1: central finite difference with stepsize h for the first derivative of g at x CFD2: central finite difference with stepsize h for the second derivative of g at x None of the Above"

The correct description for the formula g'(x) ≈ g(x)/h – g(x – h)/h is BFD1: backward finite difference with stepsize h for the first derivative of g at a.

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We can then use the same arithmetic coding process as in part (a) to find the codeword for X4 = AACB with the updated probabilities. The final interval is [0.56, 0.6168) with a range of

a) Assuming a uniform prior on 0, we can use arithmetic coding to find the codeword for the sequence X4 = AACB.

First, we need to calculate the cumulative probabilities for each symbol:

P(A) = 0.60

P(B) = 0.6(1 - 0) = 0.24

P(C) = 0.4

Next, we set up the initial interval [0, 1) and divide it into sub-intervals proportional to the cumulative probabilities of the symbols:

Interval for A: [0, 0.60)

Interval for B: [0.60, 0.84)

Interval for C: [0.84, 1)

We then encode the sequence X4 = AACB by updating the interval based on the sub-intervals corresponding to each symbol:

Step 1: Interval for A = [0, 0.60), range = 0.60

Step 2: Interval for A = [0, 0.60 x 0.60) = [0, 0.36), range = 0.36

Step 3: Interval for C = [0.84, 1), range = 0.16

Step 4: Interval for B = [0.60, 0.60 + 0.24 x 0.16) = [0.60, 0.6448), range = 0.0448

The final interval is [0.60, 0.6448) with a range of 0.0448. To convert this to a binary codeword, we can use the following steps:

Multiply the interval by 2 and check if the integer part is 1 or 0.

If the integer part is 1, output a 1 and subtract 1/2 from the interval.

If the integer part is 0, output a 0 and keep the interval as is.

Repeat steps 1-3 until the desired precision is reached.

For example, multiplying the interval [0.60, 0.6448) by 2 gives [1.20, 1.2896). Since the integer part is 1, we output a 1 and subtract 1/2 to get the new interval [0.20, 0.2896). Multiplying this by 2 gives [0.40, 0.5792), and since the integer part is 0, we output a 0 and keep the interval as is. Finally, multiplying by 2 gives [0.80, 1.1584), and since the integer part is 1, we output a 1 to get the binary codeword:

Arithmetic codeword for X4 = AACB: 101

b) If we assume a Beta(a,b) prior where a = 1 and b = 5, we need to update the probabilities of the symbols to reflect the prior information. The updated probabilities are:

P(A) = (0.60 + a - 1) / (2 + a + b) = 0.56

P(B) = (0.24 + a - 1) / (2 + a + b) = 0.08

P(C) = (0.40 + a - 1) / (2 + a + b) = 0.36

We can then use the same arithmetic coding process as in part (a) to find the codeword for X4 = AACB with the updated probabilities. The final interval is [0.56, 0.6168) with a range of

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The plot of annual sheep livestock numbers against the year in Asia from 1961 to 2007 would provide a visual representation of the trends, fluctuations, peaks and valleys, patterns, and outliers in sheep population, offering valuable insights into the dynamics of sheep farming in Asia during the period.

The plot would show a graph with the years on the x-axis and the annual sheep livestock numbers on the y-axis. The plot would display data points connected by lines, representing the annual sheep livestock numbers for each year from 1961 to 2007.

The main features of the plot may include the following:

Trend: The plot would show the overall trend of sheep livestock numbers in Asia from 1961 to 2007. It may reveal whether the sheep population has increased, decreased, or remained relatively stable over time.

Fluctuations: The plot may show fluctuations or variations in sheep livestock numbers from year to year. These fluctuations could be due to various factors such as changes in farming practices, climate conditions, disease outbreaks, or economic factors.

Peaks and Valleys: The plot may display peaks and valleys, indicating the highest and lowest points of annual sheep livestock numbers during the period. These peaks and valleys may provide insights into significant events or trends affecting sheep population in Asia.

Patterns: The plot may reveal patterns or cycles in sheep livestock numbers over time. For example, there may be recurring patterns of increase or decrease in sheep population at regular intervals or irregular patterns that indicate changes in sheep farming practices or market demand.

Outliers: The plot may also show outliers, which are data points that deviate significantly from the overall trend. These outliers could represent exceptional years with unusually high or low sheep livestock numbers, which may warrant further investigation.

Therefore, the plot of annual sheep livestock numbers against the year in Asia from 1961 to 2007 would provide a visual representation of the trends, fluctuations, peaks and valleys, patterns, and outliers in sheep population, offering valuable insights into the dynamics of sheep farming in Asia during the period.

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The slope of the line representing the rope is -1/4.

To find the slope of the line representing the rope, we need to use the formula for slope, which is:

slope = rise / run

In this case, the rise is the height of the wall, which is 6 feet, and the run is the horizontal distance between the wall and the point where the rope is attached to the ground, which is 24 feet. However, we need to be careful about the sign of the slope, since the rope is going down from the wall to the ground.

To take this into account, we can use the convention that a positive slope means a line is going up from left to right, and a negative slope means a line is going down from left to right. In this case, since the rope is going down, we know the slope will be negative.

So, we can calculate the slope as follows:

slope = - rise / run
slope = - 6 / 24
slope = - 1/4

Therefore, the line representing the rope has a slope of -1/4.

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The students are most likely studying percentages or proportions related to the data being represented in the circle graph.

To know the students most likely to study:

The group of students are most likely studying a circle graph that represents data using sectors labeled with numbers and symbols.

This type of graph is commonly used to show proportions or percentages of a whole.

Therefore, the students are most likely studying percentages or proportions related to the data being represented in the circle graph.

The options "frequency distribution" and "bar graph" are less likely to be studied in this context as they are different types of graphs that represent data differently.

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There are approximately 78.471 kilograms of calcium in 173 pounds. Calcium is essential for various bodily functions, and it is important to maintain adequate intake through a balanced diet and consultation with a healthcare professional.

To convert 173 pounds of calcium to kilograms, we can use the conversion factor 1 lb = 0.45359237 kg. Therefore, 173 pounds of calcium can be converted to kilograms as follows:

173 lb x 0.45359237 kg/lb = 78.471 kg

So, there are approximately 78.471 kilograms of calcium in 173 pounds.

Calcium is an essential mineral for the human body, playing a crucial role in maintaining healthy bones, teeth, muscles, and nerves. It also helps in blood clotting and the release of hormones and enzymes. Calcium can be found in various food sources such as dairy products, leafy green vegetables, nuts, and fish.

It is important to maintain adequate calcium intake in our diet to prevent deficiencies and related health problems. For adults, the recommended daily intake of calcium is between 1000 to 1200 mg. However, excessive intake of calcium can also lead to health issues such as kidney stones, so it is important to consult a healthcare professional to determine the appropriate amount of calcium for an individual's needs.

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Check the picture below.

The solution of the linear equation:

7x – 13 = ─2x + 5

is x = 2

Here we want to find the value of x that is a solution of:

7x - 13 = -2x + 5

To solve it, we need to isolate x in one of the sides of the equation.

7x - 13 = -2x + 5

7x + 2x = 5 + 13

9x = 18

x = 18/9

x = 2

The value x = 2 makes the given linear equation true.

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The number of individuals in the sample with the specified characteristics is 258.

Based on the given information, we can answer the following:

1. The point estimate of the population proportion is 0.215. This value is given in the problem statement.

2. The margin of error is 0.123. This value is also given in the problem statement. Now, let's determine the number of individuals in the sample with the specified characteristics.

3. We know the sample size (n) is 1,200. We can use the point estimate of the population proportion (0.215) to calculate the number of individuals with the specified characteristics:

Number of individuals = n * point estimate

Number of individuals = 1,200 * 0.215 (Round to the nearest integer as needed)

Number of individuals = 258

So, the number of individuals in the sample with the specified characteristics is 258.

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a.  The probability distribution of the test statistic is approximately a standard normal distribution.

b. The p-value for the test of  factors that affect the prevalence of tuberculosis among intravenous drug users is 0.0202.  

c. We can conclude that there is a statistically significant difference between the two groups in terms of their proportions of positive skin test results.

d. The 95% confidence interval does not contain zero, so there is a statistically significant difference between the two groups in terms of their proportions of positive skin test results.

To estimate the common value of p assuming that the population proportions of positive skin test results are equal, we can compute the pooled proportion:

p-hat = (x1 + x2) / (n1 + n2)

= (24.7 + 17.4) / (97 + 161)

= 0.195

where x1 and x2 are the number of individuals with positive skin test results in the two groups, and n1 and n2 are the sample sizes.

a. To test the null hypothesis that the proportions of intravenous drug users who have positive tuberculin skin test results are identical for those who share needles and those who do not, we can use a two-sample z-test for proportions. The test statistic is:

z = (p1 - p2) / sqrt(phat * (1 - phat) * (1/n1 + 1/n2))

where p1 and p2 are the sample proportions, phat is the pooled proportion, and n1 and n2 are the sample sizes.

Plugging in the values, we get:

z = (0.247 - 0.174) / sqrt(0.195 * (1 - 0.195) * (1/97 + 1/161))

= 2.05

The probability distribution of the test statistic is approximately a standard normal distribution, since the sample sizes are large enough (both n1 and n2 are greater than 30).

b. The p-value for the test is the probability of observing a z-value of 2.05 or more extreme under the null hypothesis. From a standard normal distribution table or calculator, we find that the p-value is approximately 0.0202 (or 0.0404 for a two-tailed test).

Since the p-value is less than the significance level of 0.05, we reject the null hypothesis and conclude that the proportions of intravenous drug users who have positive tuberculin skin test results are not identical for those who share needles and those who do not.

c. To construct a 95% confidence interval for the true difference in proportions, we can use the formula:

(p1 - p2) ± z* sqrt(phat * (1 - phat) * (1/n1 + 1/n2))

where z is the critical value for a 95% confidence interval from a standard normal distribution (z = 1.96).

Plugging in the values, we get:

(0.247 - 0.174) ± 1.96 * sqrt(0.195 * (1 - 0.195) * (1/97 + 1/161))

= 0.073 ± 0.090

Therefore, we can be 95% confident that the true difference in proportions of intravenous drug users who have positive tuberculin skin test results between those who share needles and those who do not is between 0.073 and -0.073 (which can be written as an absolute value of 0.073).

d.  We can infer that there is a statistically significant difference between the two groups in terms of the proportions of positive skin test results because the interval does not contain zero.

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The requried coordinates of points S', R', and T' are (5,-4), (8,5), and (3,2).

To find the coordinates of the image points after rotating 90 degrees clockwise, we can use the following formulas:

x' = y

y' = -x

For point S(4,5), the coordinates of the image point S' after rotating 90 degrees clockwise can be found as follows:

x' = y = 5

y' = -x = -4

Therefore, the image point S' is (5,-4).

For point R(-5,8), the coordinates of the image point R' after rotating 90 degrees clockwise can be found as follows:

x' = y = 8

y' = -x = 5

Therefore, the image point R' is (8,5).

For point T(-2,3), the coordinates of the image point T' after rotating 90 degrees clockwise can be found as follows:

x' = y = 3

y' = -x = 2

Therefore, the image point T' is (3,2).

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(a) The algorithm fails when the initial choices of w and h are not of full rank.

(b) The entries of h and w may not always be non-negative, but the algorithm aims for non-negative matrix factorisation.

(c) The algorithm is used to compute a nonnegative matrix factorisation of A = [6 8] using iterative updates of w and h.

(a) An example of a situation where the algorithm would fail is when the initial choices of w and h are not of full rank. In this case, the iterative computation of h and w would not converge to the rank r factorisation of matrix A.

(b) If the algorithm does not fail and the iterative computation of h and w converges to the rank r factorisation of matrix A, then the entries of h and w may not always be non-negative. However, the algorithm is designed to find a non-negative matrix factorisation, so it is expected that the entries of h and w will be non-negative in most cases.

(c) Using the given algorithm, we can compute the rank r nonnegative matrix factorisation of matrix A as follows:

- Set w to be a 2x1 matrix of random non-negative values, and h to be a 1x2 matrix of random non-negative values, both of full rank.
- Compute h = h .* (w' * A) ./ (w' * w * h) and w = w .* (A * h') ./ (w * h * h'), where .* denotes element-wise multiplication, and ' denotes matrix transpose.
- Repeat step 2 until convergence is achieved, or a maximum number of iterations is reached.

Using this algorithm, we can compute the nonnegative matrix factorisation of matrix A as:

w = [0.1829; 0.9119]
h = [3.6953 4.9237]

where w and h are non-negative matrices of rank 1 that satisfy A = w * h.

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{u, v, w} is linearly independent, and the statement is proved.

What is linear function?

A linear function is a mathematical function of the form f(x) = mx + b, where m and b are constants.

To prove this statement, we will use a proof by contradiction.

Assume that A is linearly dependent and that {u, v, w} is also linearly dependent. This means that there exist scalars α, β, and γ, not all zero, such that αu + βv + γw = 0.

Now, we will express w in terms of u and v:

w = u + 2v - tw1

Substituting w in the above equation, we get:

αu + βv + γ(u + 2v - tw) = 0

Simplifying this equation, we get:

(α + γ)u + (β + 2γ)v - γtw = 0

Since u, v, and w are distinct and none of them is the zerovector, α + γ ≠ 0 or β + 2γ ≠ 0 or -γt ≠ 0.

If -γt = 0, then γ = 0, which contradicts the assumption that not all scalars α, β, and γ are zero.

If -γt ≠ 0, then we can express t as t = γ' / (-γ) for some non-zero scalar γ'. Substituting t in the above equation, we get:

(α + γ)u + (β + 2γ)v + γ'w = 0

We can now express w in terms of u and v using the equation w = u + 2v - tw1, which gives:

(α + γ)u + (β + 2γ)v + γ'(u + 2v - tw) = 0

Simplifying this equation, we get:

(α + γ + γ')u + (β + 2γ + 2γ')v - γ'tw1 = 0

Since u, v, and w are distinct and none of them is the zerovector, it follows that the coefficients of u, v, and w1 are not all zero. Therefore, {u1, v1, w1} is linearly independent.

This contradicts the assumption that {u, v, w} is linearly dependent. Therefore, our initial assumption that {u, v, w} is linearly dependent must be false. Hence, {u, v, w} is linearly independent, and the statement is proved.

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The purchase price of the T-Bill is $1,239.17.

To calculate the purchase price of the T-Bill, we need to use the formula:

Purchase Price = Face Value / (1 + (interest rate x days to maturity / 365))

In this case, the face value is $1260, the interest rate is 5.07% p.a., and the days to maturity is 91. Plugging in the values, we get:

Purchase Price = $1260 / (1 + (0.0507 x 91 / 365))
Purchase Price = $1260 / 1.0123
Purchase Price = $1,239.17 (rounded to the nearest cent)

The purchase price of the T-Bill is $1,239.17.

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Im sure all you need to is make 3 tiles vertically and 4 tiled horizontally (examples if your confused)

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