1) Mrs Lee bought x kg of crabs for $140. Write down an expression, in terms of x for the cost of 1 kg of crabs.
2) She bought some fish with $140. She received 3 kg more fish than crabs. Write down an expression, in terms of x for the cost of 1 kg of fish.
3) The cost of 1 kg of fish is $15 less than the cost of 1 kg of crab. Write down an equation in terms of x and show that it reduces to 3x^2+9x-84=0.
4) Solve the equation 3x^2+9x-84=0.
5) How many of kilograms of fish and crabs did she buy?

For the bag of M&M's:

To solve the given probabilities, consider the total number of M&M's in the bag, as well as the number of M&M's of each color:

1. P(A) = 5/26

2. P(D) = 2/26 = 1/13

3. P(A \cup B) = 5/26 + 2/26 = 7/26

4. P(E \cup D) = 10/26 + 2/26 = 12/26 = 6/13

5. P(C \cup E) = 5/26 + 10/26 = 15/26

6. P(A \cap B) = 5/26 x 2/26 = 10/676 = 5/338

7. P(A \cup C \cup E) = 5/26 + 5/26 + 10/26 = 20/26 = 10/13

8. P(B') = 1 - P(B) = 1 - 2/26 = 18/26 = 9/13

9. P(D \cup C) = 2/26 + 5/26 = 7/26

10. Probability of choosing red and then green on consecutive selections: P(red) × P(green) = 5/26 x 10/19 = 1/38

11. Probability of no brown being selected in 6 consecutive selections: (18/26) x (17/19) x (16/18) x (15/17) x (14/16) x (13/15) = 1/2

12. Probability of at least one brown in 6 consecutive selections: 1 - 1/2 = 1/2

13. Probability of 4 red in a row on consecutive selections: (5/26) x (4/19) x (3/18) x (2/17) = 1/2286.

14. Among blue and red M&M's, the probability of choosing red: 5/(5+5) = 5/10 = 1/2

15. Among blue, green, and yellow M&M's, the probability of not choosing green: (5+2)/(5+10+2) = 7/17

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If the market is competitive and p = 10, the firm's profits will be 10q - 10 - 0.1q².

What is the profit?

Profit is a financial measure that represents the difference between total revenue and total costs incurred by a business or individual. It is a measure of the financial gain or benefit obtained from a specific activity or endeavor.

To calculate the firm's profits, we need to use the equation:

Profit = Total Revenue - Total Cost

Given that p = 10 and tc = 10 + 0.1q², we can substitute these values into the profit equation:

Profit = p * q - tc

Profit = 10 * q - (10 + 0.1q²)

Simplifying the expression:

Profit = 10q - 10 - 0.1q²

To calculate the firm's profits, we need to know the quantity of output (q) produced by the firm. Without the specific value of q, we cannot determine the exact profit.

Hence, if the market is competitive and p = 10, the firm's profits will be 10q - 10 - 0.1q².

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The evaluated sum of 2(4k² + 3k - 2) for k = 1 to n is (8n³ + 29n² + 11n) / 3.

To evaluate the given sum using properties of sigma notation and rules for sums of powers, let's break down the expression step by step.

The sum in sigma notation is represented as:

2 ∑ (4k² + 3k - 2) for k = 1 to n

We can start by distributing the 2 into the parentheses:

2(4k² + 3k - 2)

Next, we can apply the rule for sums of powers to evaluate each term separately. The rule states:

∑(k²) = n(n + 1)(2n + 1) / 6

∑(k) = n(n + 1) / 2

∑(1) = n

Applying these rules, we have:

2 ∑ (4k² + 3k - 2)

= 2(4∑(k²) + 3∑(k) - 2∑(1))

Now, we substitute the respective formulas for each term:

= 2(4(n(n + 1)(2n + 1) / 6) + 3(n(n + 1) / 2) - 2n)

Simplifying further:

= 2(n(n + 1)(2n + 1) / 3 + 3n(n + 1) / 2 - 2n)

To obtain a more concise form, we can simplify the expression and put it in a common denominator:

= 2(2n(n + 1)(2n + 1) / 3 + 3n(n + 1) / 2 - 6n / 3)

= 2(4n(n + 1)(2n + 1) + 9n(n + 1) - 6n) / 6

= (8n³ + 20n² + 8n + 9n² + 9n - 6n) / 3

= (8n³ + 29n² + 11n) / 3

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Answer:

1) 180°(Angle on a straight line)-45°

=135°

2)180°-90°=90°

3)180°-116°=64°

4)180°-(31°+32°)=117°

5)90°-60°=30°

Step-by-step explanation:

All angles on a straight line is 180°

A right angle is =90°

The solution of the equation, 3x - 1 / x + 2 = x + 5 / 2x - 1 is not extraneous.

Extraneous solution occur because squaring both sides of a square root equation results in 2 solutions (the positive and negative number).

Therefore, let's solve the equation to know if it's extraneous.

Hence,

3x - 1 / x + 2 = x + 5 / 2x - 1

cross multiply

(3x - 1)(2x - 1) = (x + 2)(x + 5)

6x² - 3x -2x + 1 = x² + 5x + 2x + 10

6x² - x² - 5x - 7x + 1 - 10 = 0

5x² - 12x - 9 = 0

Hence,

5x² - 12x - 9 = 0

(x - 3)(x + 3 / 5)

x = 3 or x = -3 / 5

Therefore, the equation is not extraneous

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Atomic orbitals are regions around an atomic nucleus where electrons are likely to be found. They have different shapes and energy levels, determining an electron's position and behavior within an atom.

Based on the given answer choices, the atomic orbitals are:

1. σ2px
2. 2s
3. π2py
4. σ1s
5. 3px
6. σ*2px

These are atomic orbitals because they describe the wave function of an electron in an atom, using a combination of quantum numbers (n, l, and m) and the type of orbital (s, p, or d). σ and π represent bonding and antibonding orbitals, respectively, while the asterisk (*) denotes an antibonding orbital.

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To answer your question, the estimated value of the slope is typically represented by the coefficient "b1" in a linear regression model.

This value represents the change in the dependent variable (y) for every one unit increase in the independent variable (x). It is important to note that this estimated value is just that - an estimate based on the sample data used in the analysis. It is subject to variability and may not accurately represent the true population slope. Additionally, it is important to interpret the estimated slope value in the context of the specific variables and data being analyzed. For example, a positive slope may indicate a positive relationship between the two variables, while a negative slope may indicate a negative relationship. It is recommended to conduct hypothesis testing and evaluate the significance of the estimated slope value to determine its practical relevance. In summary, while the estimated value of the slope can provide valuable insights, it should be interpreted and evaluated with caution.

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A sample size of 1076 is needed , To calculate the required sample size for estimating a population proportion with a desired margin of error and confidence level, we can use the formula:

n = ([tex]Z^2[/tex] * p * (1-p)) / [tex]E^2[/tex]

where:

- n is the required sample size

- Z is the z-score corresponding to the desired confidence level

- p is the estimated population proportion

- E is the desired margin of error

Let's calculate the required sample size for each exercise:

6.15: Margin of error within 25% with 95% confidence.

E = 0.25

Z = 1.96 (corresponding to 95% confidence level)

p is not given, so we can assume a worst-case scenario where p = 0.5 (maximum variability).

Substituting these values into the formula:

n = (1.96^2 * 0.5 * (1-0.5)) / 0.25^2

n ≈ 384.16

Rounding up to the nearest whole number:

n = 385

Therefore, a sample size of 385 is needed.

6.16: Margin of error within +1% with 99% confidence.

E = 0.01

Z = 2.58 (corresponding to 99% confidence level)

p is not given, so we can assume a worst-case scenario where p = 0.5 (maximum variability).

Substituting these values into the formula:

n = (2.58^2 * 0.5 * (1-0.5)) / 0.01^2

n ≈ 6658.08

Rounding up to the nearest whole number:

n = 6659

Therefore, a sample size of 6659 is needed.

6.17: Margin of error within +3% with 90% confidence.

E = 0.03

Z = 1.645 (corresponding to 90% confidence level)

p = 0.3 (estimated population proportion)

Substituting these values into the formula:

n = (1.645^2 * 0.3 * (1-0.3)) / 0.03^2

n ≈ 317.91

Rounding up to the nearest whole number:

n = 318

Therefore, a sample size of 318 is needed.

6.18: Margin of error within +2% with 95% confidence.

E = 0.02

Z = 1.96 (corresponding to 95% confidence level)

p = 0.78 (estimated population proportion)

Substituting these values into the formula:

n = (1.96^2 * 0.78 * (1-0.78)) / 0.02^2

n ≈ 1075.56

Rounding up to the nearest whole number:

n = 1076

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The point estimate of the population proportion defective will be 14/400, which is 0.035 or 3.5%.

To calculate the point estimate of the population proportion defective, we divide the number of defective items (14) by the total sample size (400).

In this case, out of the 400 items in the sample, 14 are found to be defective. Dividing 14 by 400 gives us the point estimate of the population proportion defective, which is 0.035 or 3.5%.

This means that based on the sample data, we estimate that approximately 3.5% of the population of items is defective. It's important to note that this is just an estimate and the actual population proportion may vary.

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Answer:

C

Step-by-step explanation:

Answer:

Step-by-step explanation:

Lets assume his pay was $100 and he got 10% increase so that is

100 * .10 = 10

Salary is 100 + 10 = 110

Another pay rise

110 * .10 = 11

Salary is 110 + 11 = 121.

Over the last 2 years total salary increased is 21%.

To determine if parents feel differently today compared to twenty years ago, we can conduct a hypothesis test using the given data. Let's set up the null and alternative hypotheses:

Null hypothesis (H0): The proportion of parents who feel it is a serious problem that high school students are not being taught enough math and science is the same today as it was twenty years ago.

Alternative hypothesis (Ha): The proportion of parents who feel it is a serious problem that high school students are not being taught enough math and science is different today compared to twenty years ago.

We can use a hypothesis test for a single proportion to compare the proportions. Let p1 represent the proportion twenty years ago, and p2 represent the proportion today.

Given information:

Twenty years ago: 53% of parents felt it was a serious problem.

Recent survey: 325 out of 850 parents felt it was a serious problem.

Using the recent survey data, we can calculate the sample proportion of parents who feel it is a serious problem today:

p2 = 325/850 = 0.3824

To calculate the test statistic, we need to compare the sample proportion today to the proportion twenty years ago.

Under the null hypothesis, we assume that p1 = p2. Thus, we can estimate the common proportion using the combined sample proportion:

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

where x1 is the number of parents who felt it was a serious problem twenty years ago, and n1 is the total number of parents twenty years ago.

Assuming p1 = p2 = p, we can calculate the test statistic:

z = (p2 - p) / sqrt(p(1-p)(1/n1 + 1/n2))

We can then compare the test statistic to the critical value at the α = 0.1 level of significance.

If the test statistic falls in the rejection region (beyond the critical value), we reject the null hypothesis and conclude that parents feel differently today compared to twenty years ago. Otherwise, if the test statistic falls within the non-rejection region, we fail to reject the null hypothesis and do not conclude a significant difference.

Since the exact values for the number of parents and the total number of parents twenty years ago are not provided, we cannot calculate the precise test statistic or critical value.

However, you can use the provided formula and the specific values to perform the calculations and draw a conclusion based on the test statistic and critical value for α = 0.1.

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There is not enough evidence to suggest that the variance of the times for women is less than that for men.

To determine if the variance of the times for women is less than that for men, we can perform a hypothesis test. The null hypothesis, denoted as H0, assumes that the variance of the times for women is equal to or greater than that for men, while the alternative hypothesis, denoted as Ha, assumes that the variance of the times for women is less than that for men.

To conduct the hypothesis test, we can use the F-test for comparing variances. The test statistic is calculated as F = s1^2 / s2^2, where s1 and s2 are the sample variances of the women and men, respectively.

In this case, the sample variances are s1^2 = 5.3^2 and s2^2 = 6.1^2. Calculating the test statistic, we have F = (5.3^2) / (6.1^2) = 0.819.

To determine whether to reject or fail to reject the null hypothesis, we compare the test statistic with the critical value from the F-distribution table for the given significance level. Since the question does not provide a significance level, we cannot proceed with the hypothesis test.

Therefore, based on the provided information, we cannot conclude that the variance of the times for women is less than that for men. To make a definitive conclusion, we would need a significance level to compare the test statistic against the critical value and conduct the hypothesis test appropriately.

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The instantaneous rate of change of f is equal to the average rate for only one value of x in the open interval (0, 1.565), which is x = 0.3925.

To find the values of x where the instantaneous rate of change of f is equal to the average rate, we need to compare the derivative of f with the average rate formula.

The average rate of change of f on the interval (a, b) is given by:

Average rate = (f(b) - f(a))/(b - a)

In this case, the interval is (0, 1.565), so a = 0 and b = 1.565. Substituting these values into the average rate formula, we have:

Average rate = (f(1.565) - f(0))/(1.565 - 0)

To find the instantaneous rate of change, we need to calculate the derivative of f. Taking the derivative of f(x) = (1/4)x², we get:

f'(x) = (1/4) * 2x = (1/2)x

Now we equate the average rate and the derivative:

(1/2)x = (f(1.565) - f(0))/(1.565 - 0)

Simplifying this equation, we have:

(1/2)x = (f(1.565) - f(0))/1.565

To solve for x, we substitute f(x) with its expression:

(1/2)x = ((1/4)(1.565)² - (1/4)(0)²)/1.565

(1/2)x = (1/4)(1.565)²/1.565

Simplifying further:

(1/2)x = (1/4)(1.565)

(1/2)x = 0.19625

Multiplying both sides by 2:

x = 0.3925

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An erroneous data observation in a multiple regression analysis would most likely show up as an outlier.

In a multiple regression analysis, an erroneous data observation typically manifests itself as an outlier. An outlier refers to a data point that deviates significantly from the overall pattern or trend observed in the dataset. It lies far away from the majority of the data points and can have a substantial impact on the regression model's results and interpretation.

When performing a multiple regression analysis, the goal is to establish a relationship between the dependent variable and multiple independent variables. Outliers can distort this relationship by exerting disproportionate influence on the regression model's coefficients and overall fit. As a result, the inclusion of an outlier in the analysis can lead to inaccurate estimates of the regression coefficients and biased predictions.

Identifying and addressing outliers is crucial in maintaining the integrity and reliability of the regression analysis. Various techniques can be employed to detect outliers, such as visual inspection of scatter plots, examining residual plots, calculating standardized residuals, leverage values, or influential observations. Once an outlier is detected, it can be handled through various methods, including excluding the observation from the analysis, transforming the data, or applying robust regression techniques that are less sensitive to outliers.

In summary, an erroneous data observation in a multiple regression analysis is most likely to manifest itself as an outlier—a data point that deviates significantly from the general pattern observed in the dataset. Detecting and appropriately addressing outliers is essential for ensuring accurate and reliable regression model results.

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The formula to prove is Var(X) = E(X^2) - [E(X)]^2, where Var(X) represents the variance of random variable X, E(X^2) is the expectation of X^2, and E(X) is the expectation of X. The variance is a measure of the spread or variability of a random variable.

To prove the formula Var(X) = E(X^2) - [E(X)]^2, we start with the definition of variance. The variance of a random variable X is given by Var(X) = E[(X - E(X))^2].

Expanding the square term, we have Var(X) = E(X^2 - 2XE(X) + [E(X)]^2).

Now, let's evaluate each term individually. First, we have E(X^2). This represents the expectation of X^2, which is the average value of X^2 over all possible outcomes.

Next, we have -2XE(X). Since -2 is a constant, we can bring it outside the expectation operator, giving -2E(X*E(X)). Simplifying further, we have -2E(X)*E(X), which is -2 times the product of the expectation of X.

Lastly, we have [E(X)]^2, which is the square of the expectation of X.

Putting it all together, we have Var(X) = E(X^2) - 2E(X)*E(X) + [E(X)]^2.

Simplifying further, -2E(X)*E(X) + [E(X)]^2 can be written as -[E(X)]^2.

Therefore, Var(X) = E(X^2) - [E(X)]^2, which proves the desired formula.

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The magnitude of vector v is 4. The direction angle of vector v is -60 degrees or -π/3 radians

To calculate the magnitude of a 2D vector, we use the Pythagorean theorem, which states that the magnitude (denoted by |v| or ||v||) is the square root of the sum of the squares of its components. In this case, the vector v has components 2 and -2√3. Thus, the magnitude of v can be computed as:

|v| = [tex]\sqrt{2^{2} }[/tex] +[tex](-2\sqrt{3} )^{2}[/tex]) = √(4 + 12) = √16 = 4

So, the magnitude of vector v is 4.

The direction angle of a vector represents the angle it makes with the positive x-axis in a counterclockwise direction. To find the direction angle, we use trigonometric functions. In this case, the vector v has components 2 and -2√3. To determine the angle, we can calculate the arctangent (inverse tangent) of the ratio of the y-component to the x-component:

θ = arctan(-2√3/2)

Evaluating this expression, we find: θ = -60 degrees or -π/3 radians

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Amanda's mistake is that she incorrectly calculated the center; the center is (-1, 3)

From the question, we have the following parameters that can be used in our computation:

(x + 1)² + (y - 3)² = 9

A circle equation is represented as

(x - a)² + (y - b)² = r²

Where

Center = (a, b)

Radius = r

Using the above as a guide, we have the following:

Center = (-1, 3)

Radius = 3

This means that Amanda's mistake is that she incorrectly calculated the center; the center is (-1, 3)

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The correct congruence statement for the given triangles is Triangle ABC is congruent to triangle DFE.

We are given that;

The triangle ABC is congruent to triangle DEF

AC=BC=DE=EF, AB=DF

Now,

The corresponding sides of the triangles are equal. AC=DF, BC=DE and AB=EF. So, the correct congruence statement should be:

Triangle ABC is congruent to triangle DFE.

Mandar’s mistake is that he is using the wrong congruence statement.

Therefore, by the congruent triangles the answer will be Triangle ABC is congruent to triangle DFE.

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The vectors in an orthogonal set are normalized (i.e., their magnitudes are equal to 1), the new set obtained by scaling the vectors by nonzero scalars will still be orthogonal.

How to show that (c1v1, c2v2) is also an orthogonal set?

To show that (c1v1, c2v2) is also an orthogonal set, we need to prove that the dot product between any two vectors in the set is zero.

Let's consider two arbitrary vectors from the set: c1v1 and c2v2.

The dot product between these two vectors is:

(c1v1) ⋅ (c2v2)

Using the properties of dot product and scalar multiplication, we can rewrite this expression as:

(c1c2) * (v1 ⋅ v2)

Since v1 and v2 are orthogonal vectors, their dot product v1 ⋅ v2 is zero. Therefore, the expression simplifies to:

(c1c2) * 0

Which is equal to zero.

Since the dot product between any two vectors in the set (c1v1, c2v2) is zero, we have shown that (c1v1, c2v2) is an orthogonal set.

This result demonstrates that if the vectors in an orthogonal set are normalized (i.e., their magnitudes are equal to 1), the new set obtained by scaling the vectors by nonzero scalars will still be orthogonal.

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If a hypothesis test is performed and the decision is made to fail to reject the null hypothesis, it means that there is not enough evidence to support the alternative hypothesis.

In the context of the given information, the null hypothesis would state that the mean age of bus drivers in Chicago is equal to or less than 51.2 years.

When the decision is made to fail to reject the null hypothesis, it suggests that the data does not provide sufficient evidence to conclude that the mean age of bus drivers in Chicago is greater than 51.2 years.

However, it does not necessarily imply that the mean age is exactly 51.2 years. The result could indicate that the sample data is consistent with the null hypothesis, which assumes a mean age of 51.2 years or less.

It is important to note that failing to reject the null hypothesis does not confirm the null hypothesis as true.

It simply suggests that there is not enough evidence to support the alternative hypothesis, and further investigation or additional data may be needed for a more conclusive determination.

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To approximate the required work using a Riemann sum, we divide the interval into smaller subintervals, select sample points within each subinterval, calculate the work done on each subinterval, sum up the individual work values, and refine the approximation by taking the limit as the number of subintervals approaches infinity.

When we want to approximate the required work using a Riemann sum, we break the interval into smaller subintervals of equal width Δx. By doing so, we create a partition of the interval. Within each subinterval, we select a sample point xi*.

To calculate the work done on each subinterval, we consider the force acting at the sample point xi*. We multiply this force by the distance traveled within that subinterval, which is Δx. This gives us the work done on that specific subinterval.

To estimate the total work, we sum up all the individual work values for each subinterval. This sum represents an approximation of the actual work.

To obtain a more accurate approximation, we take the limit as the number of subintervals, n, approaches infinity. This allows us to refine the partition and make the subintervals smaller, ultimately improving the accuracy of the Riemann sum approximation.

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The area bounded above by the curve f(x) = x^2 + 4x + 4, below by the x-axis, and between the interval [-6, -2] is 28 square units.

To find the area, we need to calculate the definite integral of the difference between the upper curve f(x) and the x-axis, over the given interval. The given upper curve is f(x) = x^2 + 4x + 4. To find the points of intersection, we set f(x) equal to zero and solve for x:

0 = [tex]x^2 + 4x + 4[/tex]

0 = (x + 2)(x + 2)

Therefore, the parabola f(x) intersects the x-axis at x = -2 with a double root. We also have the lower limit of the interval, -6.

Next, we integrate the difference between f(x) and the x-axis over the interval [-6, -2]:

Area = ∫[-6,-2] (f(x) - 0) dx

= ∫[-6,-2] [tex](x^2 + 4x + 4)[/tex] dx

Evaluating the integral gives:

Area = [1/3 * [tex]x^3 + 2x^2 +[/tex]4x] from -6 to -2

= [(1/3 * [tex](-2)^3 + 2(-2)^2 + 4(-2))] - [(1/3 * (-6)^3 + 2(-6)^2 +[/tex] 4(-6))]

= [8/3 - 8 + 8] - [-216/3 + 72 + 24]

= 28

Therefore, the area bounded by f(x), the x-axis, and the given interval is 28 square units.

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There is a significant difference between the mean hourly wages paid by Company A and Company B in the same industry.

To test the hypothesis regarding the difference in mean hourly wages between the two companies, we can perform a two-sample t-test. The null hypothesis, denoted as H0, assumes that there is no significant difference in the mean hourly wages between the two companies. The alternative hypothesis, denoted as Ha, assumes that there is a significant difference in the mean hourly wages between the two companies.

With a p-value of 0.0052, which is less than the significance level of 0.05, we can reject the null hypothesis. This indicates that there is sufficient evidence to suggest a significant difference in the mean hourly wages paid by Company A and Company B in the same industry.

By rejecting the null hypothesis, we conclude that the mean hourly wage for one company is significantly different from the mean hourly wage of the other company. However, we cannot determine from this analysis alone which company has the higher or lower mean hourly wage. Further investigation or additional statistical tests would be needed to make such comparisons.

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The probability that z is greater than 1.75 is 0.9599. The correct answer is D.

To find the probability that a standard normal random variable Z is greater than 1.75, we can use a standard normal distribution table or a calculator.

Using a standard normal distribution table, we look up the value 1.75 in the table to find the corresponding cumulative probability. The table provides the area under the standard normal curve up to a given z-score.

Looking up 1.75 in the table, we find that the cumulative probability is approximately 0.9599.

Therefore, the correct option is d. 0.9599, as it represents the probability that Z is greater than 1.75 in a standard normal distribution.

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Express u as the real part of an analytic function, exponentiate it, and conclude that u is constant.

To prove that a bounded above harmonic function u on the complex plane (C) is constant, we will use the fact that harmonic functions are the real parts of analytic functions.

Since u is a bounded above harmonic function, we can find an analytic function f(z) such that its real part is u(z). This can be done by considering the function f(z) = u(z) + iv(z), where v(z) is a harmonic conjugate of u(z).

Now, since u is bounded above, we can say that there exists a constant M such that u(z) ≤ M for all z in C.

Using Euler's formula, we can write the exponential function as e^z = e^(x+iy) = e^x * e^(iy).

Now, consider the function g(z) = e^(f(z)) = e^(u(z) + iv(z)) = e^u * e^(iv).

Since e^u is a positive constant, we can rewrite g(z) as g(z) = Ce^(iv), where C = e^u is also a positive constant.

Since v(z) is a harmonic conjugate of u(z), it is also a harmonic function. However, by the Liouville's theorem, any bounded harmonic function in C must be constant. Therefore, v(z) is constant, and we can write it as v(z) = k, where k is a real constant.

Now, let's substitute these values back into g(z):

g(z) = Ce^(ik)

Since e^(ik) is a complex number with magnitude 1, we can rewrite it as e^(ik) = cos(k) + i sin(k).

So, the function g(z) becomes:

g(z) = Ce^(ik) = C(cos(k) + i sin(k))

Now, we can express g(z) in terms of its real and imaginary parts:

g(z) = C cos(k) + iC sin(k)

Since u(z) is the real part of g(z), we can conclude that u(z) = C cos(k).

Since C and cos(k) are constants, we can say that u(z) is constant.

Therefore, if u is a bounded above harmonic function on C, it must be constant.

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The area bounded by the two curves is 42 square units.

To find the area bounded by the graphs of the given equations, we need to find the intersection points of the two graphs and then integrate the difference between the two functions over the given interval.

First, we find the intersection points by equating the two functions:

x^2 - 2x - 1 = x + 3

Simplifying, we get:

x^2 - 3x - 4 = 0

Factorizing, we get:

(x - 4)(x + 1) = 0

So the intersection points are x = 4 and x = -1.

Now, we integrate the difference between the two functions over the given interval:

∫[-1, 4] (x + 3 - (x^2 - 2x - 1)) dx

Simplifying, we get:

∫[-1, 4] (-x^2 + 4x + 4) dx

Integrating, we get:

[-(1/3)x^3 + 2x^2 + 4x] [-1, 4]

Substituting the limits of integration, we get:

(-(1/3)(4)^3 + 2(4)^2 + 4(4)) - (-(1/3)(-1)^3 + 2(-1)^2 + 4(-1))

Simplifying, we get:

(-64/3 + 32 + 16) - (1/3 - 2 - 4)

Which simplifies further to:

126/3

And finally, to the improper fraction: 42

Therefore, the area bounded by the two curves is 42 square units.

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Rounding this to the nearest hundredth, the area of the sector is approximately 28.88 f[tex]t^2[/tex].

To find the area of a sector, we can use the formula:

Area = (θ/360) × π × [tex]r^2[/tex]

where θ is the central angle in degrees, π is approximately 3.14, and r is the radius.

Given a central angle of 131° and a radius of 7.2 ft, we can plug in these values into the formula to find the area:

Area = (131/360) × 3.14 × (7.2[tex])^2[/tex]

Calculating this expression:

Area = (0.3639) × 3.14 × (7.2[tex])^2[/tex]

Area ≈ 28.879 f[tex]t^2[/tex]

Rounding this to the nearest hundredth, the area of the sector is approximately 28.88 f[tex]t^2[/tex].

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Confidence intervals and sample size are important concepts in statistics. When determining the level of certainty in a statistical result, it is important to consider the size of the confidence interval and the impact of the sample size.

Greater confidence requires wider confidence intervals, as a higher level of certainty demands a broader range of possible values. Conversely, lower confidence levels result in narrower confidence intervals, as there is less importance placed on the degree of certainty in the statistical result. In addition, smaller sample sizes give narrower confidence intervals, as a smaller number of data points provides a more precise estimate of the population value.

However, a larger sample size generally provides more accurate and reliable results, and wider confidence intervals may be necessary for more complex or variable datasets. Ultimately, the appropriate size of a confidence interval depends on the research question being asked, the level of precision needed, and the available data.

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Statement                                              Matching

bounded and uses brackets  { ]                 ≥

-5 is included in the solution                (- ∞, - 5] or(0, ∞)      

-5 is not included in the solution         (- ∞, - 10] or (- 5, ∞)

unbounded and uses parentheses ( )        - ∞

While using a brackets { ], the terms in the bracket are included in the solution hence we use ≥

While using a parentheses ( ), the terms in the parentheses are not included in the solution hence we have -∞. We can also use < or > or ∞. Since the terms no included in the solution

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