two similar hexagons have areas 36 sq. in. and 64 sq. in. the ratio of a pair of corresponding sides is 9/16. true false

The maximum population in the next 75 years is 499.

We have,

population of a species of bird, P(t) = 450 + 200 cos(0.4πt)

So, the maximum population in the next 75 years

P(2075) = 450 + 200 cos (0.4π (2075))

P(2075)= 450 +200 cos (2,606.2)

P(2075)= 450 + 200 . 0.2463

P(2075)= 499.26

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A good method for identifying inconsistencies and finding hidden meaning in a customized purchased data model is through a thorough analysis and review process. This process should involve examining the data model's structure, relationships, and data elements to ensure they align with the business's goals and objectives.

One approach is to compare the purchased data model with the organization's existing data structures, identifying any discrepancies and areas that require further investigation. Additionally, data profiling can help identify inconsistencies or gaps in the data that may impact its accuracy or completeness.

Another useful technique is to conduct data mapping, which involves tracing data elements through the data model to determine how they relate to one another and to the organization's business processes. This can help uncover any hidden meanings in the data and identify potential areas of concern or improvement.

Finally, involving subject matter experts in the analysis process can provide valuable insights and help validate the accuracy and relevance of the customized purchased data model. By using these methods, organizations can ensure that their purchased data model is fit for purpose, aligns with their business objectives, and provides meaningful insights to support informed decision-making.

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The value of the "7" in 432.0769 is 7/1000 or option A.

In the number 432.0769, the digit "7" is in the thousandths place, which means that it represents seven parts of one thousandth. The digit to the left of the thousandths place is the hundredths place, which represents one hundredth of a number. Therefore, the difference between the thousandths and hundredths place is a factor of ten, which means that the value of the digit "7" is ten times greater than the value of the digit to its right.

To put it in another way, the number 432.0769 can be broken down into its decimal representation:

4 hundreds + 3 tens + 2 ones + 0 tenths + 7 hundredths + 6 thousandths + 9 ten-thousandths

Hence the correct option is (a).

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the absolute value of ρ is less than or equal to the critical value, you cannot reject H₀ and cannot conclude a significant correlation.

To test the claim H0: rhos= 0 versus Ha: rhos ≠0 that there is a significant correlation between purchased seed expenses and fertilizer and lime expenses in the farming business with an alpha of 0.05, we can use a hypothesis test.

First, we need to collect data on the two variables and calculate the sample correlation coefficient (r). If r is close to 0, then there is no significant correlation between the two variables.

Next, we can use a t-test to determine if the correlation coefficient is significantly different from 0. We can calculate the t-value using the formula t = r(sqrt(n-2))/sqrt(1-r^2), where n is the sample size.

Finally, we can compare the t-value to the critical value from the t-distribution with n-2 degrees of freedom and an alpha of 0.05. If the t-value is greater than the critical value, we can reject the null hypothesis and conclude that there is a significant correlation between purchased seed expenses and fertilizer and lime expenses in the farming business. If the t-value is less than the critical value, we fail to reject the null hypothesis and conclude that there is no significant correlation.
To test the claim H₀: ρ = 0 (no correlation) versus Hₐ: ρ ≠ 0 (significant correlation) between purchased seed expenses and fertilizer and lime expenses in the farming business, you would perform a correlation test using α = 0.05 as your significance level.

First, gather your data on seed expenses and fertilizer & lime expenses, then calculate the Spearman rank correlation coefficient (ρ). Once you have the correlation coefficient, compare it to the critical value from a correlation table with α = 0.05. If the absolute value of ρ is greater than the critical value, you can reject H₀ and conclude there is a significant correlation between the two variables. If the absolute value of ρ is less than or equal to the critical value, you cannot reject H₀ and cannot conclude a significant correlation.

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The proportion of assemblies that require more than 25 seconds to complete is 0.5 or 50%.

The proportion of assemblies that require more than 25 seconds to complete given the probability density function f(x) = 0.1 for 20 ≤ x ≤ 30 seconds, follow these steps:

The proportion of assemblies that require more than 25 seconds to complete is 0.5 or 50%.

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Answer:

5.6ft squared

Step-by-step explanation:

4x7 = 28 divided by 5 which is 5.6

If the 95% confidence interval for the difference in mean shelf life between Brand A and Brand B includes zero, then there is no significant difference in the shelf life of the two brands of doughnuts. If the 95% confidence interval for the difference in mean shelf life between Brand A and Brand B does not include zero, then there is a significant difference in the shelf life of the two brands.

Based on the question, a grocery store wants to determine whether there is a difference in the shelf life of two different brands of doughnuts.

They took a random sample of 40 boxes of each brand and determined the shelf life in days. A 95% confidence interval for the difference in mean shelf life between Brand A and Brand B was found.

Unfortunately, you didn't provide the actual values of the confidence interval. However, I can still explain how to interpret it.

1. If the 95% confidence interval for the difference in mean shelf life between Brand A and Brand B includes zero (e.g., -2 to 3 days), then there is no significant difference in the shelf life of the two brands of doughnuts. This means that at a 95% confidence level, we cannot conclude that one brand has a longer or shorter shelf life than the other.

2. If the 95% confidence interval for the difference in mean shelf life between Brand A and Brand B does not include zero (e.g., 1 to 4 days), then there is a significant difference in the shelf life of the two brands. This means that at a 95% confidence level, we can conclude that one brand has a longer or shorter shelf life than the other, depending on the sign of the interval (positive or negative).

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The equation in spherical coordinates is :

3sinφcosθ + 3sinφsinθ + 7cosφ = 1/ρ

(a) The equation in rectangular coordinates i[tex]s 5x^2 - 4x + 5y^2 + 5z^2 = 0.[/tex] To write it in spherical coordinates, we need to replace x, y, and z with their spherical equivalents. Using the conversion formulas x = ρsinφcosθ, y = ρsinφsinθ, and z = ρcosφ, where ρ is the distance from the origin, φ is the angle down from the positive z-axis, and θ is the angle in the xy-plane measured from the positive x-axis counterclockwise, we get:

[tex]5(ρsinφcosθ)^2 - 4(ρsinφcosθ) + 5(ρsinφsinθ)^2 + 5(ρcosφ)^2 = 0[/tex]

Simplifying and factoring out [tex]ρ^2,[/tex] we get:

[tex]5ρ^2(sin^2φcos^2θ + sin^2φsin^2θ + cos^2φ) - 4ρ(sinφcosθ) = 0[/tex]

Dividing by ρ and rearranging, we get:

[tex]5(sin^2φcos^2θ + sin^2φsin^2θ + cos^2φ) = 4sinφcosθ[/tex]

This is the equation in spherical coordinates.

(b) The equation in rectangular coordinates is 3x + 3y + 7z = 1. To write it in spherical coordinates, we use the same conversion formulas as before:

3(ρsinφcosθ) + 3(ρsinφsinθ) + 7(ρcosφ) = 1

Simplifying and dividing by ρ, we get:

3sinφcosθ + 3sinφsinθ + 7cosφ = 1/ρ

This is the equation in spherical coordinates.

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The intercept in the given linear regression model, which is -631.25, does not have a reasonable interpretation in the context of ticket sales at a water park.

The intercept in a linear regression model represents the predicted value of the dependent variable (in this case, ticket sales per hour) when the independent variable (in this case, current temperature in °F) is equal to zero. However, in the context of the given model, it is not meaningful to interpret a negative intercept of -631.25 for ticket sales at a water park.

This is because ticket sales cannot be negative, and it does not make sense to predict ticket sales when the temperature is at absolute zero (0 °F), as that is not a realistic scenario. Additionally, a negative intercept implies that the model predicts negative ticket sales at extremely low temperatures, which is not feasible.

Therefore, the intercept of -631.25 in the given linear regression model does not have a reasonable interpretation in the context of ticket sales at a water park

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The value of the integral is approximately 0.168. To evaluate the integral: ∫[0,π/4] sec(θ)tan(θ) dθ

We can use the substitution u = sec(θ) + tan(θ). Then, we can use the fact that sec²(θ) - 1 = tan²(θ) to rewrite the integrand in terms of u:

sec(θ)tan(θ) = (sec²(θ) - 1)sec(θ) = (u² - 1)/u

Substituting this expression back into the integral, we have:

∫[0,π/4] sec(θ)tan(θ) dθ = ∫[1,√2] (u² - 1)/u du

Simplifying the integrand:

∫[1,√2] (u² - 1)/u du = ∫[1,√2] (u - 1/u) du = ∫[1,√2] u du - ∫[1,√2] 1/u du

Evaluating each integral separately:

∫[1,√2] u du = (1/2)(√2)^2 - (1/2)(1)^2 = (√2 - 1)/2

∫[1,√2] 1/u du = ln|u|[1,√2] = ln(√2) - ln(1) = ln(√2)

Putting it all together:

∫[0,π/4] sec(θ)tan(θ) dθ = ∫[1,√2] (u² - 1)/u du = ∫[1,√2] u du - ∫[1,√2] 1/u du = (√2 - 1)/2 - ln(√2) ≈ 0.168.

Therefore, the value of the integral is approximately 0.168.

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(a) The value of a is 0.4. (b) Therefore, the cumulative distribution function of X is: F(x) = 0 for x ≤ -2, F(x) = 0.3 for -2 < x ≤ 1, F(x) = 0.7 for 1 < x ≤ 2, F(x) = 1 for x > 2

(a) To find the value of a, we know that the sum of all probabilities in the table must be equal to 1. So, we can use the equation:
0.1 + 0.2 + a + 0.3 = 1
Simplifying the equation, we get:
a = 0.4
(b) To obtain the cumulative distribution function (CDF) of X, we need to add up the probabilities of all values of X that are less than or equal to a particular value of X.
For X = -2, the CDF is:
F(-2) = P(X ≤ -2) = 0
For X = 1, the CDF is:
F(1) = P(X ≤ 1) = 0.1 + 0.2 = 0.3
For X = 2, the CDF is:
F(2) = P(X ≤ 2) = 0.1 + 0.2 + 0.4 = 0.7
For X = 4, the CDF is:
F(4) = P(X ≤ 4) = 0.1 + 0.2 + 0.4 + 0.3 = 1

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The 98% confidence interval for the mean number of personal  computers is (2.31, 2.67) with a margin of error of 0.182.

We can use the following formula:

CI =[tex]\bar x[/tex] ± z × (σ/√n)

Where:

[tex]\bar x[/tex] = sample mean number of personal computers = 2.49

z = z-score for the desired level of confidence = 2.33 (from the standard

normal distribution table for a 98% confidence level)

σ = population standard deviation = 0.8

n = sample size = 150

Substituting the values in the formula, we get:

CI = 2.49 ± 2.33 × (0.8/√150)

CI = 2.49 ± 0.182

CI = (2.31, 2.67)

Therefore, the 98% confidence interval for the mean number of personal

computers is (2.31, 2.67) with a margin of error of 0.182.

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Therefore, the variance of the trader’s profit is not equal to var(X) + var(Y), nor is it equal to 100var(X) + 200var(Y),

A mathematical equation is a formula that connects two claims and uses the equals symbol (=) to denote equivalence. An equation in algebra is a mathematical statement that establishes the equivalence of two mathematical expressions. For instance, in the equation 3x + 5 = 14, the equal sign places a space between the variables 3x + 5 and 14. The relationship between the two sentences that are written on each side of a letter may be understood using a mathematical formula. The symbol and the single variable are frequently the same. as in, 2x - 4 equals 2, for instance.

(a) The joint pmf of X and Y can be represented in a table as follows:

X\Y -1 0 1

-2 0 0 0

-1 0 1/15 2/15

0 1/15 2/15 1/15

1 2/15 1/15 0

2 0 0 0

3 0 0 0

4 0 0 0

Note that the pmf is only defined for values of X and Y that satisfy the given condition.

(b) To find the marginal pmfs of X and Y, we can sum the joint pmf over the corresponding row or column, respectively. The marginal pmf of X is:

X -2 -1 0 1 2 3 4

P(X=x) 0 3/15 4/15 3/15 0 0 0

The marginal pmf of Y is:

Y -1 0 1

P(Y=y) 2/15 5/15 3/15

(c) The trader’s profit is given by P = 100X + 200Y. The expected value of the trader’s profit is:

E(P) = E(100X + 200Y) = 100E(X) + 200E(Y)

From part (b), we know that E(X) = (−2)(0) + (−1)(3/15) + (0)(4/15) + (1)(3/15) + (2)(0) + (3)(0) + (4)(0) = 0 and E(Y) = (−1)(2/15) + (0)(5/15) + (1)(3/15) = 1/15. Therefore,

E(P) = 100(0) + 200(1/15) = 40/3

So the expected value of the trader’s profit is 40/3.

(d) The variance of the trader’s profit is given by:

Var(P) = Var(100X + 200Y) = 100^2Var(X) + 200^2Var(Y) + 2(100)(200)Cov(X,Y)

Since X and Y are independent (as the joint pmf is uniform), their covariance is zero, so the last term in the above expression is zero. Thus, we have:

Var(P) = 100^2Var(X) + 200^2Var(Y)

Therefore, the variance of the trader’s profit is not equal to var(X) + var(Y), nor is it equal to 100var(X) + 200var(Y), nor is it equal to 10000var(X) + 40000var(Y). The correct expression is given by 100^2Var(X) + 200^2Var(Y).

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To represent the relationship between distance in kilometers (D) and the number of hours (T) at a constant speed, we can use the equation:

D = k * T

Here, "k" is the constant of proportionality, which represents the speed of the albatross in kilometers per hour (km/h).

To find two constants of proportionality for the relationship between distance in kilometers and the number of hours, you can choose any two combinations of D and T that satisfy the equation.

For example:
1. If the albatross flies at a constant speed of 20 km/h (k = 20) for 2 hours (T = 2), the distance covered will be:
D = 20 * 2 = 40 kilometers
So, one constant of proportionality is k = 20 km/h.

2. If the albatross flies at a constant speed of 30 km/h (k = 30) for 3 hours (T = 3), the distance covered will be:
D = 30 * 3 = 90 kilometers
So, another constant of proportionality is k = 30 km/h.

The relationship between these two values (20 km/h and 30 km/h) is that they both represent different constant speeds at which the albatross can fly to cover a certain distance in a given number of hours.

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The error in approximating the integral of 3! ſ sin x3/2 dx from 0 to π by the fourth-degree Taylor polynomial is less than or equal to (27/16) π^4. Note that we don't know the actual value of the error, only an upper bound on its magnitude.

To find the first four terms of the binomial series for (1 + 3x)-1/2, we can use the formula:

(1 + x)n = 1 + nx + n(n-1)/2! x^2 + n(n-1)(n-2)/3! x^3 + ...

Substituting n = -1/2 and x = 3x, we get:

(1 + 3x)-1/2 = 1 - 3/2 x + (3/2)(-1/2)/2! x^2 - (3/2)(-1/2)(-3/2)/3! x^3 + ...

Simplifying each term, we get:

(1 + 3x)-1/2 = 1 - 3/2 x + 9/8 x^2 - 27/48 x^3 + ...

To estimate the error in approximating sin x3/2 by x3/2_ *9/2, we can use Taylor's inequality:

|Rn(x)| <= M |(x-a)^(n+1)/(n+1)!|

where Rn(x) is the remainder term of the nth-degree Taylor polynomial, M is the maximum value of the (n+1)th derivative of f(x) on the interval [a,x], and a is the center of the Taylor series.

In this case, we want to estimate the error in approximating sin x3/2 by x3/2_ *9/2 in the integral of 3! ſ sin x3/2 dx over the interval [0,π]. The center of the Taylor series is a = 0, so we need to find the maximum value of the fourth derivative of sin x3/2 on the interval [0,π].

The fourth derivative of sin x3/2 is:

d^4/dx^4 sin x3/2 = 81/4 sin x3/2

This function is increasing on the interval [0,π], so its maximum value is at x = π:

d^4/dx^4 sin x3/2 = 81/4 sin (π3/2) = -81/4

Thus, M = 81/4, n = 3, a = 0, and x = π in the Taylor's inequality formula:

|Rn(π)| <= M |(π-0)^(4)/(4!)| = (81/4) (π^4/24)

Simplifying, we get:

|Rn(π)| <= (27/16) π^4

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The probability that a randomly chosen arrival takes more than 5 minutes is approximately 0.8825.

To solve this problem, we need to use the exponential distribution formula, which is:
P(X > x) = e^(-λx)
where P(X > x) is the probability that an arrival will be more than x minutes, λ is the rate parameter (15 patients per hour), and e is the base of the natural logarithm (approximately 2.718).

To find the probability that a randomly chosen arrival will be more than 5 minutes, we need to plug in the values:
P(X > 5) = e^(-15/60 * 5)
= e^(-0.125)
= 0.8825
Therefore, the probability that a randomly chosen arrival will be more than 5 minutes is 0.8825, or 88.25%.

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Answer:

x=3

Step-by-step explanation:

5(x^2 - 9) - 5 = -5

5x^2 - 45 = 0

5x^2 = 45

x^2 = 9

x = 3

Answer:

3

Step-by-step explanation:

I did the test

Hope this helps :)

Forecasting, prescriptive, predictive, descriptive, and causal are all types of analytics.

Descriptive analytics involves examining past data to understand what has happened in the past and to identify patterns and trends.

It provides insight into what has happened and why.

Predictive analytics involves analyzing past data and using statistical algorithms and machine learning techniques to predict what is likely to happen in the future.

It is used to forecast trends, behaviors, and outcomes.

Prescriptive analytics involves analyzing data and using optimization algorithms to determine the best course of action to achieve a specific goal or objective.

It is used to make decisions that will maximize outcomes or minimize risks.

Causal analytics involves identifying cause-and-effect relationships between different variables.

It is used to understand how changes in one variable may affect another, and to identify the root causes of a particular outcome or phenomenon.

Forecasting analytics involves using statistical methods and data analysis techniques to make predictions about future trends and events. It is used to estimate future demand, sales, or other variables based on past data and trends.

In summary,

Each of these types of analytics serves a unique purpose in the data analysis process and can be used to gain insights and make informed decisions.

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Answer:

see explanation

Step-by-step explanation:

(a)

∠ ABC and ∠ DBC are a linear pair and sum to 180° , that is

6.5x + x = 180

7.5x = 180 ( divide both sides by 7.5 )

x = 24

(b)

∠ ABC = 6.5x = 6.5(24) = 156°

(c)

∠ DBC = x = 24°

The probability that a resident was not inoculated and lived is approximately 82.55%.

To compute the probability that a resident was not inoculated and lived, we can use the conditional probability formula:

P(not inoculated and lived) = P(lived | not inoculated) x P(not inoculated)

From the given information, we know that P(not inoculated) = 0.9608 and P(lived | not inoculated) = 0.8588.

Substituting these values, we get:

P(not inoculated and lived) = 0.8588 x 0.9608

P(not inoculated and lived) = 0.8255 or approximately 82.55%

Therefore, the probability that a resident was not inoculated and lived is approximately 82.55%.

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Answer:

M = 462.30 / 10 = $46.23

Step-by-step explanation:

Let N be the number of nights Samantha works each month.

Then the expression to find the amount Samantha earns is:

462.30 = N * M

where M is the amount Samantha earns per night.

To solve for M, we can divide both sides by N:

M = 462.30 / N

So if Samantha works 10 nights in a month, her earnings per night would be:

M = 462.30 / 10 = $46.23

The first five nonzero terms of this solution are:

[tex]y(1) = (5 + 2\sqrt{(5)} )/5e^{((1 + \sqrt{(5)} )}/2) + (5 - 2\sqrt{(5)} )/5e^{((1 - \sqrt{(5)} )}/2) = 13.429[/tex]

[tex]y(2) = (5 + 2\sqrt{(5)} )/5e^{(1 + \sqrt{(5)} ) }+ (5 - 2\sqrt{(5)} )/5e^{(1 - \sqrt{(5)} )} =46.768\\y(3) = (5 + 2\sqrt{(5)} )/5e^{((3 +\sqrt{(5)} )}/2) + (5 - 2\sqrt{(5)} )/5e^{((3 - \sqrt{(5)} )}/2) = 163.697[/tex]

[tex]y(4) = (5 + 2\sqrt{(5)} )/5e^{(2 + \sqrt{(5)} ) }+ (5 - 2\sqrt{(5)} )/5e^{(2 - \sqrt{(5)} ) }=573.170\\y(5) = (5 + 2\sqrt{(5)} )/5e^{((5 +\sqrt{(5)} )}/2) + (5 - 2\sqrt{(5)} )/5e^{((5 - \sqrt{(5)} )}/2) = 2011.190[/tex]

The given differential equation is a second-order linear homogeneous equation with constant coefficients. The characteristic equation is given by:

[tex]r^2 - xr - 1 = 0[/tex]

Using the quadratic formula, we can find the roots of this equation:

[tex]r = (x + \sqrt{(x^2 + 4)} )/2[/tex]

The general solution of the differential equation depends on the nature of the roots. If the roots are real and distinct, the general solution is of the form:

[tex]y(x) = c1e^{(r1x)} + c2e^{(r2x)}[/tex]

If the roots are complex, the general solution is of the form:

[tex]y(x) = e^{(ax)}(c1cos(bx) + c2sin(bx))[/tex]

In this case, the roots of the characteristic equation are:

[tex]r1 = (1 + \sqrt{(5)} )/2 and r2 = (1 - \sqrt{(5)} )/2[/tex]

Since the roots are real and distinct, the general solution is:

[tex]y(x) = c1e^{((1 + \sqrt{(5)} )}/2x) + c2e^{((1 - \sqrt{(5)} })/2x)[/tex]

To find the values of the constants c1 and c2, we use the initial conditions:

y(0) = 5 and y'(0) = 8

Substituting x = 0, we get:

c1 + c2 = 5 ---(1)

and

[tex](1 + \sqrt{(5)} )/2c1 + (1 - \sqrt{(5)} )/2c2 = 8 ---(2)[/tex]

Solving these two equations simultaneously, we get:

[tex]c1 = (5 + 2\sqrt{(5)} )/5 and c2 = (5 - 2\sqrt{(5)} )/5[/tex]

Therefore, the solution of the given initial value problem is:

[tex]y(x) = (5 + 2\sqrt{(5)} )/5e^{((1 + \sqrt{(5)} )}/2x) + (5 - 2\sqrt{(5)} )/5e^{((1 - \sqrt{(5)} )}/2x)[/tex]

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Based on this poll, we can estimate that the mean number of American adults who disapprove of President Bush's overall job performance is 134.

To find the mean number of American adults who disapprove of President Bush's overall job performance based on the CNN poll, we can use the formula:

mean = (proportion of disapprovals) x sample size

The proportion of disapprovals in the poll is 67%, which we can write as a decimal: 0.67. The sample size is given as 200.

So, the mean number of American adults who disapprove of President Bush's overall job performance is:

mean = 0.67 x 200 = 134

Therefore, based on this poll, we can estimate that the mean number of American adults who disapprove of President Bush's overall job performance is 134.

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The probability that X is greater than or equal to 2 is approximately 0.9977, rounded to two decimal places.

We can standardize the variable X to a standard normal distribution, which has a mean of 0 and a standard deviation of 1, using the formula:

[tex]z = (x - \mu) /\sigma[/tex]

x is the value of the random variable,

μ is the mean, and σ is the standard deviation.

[tex]P(X > = 2)[/tex], which is equivalent to finding [tex]P(Z > = (2 - 3)/0.35) = P(Z > = -2.86)[/tex], where Z is a standard normal random variable.

A standard normal distribution table or a calculator, we can find that

[tex]P(Z > = -2.86) = 0.9977[/tex] (rounded to four decimal places).

Therefore,

[tex]P(X > = 2) = P(Z > = -2.86) = 0.9977[/tex]

By applying the following formula, we may standardise the variable X to a standard normal distribution, which has a mean of 0 and a standard deviation of 1.

[tex]z = (x - \mu) /\sigma[/tex]

The random variable's value is x, whereas the mean and standard deviation are and, respectively.

P(X > = 2), which is the same as discovering

, where Z represents a regular standard random variable.

We may determine it using a calculator or a basic normal distribution table.

P(Z > = -2.86) = 0.9977(four decimal places rounded).

Therefore,P(X > = 2) = P(Z > = -2.86) = 0.9977

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The value of c in the new equation written in standard form is:

c = 5.

The standard form of a quadratic equation is expressed as ax² + bx + c = 0.

To write the given quadratic equation, -6 =x² + 4x - 1, in standard form, we need to rewrote the equation as follows:

x² + 4x - 1 = -6

Add 6 to both sides:

x² + 4x - 1 + 6 = -6 + 6

x² + 4x + 5 = 0

The values in the standard form would be:

a = 1, b = 4, and c = 5.

Therefore, the value of c would be: c = 5.

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Answer:

Did research, the correct answer is 5

Step-by-step explanation:

The correct answer is d. 1, as the coefficient of determination cannot be negative and a value of 1 indicates a perfect fit of the regression line to the data.

The coefficient of determination, also known as R-squared, represents the proportion of the variance in the dependent variable that is explained by the independent variable(s). It ranges from 0 to 1, with higher values indicating a better fit of the regression line to the data.

Therefore, the correct answer is d. 1, as the coefficient of determination cannot be negative and a value of 1 indicates a perfect fit of the regression line to the data.

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There are 2,598,960 possible 5-card poker hands.

To calculate the number of 5-card poker hands, we need to determine the number of 5-element subsets of a 52-element set. This is the same as selecting 5 cards from a deck of 52 cards, without regard to the order in which they are selected.

We can use the formula for combinations to calculate the number of 5-element subsets. The formula for combinations is:

C(n, r) = n! / (r! * (n-r)!)

where n is the total number of items, r is the number of items to select, and the exclamation mark denotes the factorial function.

In this case, we want to select 5 cards from a deck of 52 cards, so n = 52 and r = 5. Substituting these values into the formula, we get:

C(52, 5) = 52! / (5! * (52-5)!)

Simplifying this expression:

C(52, 5) = (52 * 51 * 50 * 49 * 48) / (5 * 4 * 3 * 2 * 1)

C(52, 5) = 2,598,960

Therefore, there are 2,598,960 possible 5-card poker hands.

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The height of the cylinder that has a given surface area would be = 5.78cm.

To calculate the height of the cylinder,the formula for surface area of a cylinder should be used and it's given below.

Surface area = 2πrh +2πr²

where;

radius = Diameter/2 = 5/2 = 2.5

surface area = 130cm²

height = ?

That is

130 = 2×3.14×2.5×h + 2×3.14 × 2.5×2.5

Simplify and make h the subject of formula;

130 = 15.7h + 39.25

15.7h = 130- 39.25

= 90.75

h = 90.75/15.7

= 5.78cm

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The height of the cylinder that has a surface area of 130 cm² and a diameter of 5 cm is approximately: 5.78 cm

The surface area of a cylinder cam be calculated using the formula below:

SA = 2πr(h + r)

where r is the radius and h is the height of the cylinder.

Given the following:

r = diameter/2 = 5/2 = 2.5 cm

Surface area (SA) = 130 square centimeters

h = ?

Plug in the values:

130 = 2 × 3.14 × 2.5(h + 2.5)

130 = 15.7(h + 2.5)

130 = 15.7h + 39.25

130 - 39.25 = 15.7h

90.75 = 15.7h

90.75/15.7 = h

h ≈ 5.78 cm

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