An airline manager uses logistic regression to model individual passenger’s probability of being satisfied with the airline’s service. The following table lists out variables used in the model and corresponding parameter estimations. Assume the probability threshold is 0.5.


1. (a) A passenger is aged 32 and earns a monthly income of HK$30000. He on average travels 10 times each year. Please predict whether this passenger will be satisfied with the airline’s service or not.

(2 points)

2. (b) From the above table, one student concludes that travelers who travel more frequently are more likely to be satisfied with this airline’s service than those who travel less frequently, keeping all other factors constant. Do you agree with this conclusion? Why?

(1 points)

the area of the shaded rectangle is 14 square units.

What is a rectangle?

Rectangles are quadrilaterals having four right angles in the Euclidean plane of geometry. Various definitions include an equiangular quadrilateral, A closed, four-sided rectangle is a two-dimensional shape. A rectangle's opposite sides are equal and parallel to one another, and all of its angles are exactly 90 degrees.

To find the area of the shaded rectangle on the left side of figure B, we need to subtract the area of the unshaded rectangle (2 by 7) from the area of the larger rectangle (4 by 7). So, the area of the shaded rectangle can be expressed as:

(4 x 7) - (2 x 7)

Simplifying this expression, we get:

28 - 14 = 14

Therefore, the area of the shaded rectangle is 14 square units.

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Answer:T=(X*520N)+650

Step-by-step explanation:

The appropriate statistic is chi-squared, and the value of that statistic is 1.82.

The appropriate statistic for testing the independence of two categorical variables is the chi-squared test.

The observed values in this case are:

Shift 1 with accident: 12

Shift 1 without accident: 175

Shift 2 with accident: 7

Shift 2 without accident: 151

We can use these observed values to calculate the expected values under the assumption of independence:

Shift 1 with accident (expected): (12+7)/345 * 187 = 8.94

Shift 1 without accident (expected): (175+151)/345 * 187 = 178.06

Shift 2 with accident (expected): (12+7)/345 * 158 = 10.06

Shift 2 without accident (expected): (175+151)/345 * 158 = 147.94

The chi-squared test statistic is then:

χ² = Σ (observed - expected)² / expected

Plugging in the numbers, we get:

χ² = (12-8.94)²/8.94 + (175-178.06)²/178.06 + (7-10.06)²/10.06 + (151-147.94)²/147.94 = 1.82

The degrees of freedom for this test is (number of rows - 1) * (number of columns - 1) = 1 * 1 = 1.

Looking up the critical value of the chi-squared distribution with 1 degree of freedom and a significance level of 0.05, we find that the critical value is 3.84.

Since our calculated chi-squared value (1.82) is less than the critical value (3.84), we fail to reject the null hypothesis that the variables are independent.

Therefore, the appropriate statistic is chi-squared, and the value of that statistic is 1.82.

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The value of limx→−2(5x − x3) is -2.

To find the limit of the given function as x approaches -2, we can simply substitute -2 for x in the expression and simplify:

lim x→-2 (5x - x^3) = 5(-2) - (-2)^3 = -10 + 8 = -2

Therefore, the limit of the given function as x approaches -2 exists and is equal to -2.

Intuitively, as x approaches -2, the function 5x - x^3 becomes increasingly negative since the term x^3 dominates the expression. However, the function is still bounded and approaches a finite value, which is -2. This can be seen from the fact that as x approaches -2 from the left and from the right, the values of the function approach -2 from below and above, respectively.

In conclusion, the limit of the function exists and is equal to -2.

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The limit of the given function as x approaches 0 is e².

The given limit is:

lim(x→0) (xe²⁺ˣ - e²)

To evaluate this limit, we can use algebraic manipulation and basic limit rules. First, we can factor out e² from the expression:

lim(x→0) (xe²⁺ˣ - e²) = lim(x→0) e²(xeˣ - 1)

Next, we can use the fact that the limit of a product is the product of the limits, as long as both limits exist:

lim(x→0) e²(xeˣ - 1) = lim(x→0) e² x lim(x→0) (xeˣ - 1)

The limit of e² as x approaches 0 is simply e², so we can evaluate the second limit:

lim(x→0) (xeˣ - 1) = lim(x→0) [(eˣ - 1)/x] x x = lim(x→0) (eˣ - 1)/1 = lim(x→0) (eˣ - 1)

We can use L'Hôpital's rule to evaluate this limit:

lim(x→0) (eˣ - 1) / x = lim(x→0) eˣ / 1 = e⁰ = 1

Therefore, the original limit is:

lim(x→0) (xe²⁺ˣ - e²) = lim(x→0) e² x lim(x→0) (xeˣ - 1) = e² x 1 = e²

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Answer:

4/15

Step-by-step explanation:

there are 30 total songs.

There are 8 mambo songs, which can also be written as 8/30.

8/30 can be reduced by 2, making it 4/15.

Therefore, there is a probability of 4/15 that a mambo song will play

SORRY IF THIS DOES NOT MAKE SENSE

the scale factor is 1/2, which can also be written as the fraction ½ or the decimal 0.5. This means that the dimensions in the drawing are half the size of the actual dimensions.

Why is it?

The scale of the drawing is 8 inches = 4 feet. This means that every inch on the drawing represents 4/8 = 1/2 feet in the actual pool.

To find the scale factor, we need to divide the length of the corresponding dimension in the drawing by the length of the actual dimension. Let's assume that the length of the pool in the drawing is L inches, and the actual length of the pool is l feet. Then we have:

L inches = (1/2) l feet

To solve for the scale factor, we can divide both sides by l inches:

L/l = (1/2)

So the scale factor is 1/2, which can also be written as the fraction ½ or the decimal 0.5. This means that the dimensions in the drawing are half the size of the actual dimensions.

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The equations in cylindrical coordinates is 5r² - 7r cos θ + r² cos² θ - 9 + r cos θ = 0

To write the equation in cylindrical coordinate we use polar form  r, θ, and z.

In cylindrical coordinates, x = r cos θ, y = r sin θ, and z = z.

a) 5x² -7x + 5y² + x² = 9 - x

5(  r cos θ)² - 7( r cos θ) + 5 ( r sin θ)² + ( r cos θ)² = 9 -  r cos θ

5r² cos²θ - 7r cos θ + 5r² sin² θ + r² cos² θ = 9 - r cos θ

5r² - 7r cos θ + r² cos² θ - 9 + r cos θ = 0

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To find the most general antiderivative of the function f(u) = (u^4 + 7(u)^0.5) / u^2, we first rewrite the function to make it easier to integrate:

f(u) = u^4/u^2 + 7(u)^0.5/u^2 = u^2 + 7u^(-1.5)

Now, we find the antiderivative for each term:

∫(u^2) du = (1/3)u^3 + C1

∫(7u^(-1.5)) du = 7∫(u^(-1.5)) du = -14u^(-0.5) + C2

The most general antiderivative of f(u) is the sum of these two integrals:

F(u) = (1/3)u^3 - 14u^(-0.5) + C

Here, C = C1 + C2 is the constant of the antiderivative. To check the answer, we differentiate F(u):

F'(u) = d( (1/3)u^3 - 14u^(-0.5) + C )/du = u^2 + 7u^(-1.5)

Since F'(u) matches the original function f(u), the most general antiderivative is correct.

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Answer:

The Correct answer for the equation is C

(x+4)(x+2)

Step-by-step explanation:

x= -4,x= -2

x+4=0,x+2=0

(x+4)(x+2)

Answer:

The answer is C

Step-by-step explanation:

x= -4, x= -2

x+4=0, x+2=0

(x+4)(x+2)

Hope this helps :)

We can compute the difference in delay times between the two systems for each simulation run. The variable "Difference" stores these differences.

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The complex exponential Fourier series expression of the 4-periodic function f(x) is given by:

f(x) = Σ (C_n * [tex]e^i^n^w^_0x[/tex]), where n = -∞ to +∞, w0 = (2π)/4 = π/2, and C_n is the complex Fourier coefficient.

To find the complex Fourier coefficients C_n, use the formula:

C_n = (1/4) * ∫[f(x) * [tex]e^-^i^n^w^_0x[/tex]] dx, where the integral is taken over one period.

For the given function, f(x) = 4 for 0 ≤ x < 2, and f(x) = 10 for 2 ≤ x < 4. Therefore, the coefficients C_n can be found by integrating the two separate intervals:

C_n = (1/4) * [∫(4 * [tex]e^-^i^n^$^\pi$^/^2^x[/tex] dx) from 0 to 2 + ∫(10 *  [tex]e^-^i^n^$^\pi$^/^2^x[/tex] dx) from 2 to 4]

Evaluate the integrals and sum them up to find C_n for each n. Substitute these coefficients into the Fourier series expression to obtain the final series representation of f(x).

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The approximate value of t is 5.225. Therefore, the solution for t is approximately 2.9687. To solve for t in the given equation, we can follow these steps:

800(1.09)^t = 1400

Divide both sides by 800:

(1.09)^t = 1.75

Take the logarithm of both sides with base 1.09:

log(1.09)(1.09)^t = log(1.09)1.75

t = log(1.09)1.75

Using a calculator or LARAPCALC10, we can find that:

t ≈ 2.9687

To solve for t in the equation 800(1.09)^t = 1,400. The step-by-step explanation to find the value of t:

1. Divide both sides of the equation by 800:
(1.09)^t = 1,400/800

2. Simplify the right side:
(1.09)^t = 1.75

3. To solve for t, take the natural logarithm (ln) of both sides:
ln((1.09)^t) = ln(1.75)

4. Use the property of logarithms: ln(a^b) = b*ln(a)
t * ln(1.09) = ln(1.75)

5. Divide both sides by ln(1.09) to solve for t:
t = ln(1.75) / ln(1.09)

6. Calculate the value of t using a calculator:
t ≈ 5.225

So, the approximate value of t is 5.225.

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Answer:

Here you go!

Step-by-step explanation:

Have a great day!

<3

Given y = f(x) with f(1) = 3 and f '(1) = 4,  g'(1) if g(x) = f(x)  g'(1) = 4 h'(1) = 2.

a) To find g'(1) if g(x) = f(x), we can simply take the derivative of g(x) using the chain rule:

g'(x) = f'(x) * 1

Since g(x) = f(x), we can substitute in f'(x) for g'(x) and 1 for x:

g'(1) = f'(1) * 1 = 4 * 1 = 4


b) To find h'(1) if h(x) = f(Vx), we will need to use the chain rule again:

h'(x) = f'(Vx) * (d/dx) Vx

Since Vx represents the square root of x, we can rewrite it as x^(1/2):

h'(x) = f'(x^(1/2)) * (d/dx) x^(1/2)

Using the power rule, we can simplify (d/dx) x^(1/2) to (1/2)x^(-1/2):

h'(x) = f'(x^(1/2)) * (1/2)x^(-1/2)

Now we can substitute in 1 for x and f'(1) for f'(x^(1/2)):

h'(1) = f'(1^(1/2)) * (1/2)(1^(-1/2)) = f'(1) * 1/2

Since we know that f'(1) = 4, we can substitute that in:

h'(1) = 4 * 1/2 = 2

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Answer:

146

Step-by-step explanation:

56

=

8

+

(

9

−

1

)

d

48

=

8

d

6

=

d

The common difference is of

6

. We can now find the 24th term using the formula

t

n

=

a

+

(

n

−

1

)

d

t

24

=

8

+

(

24

−

1

)

6

t

24

=

146

Thus, the 24th term is

146

im not sure fs

The answer to this question is 133

The test is hypothesis test. and p-value is 0.00001

Based on the given information, the hypothesis test is a one-tailed (left-tailed) t-test with a level of significance of α = 0.01. The test statistic is t = -4.6 with 499 degrees of freedom.

The test is left-tailed, so the P-value is the area under the curve to the left of t = -4.6 with 499 degrees of freedom.

Using a t-distribution table or software, we can find the P-value associated with t = -4.6 and 499 degrees of freedom. The P-value is approximately 0.00001, rounded to five decimal places.

Therefore, the test is statistically significant at the 0.01 level, and we reject the null hypothesis H_0: μ = 919 in favor of the alternative hypothesis H_1: μ < 919.

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The integral I can be written as I = (2/5)x⁵ + x³ - 5/x + C, where C is the constant of integration.

To determine the integral I = S(2x⁴ + 3x² + 5/x²)dx, we need to apply the rules of integration.

We can break the integral into three separate integrals, one for each term in the function. The first term, 2x⁴, can be integrated using the power rule, which states that Sxⁿ dx = (x[tex]^(n+1))/(n+1)[/tex] + C, where C is the constant of integration. Using this rule, we get S2x⁴ dx = (2/5)x⁵ + C.

The second term, 3x², can also be integrated using the power rule to give S3x² dx = x³ + C. The third term, 5/x², can be integrated using the rule for integrating a reciprocal function, which is S1/x dx = ln|x| + C. Applying this rule, we get S5/x² dx = -5/x + C.

Therefore, the integral I can be written as I = (2/5)x⁵ + x³ - 5/x + C, where C is the constant of integration.

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The antiderivative of    [tex]f(x) = x^{3.4} - 2x^{(\sqrt{2} )}-1[/tex] is :

[tex]F(x) = (10/17)x^{4.4} - x^{(\sqrt{2} )} + C[/tex]

To find the antiderivative of [tex]f(x) = x^{3.4} - 2x^{(\sqrt{2} )}-1,[/tex] we need to find a function F(x) such that F'(x) = f(x).

Using the power rule of integration, we can integrate each term of the function as follows:

[tex]∫(x^{3.4})dx = (1/3.4)x^{4.4}} + C_1 = (10/17)x^{4.4} + C_1[/tex]

[tex]∫(-2x^{(\sqrt{2} )}-1)dx = (-2/(\sqrt{2} ))x^{(\sqrt{2} )} + C_2 = (-2/2)x^{(\sqrt{2} )} + C_2 = -x^{(\sqrt{2} } + C_2[/tex]

Where C₁ and C₂ are constants of integration.

Therefore, the antiderivative of     [tex]f(x) = x^{3.4} - 2x^{(\sqrt{2} )}-1[/tex]  is:

[tex]F(x) = (10/17)x^{4.4} - x^{(\sqrt{2} )} + C[/tex]

Where [tex]C = C_1 + C_2[/tex]is the constant of integration.

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The equation of G which is a transformation of y = sin x is

The coordinates of point P is (3π/2, 1)

Equation of graph G is as a result of transformation of graph of y = sin  x. The transformation type is translation and the rule is: up, 4 units. This is written as:

y = sin  x

G(x) = y + 4 = sin  x + 4

G(x) = sin  x + 4

The plot of the graph of G(x) = sin  x + 4 shows that the coordinate of point P is

(3π/2, 1)

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Answer:

independent

Step-by-step explanation:

We can use the formula for the margin of error to determine the required sample size:

Margin of error = z * sqrt(p*(1-p)/n)

where z is the critical value from the standard normal distribution corresponding to the desired confidence level (90% in this case), p is the population proportion (0.35 based on the survey results), and n is the sample size.

We want the margin of error to be no more than 3% of the population proportion, which means we want:

z * sqrt(p*(1-p)/n) <= 0.03*p

Solving for n, we get:

n >= (z^2 * p*(1-p)) / (0.03)^2

Plugging in the values, we get:

n >= (1.645^2 * 0.35*(1-0.35)) / (0.03)^2 = 1072.84

We need a sample size of at least 1073 to be 90% confident that the sample proportion is within 3% of the population proportion, assuming the population proportion is 0.35 based on the survey results

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The probability of 10 successes out of 12 trials with a success probability of 0.3 is 0.114.

To compute the probability of X successes using the binomial distribution table, we need to use the following formula:

P(X) = (n choose X) * p^X * (1-p)^(n-X)

where:
- P(X) is the probability of X successes
- n is the total number of trials
- p is the probability of success in each trial
- X is the number of successes we want to compute

Now, let's apply this formula to the given scenarios:

Part 1 of 4:
(a) -5, p=0.5, X=4
Since X cannot be negative, we cannot compute the probability for this scenario.

Part 2 of 4:
(b) n=9, p=0.8, X=6
P(X=6) = (9 choose 6) * 0.8^6 * 0.2^3
P(X=6) = 0.311

Therefore, the probability of 6 successes out of 9 trials with a success probability of 0.8 is 0.311.

Part 3 of 4:
(c) n=12, p=0.3, X=10
P(X=10) = (12 choose 10) * 0.3^10 * 0.7^2
P(X=10) = 0.114

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.

The bias can be corrected by using instrumental variables, which are correlated with the true value of the independent variable but uncorrelated with the measurement error.

The OLS estimator is linear because it satisfies the superposition principle.

To show that equation (2) is linear, we can write it in the form of a linear equation:

β1 = ∑(Xi - x)(Yi - y) / ∑(Xi - x)²

where β1 is the estimated slope coefficient.

To derive conditions for the OLS estimator to be unbiased, we need to assume that the error term Ui has a zero mean, constant variance, and is uncorrelated with the independent variable X1i. Under these assumptions, the OLS estimator is unbiased if and only if the expected value of the error term is zero.

Suppose we do not have access to the true independent variable X1i and instead observe a measured variable xi, which is subject to measurement error.

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The only critical point of the function f is  (-0.216, -0.308).

To find the critical point(s) of the function f, we need to solve the system of equations ∇f(x,y) = 0:

∂f/∂x = xlny + 2 = 0

∂f/∂y = 6x + y + 2 = 0

From the second equation, we can solve for y in terms of x:

y = -6x - 2

Substituting this into the first equation, we get:

xln(-6x - 2) + 2 = 0

To find critical points we use numerical methods to find an approximate solution.

x = -0.216

Substituting this value of x back into the equation y = -6x - 2, we get:

y = -0.308

Hence, the only critical point of the function f is  (-0.216, -0.308).

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The total area of the composite figure is 57 sq yd

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

The composite figure

The total area of the composite figure is the sum of the individual shapes

So, we have

Surface area = 11 * 4 + 2 * 5 + 1/2 * 2 * (12 - 5 - 4)

Evaluate

Surface area = 57

Hence. the total area of the figure is 57 sq yd

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We can write our linear equation as:

y + 1 = (4/3)*(x + 1)

or

y - 3 = (4/3)*(x - 2)

If a linear equation passes through two points (x₁, y₁) and (x₂, y₂), then the slope is:

a = (y₂ - y₁)/(x₂ - x₁).

Here the line passes through (-1, -1) and (2, 3), so the slope is:

a = (3 + 1)/(2 + 1) = 4/3

Now, if a line has a slope a and passes through a point (x₁, y₁),then we can write that line as:

y - y₁ = a*(x - x₁)

So with our two points, we can write our line as:

y + 1 = (4/3)*(x + 1)

y - 3 = (4/3)*(x - 2)

The correct option is the first one.

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The chi-square test for goodness of fit is used when we have one

categorical variable with multiple categories, and we want to compare

the observed frequencies of the categories to the expected frequencies.

c. 3 or more variables; 2 different variables.

The chi-square test for goodness of fit is used to test whether the observed frequency distribution of a categorical variable fits the expected frequency distribution. This test compares the observed data to a theoretical distribution or a known distribution, and it assesses whether there is a significant difference between them.

On the other hand, the chi-square test for independence is used to test the relationship between two categorical variables. This test examines whether the distribution of one variable is independent of the distribution of another variable. It is used to determine whether there is a statistically significant association between two categorical variables.

Therefore, the chi-square test for goodness of fit is used when we have one categorical variable with multiple categories, and we want to compare the observed frequencies of the categories to the expected frequencies. The chi-square test for independence is used when we have two categorical variables with multiple categories, and we want to determine whether there is a relationship between the categories of these two variables.

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