A sample of 60 data points is selected from a population with mean of 140 and variance of 13. Determine the mean and standard deviation for the sample.

The probability that the first student is a boy and the second student is a girl is 7/26.

To answer your question, we'll need to calculate the probabilities for each event and then multiply them together.

Probability of selecting a boy first:
There are 7 boys and 13 students total (7 boys + 6 girls), so the probability is 7/13.

Probability of selecting a girl second:
After selecting a boy, there are now 12 students remaining (6 boys + 6 girls). The probability of selecting a girl is 6/12 (which simplifies to 1/2).

Now, multiply the probabilities together: (7/13) × (1/2) = 7/26

So, the probability that the first student is a boy and the second student is a girl is 7/26.

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The angle is 86.8 degrees and its complement is 3.2 degrees.

let x be the angle and y be the Complementary angle.

If the angles are complementary, then their sum is 90 degrees.

x + y = 90................(1)

and, the angle measures 83.6 degrees more than its complement.

x = y + 83.6

y + 83.6 + y = 90

Solving the equation for y we get

2y + 83.6 = 90

2y = 90 - 83.6

2y = 6.4

y= 3.2

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The average temperature during the period from 9am to 9pm is approximately 32.51 degrees Fahrenheit

To find the temperature at 9am, we can simply plug in t=0 into the given function:

T(0) = 30 + 19 sin(0) = 30

So the temperature at 9am is 30 degrees Fahrenheit.

To find the temperature at 3pm, we need to find the value of t that corresponds to 3pm. Since 3pm is 6 hours after 9am, we have t=6:

T(6) = 30 + 19 [tex]sin((pi/12)*6[/tex]) = 30 + 19 s[tex]in(pi/2)[/tex] = 30 + 19 = 49

So the temperature at 3pm is 49 degrees Fahrenheit.

To find the average temperature during the period from 9am to 9pm, we need to find the average value of the function T(t) over the interval [0,12]. We can use the formula for the average value of a function:

avg(T) = (1/(b-a)) * ∫[a,b] T(t) dt

In this case, a=0 and b=12, so we have:

avg(T) =[tex](1/12) * ∫[0,12] (30 + 19 sin(pit/12[/tex])) dt

Integrating term by term, we get:

avg(T) = (1/12)[tex]* (30t - (19/12) *[/tex] ([tex]12cos(pit/12[/tex])) |[0,12]

Evaluating the expression at t=12 and t=0, we get:

[tex]avg(T) = (1/12)[/tex] [tex]* (3012 - (19/12) * (12cos[/tex][tex](pi)) - (300 - (19/12) * (12cos(0))))[/tex]

Simplifying, we get:

[tex]avg(T) = (1/12) *[/tex] (360 + 38.13) = 32.51

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If an expression for x that does not use an absolute value is y, rewrite |x-y| as x - y.

It is the same as rearranging one expression to plug it into another expression when rewriting algebraic expressions using structure. Solving for one of the variables and then plugging the resulting expression for that variable into the other expression is the initial step to take in these kinds of issues.

Any mathematical statement that includes numbers, variables, and an arithmetic operation between them is known as an expression or algebraic expression. In the phrase 4m + 5, for instance, the terms 4m and 5 are separated from the variable m by the arithmetic sign +.

Here given :

|x−y| , if x then :

y, x - y is rewritten for  |x−y|

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Correct Question:

Rewrite each of the following expressions without using absolute value.

1. |x−y| , if x

The Mean Value Theorem applies over the interval (-5, 5) and (5, ∞).

To determine the interval(s) where the Mean Value Theorem (MVT) applies for the function y=√(x^2-25), we need to ensure that the function is continuous and differentiable on the given interval.

1. The function is continuous when the expression under the square root is non-negative, which means x^2-25≥0. Solving for x, we get x≥5 or x≤-5. In interval notation, the domain for continuity is (-∞,-5] U [5,∞).

2. To check for differentiability, we need to find the derivative of the function. The derivative of y=√(x^2-25) is:

y' = (1/2)(x^2-25)^(-1/2) * 2x
y' = x/√(x^2-25)

Now, we need to ensure that the derivative is defined on the given interval. Since x=5 or x=-5 makes the denominator zero, we should exclude these points. Hence, the interval for differentiability is (-∞,-5) U (5,∞).

Since the MVT requires both continuity and differentiability, the applicable interval(s) for the Mean Value Theorem are (-∞,-5) U (5,∞).

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The critical F-value (3.098), we can reject the null hypothesis and conclude that there is a significant difference between the mean amount of snowfall for the four regions.

To learn

To test whether there is a significant difference between the mean amount of snowfall for the four regions, we can use a one-way ANOVA test. The null hypothesis for this test is that the mean amount of snowfall is the same for all four regions, while the alternative hypothesis is that at least one region has a significantly different mean amount of snowfall than the others.

To begin, we can calculate the sample means and sample standard deviations for each region:

Region 1: Mean = 3.10, SD = 0.283

Region 2: Mean = 3.00, SD = 0.418

Region 3: Mean = 2.57, SD = 0.182

Region 4: Mean = 3.09, SD = 0.499

Next, we can calculate the overall mean and overall variance of the sample data:

Overall mean = (3.10 + 3.00 + 2.57 + 3.09) / 4 = 2.94

Overall variance = (([tex]0.283^2[/tex] + 0.418^2 + [tex]0.182^2[/tex] + [tex]0.499^2[/tex]) / 3) / 4 = 0.00937

Using these values, we can calculate the F-statistic for the one-way ANOVA test:

F = (Between-group variability) / (Within-group variability)

Between-group variability = Sum of squares between groups / degrees of freedom between groups

Within-group variability = Sum of squares within groups / degrees of freedom within groups

Degrees of freedom between groups = k - 1 = 4 - 1 = 3

Degrees of freedom within groups = N - k = 20 - 4 = 16

Sum of squares between groups = (n1 * (x1bar - overall_mean)[tex]^2[/tex] + n2 * (x2bar - overall_mean)[tex]^2[/tex] + n3 * (x3bar - overall_mean)[tex]^2[/tex] + n4 * (x4bar - overall_mean)[tex]^2[/tex]) / (k - 1)

= ((9 * (3.10 - 2.94)[tex]^2[/tex] + 9 * (3.00 - 2.94)[tex]^2[/tex] + 7 * (2.57 - 2.94)[tex]^2[/tex] + 3 * (3.09 - 2.94)[tex]^2[/tex]) / 3

= 3.602

Sum of squares within groups = (n1 - 1) * s[tex]1^2[/tex] + (n2 - 1) * s[tex]2^2[/tex] + (n3 - 1) * s[tex]3^2[/tex] + (n4 - 1) * s[tex]4^2[/tex]

= (8 *[tex]0.283^2[/tex] + 8 * 0.[tex]418^2[/tex] + 6 * [tex]0.182^2[/tex] + 2 * [tex]0.499^2[/tex])

= 1.055

F = (Between-group variability) / (Within-group variability) = 3.602 / 1.055 = 3.415

We can then use an F-distribution table or calculator to find the critical F-value for a significance level of 0.05, with degrees of freedom between groups = 3 and degrees of freedom within groups = 16. The critical F-value is 3.098.

Since our calculated F-value (3.415) is greater than the critical F-value (3.098), we can reject the null hypothesis and conclude that there is a significant difference between the mean amount of snowfall for the four regions.

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The measure of the angle ∠CMD is 45.

We have,

From the figure,

∠FMD = 15

∠BMC = 30

Now,

∠BMF = 90

This can be written as,

∠BMC + ∠CMD + ∠FMD = 90

30 + ∠CMD + 15 = 90

∠CMD = 90 - 45

∠CMD = 45

Thus,

The measure of the angle ∠CMD is 45.

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The group described by 16% of the population is 73.

For a normal distribution curve, the population are often divided into 2% below the mean, 14 % below the mean, 34% below the mean, the mean, 34% above the mean, 16% above the mean and 2 % above the mean.

for 34% below the mean, the population = M - 1std

for 16% below the mean, the population = M - 2std

So the population represented by 16% is calculated as follows;

= M - 2std

where;

= 79 - 2 (3)

= 79 - 6

= 73

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The domain of the function shown in the graph is the one in option A:

6 ≤ k ≤ 11

The domain of a function y = f(x) is the set of the inputs of the function. To identify the domain in a graph, we need to look at the horizontal axis (also called the x-axis).

On the graph we can see that it starts at x = 6 with a closed dot, and it ends at x = 11 also with a closed dot.

That means that these values belong to the domain, so we can write the domain as follows:

Domain = 6 ≤ k ≤ 11

(notice that the variable in the horizontal axis is k).

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The rule for the nth term of the geometric sequence is given as follows:

[tex]a_n = 2^n[/tex]

Hence the 10th term of the sequence is given as follows:

1024.

A geometric sequence is a sequence of numbers where each term is obtained by multiplying the previous term by a fixed number called the common ratio q.

The explicit formula of the sequence is given as follows:

[tex]a_n = a_0q^{n}[/tex]

In which [tex]a_0[/tex] is the first term.

The parameters in this problem are given as follows:

Hence the rule is given as follows:

[tex]a_n = 2^n[/tex]

Hence the 10th term of the sequence is given as follows:

2^10 = 1024.

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The  value of a= 17.5, b=-14 and c=-28.

A function is represented as a rule that produces a distinct result for each input x. In mathematics, a function is indicated by a mapping or transformation

Given function:

f (x) = 3x – 1.

Also,

x        f(x)

-5       a

-2       7

4        b

8       c

Now, using the proportionality

k = y/x

k = 7 / (-2)

k = -3.5

So, -3.5 = a/ (-5)

-3.5 x (-5) = a

a= 17.5

again, -3.5 = b/4

b= -14

Lastly, -3.5 = c/8

c= -28.

Hence, the value of a= 17.5, b=-14 and c=-28.

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The length of curve DE is equal to 26.25 units.

In Mathematics and Geometry, Pythagorean's theorem is modeled by the following mathematical expression:

x² + y² = z²

Where:

x, y, and z represents the length of sides or side lengths of any right-angled triangle.

In order to determine the length of the hypotenuse in this right-angled triangle, we would have to apply Pythagorean's theorem as follows;

AC² + BC² = AB²

20² + 15² = AB²

AB² = 400 + 225

AB = √625

AB = 25 units.

For the length of curve DE, we have:

DE = 105% of AB

DE = 1.05 × 25

DE = 26.25 units.

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Answer:

-3 times n+7>-17

Step-by-step explanation:

(a) The function f(x) = 0.2, 1 < x < 6 is a probability distribution function (pdf) because it is non-negative for all x in its domain and the total area under the curve is equal to 1.

(b) The cumulative distribution function (cdf) F(x) for 1 < x < 6 is given by F(x) = 0.2(x-1), where F(x) = 0 for x ≤ 1 and F(x) = 1 for x ≥ 6.

(c) The probability P(2 < X < 4) is 0.4, which can be calculated by integrating the pdf f(x) = 0.2 over the interval [2, 4].

(d) The probability P(X > 4) is 0.6, which is obtained by subtracting the cumulative probability F(4) = 0.2(4-1) from 1.

(a) To show that f(x) = 0.2, 1 < x < 6 is a probability distribution function (pdf), we need to show that:

f(x) is non-negative for all x in its domain: f(x) = 0.2 is non-negative for all x between 1 and 6.

The total area under the curve of f(x) is equal to 1:

∫1^6 0.2 dx = 0.2(x)|1^6 = 0.2(6-1) = 1

Since both conditions are satisfied, f(x) is a pdf.

(b) The cumulative distribution function (cdf) F(x) is given by:

F(x) = ∫1^x f(t) dt

For 1 < x < 6, we have:

F(x) = ∫1^x 0.2 dt = 0.2(t)|1^x = 0.2(x-1)

For x ≤ 1, F(x) = 0, and for x ≥ 6, F(x) = 1.

(c) P(2 < X < 4) is given by:

P(2 < X < 4) = ∫2^4 f(x) dx = ∫2^4 0.2 dx = 0.2(x)|2^4 = 0.4

(d) P(X > 4) is given by:

P(X > 4) = 1 - P(X ≤ 4) = 1 - F(4) = 1 - 0.2(4-1) = 0.6

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Answer:

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If x=2 and x=3 are roots of the equation 3x

2

−2kx+2m=0 then (k,m)=

Medium

Solution

verified

Verified by Toppr

Correct option is A)

Since x=2 and x=3 are roots of the equation 3x

2

−2kx+2m=0

⇒12−4k+2m=0⇒2k−m=6 ...(i)

and ⇒27−6k+2m=0⇒6k−2m=27 ...(ii)

On multiplying (i) by 3 and subtracting (ii) from it, we get

6k−3m=18

−

6

k

+

−

2m=

−

2

7

−m=−9

∴m=9

On putting m=9 in (i), we get

2k=15⇒k=

2

15

∴(k,m)=(

2

15

,9)

Hence, Option A is correct.

Answer:

A. 2x+3 and B. 3y² - 2y + 4 and E. x²/2 - 3x + 7 are polynomial expressions.

(1) The probability of getting a soft chicken is 16.67%.

(2) The probability of getting a crunch beef is 16.67%.

(3) The probability of getting a fish  (crunchy or soft) is 33.33%.

The probability of getting a soft chicken is calculated as follows;

total outcome = 6

number of soft chicken = 1

Probability = 1/6 = 16.67%

The probability of getting a crunch beef is calculated as follows;

total outcome = 6

number of crunch beef = 1

Probability = 1/6 = 16.67%

The probability of getting a fish  (crunchy or soft) is calculated as follows;

total outcome = 6

number of soft fish = 1

number of crunch fish  = 1

P(soft or crunch) = 1/6 + 1/6 = 2/6 = 1/3 = 33.33%

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The equality statement is False (b) 7= -(-(7)).

The expression on the right side of the equation simplifies to -(-7), which is equal to 7, making the statement untrue. Therefore, 7=-(-7) should be used as the right equality declaration.

In other words, 7 is equal to the opposite of -(-7)

The area of mathematics known as algebra aids in the representation of circumstances or problems as mathematical expressions. Mathematical operations like addition, subtraction, multiplication, and division are combined with variables like x, y, and z to produce a meaningful mathematical expression.

The associative, commutative, and distributive laws are the three fundamental principles of algebra. They facilitate the simplification or solution of problems and aid in illustrating the connection between different number operations.

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The surface with equation (y^2 + 1)e^z – (z^2 + 1)e^x + y^2z^2e^y = 0 can be parametrized as follows 1:

x = u

y = v

z = ln((v^2 + 1) / (u^2 + 1))

Parametrization of a surface is a mathematical technique used to describe a surface in terms of parameters. It involves expressing the coordinates of points on the surface as functions of two or more parameters. A common way to parametrize a surface is to use two parameters u and v to represent the coordinates of points on the surface. This is called a parametric representation or a parametric equation of the surface. Another way to parametrize a surface is to use a vector-valued function, which maps a point in a domain onto a point on the surface. Both of these techniques allow us to describe the surface in a way that is useful for mathematical analysis and visualization.

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The producer’s risk is 0.99 and the consumer’s risks for each sampling plan are 0.347, 0.049, and 0.176 for acceptance criteria 0, acceptance criteria 1, and acceptance criteria 2 respectively.

a. The operating characteristic curve (OC curve) shows the probability of accepting a lot with a given quality level, based on the sample size and acceptance criteria. Here are the OC curves for acceptance criteria of 0, 1, and 2, assuming a binomial distribution:

Acceptance Criteria = 0:
Sample size: 20
Probability of acceptance (p): 0.01
Probability of rejection (1-p): 0.99
OC Curve:
Quality Level (proportion defective) | Probability of acceptance
0% | 0.994
1% | 0.988
2% | 0.977
3% | 0.958
4% | 0.928
5% | 0.883
6% | 0.821
7% | 0.743
8% | 0.653
9% | 0.556
10% | 0.458

Acceptance Criteria = 1:
Sample size: 20
Probability of acceptance (p): 0.92
Probability of rejection (1-p): 0.08
OC Curve:
Quality Level (proportion defective) | Probability of acceptance
0% | 1.000
1% | 1.000
2% | 1.000
3% | 1.000
4% | 1.000
5% | 1.000
6% | 0.999
7% | 0.998
8% | 0.993
9% | 0.981
10% | 0.951

Acceptance Criteria = 2:
Sample size: 20
Probability of acceptance (p): 0.83
Probability of rejection (1-p): 0.17
OC Curve:
Quality Level (proportion defective) | Probability of acceptance
0% | 1.000
1% | 1.000
2% | 1.000
3% | 1.000
4% | 0.999
5% | 0.998
6% | 0.992
7% | 0.978
8% | 0.949
9% | 0.898
10% | 0.824

b. The producer's risk (Type I error) is the probability of rejecting a good lot, while the consumer's risk (Type II error) is the probability of accepting a bad lot. Here are the calculations for each sampling plan:

Acceptance Criteria = 0:
Producer's risk = α = 1 - p0 = 0.99
Consumer's risk = β = 1 - OC at p1 = 1 - 0.653 = 0.347

Acceptance Criteria = 1:
Producer's risk = α = 1 - p0 = 0.99
Consumer's risk = β = 1 - OC at p1 = 1 - 0.951 = 0.049

Acceptance Criteria = 2:

Producer's risk = α = 1 - p0 = 0.99
Consumer's risk = β = 1 - OC at p1 = 1 - 0.824 = 0.176

Note that the producer's risk is the same for all three sampling plans since it is based on the specified probability of a defective unit in the lot. The consumer's risk, however, varies depending on the acceptance criteria and sample size. Generally, a more lenient acceptance criterion (higher p-value) or a larger sample size will result in lower consumer risk.

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The energy for vacancy formation per atom in the metal M is 0.91 eV/atom.

To calculate the energy (in eV/atom) for vacancy formation in the metal M, we can use the following formula:

E_v = RT * ln(N_v/N)

Where:
- E_v is the energy for vacancy formation per atom
- R is the gas constant (8.314 J/mol*K or 0.008314 eV/mol*K)
- T is the temperature in Kelvin (317°C = 590K)
- N_v is the equilibrium number of vacancies (6.67 × 10^23 m^-3)
- N is the number of atoms per unit volume, which can be calculated using the density and atomic weight of the metal as follows:

N = (6.40 g/cm^3) * (1 mol/27.00 g) * (6.022 × 10^23 atoms/mol) = 1.51 × 10^22 atoms/m^3

Plugging in these values, we get:

E_v = (0.008314 eV/mol*K) * (590 K) * ln(6.67 × 10^23 m^-3 / 1.51 × 10^22 atoms/m^3)
E_v = 0.91 eV/atom

Therefore, the energy for vacancy formation per atom in the metal M is 0.91 eV/atom.

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Answer:

357=10.5*4*x

8.5x

Step-by-step explanation:

357=10.5*4*x

357=42*x

8.5=x

Answer:

D. 13.34

Step-by-Step Explanation:

distance formula

[tex]d=\sqrt{(x2-x1)^2 +(y2-y1)^2} \\d=\sqrt{(6-3)^2 +(9-(-4))^2} \\d=\sqrt{178}[/tex]

Answer:

24

Step-by-step explanation:

starting with the "top floor" :

3 single small triangles.

then the left 2 combined and the right 2 combined.

and then all 3 combined.

that is 3 + 2 + 1 = 6 triangles.

now we extend the triangles from the top floor to the next floor below.

we have the same number of triangles, they are just longer.

6 triangles there.

and we extend the triangles to the next floor below.

we have again the same number of triangles, they are just even longer.

6 triangles there.

and we extend the triangles to the next (and last) floor below.

we have again the same number of triangles, they are just very long.

6 triangles there.

that makes for the 4 floors

6×4 = 24 triangles.

(a) The box can be represented as a population of 200,000 registered voters, with 120,000 of them supporting the ballot proposition and 80,000 opposing it. Each voter can be represented by a ticket, and the box would contain 120,000 tickets labeled "Support" and 80,000 tickets labeled "Oppose."

(b) If we drew 200 times with replacement from this box, there is a 95% chance that the sum of draws (number of voters contacted who support the ballot proposition) would be between 106 and 134. Expressed as a percentage of the 200 people, this is between 53% and 67%.

(a) The box can be represented as a collection of 200,000 balls, where each ball corresponds to a registered voter. Among these, 120,000 balls are labeled as "support" to indicate that the voter supports the ballot proposition, and the remaining 80,000 balls are labeled as "oppose" to indicate that the voter does not support the proposition. When we randomly select 200 voters without replacement and count the number of supporters among them, we are essentially drawing 200 balls from this box without replacement and counting the number of balls labeled as "support".

(b) Since we are drawing with replacement, each draw is independent and has a Bernoulli distribution with a probability of success p = 0.6 (since 60% of the voters support the proposition). The sum of these draws has a binomial distribution with parameters n = 200 (the number of trials) and p = 0.6 (the probability of success). The mean of this distribution is μ = np = 200 x 0.6 = 120, and the standard deviation is σ = sqrt(np(1-p)) = sqrt(200 x 0.6 x 0.4) ≈ 7.75.

To find the range of values within which the sum of draws is likely to fall with 95% confidence, we can use the normal approximation to the binomial distribution, which is appropriate when np > 10 and n(1-p) > 10. In this case, we have np = 120 and n(1-p) = 80, so the normal approximation is valid.

We want to find the values of k such that P(μ - kσ < X < μ + kσ) = 0.95, where X is the sum of draws. Using the standard normal distribution, we have:

P(-k < Z < k) = 0.95,

where Z = (X - μ)/σ is a standard normal variable. From standard normal tables, we find that k ≈ 1.96.

Therefore, the 95% confidence interval for the sum of draws is:

120 - 1.96 x 7.75 ≈ 104.1 to 120 + 1.96 x 7.75 ≈ 135.9.

As a percentage of the 200 people polled, this is between:

104.1/200 x 100 ≈ 52.05% and 135.9/200 x 100 ≈ 67.95%.

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Let A ∈ R^(nxn) and let C ∈ R^(nxm). We will prove the following:

(1) Assume A is positive semidefinite. We need to show that tr(A) = 0 if and only if A = 0.

Proof:

(i) If A = 0, then tr(A) = 0 since the trace of the zero matrix is 0.

(ii) Assume tr(A) = 0. Recall that A is positive semidefinite, which means that its eigenvalues are non-negative. Since the trace of a matrix is the sum of its eigenvalues, having tr(A) = 0 implies that all eigenvalues of A must be zero. Consequently, A is a diagonal matrix with all diagonal elements equal to 0. Therefore, A = 0.

Thus, we have shown that tr(A) = 0 if and only if A = 0.

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Step-by-step explanation:

You are given $  and  cartons and you want  $/carton

$ 48 / 12 cartons =  $ 4 / carton    <====unit rate

The measures of the following angles are; Angle 1 =44

Given that HC is the diameter of the circle, then:

HA = 83°, BC= 50°, HD = 135°, GF = 32°, HG = 45°, and FE = 55°

From the given figure, HC can be expressed as:

HC = HA + BC + AB

Substituting with HC = 180°, HA = 83°, and BC = 50°, and solving for AB:

180 = 83 + AB + 50

AB = 47

The relation between the angle outside the circle, ∠1, and the intersected arcs AB and HD are:

Angle = 1/2(HD - AB)

Substituting with HD = 135°, and AB = 47°:

Angle 1 = 1/2(135 - 47)

Angle 1 =44

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If Crunchy-Critters produces chips bags with mean weight as 16 oz, the the probability that weight of the bag is less than 15.4 oz is 0.0228.

We use the standard normal distribution to find the required probability. First, we need to standardize the value of 15.4 oz using the formula : z = (x - μ) / σ,

where x is = value we are interested in, μ is = mean weight, σ is = standard deviation, and z is the standardized score.

The mean-weight of the chips is (μ) = 16 oz,

The standard-deviation of weight (σ) is 0.3 oz,

Substituting the values we have, we get:

⇒ z = (15.4 - 16)/0.3,

⇒ z = -2, and

We know that, P(X < 15.4) = P(Z < -2) = 0.0228

Therefore, the required probability is 0.0228 or 2.28%.

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