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Answer: n=-14

Step-by-step explanation:

CONCEPT:

SOLVE:

The expression in the questions is [tex](x^{3} )(x^{-17})[/tex] which is MULTIPLYING, which means we should add the exponents together

[tex](x^{3} )(x^{-17})[/tex]=[tex]x^n[/tex] ⇔ Given

[tex]x^{3+(-17)}[/tex]=[tex]x^n[/tex] ⇔ Adding Exponents Together

[tex]x^{3-17}[/tex]=[tex]x^n[/tex] ⇔ Simplify

[tex]x^{-14}[/tex]=[tex]x^n[/tex] ⇔ Simplify

[tex]n[/tex]=[tex]-14[/tex] ⇔ Correspondingly

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Answer:

Supplementary Angles are 180 degrees. A triangle has 180 degrees. One angle is already 90 degrees, so 2 more angles with 180 degrees is impossible.

Step-by-step explanation:

Answer:

[tex] \frac{9}{10} = 0.9[/tex]

Answer:

The answer is d

Step-by-step explanation:

Answer:

6 units

Step-by-step explanation:

Using readings taken from the coordinate upon which the triangle is drawn :

The area of triangle is given as :

Area of triangle = 0.5 * base * height

The base of the triangle = 4 units

The height of the triangle = 3 units

The Area of the triangle is thus :

0.5 * 4 * 3

0.5 * 12

= 6 units

Answer:

The area of the given triangle is 6 units²

Intuitively, a larger sample should lead to a smaller confidence interval (fixing the level of confidence). The following most accurately gives a reason for this in the mathematics we use to make the confidence interval:

The standard error goes down because of the greater sample size in the denominator. (And other aspects remain the same.) And the t'-value associated to the t-distribution goes down because t_n have "smaller tails" as n gets large. This is true.

The standard error goes down because of the greater sample size in the denominator. This is because the formula for the standard error involves taking the square root of the sample size in the denominator. Therefore, as the sample size increases, the denominator of the standard error formula increases, causing the standard error to decrease. And the t'-value associated to the t-distribution goes down because t_n have "smaller tails" as n gets large. This is because the t-distribution is symmetrical and bell-shaped, with fatter tails than the normal distribution. As the sample size n increases, the t-distribution approaches the normal distribution, with thinner tails, which means that the t-values become smaller as n increases.

Hence, the correct option is (O) The standard error goes down because the standard deviation of the sample will go down. (And other aspects remain the same.) The mean will be more accurate with a larger sample size. The standard error goes down because of the greater sample size in the denominator. And the t-value associated with the t-distribution goes down because t_n has "smaller tails" as n gets large.

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Answer:

30%

Step-by-step explanation:

The operation that results in the simplified expression x⁵ + x⁴ - 5x³ - 3x² + x - 8 is; P + Q

We are given the Polynomials as;

P(x) = x⁴ + 3x³ + 2x² - x + 2

Q(x) = (x³ + 2x² + 3)(x² - 2)

We want to find the combination of P and Q that would yield;

x⁵ + x⁴ - 5x³ - 3x² + x - 8

Let us expand Q(x) to get;

Q(x) = x⁵ + 2x⁴ + 3x² - 2x³ - 4x² - 6

Q(x) = x⁵ + 2x⁴ - 2x³ - x² - 6

Now, the combined polynomial shows us that coefficient of x⁴ is 1 and coefficient of x³ is - 5.

By inspection, we can say that the combination that would produce the required result is;

Q(x) - P(x) = x⁵ + 2x⁴ - 2x³ - x² - 6 -  x⁴ - 3x³ - 2x² + x - 2

Q(x) - P(x) = x⁵ + x⁴ - 5x³ - 3x² + x - 8

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SJxz dV where E is the region in the first octant inside the ball of radius 2.

∭E xz dV = ∫₀² ∫₀√(4-x²-z²) ∫₀√(4-x²-y²) xz dy dz dx.

Integrating with respect to y first, then z, and finally x, we can calculate the value of the integral.

To evaluate the integral ∭E xz dV, where E is the region in the first octant inside the ball of radius 2, we need to set up the limits of integration.

Since we are integrating over the region inside the ball of radius 2 in the first octant, we can set up the limits as follows:

0 ≤ x ≤ 2,

0 ≤ y ≤ √(4 - x^2 - z^2),

0 ≤ z ≤ √(4 - x^2 - y^2).

Note that we are using the equation of the sphere x^2 + y^2 + z^2 = 4 to determine the limits of integration for y and z.

Now we can evaluate the integral as follows:

∭E xz dV = ∫₀² ∫₀√(4-x²-z²) ∫₀√(4-x²-y²) xz dy dz dx.

Integrating with respect to y first, then z, and finally x, we can calculate the value of the integral.

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Answer:

Option C (None Of The Above)

Step-by-step explanation:

Given expression = [tex] - (\frac{ - e}{ - f} )[/tex]

Lets multiply the numerator and denominator by -1.

[tex] = > - ( \frac{ - e \times - 1}{ - f \times - 1} )[/tex]

[tex] = > - ( \frac{e}{f} )[/tex]

Now , lets open the brackets.

[tex] = > \frac{ - e}{f} [/tex]

But as this expression is not there in the given options , the correct answer will be Option C.

Answer:

C. None Of The Above

Step-by-step explanation:

Hope this helps

Answer:

the answer is D

Step-by-step explanation:

3x-21=x+7

2x-21=7

2x=28

x=14

substitute x for 14

14+7=21

2X14-21+21

Answer:

D

Step-by-step explanation:

Answer:

4 is choice C. THis is because the y intercept is 1 and when you go up 2 units, and go right once, you go to another point (rise over run)

Step-by-step explanation:

Answer:

26

Step-by-step explanation:

Given

4x - a ← substitute a = - 2, x = 6 into the expression

= 4(6) - (- 2) [ note - (- ) is equivalent to + ]

= 24 + 2

= 26

False. "If the conclusion of a valid argument is false, then all of the premises must be false" is incorrect.

The statement "If the conclusion of a valid argument is false, then all of the premises must be false" is incorrect. In a valid argument, the truth of the premises guarantees the truth of the conclusion, but it does not guarantee the truth of the conclusion in reverse. This means that even if the conclusion of a valid argument is false, it does not necessarily imply that all of the premises must be false.

A valid argument is one in which the conclusion logically follows from the premises. It is possible for the premises to be true and still lead to a false conclusion due to errors in reasoning or incorrect logical connections. In such cases, the argument is considered valid but unsound.

To illustrate this, consider the following example:

Premise 1: All birds have feathers.

Premise 2: Penguins are birds.

Conclusion: Therefore, penguins can fly.

This argument is logically valid because the conclusion follows logically from the premises. However, the conclusion is false because penguins cannot fly. In this case, the premises are true, but the conclusion is false.

Therefore, the statement that all of the premises must be false if the conclusion of a valid argument is false is incorrect.

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The problem involves calculating the probability that a patient who tests positive for HIV is not actually infected with HIV. Given that 8% of the patients tested are infected and that the test has a 92% true positive rate for infected patients and a 9% false positive rate for non-infected patients, we need to determine the probability of a false positive result.

Let's denote the events as follows:

A: Patient is infected with HIV

B: Patient tests positive for HIV

We are interested in finding P(A'|B), which represents the probability that a patient is not infected (A') given that they test positive (B).

According to Bayes' theorem, we can express this probability as:

P(A'|B) = (P(B|A') * P(A')) / P(B)

First, let's calculate P(B|A'), which represents the probability of testing positive given that the patient is not infected. Since the false positive rate is given as 9%, we have P(B|A') = 0.09.

Next, we need to calculate P(A'), which is the probability of not being infected. Since 8% of the patients are infected, the complement event (not being infected) has a probability of 1 - 0.08 = 0.92.

To calculate P(B), the probability of testing positive, we need to consider the total probability of testing positive, which includes both infected and non-infected patients. Therefore, we have:

P(B) = P(B|A) * P(A) + P(B|A') * P(A') = 0.92 * 0.08 + 0.09 * 0.92.

Finally, substituting these values into Bayes' theorem, we can calculate P(A'|B), the probability that a patient testing positive is not infected with HIV.

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Answer: 6 × 1

To write a number in expanded form, add the places by it's number.

Ex: 231 → 200 + 30 + 1 = 231

In the problem, 6 is what we're expanding.

Since 6 is only just 1 digit, it is possible to expand a 1 digit number. All you'll need to do it multiply the digit by 1.

6 → 6 × 1 = 6

Therefore, 6 × 1 is the expanded form of 6.

The  probability that no letter is repeated in the password is approximately 0.0000194293, or about 0.0019%.

To calculate the probability that no letter is repeated in a password created using 8 letters from the alphabet (repetition allowed), we need to consider the total number of possible passwords and the number of passwords without repeated letters.

The number of possible passwords can be calculated by considering that each letter in the password can be chosen independently from the 26 letters in the alphabet. Therefore, there are 26 choices for each of the 8 positions, resulting in a total of 26^8 possible passwords.

To calculate the number of passwords without repeated letters, we can consider the choices for each position. For the first position, we have 26 options. For the second position, we have 25 options (since we cannot repeat the letter chosen for the first position). Similarly, for the third position, we have 24 options, and so on.

Using the multiplication principle, the number of passwords without repeated letters is given by 26 * 25 * 24 * 23 * 22 * 21 * 20 * 19.

Therefore, the probability that no letter is repeated in the password can be calculated as:

Probability = (Number of passwords without repeated letters) / (Total number of possible passwords)
= (26 * 25 * 24 * 23 * 22 * 21 * 20 * 19) / (26^8)

Calculating this probability:

Probability ≈ 0.0000194293

So, the probability that no letter is repeated in the password is approximately 0.0000194293, or  about 0.0019%.

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Answer:

Step-by-step explanation:

25(w+10) + 13.5(w +10)

Answer:

25(w+10)+13.5(w+10)

Step-by-step explanation:

hope it helps

Answer:

2k

Step-by-step explanation:

-k+3k

2k

The formula for calculating an unbiased estimate

of the covariance coefficient of variables x and y of a large (but finite) population, based on a random sample of n items is:

(1/n-1) * ∑(Xi - X bar) * (Yi - Y bar)`,

where Xi and Yi are the values of the it h observation of x and y, X bar and Y bar are the means of x and y, respectively, and n is the sample size.

If we have to get an unbiased estimate of the covariance coefficient of variables x and y of a large (but finite) population, based on a random sample of n items, then we can use the formula:

(1/n-1) * ∑(Xi - X bar) * (Yi - Y bar)

where, the unbiased estimate of the covariance coefficient of x and y

Xi = the value of the it h observation of x

Yi = the value of the it h observation of y

X bar = the mean of x

Y bar = the mean of y

n = the sample size of the population

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The solution to the given problem is as follows:N in this study = 1017P in this study = 143/1017 = 0.1407 (rounded to 4 decimal places)

Given, the sample size n = 1017, and the sample proportion of people who would avoid seeking treatment due to high healthcare costs = 143/1017 = 0.1407

Since we need to calculate the 95% confidence interval for the proportion of the American adult population who would avoid seeking treatment for COVID-19 due to the high costs of healthcare, we need to calculate the standard error of the proportion.

The formula to calculate the standard error of the proportion is:

Standard error of the proportion = sqrt [ p * (1 - p) / n ]

Substituting the values, we get:

Standard error of the proportion = sqrt [ 0.1407 * (1 - 0.1407) / 1017 ]= 0.0141 (rounded to 4 decimal places)

To calculate the 95% confidence interval, we use the following formula:

95% confidence interval = sample proportion ± margin of error

Margin of error = Z * (standard error of the proportion)

where Z is the Z-score corresponding to the confidence level.

For 95% confidence level, Z = 1.96.

Substituting the values, we get:

Margin of error = 1.96 * 0.0141= 0.0276 (rounded to 4 decimal places)

Therefore, the 95% confidence interval for the proportion of the American adult population who would avoid seeking treatment for COVID-19 due to the high costs of healthcare is given by:

0.1407 ± 0.0276= (0.1131, 0.1683) (rounded to 4 decimal places)

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The sets X and Y are defined based on specific conditions on positive integers. In functional form, X n Y can be represented as X n Y = {x € Z + : x ≤ 20, x ≤ 24, and x^2 € Z +, sqrt(x) € Z +}.

a) To find X U Y (the union of X and Y), we need to identify all the positive integers that satisfy either the condition for X or the condition for Y. In enumeration form, X U Y = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 24}. In functional form, X U Y can be represented as X U Y = {x € Z + : (x ≤ 20 and x^2 € Z +) or (x ≤ 24 and sqrt(x) € Z +)}.

To find X n Y (the intersection of X and Y), we need to identify the positive integers that satisfy both the condition for X and the condition for Y. In enumeration form, X n Y = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}. In functional form, X n Y can be represented as X n Y = {x € Z + : x ≤ 20, x ≤ 24, and x^2 € Z +, sqrt(x) € Z +}.

b) To determine the sets X n Z + and Y n Z +, we need to identify the positive integers that satisfy the conditions for X and Y, respectively, and also belong to the universal set of positive integers, Z +. Since X and Y are subsets of Z +, X n Z + = X and Y n Z + = Y.

To find X U Z +, we need to identify all the positive integers that satisfy either the condition for X or belong to Z +. In this case, X U Z + = Z + since all positive integers are included in X. Similarly, Y U Z + = Z + since all positive integers are included in Y.

In summary, X U Y = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 24} and X n Y = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}. The sets X n Z + and Y n Z + are equal to X and Y, respectively, while X U Z + and Y U Z + are both equal to Z +.

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There are reasons to question the results of the survey comparing the happiness of Norwegian and American citizens due to potential issues with defining the variable of interest.

The given options present various perspectives on whether there are reasons to question the results of the survey comparing the happiness of Norwegian and American citizens. Among the provided options, options B, C, and D are the most appropriate selections.

B. Yes, there is reason. It is not clear how the variable of interest is defined:

C. Yes, there is reason. The people being surveyed will likely not be representative of the population:

D. Yes, there is reason. It is not clear how the variable of interest is measured:

By considering these factors, it becomes apparent that there are reasons to question the survey results, highlighting the importance of clear definitions, representative sampling, and transparent measurement methods to ensure the validity and reliability of the study.

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Therefore, the total payment made today by the payee is $2,149.01

To total payment made today would place the payee in the same financial position as the scheduled payments if money can earn 6.25% we will use the  formula below.

PV = FV /(1 + Rr)^n

r =6.25%

FV = $1,260

PV = $ 1,260 / (1+ 0.0625) ^(7/12)

PV = $ 1,179.01

The value of the second payment is  $ 1,179.01.

Lets find the total payment. We can represent the  total payment by X.

X - $ 970 = $ 1,179.01.

To isolate X, we will add $ 970  to both sides.

X = $ 970 + $ 1,179.01.

X = $2,149.01

Therefore, the total payment made today by the payee is $2,149.01

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Answer:

5

Step-by-step explanation:

Distance Equation Solution:

[tex]d=\sqrt{(2-(-1)^2+(6-2)^2}\\d=\sqrt{(3)^2+(4)^2} \\d= \sqrt{9+6}\\d= \sqrt{25}\\d=5[/tex]

The blue region indicated by the Venn diagram can be notated using set notation as A ∩ B or using symbolic representation as C = A ∩ B.

When representing the blue region of a Venn diagram, there are two common ways to notate it: using set notation and using symbolic representation.

1. Set Notation: In set notation, each circle in the Venn diagram represents a set. Let's assume the sets represented by the circles are A and B. The blue region corresponds to the intersection of sets A and B, meaning the elements that are common to both sets. To notate this, we use the symbol ∩, which represents the intersection. Therefore, the blue region can be notated as A ∩ B.

2. Symbolic Representation: Another approach is to use a symbolic representation to notate the blue region. In this case, we can assign a variable, such as C, to represent the blue region. To indicate that C represents the intersection of sets A and B, we write C = A ∩ B. This notation clarifies that C represents the elements that belong to both sets A and B.

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Answer:

253 mmHg

Step-by-step explanation:

Since the atmospheric pressure, [tex]P(a) = 760e^{-0.13a}[/tex]

when a = height (in km) = 8.47 km, then the atmospheric pressure P(a) is

[tex]P(a) = 760e^{-0.13a}\\P(8.47) = 760e^{-0.13X8.47}\\P(8.47) = 760e^{-1.1011} \\P(8.47 )= 760 X 0.33251 \\P(8.47)= 252.7 mmHg\\[/tex]

P(8.47) ≅ 253 mmHg

The correct answer is:

[tex]\textbf{b.}[/tex] Because the p-value is > 0.05, we fail to reject H0 and conclude that age cannot be used to predict disease activity scores.

What is a null hypothesis?

The null hypothesis, denoted as H0, is a fundamental concept in statistical hypothesis testing. It is a statement or assumption that suggests there is no significant difference, effect, or relationship between variables in a population.

The decision and conclusion depend on the p-value obtained from the analysis of variance (ANOVA) table.

If the p-value is greater than 0.05, we fail to reject the null hypothesis (H0), implying that there is not enough evidence to support the alternative hypothesis.

If the p-value is less than 0.05, we reject the null hypothesis (H0), indicating that there is sufficient evidence to support the alternative hypothesis.

Therefore, the correct answer is:

Because the p-value > 0.05, we fail to reject H0 and conclude that age cannot be used to predict disease activity score.Using a 0.05 significance level, what decision and conclusion should you make regarding the null hypothesis?

\textbf{Answer:}

The decision and conclusion depend on the p-value obtained from the analysis of variance (ANOVA) table.

If the p-value is greater than 0.05, we fail to reject the null hypothesis (H0), implying that there is not enough evidence to support the alternative hypothesis.

If the p-value is less than 0.05, we reject the null hypothesis (H0), indicating that there is sufficient evidence to support the alternative hypothesis.

Therefore, the correct answer is:

\textbf{b.} Because the p-value > 0.05, we fail to reject H0 and conclude that age cannot be used to predict disease activity score.Using a 0.05 significance level, what decision and conclusion should you make regarding the null hypothesis?

\textbf{Answer:}

The decision and conclusion depend on the p-value obtained from the analysis of variance (ANOVA) table.

If the p-value is greater than 0.05, we fail to reject the null hypothesis (H0), implying that there is not enough evidence to support the alternative hypothesis.

If the p-value is less than 0.05, we reject the null hypothesis (H0), indicating that there is sufficient evidence to support the alternative hypothesis.

Therefore, the correct answer is:

\textbf{b.} Because the p-value > 0.05, we fail to reject H0 and conclude that age cannot be used to predict disease activity score.Using a 0.05 significance level, what decision and conclusion should you make regarding the null hypothesis?

\textbf{Answer:}

The decision and conclusion depend on the p-value obtained from the analysis of variance (ANOVA) table.

If the p-value is greater than 0.05, we fail to reject the null hypothesis (H0), implying that there is not enough evidence to support the alternative hypothesis.

If the p-value is less than 0.05, we reject the null hypothesis (H0), indicating that there is sufficient evidence to support the alternative hypothesis.

Therefore, the correct answer is:

\textbf{b.} Because the p-value > 0.05, we fail to reject H0 and conclude that age cannot be used to predict disease activity score.Using a 0.05 significance level, what decision and conclusion should you make regarding the null hypothesis?

\textbf{Answer:}

The decision and conclusion depend on the p-value obtained from the analysis of variance (ANOVA) table.

If the p-value is greater than 0.05, we fail to reject the null hypothesis (H0), implying that there is not enough evidence to support the alternative hypothesis.

If the p-value is less than 0.05, we reject the null hypothesis (H0), indicating that there is sufficient evidence to support the alternative hypothesis.

Therefore, the correct answer is:

\textbf{b.} Because the p-value > 0.05, we fail to reject H0 and conclude that age cannot be used to predict disease activity score.Using a 0.05 significance level, what decision and conclusion should you make regarding the null hypothesis?

\textbf{Answer:}

The decision and conclusion depend on the p-value obtained from the analysis of variance (ANOVA) table.

If the p-value is greater than 0.05, we fail to reject the null hypothesis (H0), implying that there is not enough evidence to support the alternative hypothesis.

If the p-value is less than 0.05, we reject the null hypothesis (H0), indicating that there is sufficient evidence to support the alternative hypothesis.

Therefore, the correct answer is:

[tex]\textbf{b.}[/tex] Because the p-value > 0.05, we fail to reject H0 and conclude that age cannot be used to predict disease activity scores.

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Answer:

0

Step-by-step explanation:

hope this helps :)