solve for x. x - 16 = -20

PLEASE HELPP

Answer:

The answer is root 6 + root 2/4

Step-by-step explanation:

Cos (15)

Rewrite the expression

write (15 degrees) as a difference

cos ( 45 degrees - 30 degrees)

cos ( 45 degrees - 30 degrees)

Expand the expression

Use cos ( t - s ) = cos (t) cos (s) + sin (t) sin (s)

to expand the expression

cos (45 degrees) cos ( 30 degrees) + sin ( 45 degrees ) sin ( 30 degrees)

cos (45 degrees) cos ( 30 degrees) + sin ( 45 degrees ) sin ( 30 degrees)

calculate the expression

use the trigonometric values table or unit circle to calculate the expression

root 2/root 2 × cos (30 degrees) + sin (45 degrees) sin (30 degrees)

root 2/root 2 × root 3/root 2 + sin (45 degrees) sin (30 degrees)

root 2/ root 2 × root 3/root 2 + root 2/root 2 × sin (30 degrees)

root 2/root2 × root 3/root 2 + root 2/root 2 × 1/2

root 2/root2 × root 3/root 2 + root 2/root 2 × 1/2

Multiply

root 2/root 2 × root 3/root 2 only

Multiply by the fractions

root 6/ root 4 + root 2/ root 2 × 1/2

Multiply root 2/ root 2 × 1/2 only

6 root / root 4 + root 2/ root 4

write all numerators above the denominator

root 6 + root 2 / 4

We have the next equation

42=6a

we need to clear a

a=42/6

a=7

the answer is 7

Answer:

B?

Step-by-step explanation:

Answer: C. I tried it out and I got it right.

Step-by-step explanation: Brainliest please.

The inequality |u| > c is equivalent to u > c or -u > c, depending on whether u is positive or negative.

To solve the inequality |u| > c, we need to consider two cases: when u is positive and when u is negative.

Case 1: When u is positive, the inequality |u| > c is equivalent to u > c. This is because the absolute value of a positive number is the number itself.

Case 2: When u is negative, the inequality |u| > c is equivalent to -u > c. This is because the absolute value of a negative number is the opposite of the number.

In summary, the inequality |u| > c is equivalent to u > c or -u > c, depending on whether u is positive or negative.

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The slope-intercept form is y=mx+b y = m x + b , where m is the slope and b is the y-intercept. Find the values of m and b using the form y=mx+b y = m x + b . The slope of the line is the value of m , and the y-intercept is the value of b .

Step-by-step explanation:

Answer:

let me see what i can do

Step-by-step explanation:

isolate the variable by dividing each side by factors that don´t contain the variable x = - 4/11 + 6/11

Answer:

1.5hours

Step-by-step explanation:

1. 14w - 3w

Variable term: 14w, -3w

Constant term: None

Coefficient: 14, -3

2. -2a + 9 - 5a

Variable term: -2a, -5a

Constant term: 9

Coefficient: -2, -5

3. 8m - 2 - 10 + m

Variable term: 8m, m

Constant term: -2, -10

Coefficient: 8, 1

4. -2p - 17 + 6p - p + 4

Variable term: -2p, 6p, -p

Constant term: -17, 4

Coefficient: -2, 6, -1

5. 14x - 3y + 5y - 26 - 7x

Variable term: 14x, -3y, 5y, -7x

Constant term: -26

Coefficient: 14, -3, 5, -7

6. -6C + 18 - 15d + C - 11 - 3c

Variable term: -6C, -15d, C, -3c

Constant term: 18, -11

Coefficient: -6, -15, 1, -3

In each expression, variable terms are the terms that contain variables (letters representing unknowns), constant terms are the terms that do not contain variables, and coefficients are the numbers multiplying the variables. Let's identify the variable terms, constant terms, and coefficients for each expression:

In each expression, the variable terms consist of the terms containing variables, the constant terms are the terms without variables, and the coefficients are the numbers multiplying the variables.

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Answer:

1) 6.27

2)1.254

Step-by-step explanation:

The true statements about the independent and dependent variables in this scenario are:

The independent variable is the number of plays he carries the ball.

The dependent variable is the number of yards he gains.

In this case, the number of plays Tom carries the ball is the independent variable because it is the factor that is being manipulated or controlled. The coach keeps track of this variable to observe its effect on other factors.

On the other hand, the number of yards Tom gains is the dependent variable because it depends on the independent variable, which is the number of plays he carries the ball. The coach keeps track of this variable to measure the outcome or response that is influenced by the independent variable.

The number of touchdowns he scores is not explicitly mentioned in relation to being an independent or dependent variable in the given information. Therefore, we cannot determine its classification based on the provided context.

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Answer:

i believe it is the second answer choice

Step-by-step explanation:

B. No, because the Central Limit Theorem states that the sampling distribution of x is approximately normally distributed only if the sample size is large enough.

The Central Limit Theorem (CLT) is a fundamental concept in statistics that states that as the sample size increases, the sampling distribution of the sample means will approach a normal distribution. However, this is only true if certain conditions are met, one of which is having a large enough sample size.
The CLT states that the sampling distribution of x will be approximately normally distributed if the sample size is large enough (usually greater than 30). If the sample size is small, the sampling distribution may not be normally distributed. In such cases, other statistical techniques like the t-distribution should be used.
Furthermore, the CLT assumes that the population being sampled is not necessarily normally distributed, but it does require that the population has a finite variance. This means that even if the population is not normally distributed, the sampling distribution of x will still be approximately normal if the sample size is large enough.
In conclusion, the answer is B, as the Central Limit Theorem states that the sampling distribution of x is approximately normally distributed only if the sample size is large enough.

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Check the picture below.

\(tan(A)=\cfrac{\stackrel{opposite}{55}}{\underset{adjacent}{48}}\implies A=tan^{-1}\left( \cfrac{55}{48} \right)\implies A\approx 48.9^o \\\\\\ tan(B)=\cfrac{\stackrel{opposite}{48}}{\underset{adjacent}{55}}\implies B=tan^{-1}\left( \cfrac{48}{55} \right)\implies B\approx 41.1^o\)

Make sure your calculator is in Degree mode.

(a) The given problem can be written as:
Minimize: \(3X^2 - 22X + 2XY + Y^2 - 16Y + 60\)
(b) Subject to: \(X + 5Y ≤ 8\)

(c) We can find the difference between the new optimal objective function value and our estimated change.

a. To find the minimum solution to the given problem, we need to solve the optimization problem by minimizing the objective function subject to the constraint.

The given problem can be written as:
Minimize: \(3X^2 - 22X + 2XY + Y^2 - 16Y + 60\)
Subject to: \(X + 5Y ≤ 8\)

To find the minimum solution, we need to solve this problem using optimization techniques like linear programming or calculus.

b. If the right-hand side of the constraint is increased from 8 to 9, we need to analyze the change in the objective function.

To do that, we can find the difference between the objective function value at the new solution and the old solution.

c. Re-solving the problem with a new right-hand side of 9 will give us a new optimal objective function value.

To compare the actual change with our estimate, we can find the difference between the new optimal objective function value and our estimated change.

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TRUE. The ANOVA table from a multiple regression includes the F statistic for assessing overall fit. In this case, the F statistic is 2.83. The F statistic is a ratio of two variances, the between-group variance and the within-group variance.

It is used to test the null hypothesis that all the regression coefficients are equal to zero, which implies that the model does not provide a better fit than the intercept-only model. If the F statistic is larger than the critical value at a chosen significance level, the null hypothesis is rejected, and it can be concluded that the model provides a better fit than the intercept-only model.The F statistic can also be used to compare the fit of two or more models. For example, if we fit two different regression models to the same data, we can compare their F statistics to see which model provides a better fit. However, it is important to note that the F statistic is not always the most appropriate measure of overall fit, and other measures such as adjusted R-squared or AIC may be more informative in some cases.Overall, the F statistic is a useful tool for assessing the overall fit of a multiple regression model and can be used to make comparisons between different models. In this case, the F statistic of 2.83 suggests that the model provides a better fit than the intercept-only model.

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The linear regression model for the weekly cost data is given by C(z) = ma + k, where z is the independent variable representing the weekly demand.



The values of m and k are determined through the regression analysis. Using this model, we can estimate the total weekly cost for a given weekly demand and calculate the per unit variable cost and weekly fixed cost.To find the linear regression model, we need to perform a regression analysis on the given weekly cost data. This analysis will determine the values of m and k in the equation C(z) = ma + k, where C(z) represents the weekly cost and z represents the weekly demand.

Once the values of m and k are obtained, we can use the model to estimate the total weekly cost when the weekly demand is 210. By plugging in z = 210 into the equation C(z) = ma + k, we can calculate the total weekly cost rounded to the nearest dollar.The per unit variable cost can be determined by the value of m in the model. It represents the change in cost per unit change in demand. By rounding m to one decimal place, we can obtain the per unit variable cost rounded to one decimal place.

The weekly fixed cost can be determined by the value of k in the model. It represents the cost that does not depend on the weekly demand. By rounding k to the nearest integer, we can obtain the weekly fixed cost rounded to the nearest dollar.Overall, by applying the linear regression model to the given data, we can estimate the total weekly cost, calculate the per unit variable cost, and determine the weekly fixed cost of producing sleeping bags.

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Rudy has 3003 different ways to choose 6 toppings from a menu of 14 toppings if each topping can only be chosen once.

The number of ways to choose 6 toppings from a menu of 14 toppings where each topping can only be chosen once is 14!/(14-6)! = 14!/(8!) = 3003. This is because for the first topping, Rudy has 14 options, for the second topping he has 13 options, for the third topping he has 12 options, and so on. To calculate the total number of combinations, we use the formula n!/(n-r)! where n is the number of items and r is the number of items to choose. This formula gives us the number of ways to choose r items from a set of n items without regard to order, which is the number of combinations.

The formula for combinations is given by n!/(n-r)! where n is the number of items and r is the number of items to choose. Here, n = 14 (the number of toppings on the menu) and r = 6 (the number of toppings Rudy wants to choose). So, the number of combinations is 14!/(14-6)! = 14!/(8!).

The factorial (!) is the product of all positive integers up to that number. For example, 4! = 4 x 3 x 2 x 1 = 24.

So, 14! = 14 x 13 x 12 x ... x 2 x 1 = 87178291199, and 8! = 8 x 7 x 6 x ... x 2 x 1 = 40320.

Now, to calculate the number of combinations, we divide 14! by 8!: 14!/(8!) = 87178291199/40320 = 3003.

Therefore, Rudy has 3003 different ways to choose 6 toppings from a menu of 14 toppings if each topping can only be chosen once.

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Regression is a statistical procedure for determining the linear relationship between a number of variables. A simple regression model has one dependent variable and one independent variable \((y)\)

It is used to forecast the amount of a dependent variable according to the results of one independent variable. \((x)\)

The estimated least-squares line has the following shape :\(y=bo+b1x\).

In this case, the dependent variable is, the independent variable is, the intercept is, and the slope coefficient is.

The slope of a linear regression describes how a unit change in x changes the y value.

When, the intercept represents the expected mean value of Y. When X never equals zero, the intercept has no intrinsic meaning.

Step  (1) The process of estimating the linear relationship between two or more variables is known as regression. Only if there is a linear relationship between both variables can a regression equation be used to predict future.

Linear regression is the most fundamental a kind of regression and is the most widely used proposed model. A regression line can be used to predict dependent variables based on independent variables.

Step: (2) Outliers have a significant impact on the slope of the regression line. Influential points are also referred to as outliers in regression analysis.

Outliers can have a significant impact on the regression equation, so remove any outliers that are recognized errors. Consider the effects of any known inconsistencies that are outliers.

Step 3: Because outliers can have a large impact on the regression line, they are also referred to as important and powerful points.

An outlier is a data point that is located far away first from remaining pieces of data. These are additionally known as influencing points. Outliers have a significant influence on the slope of something like the regression line.

For linear associations, regression models can be utilized.

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The correct answer is that the probability of a cash flow being between $16,000 and $19,000 is approximately 18.59%.

To calculate the probability of a cash flow being between $16,000 and $19,000, we can use the standard deviation and assume a normal distribution.

We are given that the average cash flow is $14,000 with a standard deviation of $5,000. These values are necessary to calculate the probability.

The probability of a cash flow falling within a certain range can be determined by converting the values to z-scores, which represent the number of standard deviations away from the mean.

First, we calculate the z-score for $16,000 using the formula: z = (x - μ) / σ, where x is the cash flow value, μ is the mean, and σ is the standard deviation. Plugging in the values, we get z1 = (16,000 - 14,000) / 5,000.

z1 = 2,000 / 5,000 = 0.4.

Next, we calculate the z-score for $19,000: z2 = (19,000 - 14,000) / 5,000.

z2 = 5,000 / 5,000 = 1.

Now that we have the z-scores, we can use a standard normal distribution table or calculator to find the corresponding probabilities.

Subtracting the probability corresponding to the lower z-score from the probability corresponding to the higher z-score will give us the probability of the cash flow falling between $16,000 and $19,000.

Looking up the z-scores in a standard normal distribution table or using a calculator, we find the probability for z1 is 0.6554 and the probability for z2 is 0.8413.

Therefore, the probability of the cash flow being between $16,000 and $19,000 is 0.8413 - 0.6554 = 0.1859, which is approximately 18.59%.

So, the correct answer is that the probability of a cash flow being between $16,000 and $19,000 is approximately 18.59%.

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The probability of a cash flow between $16,000 and $19,000 is approximately 18.59%.

To calculate the probability of a cash flow being between $16,000 and $19,000, we can use the standard deviation and assume a normal distribution.

We are given that the average cash flow is $14,000 with a standard deviation of $5,000. These values are necessary to calculate the probability.

The probability of a cash flow falling within a certain range can be determined by converting the values to z-scores, which represent the number of standard deviations away from the mean.

First, we calculate the z-score for $16,000 using the formula: z = (x - μ) / σ, where x is the cash flow value, μ is the mean, and σ is the standard deviation. Plugging in the values, we get z1 = (16,000 - 14,000) / 5,000.

z1 = 2,000 / 5,000 = 0.4.

Next, we calculate the z-score for $19,000: z2 = (19,000 - 14,000) / 5,000.

z2 = 5,000 / 5,000 = 1.

Now that we have the z-scores, we can use a standard normal distribution table or calculator to find the corresponding probabilities.

Subtracting the probability corresponding to the lower z-score from the probability corresponding to the higher z-score will give us the probability of the cash flow falling between $16,000 and $19,000.

Looking up the z-scores in a standard normal distribution table or using a calculator, we find the probability for z1 is 0.6554 and the probability for z2 is 0.8413.

Therefore, the probability of the cash flow being between $16,000 and $19,000 is 0.8413 - 0.6554 = 0.1859, which is approximately 18.59%.

So, the correct answer is that the probability of a cash flow being between $16,000 and $19,000 is approximately 18.59%.

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The dimensions of the poster with the smallest area are as follows: The width is 20 cm, and the height is 16 cm.

To determine these dimensions, we need to consider the area of the printed material on the poster, which is fixed at 388 square centimeters. Let's assume the width of the printed material is x cm.

Since the side margins are each 2 cm, the total width of the poster (including the margins) becomes x + 2 cm + 2 cm = x + 4 cm.

Similarly, the total height of the poster is x + 4 cm as well because the top and bottom margins are both 4 cm.

To calculate the area of the entire poster, we multiply the width by the height: (x + 4 cm) * (x + 4 cm) = (x + 4)^2 cm^2.

According to the problem, the area of the printed material is 388 square centimeters. Therefore, we have the equation (x + 4)^2 cm^2 = 388 cm^2.

Solving this equation, we find x + 4 = 19 cm, which means x = 15 cm.

Hence, the dimensions of the poster, width is 15 cm + 4 cm = 19 cm, and the height is also 15 cm + 4 cm = 19 cm.

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Answer:

x = 38.7

Step-by-step explanation:

Since this is a right triangle, we can use trig functions

tan theta = opposite/ adjacent

tan x = 8/10

Take the inverse tan of each side

tan ^-1 ( tan x) = tan ^-1 (8/10)

x = tan ^-1 (8/10)

x =38.65980825

To the nearest tenth

x = 38.7

Answer:

38.7

Step-by-step explanation:

Answer:

above sea level

Step-by-step explanation:

Answer:

Option D, 4

Step-by-step explanation:

2 pizzas x 6 slices per pizza = 12 slices of pizza

12 slices of pizza divided by 3 friends eating equal slices = 4 slices per friend

Option D, 4, is your answer

Shania can run approximately 111.501 meters in 3 minutes 43 seconds.

To find the distance Shania can run in 3 minutes 43 seconds, we can use the formula Distance = Speed × Time.

First, we need to convert the time to a consistent unit, which in this case is minutes. We can do this by dividing the number of seconds by 60, since there are 60 seconds in a minute.

3 minutes 43 seconds is equivalent to 3 + (43/60) minutes. Simplifying the fraction, we get:

3 + (43/60) = 3 + 0.7167 = 3.7167 minutes (approximately)

Now, we can use the average speed of 30 m/min and the time of 3.7167 minutes to calculate the distance:

Distance = 30 m/min × 3.7167 min

Distance ≈ 111.501 meters (rounded to three decimal places)

Therefore, Shania can run approximately 111.501 meters in 3 minutes 43 seconds.

It's important to note that the answer is rounded to three decimal places since the average speed was given as 30 m/min, implying a certain level of precision. If more decimal places were provided in the average speed or if greater precision was required, the final answer could be adjusted accordingly.

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When collecting quantitative data you can use a statistical formula to give you a rough estimate of the sample size you will need. When collecting qualitative data, however, the rule of thumb is to have as representative of a sample as possible that will allow you to exhaust the knowledge you could learn from the population.

Macroinvertebrate sampling provides a relatively easy way to assess the water quality of a stream. The types and numbers of macroinvertebrates (mostly insect larvae/nymphs) that form the biological community at a particular stream location are shaped by both the nature of the stream and the quality of water that drains into the stream.

Answer:

12) a = 5

Step-by-step explanation:

a is found by dividing the second difference by 2.

10 = 2a

5   = a