Luis created a spreadsheet of his expenses for three months. Which of Luis's expenses are variable expenses


utility bill


Expenses Jan Feb Mar


rent


$1,250,00 $1,250,00 $1,250,00


$124. 11 $108. 72 $121. 69


car loan payment $384. 00 3384,00 $384. 00


Insurance payment 397. 18 597. 18 $97. 18


groceries


$315,43 $367. 25 $341. 04


clothing


$72. 18 $152. 74 $0. 00


fuel


$108. 71 $117. 46 $127. 34

Answer:

1200?

Step-by-step explanation:

Answer:

A 45

Step-by-step explanation:

45 is the answer and hope this helps!

Answer:

x = 3

Step-by-step explanation:

2x = 5x - 9

add 9 to each side

2x + 9 = 5x

subtract 3x from each side

9 = 3x

divide each side by 3

3 = x

x = 3

If my answer is incorrect, pls correct me!

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Incomplete Question:

The content of the pie chart is as follows:

Hamsters  = 9% ; Snakes  = 10% ; Cats  = 23%

Birds  = 21% ;  Dogs  = 26% ;  Fish  = 11%

Answer:

The number of citizens who chose cat or fish is 47,600

Step-by-step explanation:

Given

Number of citizens = 140,000

Required

Determine the number of those that chose fish or cats

First, we need to calculate the percentage of those whose pets are either cats or fish

\(Percentage = Cat + Fish\)

Substitute 23% for cat and 11% for fish

\(Percentage = 23\% + 11\%\)

\(Percentage = 34\%\)

Next, is to multiply the calculated percentage by the number of citizens

\(Cat\ or\ Fish = Percentage * Number\ of\ Citizens\)

\(Cat\ or\ Fish = 34\% * 140000\)

\(Cat\ or\ fish = 47600\)

Hence, the number of citizens who chose cat or fish is 47,600

the number of citizens who chose cat or fish is 47,600

= Number of citizens × total percentage

\(= 140,000 \times (23\% + 11\%)\\\\= 140,000 \times 34\%\)

= 47,600

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Answer:

Step-by-step explanation:

a1 = 51 + (1 -1 ) * 0.8

a1 = 51

a2 = 51 + (2 - 1)*0.8

a2 = 51 + 0.8

a2 = 51.8

a3 = 51 + (3 - 1)*0.8

a3 = 51 + 2*0.8

a3 = 51 + 1.6

a3 = 52.6

a4 = 51 + (4 - 1)*0.8

a4 = 51 + 2.4

a4 = 53.4

Answer: The slope of the line is 6.

You can use the distance formula to find the slope (Y2-Y1/X2-X1). You can plug in any two points seen on the graph into the formula. I will use (0,-1) and (1,5). When I plug in the values, 5-(-1)/1-0=6/1=6. So the slope of the line is 6.

:)

Spherical harmonics are an integral part of quantum mechanics. They describe the shape of the orbitals, which electrons occupy in atoms. Moreover, the spherical harmonics provide the angular distribution of a wave in spherical coordinates. In 3D, the spherical harmonics can be written as:

Ylm(θ, φ) = √(2l + 1)/(4π) * √[(l - m)!/(l + m)!] * Plm(cosθ) * e^(imφ)

Here, l and m are known as the angular quantum numbers. They define the shape and orientation of the orbital. Plm(cosθ) represents the associated Legendre polynomial, and e^(imφ) is the exponential function. The spherical harmonics have various forms, including:

Y1,1 = -Y1,-1 = 1/2 √(3/2π) sinθe^(iφ)

Y1,0 = 1/2 √(3/π)cosθ

Y2,2 = 1/4 √(15/2π)sin²θe^(2iφ)

Y2,1 = -Y2,-1 = 1/2 √(15/2π)sinθcosφ

Y2,0 = 1/4 √(5/π)(3cos²θ-1)

Y0,0 = 1/√(4π)

The spherical harmonics have various applications in physics, including quantum mechanics, electrodynamics, and acoustics. They play a crucial role in understanding the symmetry of various systems. Hence, the spherical harmonics are an essential mathematical tool in modern physics. Thus, this is how one can show another form for spherical harmonics.

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Answer:

That would be Thirty-two

Step-by-step explanation:

Because is we are doubling 32 its going to be 64 and if we are subtracting 32 then its going to be 32

Mark brainliest pls :)

Expectancy theory would best provide perspective on the question 'if i study for 12 hours, what is the probability that i'll get an on the exam?".

 Expectancy theory (expectancy theory of motivation) was put forward by Victor Vroom in 1964. Vroom put more emphasis on outcome factors, rather than needs as proposed by Maslow and Herzberg.

This theory states that the intensity of the tendency to perform in a certain way depends on the intensity of the expectation that performance will be followed by a definite outcome and on the attractiveness of the result to the individual.

Vroom argues that people will be motivated to do certain things to achieve goals if they believe that their actions will lead to the achievement of these goals.

There are three main assumptions of Vroom in expectancy theory.  These assumptions are as follows:

-Every individual believes that if he behaves in a certain way, he will get certain things.  This is called an outcome expectancy as a person's subjective assessment of the likelihood that a certain outcome will arise from that person's actions.

-Each outcome has value, or appeal to a particular person.  This is called valence (valence) as the value that people give to an expected result.

-Each outcome is associated with a perception of how difficult it is to achieve that outcome.  This is called effort expectancy as the possibility that one's efforts will result in the attainment of a certain goal.

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Answer:

Basic Cable Company A= 30x + 75

Basic Cable Company B= 40x

After 7.5 months, it's more economical to go with Company A. Before 7.5 months, Company B is cheaper

Step-by-step explanation:

Answer: 0.92 pounds

Step-by-step explanation:

Roberto will make 11 pounds of fudge but wants to give it away as presents to 12 friends.

Each friend will therefore get;

= 11 pounds/ 12

= 0.92 pounds

Answer:5

Step-by-step explanation:

use the slope formula

Answer:

In this section we will discuss the method of graphing an equation in two variables. In other words, we will sketch a picture of an equation in two variables.

Step-by-step explanation:

Answer:

Step-by-step explanation:

All you have to do is subtract 6 from 14. The answer is 8. If the question is something like this one, always take the remainder and subtract it from how many you had in the beginning to get the answer.

Good luck

Peyton

Answer:

B. x=3

Step-by-step explanation:

3x+4y=12

9x-4y=24

Add 9x and 3x

Subtract -4y from 4y which will cancel out

Add 12 and 24

12x=36

divide both sides by 12

x=3

d. Dummy coding. Categorical variables need to be converted into numerical variables to be used in regression analysis. Dummy coding involves creating binary variables for each category of the categorical variable.

For example, if the categorical variable is "color" with categories "red," "green," and "blue," dummy coding would involve creating three binary variables: "red" (0 or 1), "green" (0 or 1), and "blue" (0 or 1). These binary variables can then be used in the regression analysis. In conclusion, to use categorical variables in regression analysis, dummy coding is necessary.


In order to use categorical variables in a regression analysis, they must be converted into numerical values. This process is called dummy coding (also known as one-hot encoding). Dummy coding involves creating new binary variables (0 or 1) for each category of the categorical variable. This allows the regression model to incorporate the categorical data while maintaining its numerical nature.

To use categorical variables in a regression analysis, you must apply dummy coding to convert them into numerical values.

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Answer:

w

the square root of 95 =9.75 and w is placed at about 9.75

Answer:

W

Step-by-step explanation:

square root of 95 is 9.74 smth

Answer:

Hey! so the answer is N=2

Step-by-step explanation:

Add 12 to both sides of the equation

Simplify

Add 3 to both sides of the equation

Simplify

Divide both sides of the equation by the same term

Simplify

Solution

=2

Hoped this helped Im Eve btw. Hope you have a great day and consider marking this brainliest Thank you so much if you do! ✨

A(n) "correlational" design is used when the researcher wants to test a relationship between existing variables. This design aims to determine the degree to which two variables are related or associated with each other. In a correlational design, the researcher measures the variables of interest and examines the statistical relationship between them, typically using correlation coefficients.

On the other hand, a(n) "experimental" design is used when a researcher wants to test a causal link between variables. In an experimental design, the researcher manipulates one or more independent variables and measures the effect on a dependent variable. By controlling for confounding variables and using random assignment, an experimental design allows for causal inferences to be made.

It's important to note that while correlational designs can provide valuable insights into the relationships between variables, they do not establish causality. Experimental designs, on the other hand, provide stronger evidence for causal relationships by systematically manipulating variables.

A(n) \(\textbf{correlational}\) design is used when the researcher wants to test a relationship between existing variables.

A(n) \(\textbf{experimental}\) design is used when a researcher wants to test a causal link between variables.

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In order to check if the new industrial product is profitable, we need to determine if the mean of the advance orders (4 units) is greater than or equal to the required number of units per customer (4.2 units).

Industrial products are physical goods or services produced by industries. These products are used in the production of other goods or services.

We can use a one-sample t-test to determine if the difference in means is statistically significant at the 1% level. The null hypothesis for this test is that the mean of the sample is equal to the required number of units per customer (4.2 units).

The one-sample t-test yields a p-value of 0.73, which is greater than the 1% level. Therefore, we cannot reject the null hypothesis and conclude that the mean of the sample is not significantly different from the required number of units per customer. As such, the new industrial product is likely to be profitable.

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Answer:

B, C and E

Step-by-step explanation:

Given

See attachment for table

Required

Select two correct options

Checking the greatest maximum

The maximum of each function is:

\(f(x) = 14\)

\(g(x) = 49\)

\(h(x) = 0\)

Compare these three values, we have:

\(g(x) > f(x) > h(x)\)

Because:

\(49 > 14 > 0\)

This means that g(x) has the greatest maximum --- option E

Checking the x intercept

When a function equals 0, then it has an x intercept

Of the three functions, we have:

\(f(0) = 0\)

\(h(0) = 0\)

g(x) do not equal 0, at all

This means that only f(x) and h(x) have x intercept --- option B

Checking the least maximum

The minimum of each function is:

\(f(x) = -14\)

\(g(x) = \frac{1}{49}\)

\(h(x) = -28\)

Compare these three values, we have:

\(h(x) < f(x) < g(x)\)

Because:

\(-28 < -14 < \frac{1}{49}\)

This means that h(x) has the least minimum --- option C

Answer:\(\frac{-5}{4}, \frac{3}{4}\)

Step-by-step explanation:

Given

\(\theta\) is in the third quadrant and

\(\Rightarrow \cos \theta=\frac{-3}{5}\)

tan and cot is positive in the third quadrant and the rest are negative

using identity

\(\sin^2 \theta+\cos^2 \theta=1\\\Rightarrow \sin \theta=-\sqrt{1-\cos^2 \theta}\\\Rightarrow \sin \theta =-\sqrt{1-(-\frac{3}{5}^2)}\\\Rightarrow \sin \theta =-\frac{4}{5}\\\Rightarrow\text{cosec} \theta=-\frac{5}{4}\\\Rightarrow \cot \theta=\frac{\cos \theta}{\sin \theta}=\frac{\frac{-3}{5}}{\frac{-4}{5}}=\frac{3}{4}\)

The table shows the estimated digit and significant figures of each measurement and their representation with x.

Number of Measurement Estimated Digit Significant Figures

14.8 m 3 ✓

$10.25 - ✓

0.05 L - ✓

1.000 g/mL - ✓

1.200 - ✓

6200 cm - ✓

403 kg - ✓

For each measurement, the estimated digit is replaced with an x to represent the number with the correct number of significant figures. The correct representations for each measurement are:

Note that in some cases, the representation requires adding zeros to the right of the decimal point to indicate the number of significant figures. In other cases, the representation requires rounding up or down to the correct number of significant figures.

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The given argument is invalid. It says that if one person has more gray hair in their beard than another person, then they have a higher percentage of gray hair in their beard.

The argument is invalid because it is possible for one person to have a higher percentage of gray hair in their beard but a lower number of gray hairs in total.One possible counterexample by model is:Suppose Plato has 200 hairs in his beard and 50 of them are gray. So, 1/4 of his hairs are gray. Now, let's suppose Aristotle has 400 hairs in his beard, and 300 of them are gray. So, 3/4 of his hairs are gray. Even though Plato has fewer gray hairs than Aristotle, his percentage of gray hairs is more significant (1/4) than Aristotle's (3/4).

Therefore, the given argument is invalid since Aristotle's beard contains more gray hairs in quantity, but Plato has a higher percentage of gray hair in his beard.

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Marco's data points have a better line of best fit based on a comparison of Correlation coefficients.

Marco is incorrect in stating that his data points have a better line of best fit than Landon based solely on the comparison of correlation coefficients. The magnitude of the correlation coefficient alone does not determine the quality or strength of the line of best fit.

The correlation coefficient measures the strength and direction of the linear relationship between two variables. It ranges from -1 to +1. A positive correlation coefficient (such as 0.85) indicates a positive linear relationship, while a negative correlation coefficient (such as -0.85) indicates a negative linear relationship. The closer the correlation coefficient is to -1 or +1, the stronger the relationship. A correlation coefficient of 0 indicates no linear relationship.

In the case of Landon and Marco, both correlation coefficients have the same absolute value of 0.85, suggesting a strong linear relationship. However, the negative sign for Landon's correlation coefficient indicates a negative linear relationship, while the positive sign for Marco's correlation coefficient indicates a positive linear relationship.

The comparison between -0.85 and 0.85 should not be made in terms of greater or lesser quality of the line of best fit. The choice of a positive or negative correlation depends on the context and nature of the variables being analyzed.

The appropriateness of the line of best fit and the goodness-of-fit of the model should be evaluated based on additional factors such as the data points' distribution around the line, residuals, and the overall context of the analysis. These aspects provide more comprehensive insights into the quality of the fit and the reliability of the relationship being represented.

Therefore, Marco's data points have a better line of best fit solely based on a comparison of correlation coefficients. The interpretation of the correlation coefficient requires considering the nature of the variables and other factors influencing the analysis.

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With linear inequalities in two variables, you still have y=mx+b and it still tells you the same thing.

The inequality portion adds two additional layers on top of the y=mx+b line.

If the inequality is > or <, then you draw the line as a dashed line.

If the inequality is ≥ or ≤, then you draw the line as a solid line.

The difference is that points on the > or < line do NOT make the inquality true, while points on the ≥ or ≤ line do.

The other piece is that all the points on one side of the line will also make the inequality true.

If you have "y>mx+b" or "y≥mx+b", then you shade above the line.

If you have "y<mx+b" or "y≤mx+b", then you shade below the line.

So there are three parts:

1. Where is the line?  

2. Is the line solid or dashed?

3. Do you shade above or below the line?

The three graphs are attached.

Answer: