which of the following conclusions is appropriate at a 5% level of significance? check all that apply.group of answer choicesimipramine is more effective because the mean time to recurrence of depression symptoms is longer for those taking imipramine.the differences observed in sample means do not provide strong evidence of a difference in mean recurrence time for the three treatment types in the population.there are statistically significant differences in mean time to recurrence of depression symptoms for patients in the three treatment groups. this suggests that there is a treatment effect.for the population of depressed people who take lithium or imipramine or who do not receive treatment, the mean time it takes for depression to reoccur differs.no conclusion is possible because conditions for use of the anova f-test are not met.

Step-by-step explanation:

This equals   2/3   * 2/3   =  (2*2) / ( 3*3 ) = 4/9

The factoring method used to factor x² - 64 is the difference of squares.

The given expression is x² - 64.
We have to find the factor method.

The difference of squares is a factoring pattern used when we have a binomial of the form a² - b².

In this case, x² - 64 fits this pattern because it can be expressed as (x)² - (8)².

Applying the difference of squares method, we can factor x² - 64 as (x - 8)(x + 8).

Hence, the factors are (x - 8) and (x + 8).

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The man is standing about 40.44 feet away from the base of the streetlight and  height of the streetlight is 32 ft

We can use the fact that the man's height and shadow length are proportional to the streetlight's height and shadow length.

Let the streetlight's height be "h".

(6 ft) / (15 ft) = h / (80 ft)

Simplifying this proportion, we get:

h = (6/15) × 80

h = 32 ft

Now we have found the height of the streetlight.

We can use trigonometry to find the distance from the man to the base of the streetlight.

Let's call this distance "d".

We know the angle of elevation is 52.5 degrees, and we can use the tangent function:

tan(52.5) = h / d

Solving for d, we get:

d = h / tan(52.5)

d = 32 / tan(52.5)

d ≈ 40.44 ft

Therefore, the man is standing about 40.44 feet away from the base of the streetlight.

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After 6 hours, the population of bacteria in the Petri dish would be 4800.

If the population of bacteria in a Petri dish doubles every 2 hours, we can calculate the population at any given time using the formula

P = P₀[tex]\times 2^{(t/d),[/tex]

where P is the final population, P₀ is the initial population, t is the time elapsed, and d is the doubling time.

In this case, the initial population (P₀) is 600 bacteria, and the doubling time (d) is 2 hours. Let's calculate the population after a certain time, say 6 hours:

[tex]P = 600 \times 2^{(6/2)}\\P = 600 \times 2^3\\P = 600 \times 8\\P = 4800[/tex]

Therefore, after 6 hours, the population of bacteria in the Petri dish would be 4800.

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In a two-player game with one player having four available strategies and the other player having three available strategies, there can be a total of twelve outcomes. This is determined by the four strategies available to the first player multiplied by the three strategies available to the second player.

(A) qualitative and quantitative categories. (A) True. (C) is highly susceptible when used outside tested range of X values.

Data is broken into (A) qualitative and quantitative categories, which refers to the nature of the data being analyzed. Qualitative data is non-numeric and describes characteristics or attributes, while quantitative data is numeric and describes quantities or measurements.

The statistical calculation r shows the correlation between variables (Y, X) in a regression analysis. This statement is true. The correlation coefficient r is a measure of the strength and direction of the linear relationship between two variables. It ranges from -1 to 1, where -1 indicates a perfect negative correlation, 0 indicates no correlation, and 1 indicates a perfect positive correlation.

Linear regression used for estimation of A is susceptible when used outside the tested range of X values. This statement is true. Linear regression models are based on the assumption of a linear relationship between the dependent variable Y and the independent variable X within a certain range of X values. When used outside of this range, the linear regression model may not accurately predict the values of Y. This is because the linear relationship between Y and X may not hold outside of the tested range, or other factors may come into play that were not accounted for in the model. Therefore, it is important to carefully consider the range of X values used in the regression analysis and to exercise caution when making predictions outside of this range.

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The equation of the tangent to the curve at the given point. x = 7 sin(t), y = t2 t, (0, 0) is y = 0, To find the equation of the tangent to the curve at the given point (0, 0), we first need to find the derivative of x and y with respect to t, and then find the slope of the tangent at the given point.

Given: x = 7sin(t), y = t^2

Find dx/dt and dy/dt:
dx/dt = 7cos(t)
dy/dt = 2t

Now, find the slope of the tangent at the point (0, 0) by dividing dy/dt by dx/dt:

Slope = (dy/dt) / (dx/dt) = (2t) / (7cos(t))

At t = 0, the slope is:
Slope = (2*0) / (7cos(0)) = 0 / 7 = 0

Now we use the point-slope form of the equation to find the equation of the tangent line:

y - y1 = slope * (x - x1)

Since the point is (0, 0) and the slope is 0, the equation becomes:

y - 0 = 0 * (x - 0)

Simplifying, we get the equation of the tangent line as:

y = 0

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Answer:

A

Step-by-step explanation:

the graph goes up and the value of y increases

The  new estimated demand is equal to the original estimated demand (ŷ) minus 12. This means that when the price is increased by 3 units, the estimated demand decreases by 12 units.

The equation ŷ = 70 - 4x represents a linear demand function for the product, where y is the estimated demand for the product and x is its price.

To answer the question, we can evaluate the change in demand when the price is increased by 3 units. We can do this by comparing the estimated demand at the original price (x) to the estimated demand at the new price (x + 3).

Original estimated demand:

ŷ = 70 - 4x

New estimated demand:

ŷ' = 70 - 4(x + 3) = 70 - 4x - 12 = ŷ - 12

Therefore, the new estimated demand is equal to the original estimated demand (ŷ) minus 12. This means that when the price is increased by 3 units, the estimated demand decreases by 12 units.

In other words, the demand for the product is negatively related to its price (as indicated by the negative coefficient of x in the demand function). When the price goes up, the estimated demand goes down, and vice versa. The magnitude of this effect is given by the coefficient of x, which in this case is 4. This means that for every one-unit increase in price, the estimated demand decreases by 4 units. Therefore, a 3-unit increase in price would lead to a decrease in estimated demand of 4 * 3 = 12 units.

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Based on the given information, we can assume that for each pair (xi, yi), the outcome of yi is dependent on the value of xi. More specifically, we can say that yi follows a Bernoulli distribution, with a success probability that is conditional on the value of xi.

A Bernoulli distribution is a probability distribution that models a single binary outcome, such as a coin flip resulting in heads or tails. The distribution is characterized by a single parameter, the success probability p, which represents the probability of observing a "success" outcome (in our case, yi = 1).

In this scenario, the success probability for each yi is not fixed but rather varies depending on the value of xi. We can express this as P(yi=1 | xi) = pi, where pi represents the success probability for the ith pair, given the value of xi.

So, for example, if we observe xi = 0.5, we can use the corresponding success probability pi to calculate the probability of observing yi = 1. This would be given by P(yi=1 | xi=0.5) = pi.

Overall, this information allows us to model the relationship between xi and yi as a conditional Bernoulli distribution, where the success probability varies based on the value of xi.

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Given that,

Resistivity- Resistivity is a measure of the electrical resistance of a material per unit length and per unit cross-sectional area.

The resistance of a wire is given by

 R=ρL/A

In this case [tex]A=\pi r^2 =\pi (0.50*10^(-3) ) ^2\\=7.85*10^-7\\[/tex]

[tex]\frac{(50*10^-3m)(7.85*1^-7m)}{2m} \\=2.0*10^-8[/tex]

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Correct statement  are,

I. All isosceles trapezoids consist of two parallel sides.

II. The base angles of all isosceles trapezoids are equal in measure.

We have to given that;

All statements are,

I. All isosceles trapezoids consist of two parallel sides.

II. The base angles of all isosceles trapezoids are equal in measure.

III. The lengths of the legs of all isosceles trapezoids are equal in measure.

Since, We know that;

In a trapezoid, one pair of opposite sides are parallel.

And, The base angles of an isosceles trapezoid are equal in measure (there are in fact two pairs of equal base angles, where one base angle is the supplementary angle of a base angle at the other base).

Since, the two other sides (the legs) are of equal length.

Hence, Correct statement  are,

I. All isosceles trapezoids consist of two parallel sides.

II. The base angles of all isosceles trapezoids are equal in measure.

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The best guess for how many times you will choose a blue marble out of 12 trials is 8 times.

The likelihood of choosing a blue marble on each trial stays constant over the course of the 12 trials if there are 3 marbles total—2 blue and 1 green—and you choose one without looking.

The likelihood of selecting a blue marble on any given trial is 2/3, or 2 out of 3.

You may multiply the likelihood of selecting a blue marble on each trial by the number of trials to determine how many times you will choose a blue marble over the course of 12 trials:

E = Probability of blue marble * Number of trials

E = (2/3) * 12

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We conclude that there is not enough evidence to suggest that there is a significant difference among the mean meal prices for italian, seafood, and steakhouse restaurants.

to test whether there is a significant difference among the mean meal prices for italian, seafood, and steakhouse restaurants, we can use an analysis of variance (anova) test. the null hypothesis is that there is no significant difference among the means, while the alternative hypothesis is that there is a significant difference among the means.here are the steps to conduct the anova test:1. calculate the sample means for each category of restaurants:- italian: (12 + 16 + 24 + 13 + 18 + 19 + 15 + 17) / 8 = 17.25- seafood: (23 + 21 + 20 + 15 + 22 + 17 + 19 + 27) / 8 = 20.75

- steakhouse: (18 + 31 + 24 + 18) / 4 = 22.752. calculate the overall mean:(12 + 16 + 24 + 13 + 18 + 19 + 15 + 17 + 23 + 17 + 26 + 25 + 18 + 23 + 21 + 20 + 15 + 22 + 17 + 19 + 27 + 24 + 18 + 31) / 24 = 20.3753. calculate the sum of squares between groups (ssb):

ssb = 8 x (17.25 - 20.375)² + 8 x (20.75 - 20.375)² + 4 x (22.75 - 20.375)²    = 38.54. calculate the sum of squares within groups (ssw):ssw = (12 - 17.25)² + (16 - 17.25)² + ... + (18 - 22.75)² + (31 - 22.75)²    = 598.5

5. calculate the degrees of freedom for between groups (dfb):dfb = k - 1 = 3 - 1 = 26. calculate the degrees of freedom for within groups (dfw):dfw = n - k = 24 - 3 = 21

7. calculate the mean square between groups (msb):msb = ssb / dfb = 38.5 / 2 = 19.258. calculate the mean square within groups (msw):msw = ssw / dfw = 598.5 / 21 = 28.5

9. calculate the f-statistic:f = msb / msw = 19.25 / 28.5 = 0.6810. look up the critical f-value from an f-distribution table with dfb = 2 and dfw = 21 and a significance level of 0.05. the critical f-value is 3.10.

11. compare the f-statistic to the critical f-value. since the f-statistic (0.68) is smaller than the critical f-value (3.10), we fail to reject the null hypothesis. the test   statistics    value from our analysis is f = 0.68.

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Francesca drew a scale drawing of an Italian restaurant, and the scale of the drawing was not given. Therefore, it is impossible to determine how wide the kitchen is in the drawing.

A scale drawing is a representation of an object or space that is smaller or larger than the actual object or space. It is created using a scale that is agreed upon before the drawing is made.

The scale is usually expressed as a ratio or fraction, such as 1:10 or 1/4. This ratio means that every unit of measurement on the drawing represents a certain number of units on the actual object or space.

Without knowing the scale of Francesca's drawing, we cannot calculate how wide the kitchen is.

It is important to have the scale when working with scale drawings to ensure accurate measurements and proportions. If the scale is not given, it is best to ask for it or assume a standard scale, such as 1:100.

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There are 4,080 different arrangements of first, second, and third place possible for the 17 students running in the race.

To determine the number of different arrangements of first, second, and third place, we need to use the permutation formula.

The number of permutations of n objects taken r at a time is given by:

P(n,r) = n!/(n-r)!

In this problem, we have 17 students running, and we want to determine the number of different arrangements of first, second, and third place, which means we need to find the number of permutations of 17 objects taken 3 at a time.

Using the permutation formula, we get:

P(17,3) = 17!/(17-3)!

= 17!/14!

= 171615

= 4,080

Therefore, there are 4,080 different arrangements of first, second, and third place possible for the 17 students running in the race.

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A sample size of at least 538 freshmen to estimate the proportion of freshmen who do not visit their advisors regularly with a margin of error of 4% and a confidence level of 95%.

To answer your question, we need to use the formula for sample size calculation for proportion:
n = [(Z-score)^2 * p(1-p)] / E^2

Where:
n = required sample size
Z-score = the critical value for the desired confidence level (95% confidence level corresponds to a Z-score of 1.96)
p = the estimated population proportion (34% or 0.34)
1-p = the complement of p
E = the desired margin of error (4% or 0.04)

Plugging in the values:
n = [(1.96)^2 * 0.34(1-0.34)] / (0.04)^2
Simplifying the equation:
n = [(3.8416) * 0.2244] / 0.0016
n = 537.38

We need a sample size of at least 538 freshmen to estimate the proportion of freshmen who do not visit their advisors regularly with a margin of error of 4% and a confidence level of 95%. Keep in mind that this is just an estimate based on the historical data and assumes that the population proportion has not changed significantly.

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The required,
A. Expression by factoring out the greatest common factor is 3xy(2x - 1 - 8y + 4y),
B.  The completely factored form of the expression 6x²y - 3xy - 24xy²+ 12y² is (2x - 1)(3xy - 12y²).

Part A: To factor out the greatest common factor (GCF) from the expression 6x²y - 3xy - 24xy² + 12y², we need to find the common factors of all the terms.

The common factors are 3, x, y.

Taking out the GCF, we have:

GCF: 3xy

Rewritten expression: 3xy(2x - 1 - 8y + 4y)

Part B: Now let's factor the entire expression completely.

Given expression: 6x²y - 3xy - 24xy² + 12y²

Group the terms:

(6x²y - 3xy) + (-24xy² + 12y²)

Factor out the GCF from each group:

3xy(2x - 1) - 12y²(2x - 1)

Notice that we now have a common binomial factor, (2x - 1).

Factor out the common binomial factor:

(2x - 1)(3xy - 12y²)

Therefore, the completely factored form of the expression 6x²y - 3xy - 24xy²+ 12y² is (2x - 1)(3xy - 12y²).

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If the p-value is smaller than α, we reject the null hypothesis; otherwise, we fail to reject it.

To approximate the p-value, we use the t-distribution because the population variance is unknown.

(a) t0 = 2.05:

To calculate the p-value, we need to find the area under the t-distribution curve beyond the test statistic in both tails. Since our alternative hypothesis (H1) is μ ≠ 7, we need to consider both tails of the distribution.

Using a t-table or statistical software, we can find the p-value associated with t0 = 2.05 and degrees of freedom = 19. Let's assume the p-value is P1.

P-value for t0 = 2.05 (P1) = 2 * (1 - P(Z < |t0|)), where P(Z < |t0|) is the cumulative probability of the standard normal distribution at |t0|.

Note: Since we have a two-tailed test, we multiply the probability by 2 to account for both tails.

(b) t0 = -1.84:

Similarly, for t0 = -1.84, we calculate the p-value by finding the area under the t-distribution curve beyond the test statistic in both tails. Since our alternative hypothesis (H1) is μ ≠ 7, we consider both tails.

Using a t-table or statistical software, we can find the p-value associated with t0 = -1.84 and degrees of freedom = 19. Let's assume the p-value is P2.

P-value for t0 = -1.84 (P2) = 2 * P(Z < |t0|)

(c) t0 = 0.4:

For t0 = 0.4, we calculate the p-value in a similar manner as before, considering both tails of the t-distribution.

Using a t-table or statistical software, we can find the p-value associated with t0 = 0.4 and degrees of freedom = 19. Let's assume the p-value is P3.

P-value for t0 = 0.4 (P3) = 2 * P(Z > |t0|)

Once you have the p-values (P1, P2, and P3) for each test statistic, you can compare them to the significance level (α) chosen for the hypothesis test. The significance level represents the threshold below which we reject the null hypothesis.

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Complete Question:

For the hypothesis test H0: μ = 7 against H1: μ ≠ 7 with variance unknown and n = 20, approximate the P-value for each of the following test statistics. (a) t0 = 2.05 (b) t0 = − 1.84 (c) t0 = 0.4

The census bureau would consider a household of three that earns $12,000 to be living below the poverty threshold. The poverty threshold is the minimum income required to meet basic needs such as food, shelter, and clothing. If a household earns less than the poverty threshold, it means they are unable to afford these basic necessities. In the case of a household of three, the poverty threshold is approximately $16,000. Therefore, a household earning $12,000 falls short of this minimum requirement and is considered to be living in poverty.

The poverty threshold is an important benchmark used by the census bureau to determine the poverty status of households. It is based on the income level required to meet basic needs such as food, shelter, and clothing. The poverty threshold varies based on the size of the household and is adjusted annually for inflation.

A household of three that earns $12,000 would be considered to be living below the poverty threshold by the census bureau. This means that they are unable to afford basic necessities and are experiencing financial hardship. It highlights the need for policies and programs that address poverty and support those who are struggling to make ends meet.

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The diagonal of the given rectangle is 18.6 units.

As per the question, the length of the rectangle is 15 units and the width is 11 units.

Therefore, we can consider the length as one side of the right triangle and the width as the other side.

As we know that Pythagoras's theorem states that in a right-angled triangle, the square of one side is equal to the sum of the squares of the other two sides.

Using the Pythagorean theorem:

diagonal² = length² + width²

diagonal² = 15² + 11²

diagonal² = 225 + 121

diagonal² = 346

To find the length of the diagonal, we take the square root of both sides:

diagonal = √346

diagonal = 18.6

Hence, the diagonal is approximately 18.6 units.

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The one-sample t-test with a significance level of 5% fails to reject the null hypothesis that the mean survival time for all bronchial cancer patients who receive that treatment is equal to 90 days, and the P-value is 0.0974.

To determine whether the average survival time for all bronchial cancer patients who receive that treatment is different from 90 days, we can perform a one-sample t-test. The null hypothesis is that the mean survival time is equal to 90 days, and the alternative hypothesis is that the mean survival time is different from 90 days.

We can use the following formula to calculate the t-statistic:

[tex]$t = \frac{\bar{x} - \mu}{s/\sqrt{n}}$[/tex]

Substituting the given values, we get:

t = (111.3 - 90) / (62.9 / √12) = 1.83

Using a t-distribution table with 11 degrees of freedom, and a significance level of 5%, we find the critical values to be ±2.201.

Since the calculated t-value of 1.83 falls within the range of -2.201 to +2.201, we fail to reject the null hypothesis. This means that we do not have sufficient evidence to conclude that the average survival time for all bronchial cancer patients who receive that treatment is different from 90 days.

To find the P-value using a calculator, we can use the t-distribution with 11 degrees of freedom and calculate the probability of getting a t-value greater than 1.83 or less than -1.83 (since it is a two-tailed test). The P-value turns out to be 0.0974. Rounded to the nearest 4th decimal digit, the P-value is 0.0974.

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Complete question:

Twelve bronchial cancer patients who received a certain treatment had an average survival time of 111.3 days with a standard deviation of 62.9 days. Can we conclude from this that the average survival time for all bronchial cancer patients who receive that treatment will be different from 90 days? We will use a 5% significance level. Do the T-test on your calculator and enter the P-value below. Enter it as a decimal (not a percentage), and round it to the nearest 4th decimal digit.

Based on the information you provided, R(t) is a differentiable function that represents the rate at which people leave a restaurant in people per hour after 6 hours since opening. In other words, R(t) describes the speed at which customers are leaving the restaurant as time goes by.

It's important to note that R(t) is only a function of time t, and not a function of the number of people currently in the restaurant or any other variables. This means that if the restaurant is empty at 6 hours since opening, R(t) will give you the rate at which people leave the restaurant from that point forward, regardless of whether there are any customers in the restaurant or not.

In terms of the restaurant's function, R(t) is a key component in understanding how many customers the restaurant is likely to have at any given time. By subtracting R(t) from the restaurant's initial capacity (i.e. the number of seats or tables available), you can estimate how many customers are likely to be in the restaurant at any given time.

Overall, R(t) is a powerful tool for understanding the behavior of customers in a restaurant and can help the restaurant make informed decisions about staffing, marketing, and other aspects of their business.

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The solutions of the quadratic equation x² - 7x = -12 are x = 3 or x = 4

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

x² - 7x=-12

Express properly

So, we have

x² - 7x = -12

Add 12 to both sides

x² - 7x + 12 = 0

When factored, we have

(x - 3)(x - 4) = 0

Using the zero product property , we have

x - 3 = 0 or x - 4 = 0

Evaluate

x = 3 or x = 4

Hence, the solutions of the quadratic equation are x = 3 or x = 4

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Answer: 108

Step-by-step explanation: 24 copies x 4 minutes = 96

24/2 = 12 copies/ 30 seconds

96+12 = 108

:)

Answer:

108 copies

Step-by-step explanation:

We Know

A copy machine makes 24 copies per minute.

How many copies does it make in 4 minutes and 30 seconds?

Let's solve

4 minutes and 30 seconds = 4.5 minutes

We Take

24 x 4.5 = 108 copies

So, it make 108 copies in 4 minutes and 30 seconds.

The graph of the system of inequalities is attached to the solution.

Given is a system of inequalities, y > -x/3+5 and y ≥ 3,

So, we will simply find the coordinates of both the inequalities, and plot them,

We know that the solution of a system of inequalities is all the part which is common in both the inequalities.

So, here the first inequality,

y > -x/3+5

Finding the coordinates,

y = -x/3+5

Put x = 0

y = 5

(0, 5)

Put y = 0,

x = 15

(15, 0)

Therefore, the inequality will pass from these two lines, and since the sing is > so the shaded part will be above the line and the line will be dotted.

And y ≥ 3,

In this inequality the graph will simply pass by y = 3 and since the sing is ≥ so the shaded part will be above the line and the line will solid line.

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The angles and coordinates of vectors are listed below:

Case A: θ = 0°, θ = 0 rad, (x, y) = 5 · (1, 0)

Case B: θ = 40°, θ = 2π / 9 rad, (x, y) = 5 · (0.766, 0.643)

Case C: θ = 80°, θ = 4π / 9 rad, (x, y) = 5 · (0.174, 0.985)

Case D: θ = 120°, θ = 2π / 3 rad, (x, y) = 5 · (- 0.5, 0.866)

Case E: θ = 160°, θ = 8π / 9 rad, (x, y) = 5 · (- 0.939, 0.342)

Case F: θ = 200°, θ = 10π / 9 rad, (x, y) = 5 · (- 0.939, - 0.342)

Case G: θ = 240°, θ = 4π / 3 rad, (x, y) = 5 · (- 0.5, - 0.866)

Case H: θ = 280°, θ = 14π / 9 rad, (x, y) = 5 · (0.174, - 0.985)

Case I: θ = 320°, θ = 16π / 9 rad, (x, y) = 5 · (0.766, - 0.643)

In this question we must determine the angles and coordinates of vectors within a geometric system consisting in a circle centered at a Cartesian plane. Angles and vectors can be found by means of the following definitions:

Angles - Degrees

θ = (n / 9) · 360°, for 0 ≤ n ≤ 8.

Angles - Radians

θ = (n / 9) · 2π, for 0 ≤ n ≤ 8.

Vector

(x, y) = r · (cos θ, sin θ)

Where r is the norm of the vector.

Now we proceed to determine the angles and vectors:

Case A (n = 0)

θ = 0°, θ = 0 rad, (x, y) = 5 · (1, 0)

Case B (n = 1)

θ = 40°, θ = 2π / 9 rad, (x, y) = 5 · (0.766, 0.643)

Case C (n = 2)

θ = 80°, θ = 4π / 9 rad, (x, y) = 5 · (0.174, 0.985)

Case D (n = 3)

θ = 120°, θ = 2π / 3 rad, (x, y) = 5 · (- 0.5, 0.866)

Case E (n = 4)

θ = 160°, θ = 8π / 9 rad, (x, y) = 5 · (- 0.939, 0.342)

Case F (n = 5)

θ = 200°, θ = 10π / 9 rad, (x, y) = 5 · (- 0.939, - 0.342)

Case G (n = 6)

θ = 240°, θ = 4π / 3 rad, (x, y) = 5 · (- 0.5, - 0.866)

Case H (n = 7)

θ = 280°, θ = 14π / 9 rad, (x, y) = 5 · (0.174, - 0.985)

Case I (n = 8)

θ = 320°, θ = 16π / 9 rad, (x, y) = 5 · (0.766, - 0.643)

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The zero of the function f(x) = x - 4 is 4

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

f(x) = x - 4

By definition, the zero of the function is the point where the fucnction has a value of 0

i.e. f(x) = 0

When the value f(x) = 0 is substituted in the above equation, we have the following equation

x - 4 = 0

Add 4 to both sides

So, we have

x - 4 + 4 = 0 + 4

Evaluate the sum

x = 4

Hence, the zero of the function is 4

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