Answer:

i thing the answer is D the last chart

WITH GRAPHWhat is the y-intercept of this line? A. (-2,0) B. (0,-2) C. (0,1) D. (1,0)

Given the linear programming problem, use the method of corners to determine where the minimum occurs and give the minimum value.MinimizeExam ImageSubject tox ≤ 3y ≤ 9x + y ≥ 9x ≥ 0y ≥ 0

So i just learned multiplying negative number and i have to put them on a number line pls help here is the Qustion -2 x 5 = please explain

An electronics store has a current inventory of 50 stereo systems. The lowest priced stereo system in the store sells for $800, The lowest priced stereo system in the store sells for 800 dollars, and the highest priced stereo system sells for $3000. and the highest priced stereo system sells for 3000 dollars. Which of the following is the maximum amount of $3000 systems on hand Which of the following is the maximum amount of 3000 dollar systems on hand if the current inventory totals $111,000?The answer is 32, I need an explanation how to find the answer.

I WILL GIVE 100 POINTS IF YOU ANSWER THE QUESTIONS BELOW

Given the linear programming problem, use the method of corners to determine where the minimum occurs and give the minimum value.MinimizeExam ImageSubject tox ≤ 3y ≤ 9x + y ≥ 9x ≥ 0y ≥ 0

So i just learned multiplying negative number and i have to put them on a number line pls help here is the Qustion -2 x 5 = please explain

An electronics store has a current inventory of 50 stereo systems. The lowest priced stereo system in the store sells for $800, The lowest priced stereo system in the store sells for 800 dollars, and the highest priced stereo system sells for $3000. and the highest priced stereo system sells for 3000 dollars. Which of the following is the maximum amount of $3000 systems on hand Which of the following is the maximum amount of 3000 dollar systems on hand if the current inventory totals $111,000?The answer is 32, I need an explanation how to find the answer.

I WILL GIVE 100 POINTS IF YOU ANSWER THE QUESTIONS BELOW

Answer:

-937.5π

Step-by-step explanation:

F (r) = r = (x, y, z) the surface equation z = 3(x^2 + y^2) z_x = 6x, z_y = 6y the normal vector n = (- z_x, - z_y, 1) = (- 6x, - 6y, 1)

Thus, flux ∫∫s F · dS is given as;

∫∫ <x, y, z> · <-z_x, -z_y, 1> dA

=∫∫ <x, y, 3x² + 3y²> · <-6x, -6y, 1>dA , since z = 3x² + 3y²

Thus, flux is;

= ∫∫ -3(x² + y²) dA.

Since the region of integration is bounded by x² + y² = 25, let's convert to polar coordinates as follows:

∫(θ = 0 to 2π) ∫(r = 0 to 5) -3r² (r·dr·dθ)

= 2π ∫(r = 0 to 5) -3r³ dr

= -(6/4)πr^4 {for r = 0 to 5}

= -(6/4)5⁴π - (6/4)0⁴π

= -937.5π

To set up a **double****integral **for calculating the flux of the vector field through the given surface, parameterize the surface using the equation provided and the given condition. Calculate the cross product of the partial derivatives of x and y to find the normal vector. Finally, set up the double integral for the flux using the vector field and the normal vector.

To set up a double integral for calculating the flux of the vector field through the given surface, we first need to **parameterize** the surface. Given the equation of the surface **z = 3(x^2 + y^2)** and the condition **x^2 + y^2 ≤ 25**, we can parameterize the surface as follows:

**x = rcosθ, y = rsinθ, z = 3r^2**

We can now calculate the cross product of the **partial****derivatives** of x and y to find the normal vector, which is: **n = (3rcosθ, 3rsinθ, 1)**

Finally, the double integral for calculating the flux through the surface is:

**∬ F · n dA = ∬ (x, y, z) · (3rcosθ, 3rsinθ, 1) dA**

#SPJ12

What is the geometric mean of the measures of the line segments A Dand DC? Show your work.

**Answer:**

AC2 = AB2 + BC2 ---> AC2 = 122 + 52 ---> AC = 13

AD / AB = AB / AC ---> AD / 12 = 12 / 13 ---> AD = 144/13

DC = AC - AD ---> DC = 13 - 144/13 ---> DC = 25/13

AD / DB = DB / DC ---> DB2 = AD · DC ---> DB2 = (144/13) · (25/13) ---> DB = 60/13

DB is the geometric mean of AD and DC.

**Step-by-step explanation:**

**Answer:**

Option A The linear model on tar content accounts for 92.4% of the variability in nicotine content.

**Step-by-step explanation:**

R-square also known as coefficient of determination measures the variability in dependent variable explained by the linear relationship with independent variable.

The given scenario demonstrates that nicotine content is a dependent variable while tar content is an independent variable. So, the given R-square value 92.4% describes that 92.4% of variability in nicotine content is explained by the linear relationship with tar content. We can also write this as "The linear model on tar content accounts for 92.4% of the variability in nicotine content".

The Upper R squared or the coefficient of determination here represents the percentage of the variability in the nicotine content that can be explained by the tar content in the regression model, which in this case is 92.4%.

In this context, the meaning of Upper R squared is represented by option A. The **linear model** on tar content accounts for **92.4%** of the variability in nicotine content. This indicates that 92.4% of the change in nicotine content can be explained by the amount of tar content based on the linear regression model used. This measure is also known as the coefficient of determination. Meanwhile, options B, C, and D are not correct interpretations of the R squared in this context. Both B and D wrongly relate the percentage to the predictability of the data points and option C incorrectly associates this percentage with the residual magnitude.

#SPJ3

:) sorry i put the wrong answer before bc i thought i knew it but it was the wrong one and i dont know how to delete the answer sorrryyyyyyyyyyyyyyyy

**Answer:**

**Step-by-step explanation:**

Solution:-

State the hypotheses. The first step is to state the null hypothesis and an alternative hypothesis.

Null hypothesis: The distribution of severity of psoriasis cases at the end and prior are same.

Alternative hypothesis: The distribution of severity of psoriasis cases at the end and prior are different.

Formulate an analysis plan. For this analysis, the significance level is 0.05. Using sample data, we will conduct a chi-square goodness of fit test of the null hypothesis.

Analyze sample data. Applying the chi-square goodness of fit test to sample data, we compute the degrees of freedom, the expected frequency counts, and the chi-square test statistic. Based on the chi-square statistic and the degrees of freedom, we determine the P-value.

DF = k - 1 = 4 - 1

D.F = 3

(Ei) = n * pi

** Category observed Num expected num [(Or,c -Er,c)²/Er,c]**

Remission 380 20 6480

Mild

symptoms 520 770 81.16883117

Moderate

symptoms 95 160 24.40625

Severe

symptom 5 50 40.5

**Sum 1000 1000 6628.075081**

Χ2 = Σ [ (Oi - Ei)2 / Ei ]

Χ2 = 6628.08

Χ2Critical = 7.81

where DF is the degrees of freedom, k is the number of levels of the categorical variable, n is the number of observations in the sample, Ei is the expected frequency count for level i, Oi is the observed frequency count for level i, and Χ2 is the chi-square test statistic.

The P-value is the probability that a chi-square statistic having 3 degrees of freedom is more extreme than 6628.08.

We use the Chi-Square Distribution Calculator to find P(Χ2 > 19.58) =less than 0.000001

Interpret results. Since the P-value (almost 0) is less than the significance level (0.05), we cannot accept the null hypothesis.

We reject H0, because 6628.08 is greater than 7.81. We have statistically significant evidence at alpha equals to 0.05 level to show that distribution of severity of psoriasis cases at the end of the clinical trial for the sample is different from the distribution of the severity of psoriasis cases prior to the administration of the drug suggesting the drug is effective.

The chi-square test is a statistical method that determines if there's a significant difference between observed and expected frequencies in different categories, such as symptom status in this clinical trial. Without post-treatment numbers, we can't run the exact test. However, if the test statistic exceeded the critical value, we could conclude that the drug significantly affected **symptom statuses.**

This question pertains to the use of a chi-squared test, which is a statistical method used to determine if there's a significant difference between observed **frequencies **and expected frequencies in one or more categories. For this case, the categories are the symptom statuses (remission, mild, moderate, and severe).

To conduct a chi-square test, you first need to know the observed frequencies (the initial percentages given in the question) and the expected frequencies (the percentages after treatment). As the question doesn't provide the numbers after treatment, I can't perform the exact chi-square test.

If the post-treatment numbers were provided, you would compare them to the pre-treatment numbers using the chi-squared formula, which involves summing the squared difference between observed and expected frequencies, divided by expected frequency, for all categories. The result is a chi-square test statistic, which you would then compare to a critical value associated with a chosen significance level (commonly 0.05) to determine if the treatment has a statistically significant effect.

To interpret a chi-square test statistic, if the calculated test statistic is larger than the critical value, it suggests that the drug made a significant difference in the distribution of symptom statuses. If not, we can't conclude the drug was effective.

#SPJ3

The solution to the **equation **15.6 + (-1.8) is 13.8.

The equation to solve is 15.6 + (-1.8).

First, add the two **numbers**: 15.6 + (-1.8) = 13.8.

The result is 13.8.

In this equation, you're adding a positive value (15.6) and a negative value (-1.8), which results in a smaller positive value. When you add a **positive number** and a negative number, you can think of it as moving to the right on the number line (positive direction) but not as far as you would if you were only considering the positive value.

So, the solution to the equation 15.6 + (-1.8) is 13.8.

To know more about **equation:**

Since +(-) becomes (-) we can write the given problem as

15.6-1.8

Subtraction gives us 13.8

**:)**