1 over 5 +2 over 25 =

2x(x+4)+7+=(x+8)+ 2x(x+1)+12=

Katena32 [7]

Answer:

x=2.600

Step-by-step explanation:

2x(x+4)+7+=(x+8)+ 2x(x+1)+12

Rearrange the equation by subtracting what is to the right of the equal sign from both sides of the equation :

-> 2*x*(x+4)+7+-((x+8)+2*x*(x+1)+12)=0

-> ((2x•(x+4))+7)-(((x+8)+2x•(x+1))+12) = 0

-> (2x • (x + 4) + 7) - (2x2 + 3x + 20) = 0

-> 5x - 13 = 0

-> Add 13 to both sides of the equation :

5x = 13

Divide both sides of the equation by 5:

x = 13/5 = 2.600

Please mark as brainliest

8 0

2 years ago

Which graph represents the solution set of y = x2 − 4 and x + y + 2 = 0?

statuscvo [17]

Answer:

Graph of solutions of:

y = x^2 -4 (red)

x + y + 2 = 0 (purple)

Step-by-step explanation:

8 0

2 years ago

5.2.14. For the negative binomial pdf p (k; p, r) = k+r−1 (1 − p)kpr, find the maximum likelihood k estimator for p if r is know

Volgvan

Answer:

$\hat p = \frac{r}{\bar x +r}$

Step-by-step explanation:

A negative binomial random variable "is the number X of repeated trials to produce r successes in a negative binomial experiment. The probability distribution of a negative binomial random variable is called a negative binomial distribution, this distribution is known as the Pascal distribution".

And the probability mass function is given by:

$P(X=x) = (x+r-1 C k)p^r (1-p)^{x}$

Where r represent the number successes after the k failures and p is the probability of a success on any given trial.

Solution to the problem

For this case the likehoof function is given by:

$L(\theta , x_i) = \prod_{i=1}^n f(\theta ,x_i)$

If we replace the mass function we got:

$L(p, x_i) = \prod_{i=1}^n (x_i +r-1 C k) p^r (1-p)^{x_i}$

When we take the derivate of the likehood function we got:

$l(p,x_i) = \sum_{i=1}^n [log (x_i +r-1 C k) + r log(p) + x_i log(1-p)]$

And in order to estimate the likehood estimator for p we need to take the derivate from the last expression and we got:

$\frac{dl(p,x_i)}{dp} = \sum_{i=1}^n \frac{r}{p} -\frac{x_i}{1-p}$

And we can separete the sum and we got:

$\frac{dl(p,x_i)}{dp} = \sum_{i=1}^n \frac{r}{p} -\sum_{i=1}^n \frac{x_i}{1-p}$

Now we need to find the critical point setting equal to zero this derivate and we got:

$\frac{dl(p,x_i)}{dp} = \sum_{i=1}^n \frac{r}{p} -\sum_{i=1}^n \frac{x_i}{1-p}=0$

$\sum_{i=1}^n \frac{r}{p} =\sum_{i=1}^n \frac{x_i}{1-p}$

For the left and right part of the expression we just have this using the properties for a sum and taking in count that p is a fixed value:

$\frac{nr}{p}= \frac{\sum_{i=1}^n x_i}{1-p}$

Now we need to solve the value of $\hat p$ from the last equation like this:

$nr(1-p) = p \sum_{i=1}^n x_i$

$nr -nrp =p \sum_{i=1}^n x_i$

$p \sum_{i=1}^n x_i +nrp = nr$

$p[\sum_{i=1}^n x_i +nr]= nr$

And if we solve for $\hat p$ we got:

$\hat p = \frac{nr}{\sum_{i=1}^n x_i +nr}$

And if we divide numerator and denominator by n we got:

$\hat p = \frac{r}{\bar x +r}$

Since $\bar x = \frac{\sum_{i=1}^n x_i}{n}$

4 0

2 years ago

Solve this equation: 2x = 5

Scrat [10]

Answer:

2.5

Step-by-step explanation:

2x = 5

x = 5/2 = 2.5

I hope im right!!

7 0

2 years ago

Read 2 more answers

Run a multiple regression where the dependent variable is ratings and the independent variables are star and fact. Use data from

Vanyuwa [196]

Answer:

See explaination

Step-by-step explanation:

See attachment for diagram

The r value is 0.373 (low). This implies a weak correlation between the dependent and independent variables for this sample.

The overall p- value for the regression model is 0.0017. This implies that at least one of the two independent variables (x1 or x2) in the model is significant predictor of the dependent variable y.

p- values for the both "Fact" and "Star" are < 0.05. This means both the independent variables are significant predictors of the "Rating" at 95% confidence level. The variable "Fact" is significant at 99% level of confidence also. This means the rating viewers award to a movie depends upon both the storyline (fact or Fiction) and the presence or absence of stars.

Expected rating for a fact based movie with no stars = 1.7991(1) + 1.2586(0) + 12.5685 = 14.37

Expected rating for a fiction based movie with a star = 1.7991(0) + 1.2586(1) + 12.5685 = 13.83

So, one may expect a fact based movie without any stars to get better ratings than a fiction based movie with one star.

7 0

2 years ago