Look at the subtraction expression below.

Helpppp, I need somebody Simplify √3/10.

maks197457 [2]

Answer:

$\frac{\sqrt{30} }{10}$

Step-by-step explanation:

Simplify the radical by breaking the radicand up into a product of known factors.

3 0

3 years ago

Help please

hoa [83]

1.) One hundred twenty-five and forty-seven thousandths

2.) 91/100.

3.) 2769/1000

4.) 0.074

5.) 3.705

6.) 75.69

7.) 0.162 is greater than 0.07

8.) 8.049 is greater than 8.0094

9.) 6

10.) 5.678

11.) 5.7

7 0

3 years ago

The price of a ski jacket was $92.00. The tax rate was 5%.

kenny6666 [7]

$92.00 X 5% = $4.60

$92.00 + $4.60 = $96.60

A. What was the tax on the ski jacket? $4.60


B. What was the total price of the ski jacket including tax? $96.60

8 0

3 years ago

Read 2 more answers

The difference between the observed value of the dependent variable and the value predicted using the estimated regression equat

Elenna [48]

Answer:

For this case we define the dependent variable as Y and the independent variable X. We assume that we have n observations and that means the following pairs:

$(x_1, y_1) ,....,(x_n,y_n)$

For this case we assume that we want to find a linear regression model given by:

$\hat y = \hat m x +\hat b$

Where:

$\hat m$ represent the estimated slope for the model

$\hat b$ represent the estimated intercept for the model

And for any estimation of the dependent variable $\hat y_i , i=1,...,n$ is given by this model.

The difference between the observed value of the dependnet variable and the value predicted using the estimated regression equation is known as residual, and the residual is given by this formula:

$e_i = y_i -\hat y_i , i=1,...,n$

So the best option for this case is:

d. residual

Step-by-step explanation:

For this case we define the dependent variable as Y and the independent variable X. We assume that we have n observations and that means the following pairs:

$(x_1, y_1) ,....,(x_n,y_n)$

For this case we assume that we want to find a linear regression model given by:

$\hat y = \hat m x +\hat b$

Where:

$\hat m$ represent the estimated slope for the model

$\hat b$ represent the estimated intercept for the model

And for any estimation of the dependent variable $\hat y_i , i=1,...,n$ is given by this model.

The difference between the observed value of the dependnet variable and the value predicted using the estimated regression equation is known as residual, and the residual is given by this formula:

$e_i = y_i -\hat y_i , i=1,...,n$

So the best option for this case is:

d. residual

7 0

3 years ago

What is the domain of the function?

Mkey [24]

Answer:

0 $\leq$ x < ∞

Step-by-step explanation:

6 0

2 years ago