Discuss some ways that the concepts of covariance and correlation are similar or different.

Step-by-step explanation:

Key Differences Between Covariance and Correlation

The following points are noteworthy so far as the difference between covariance and correlation is concerned:

1. A measure used to indicate the extent to which two random variables change in tandem is known as covariance. A measure used to represent how strongly two random variables are related known as correlation.

2. Covariance is nothing but a measure of correlation. On the contrary, correlation refers to the scaled form of covariance.

3. The value of correlation takes place between -1 and +1. Conversely, the value of covariance lies between -∞ and +∞.

4. Covariance is affected by the change in scale, i.e. if all the value of one variable is multiplied by a constant and all the value of another variable are multiplied, by a similar or different constant, then the covariance is changed. As against this, correlation is not influenced by the change in scale.

5. Correlation is dimensionless, i.e. it is a unit-free measure of the relationship between variables. Unlike covariance, where the value is obtained by the product of the units of the two variables.

You can find more here:  http://keydifferences.com/difference-between-covariance-and-correlation.html#ixzz4qg5YbiGj

a)(x-2)^2+(y-1)^2=9

step-by-step explanation:

(picture explains)

center (1,3) with radius 6

step-by-step explanation:

we must complete the square to find the center and radius of the circle.

first make sure the x and y squared terms have 1 as their coefficients. we also make sure x and y terms together.

we now create space between the x and y terms with parenthesis.

we complete the square by taking the middle terms -2x and the -6y - divide each and square them.

we add the squares to both sides.

simplify.

and write the quadratics in factored form.

the center is (h,k) or (1,3). the radius is the square root of 36 which is 6.