We run linear regression for Revenue (as the response, in million $) and Sales- force(as the predictor) based on historical data in the previous 200 periods (weeks). The following is part of the summary output of this linear regression. Call:
lm(formula = Revenue ~ Salesforce, data = mydata)
Residuals:
Min 1Q Median 3Q Max
-8.3860 -1.9545 -0.1913 2.0671 7.2124

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.00 0.88 15.36 <2e-16
Salesforce 0.10 0.06 17.6 70.025

Required:
a. What proportion of variation in the data set is explained by the linear regres-sion model?
b. Is the slope coefficient significantly different from zero? How do you know? Ifyes, at what significance level?
c. Compute the 95% confidence intervals for the intercept and slope. Commentthe confident intervals.

idk i would have to be there to under stand it

step-by-step explanation:

idk i would have to be there to under stand it

step-by-step explanation:

to find if any function is an inverse of the other, put the first equation in as x in the next one. then simplify and solve. if at the end you get the statement y = x, then it is an inverse. if you get anything else, it is not.

a. 12.5 kg

step-by-step explanation:

f = ma

so

m = f/a = (2500 n)/(200 m/s²) = 12.5 kg

1 n = 1 kg·m/s²

"image"

step-by-step explanation:

in most of these problems, the notation is that the point with a "prime" is the image of the point without. that is, b' is the image of b.

here, there is no dilation or reflection. there is simply translation (with no direction shown). which figure you call the "preimage" and which one you call the "image" is up to you. as a matter of convention, the naming is as described above.