1. The least squares regression line given above is said to be a line which "best fits" the sample data. The term "best fits" is used because the line has an equation that minimizes the ______, which for these data is______.

2. For the data point (225.3, 308.1), the value of the residual is_____. (Round your answer to at least 2 decimal places.)

3. The total variation in the sample y values is given by the _____, which for these data is_____.

4. The value r2 is the proportion of the total variation in the sample y values that is explained by the estimated linear relationship between x and y. For these data, the value of r2 is____.

Harry spent the last few days at a lake and caught fish and the data is given below. Perform a LeastSquares Regression for this data.
Hours at lake (X) Fish caught(Y)
2 5
3 5
2 4
1 3
4

In fitting a leastsquares line to n=15 data points, the following quantities were computed: SSxx=55, SSyy=198, SSxy=-88, x-bar=1.3, and y-bar=35.
a.) Find the leastsquares line.
b.) Describe the graph of the leastsquares line.
c.) Calculate SSE
d.) Calculate s^2.

In a regression model involving 30 observations, the following estimated regression equation was obtained:
Y(hat) = 17 + 4X1 - 3X2 + 8X3 + 8X4
For this model SSR = 700 and SSE = 100.
The coefficient of determination for the above model is approximately
A. -0.875
B. 0.875
C. 0.125
D. 0.144
The c

An advertising firm wishes to demonstrate to potential clients the effectiveness of the advertising campaigns it has conducted. The firm is presenting data from recent campaigns, with the data indicating an increase in sales for an increase in the amount of money spent on advertising. In particular, the least-squares regression

For a set of data, the total variation or sum of squares for y is SST - 143.0 and the error sum of squares is SSE = 24.0. What proportion of the variation in y is explained by the regression equation?
2. A. Determine the leastsquares regression line and calculate r.
B. What proportion of the variability in steel

a) Means, sums of squares and cross products, standard deviations, and the correlation between X and Y.
b) Regression equation of Y on X.
c) Regression and residual sum of squares.
d) F ratio for the test of significance of the regression of Y on X, using the sums of squares (i.e., SSreg and SSres) and r_xy^2.
e) Variance o

Consider the following partial computer output for a multiple regression model.
Predictor Coefficient Standard Deviation
Constant 41.225 6.380
X1 1.081 1.353
X2 -18.404 4.547
Analysis of V