Method of Least Squares, Evaluation of Cost Equation Lassiter Company used the method of least squares to develop a cost equation to predict the cost of moving materials. There were 80 data points ft the regression, and the following computer output was generated: Intercept $19,050 Slope Coefficient of correlation 0.91 Standard error $220 The activity driver used was the number of moves. Required:
1. What is the cost formula?
2. Using the cost formula, predict the cost of moving materials if 340 moves are made.
3. What percentage of the variability in moving cost is explained by the number of moves? (Round percentage to two decimal places.)

(1) The cost formula is: y = $19,050 + $12·x.

(2) The cost of moving materials if 340 moves are made is $23,130.

(3) 82.81% of the variability in moving cost is explained by the number of moves.

Step-by-step explanation:

The computer output for the regression analysis of 80 data points is as follows:

Intercept:  $19,050

Slope: 12

Coefficient of correlation: 0.91

Standard error:  $220

(1)

The general formula of regression equation is:

y = a + b·x

Here,

a = intercept

b = slope

The cost formula is:

y = $19,050 + $12·x

(2)

Predict the cost of moving materials if 340 moves are made as follows:

Thus, the cost of moving materials if 340 moves are made is $23,130.

(3)

The coefficient of determination R² specifies the percentage of the variance in the dependent variable (Y) that is forecasted or explained by linear regression and the forecaster variable (X, also recognized as the independent-variable).

The coefficient of determination R² can be computed by squaring the correlation coefficient value.

Thus, 82.81% of the variability in moving cost is explained by the number of moves.

step-by-step explanation: where's the phrase?

(1,4) is on graph of f(x)

f(1) = 4

so 1/4 f(x)

1/4 f(1)

1/4. ×4

1

so point for function 1/4. f(x) is (1,1)