MarciaMarcia

StantonStanton is the new manager of the materials storeroom for Tudor Manufacturing. MarciaMarcia has been asked to estimate future monthly purchase costs for part #696, used in two of TudorTudor's products.MarciaMarcia has purchase cost and quantity data for the past 9 months as follows:

Month	Cost of Purchase	Quantity Purchased
January	$12,687	2,740	parts
February	12,220	2,830
March	17,373	4,137
April	15,838	3,756
May	13,125	2,901
June	14,049	3,376
July	15,300	3,655
August	10,061	2,309
September	14,990	3,512

Month	Purchase Quantity Expected
October	3,370	parts
November	3,720
December	3,100

Requirement 1. The computer in

MarciaMarcia's

office is down, and

MarciaMarcia

has been asked to immediately provide an equation to estimate the future purchase cost for part #696.

MarciaMarcia

grabs a calculator and uses thehigh-low method to estimate a cost equation. What equation does she get?

=

+ (

x

)

Requirement 2. Using the equation from requirement 1, calculate the future expected purchase costs for each of the last 3 months of the year.

			High-Low Method
Month	Purchase Quantity Expected		Expected Cost
October	3,370	parts
November	3,720	parts
December	3,100	parts

Requirement 3. After a few hours

MarciaMarcia's

computer is fixed.

MarciaMarcia

uses the first 9 months of data and regression analysis to estimate the relationship between the quantity purchased and purchase costs of part#696. The regression line

MarciaMarcia

obtains is as follows: y $1,922.9

+

3.713.71X

Run a regression on the 9 months of data using Microsoft Excel to determine the R Square and t Stat for the X Variable 1 statistics. (Enter the R Square and t Stat amounts to six decimal places, 0.XXXXXX.)

Regression Statistics
R Square
	t Stat
X Variable 1

Evaluate the regression line using the criteria of economic plausibility, goodness of fit, and significance of the independent variable.

Economic plausibility
Goodness of fit
Significance of independent variable

Compare the regression equation to the equation based on the high-low method. Which is a better fit? Why?

The

high-low method

regression

is a better fit and more accurate estimate because it uses

all available data (all nine data points)

four data points

two data points

while the other method only relies on

all

two

zero

data points and may therefore miss some important information contained in the other data.

Requirement 4. Use the regression results to calculate the expected purchase costs for October, November, and December. Compare the expected purchase costs to the expected purchase costs calculated using thehigh-low method in requirement 2. Comment on your results.

Begin by using the regression results to calculate the expected purchase costs for October, November, and December. (Use amounts as given in the information to two decimal places. Do not round interimcalculations, but then do round your final answers [expected cost amounts] to the nearest whole dollar.)

			Regression Equation
Month	Purchase Quantity Expected		Expected Cost
October	3,370	parts
November	3,720	parts
December	3,100	parts

Compare the expected purchase costs to the expected purchase costs calculated using the high-low method in requirement 2. Comment on your results.

Although the two equations are

different in both fixed element and variable rate

the same for the fixed element, but different for the variable rate

the same for the variable rate, but different for the fixed element

the same in both fixed element and variable rate

, within the relevant range they give

somewhat similar

drastically different

expected costs. This implies that the high and low points of the data are

a reasonable

not a reasonable

representation of the total set of points within the relevant range.