Simple Regression to Estimate Parameters. Simple regression model with sales revenue as the dependent

Simple Regression to Estimate Parameters.

Simple regression model with sales revenue as the dependent Y-variable and R&D expenditures
independent X-variable.
Simple regression model with profits as the dependent Y-variable and R&D expenditures

independent X-variable.

Submit printout of excel regression computation.
2. Write out linear equation with the estimated parameters.
3. Interpret the meaning of the coefficient, i.e. explain the impact of a one unit
change in the coefficient (parameter) value on the predicted (dependent) variable.
4. Use the estimated equation to calculate a new predicted value of the dependent
variable, i.e. make a forecast.
5. Explain the meaning of R square.
6. Explain the meaning of the F test of significance.
(Text reference p. 100, ST3.2)
II. Multiple Regression to Estimate Parameters.

Demand Function Electronic Data Processing, Inc.

Y	P	SE	AD
Unit Sales	Price	Selling Ex	Expenses
100	3800	14250	13500
110	3700	15000	15000
130	3500	17000	17250
170	3200	18750	22500
140	3900	21750	18000
210	2500	23250	16500
230	2300	22500	24000
250	2100	24000	15000
200	3400	21000	24750
220	2500	24750	19500
240	2400	25500	24750
200	3300	29250	12000

Submit printout of excel computation.
Write out linear equation with the estimated parameters.
Interpret the meaning of the coefficients, i.e. explain the impact of a one unit
change in each coefficient (parameter) value on the predicted (dependent) variable.
Using the t-test of significance, indicate which variables are statistically significant enough to
use to forecast.
Use the estimated equation to calculate a new predicted value of the dependent
variable, i.e. make a forecast.
Explain the meaning of R square.
Explain the meaning of the F test of significance.

(Text reference p.234, ST6.2.)

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Solution: The downloadable solution consists of 8 pages, 558 words and 3 charts.
Deliverable: Word Document