Multiple linear regression is used to analyse the effect of a number of independent variables (ISO9000 certification, number of employees, sales, FDI dummy, and age of company) on the dependent variable (profit of a company). The following tables are multiple linear regression output.

Regression Statistics

Multiple R 0.77

R Square 0.59

Adjusted R Square 0.58

Standard Error 4611.66

Observations 5717

ANOVA:

df SS MS F Significance F

Regression 5.00 171446253958.93 34289250791.79 1612.29 0.00

Residual 5711.00 121458393190.81 21267447.59

Total 5716.00 292904647149.74

Coefficients Standard Error t Stat P-value

Intercept -65.25 93.51 -0.70 0.49

Certification -1013.31 230.97 -4.39 0.00

Employee number 6.49 0.96 6.79 0.00

Sales 0.16 0.00 75.85 0.00

FDI -277.29 361.39 -0.77 0.44

Company age 7.26 8.85 0.82 0.41

Note: For variable certification, not certified = 0, certified =1; for variable FDI, Without FDI=0, with FDI=1. FDI is foreign direct investment.

1)Is there a linear relationship between the dependent variable and all the independent variables as a group? Give your evidence.

2)What percentage of variance (Coefficient of Determination) of company profit is explained by the five independent variables as a group?

Value of coefficient of determination: ..................................

3)Please identify the independent variables that have relationships with profit of a company.

4)If there is a relationship between any independent variables and profit of company price, what is the direction and estimated regression coefficient of the relationship for each independent variable?