A research project is conducted on sales of 701 UK MNEs The following data are c

Question

A research project is conducted on sales of 701 UK MNEs The following data are collected: Y: BinSales (dummy variable, 1 = the MNE's sales exceed 100.000 0 = the MNE's sales do not exceed 100,000) X1: Capital X2: Employees X3: FSTS (the ratio of foreign sales to total sales) A logit model is applied in this project and the SPSS outputs for the intercept-only model are as follows: Constant is included in the model The out value in 500 -2 log is 969.393 And the SPSS outputs for the unconstrained model are: You are required to answer the following questions: Write down the statistical model for the unconstrained model Then use it to predict whether an MNE's sales exceeding 100,000 if it has 1,000 employess, 50,000 capital, and FSTS of 0.6 Interpret the regression results. Briefly discuss the relationship between the dependent and independent variables. entered on step 1: Capital, Employees, FSTS. You are required to answer the following questions: a. Write down the statistical model for the unconstrained model. Then use it to predict whether an MNE's sales exceeding 100,000 if it has 1,000 employees, 50,000 capital, and FSTS of 0.6. b. Interpret the regression results. Briefly discuss the relationship between the dependent and independent variables. MNEs' managers in UK approach you for suggestions on how to improve their sales. Based on the SPSS outputs, would you recommend the use of the statistical model in (a) to predict whether an MNE's sales exceed 100,000?

Explanation / Answer

Statistical model for the unconstrain model is

Ycap=Bo+B1X1+B2X2+B3X3

here Bo=constant=-6.182, B1=0, B2=0.006, B3=-1.937

Then equation become

Ycap=-6.182+0(x1)+0.006 x2-1.937 x3

X1=1000. X2=50000. X3=0.6

Then the predicted value becomes

Ycap=-6.182+(0×1000)+(50000×0.006)-(1.937×0.6)

=-6.182+0+300-1.1622

= 292.6558

B)as here the p values of all independent variable are less than 0.05 hence all independent variable are significant

Here we can see that there is strong correlation between the dependent and indepent variable as the value of R square is more then 0.65

3)Yes the model is good as all independent variablr are significant and they give us

And independent variable exaplain the more than 70% of variation so it gives better preficted values

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