Regression analysis

Regressionanalysis

In order to achieve the requirements of thisproject, a number of regression analyses were conducted in order totest the influence among predictorvariables. We made good use of the statistical package for both thesocial science SPSS V22.00 in coding, you can enter and evaluate therequired measurements of many (Kacapyr, 2011)

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.953285

R Square

0.908752

Adjusted R Square

0.902116

Standard Error

5.463931

Observations

60

ANOVA

&nbsp

do

SS

MS

F

Significance F

Regression

4

16353

4088.25

136.9389

6.62E-28

Residual

55

1642

29.85455

Total

59

17995

TheR-square method is mostly used statistically to estimate the fit ofthe model. Theadjusted R2,also called the coefficient of multiple is evaluation, the variancepercentage within the dependent is well explained in a unique way ortogether by the independent variables (Abu Dhabi Food ControlAuthority, 2005). Themodel had an average coefficient of determination (R2)of 0.9087 and which implied that 90.87% of the variations inoperational efficiency are caused by the independent variablesunderstudy (advertisement, awareness of health risks associated withconsumption of junk foods, affordability of junk foods and governmentregulations (Kennedy, 2003).

REGRESSIONAANALYSIS

Thispart of the project required that we calculate the regressionanalysis in order to determine the influence among the predictorvariable, since we do not have the required values of differentvariables we estimated the variable depending on the size of thevariables and the estimated effect that it will have on the resultsof the project according to the research results. The schedule aboverepresents the size of the target group, the respondents and Achievedresponse ratio. Using the method that you asked me to use accordingto the file that I did send. I managed to get the regression analysisvalues