# 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   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