Statistic - Research Paper Example

Lifestyle on Satisfaction of social life Affiliation H0: The hypothesis derived from the two variables is that there is difference between lifestyle on satisfaction and social life. H1: There is no difference between lifestyle on satisfaction and social life. Descriptive StatisticsNMinimumMaximumMeanStd. DeviationLifestyle48132.42.739Social48153.581.033Age48193821.792.939Valid N (listwise)48 According to the descriptive statistics done using SPSS program, 48 participants undertook the experiment.

The participants were of age 19 to 38. The mean age of the participants were M=21.79, indicating that almost all of the participants were at their early 20s. Lifestyle M=2.42; SD=0.739, social M=3.58; SD=1.033, age M=21.79; SD=2.939. Tests of Between-Subjects EffectsDependent Variable:LifestyleSourceType III Sum of SquaresdfMean SquareFSig.Partial Eta SquaredCorrected Model4.484a6.7471.447.221.175Intercept80.114180.114155.067.000.791Social4.4846.7471.447.221.175Error21.18241.517Total306.00048Corrected Total25.66747a. R Squared = .

175 (Adjusted R Squared = .054)An Anova analysis was done with lifestyle as the dependent variable and social life as the independent variable. Anova analysis was done to determine if lifestyle and social life were statistically significant and if there had any relationship. Identifying the relationship between the variable would depict if there go hand in hand. According to the results of the Anova analysis, the p-value P=0.221 which is higher than the alpha value used for the study α= .05. With a P=0.

222 when value is high than .05 meaning that the data is not statistically significant hence the null hypothesis is supported for the study since the p-value shows that there is no relationship between lifestyle on satisfaction of your social life. CorrelationsSocialLifestyleKendalls tau_bSocialCorrelation Coefficient1.000.151Sig. (1-tailed)..117N4848LifestyleCorrelation Coefficient.1511.000Sig. (1-tailed).117.N4848 In order to proof the significance relationship undertaken using ANOVA analysis as above, a correlation analysis done using Kendall’s was under taken an it depicted that there is a correlation between lifestyle and social life at .

151, however it was a weak positive correlation and it is not statistically significant (p=.117. The main effect of the analysis is that there is a week correlation between lifestyle and social life at .151, and it is not statistically significant (p=.117). Hence, the null hypothesis was supported for the study. This explains that despite the fact that, human beings have different lifestyle it has a small impact on the social life of individuals. However, the small difference depicted from the study indicates that small portion of people is affected by the lifestyle to an extent of destroying their social life.

For example, those taking drugs and living unhealthy life can be affected by the lifestyle they are taking. According to United States 2013, health fact sheet the number of obese individual has increased from 1 million people to 1.2 million over the last 5 years and these has caused various social dysfunctions such as lack of human capital because of obese “syndrome” (Babbie, 2013). This is shown using the scatter graphy belowReferenceBabbie, E. R. (2013). The practice of social research.

Cengage Learning.