According to the statistical data, analyze and explain which tables results conc

Question

According to the statistical data, analyze and explain which tables results conclude how does old age and gender predict loneliness? Please be specific as possible.

Regression Descriptive Statistics Mean Std ne grow_old female Std. Deviation 983 496 452 2.06 166 166 166 72 Correlations lonely grow old female 030 030 1.000 353 352 Pearson Correlation lonely 1.000 .158 030 .158 1.000 .030 021 grow old female lonely grow old female lonely grow old female Sig. 1-tailed) 021 353 166 166 166 352 166 166 166 166 166 166 Variables Entered/Removeda Variables Entered Variables Removed Model Method female row old Enter a. Dependent Variable: lonely b. All requested variables entered

Explanation / Answer

Age, gender and lonely variable have low correlation between them. They are not multicollinear, no problems here.

Now, The regression exercise reveals that R saure is .026, which is quiet low, indicating very low power of prediction of the existing independent variables. Only 2.6% of variance in Loniliess is being explained by Gender and Age.

Gender(Female variable) is significant, the other is insignificant.

There are better estimators of Loniliness that model couldn't captured. According to the regression model, both of these variabels negatively effect loniliess by the coefficients of the respective variables.

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