In the Chi-Square test table below, the “Asymp. Sig. (2-tailed)” determines the level of confidence. This particular level of confidence is less than 0.5, which means the difference is statistically significant. The null hypothesis is thus rejected.
Chi-Square Tests Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 12.747a 6 .047
Likelihood Ratio 13.057 6 .042
Linear-by-Linear Association 5.178 1 .023
N of Valid Cases 742
Correlation and Regression with SPSS
Pearson’s r value and (possibly) significance values, there was a correlation between the two variables [r = .208, n = 906, p = .000].
RESPONDENTS INCOME HIGHEST YEAR OF SCHOOL COMPLETED
RESPONDENTS INCOME Pearson Correlation 1 .208** Sig. (2-tailed) .000 N 906 906 …show more content…
(2-tailed) .000 N 906 1499
**. Correlation is significant at the 0.01 level (2-tailed).
In this chart, there was a positive correlation between the years of high school completed and the income of the respondent. The more years of high school that were completed led to a higher income for the respondent. This was calculated by a Pearson product-moment correlation coefficient. The values that were found were positive with r=0.208, n=906, p=0.000. This is summarized by the scatterplot above. The Sig value strengthens the argument for a positive correlation between years and school and a higher income level, as the value is 0.000. This is less than 0.05 and thus statistically significant.
Positive correlation in a scatterplot
The line that is imagined in below shown scattered graph slopes upward from zero, and I am concluding that there is a positive correlation between
Chi-Square Tests Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 12.747a 6 .047
Likelihood Ratio 13.057 6 .042
Linear-by-Linear Association 5.178 1 .023
N of Valid Cases 742
Correlation and Regression with SPSS
Pearson’s r value and (possibly) significance values, there was a correlation between the two variables [r = .208, n = 906, p = .000].
RESPONDENTS INCOME HIGHEST YEAR OF SCHOOL COMPLETED
RESPONDENTS INCOME Pearson Correlation 1 .208** Sig. (2-tailed) .000 N 906 906 …show more content…
(2-tailed) .000 N 906 1499
**. Correlation is significant at the 0.01 level (2-tailed).
In this chart, there was a positive correlation between the years of high school completed and the income of the respondent. The more years of high school that were completed led to a higher income for the respondent. This was calculated by a Pearson product-moment correlation coefficient. The values that were found were positive with r=0.208, n=906, p=0.000. This is summarized by the scatterplot above. The Sig value strengthens the argument for a positive correlation between years and school and a higher income level, as the value is 0.000. This is less than 0.05 and thus statistically significant.
Positive correlation in a scatterplot
The line that is imagined in below shown scattered graph slopes upward from zero, and I am concluding that there is a positive correlation between