Final answer:
The t-ratio (t-test) is used in statistics to determine if a significant relationship exists between a predictor variable and a dependent variable, based on the correlation coefficient and sample size.
Step-by-step explanation:
The indicator that tells us whether the predictor variable has a "significant" relationship with the dependent variable, in statistical terms, is the c. t-ratio. When referring to correlation and regression analyses, the t-test is often used to determine if the correlation coefficient (r) is significantly different from zero, which would indicate a significant linear relationship between the independent (predictor) variable and the dependent variable. The hypothesis testing involving the t-ratio involves calculating the t value from the correlation coefficient and the sample size and comparing this to a critical value based on the degrees of freedom (n−2) and a significance level (commonly a = 0.05). If the computed t is higher than the critical value, or if the p-value is less than the significance level, then the null hypothesis (no relationship) is rejected, confirming a significant relationship.