Final answer:
Bootstrap support indicates how often the correct relationship between variables was found in the bootstrap samples.
Step-by-step explanation:
In statistics, the bootstrap method is a resampling technique used to estimate the variability of a statistic from a sample by resampling the sample with replacement. Bootstrap support indicates how often the correct relationship between variables was found in the bootstrap samples.
If the support is high, it suggests that the observed relationship is likely to be the correct one. For example, if the bootstrap support of a correlation coefficient is 0.95, it means that in 95% of the bootstrap samples, the correlation coefficient indicated the correct relationship between the variables.
A student has asked what bootstrap support indicates that the correct relationship was probably found. In statistics and data analysis, bootstrap support refers to a measure of confidence in a finding, often used in the context of developing models based on sampled data.
Bootstrap methods involve resampling data and recalculating a statistic multiple times to estimate the precision of the original calculation.
Remember, in the context of correlation analysis, confidence in the relationship between variables can be gauged using the correlation coefficient and the coefficient of determination (R-squared).
When the coefficient of determination is at least 0.50, this implies that at least 50% of the variance in one variable is predictable from the other variable.
To achieve a coefficient of determination of 0.50, the absolute value of the correlation coefficient (r) must be at least 0.71 when rounded to two decimal places since R-squared is calculated as the square of the correlation coefficient (r^2).