The correct term for the proportion of the variance in the dependent variable that is predictable from the independent variable is:

Coefficient of Determination.

This is commonly represented as $R^2$ in regression analysis. It indicates how well the independent variable(s) can explain the variability in the dependent variable. For example, if $R^2 = 0.80$, it means that 80% of the variance in the dependent variable can be explained by the independent variable(s).

Brief explanations of the other terms:

• Chi-Square Test for Goodness of Fit: A statistical test used to determine whether observed data fit a particular distribution or model. It is typically used with categorical data to assess how well a theoretical distribution matches observed data.

• Bonferroni: A statistical correction method used when performing multiple comparisons or tests. The Bonferroni correction adjusts the significance level to control for Type I errors (false positives) when multiple tests are conducted.

• Pearson Correlation Coefficient: A measure of the linear correlation between two variables. It ranges from -1 (perfect negative correlation) to +1 (perfect positive correlation), with 0 indicating no linear correlation.

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Would you like further details on any of these concepts?

Here are 5 related questions:

1. What is the formula for the coefficient of determination ($R^2$) in simple linear regression?
2. How is the Pearson correlation coefficient different from the coefficient of determination?
3. What is the purpose of the Bonferroni correction in multiple hypothesis testing?
4. How do you interpret a Chi-Square statistic in a Goodness of Fit test?
5. Can the coefficient of determination ever be negative?

Tip: When interpreting $R^2$, remember that it tells you the proportion of the variance explained by the model. However, a higher $R^2$ doesn't always mean the model is better, especially if overfitting occurs.