Linear Regression (Katas)

A curved pattern in residuals plotted against the independent variable most likely suggests a violation of:

A smaller standard error of the estimate most likely means that observed Y values are:

The assumption that residual variance is the same for all observations is called:

For large samples, the normality assumption is most accurately described as:

The intercept is most likely meaningful when:

A prediction interval is built around the predicted value by adding and subtracting the:

The predicted value of the dependent variable at a chosen forecasted X is most accurately found by:

The standard error of the estimate is equal to the:

In a log-lin model, the dependent variable is most accurately described as:

Ordinary least squares chooses the fitted line by minimizing the:

In a log-log model, the slope coefficient most likely measures the:

A repeating seasonal pattern in residuals is most likely evidence against:

In simple linear regression, mean square error is most accurately calculated as SSE divided by:

With n observations in simple linear regression, the t-test for the slope uses how many degrees of freedom?

In simple linear regression, the coefficient of determination is equal to the:

In simple linear regression, the dependent variable is most accurately described as:

In a lin-log model, the independent variable is most accurately described as:

The standard error of the forecast is smallest when the forecasted value of X is:

If covariance between X and Y is positive, the slope and the correlation are most likely:

The F-statistic in simple linear regression most likely tests whether:

If the estimated slope is 1.25, a one-unit increase in the independent variable is most likely associated with a: