Chapter 12 business stat and opt
The F-value to test the overall significance of a regression model is computed by dividing the sum of squares regression (SSreg) by the sum of squares error (SSerr).
False
The proportion of variability of the dependent variable (y) accounted for or explained by the independent variable (x) is called the coefficient of correlation.
False
The range of admissible values for the coefficient of determination is −1 to +1.
False
The slope of the regression line, y = 21 − 5x, is 21.
False
The slope of the regression line, y = 21 − 5x, is 5.
False
The standard error of the estimate, denoted se, is the square root of the sum of the squares of the vertical distances between the actual Y values and the predicted values of Y.
False
The strength of a linear relationship in simple linear regression change if the units of the data are converted, say from feet to inches.
False
One of the major uses of residual analysis is to test some of the assumptions underlying regression.
True
Regression output from Excel software includes an ANOVA table.
True
Data points that lie apart from the rest of the points are called deviants.
False
If the correlation coefficient between two variables is -1, it means that the two variables are not related.
False
In a simple regression the coefficient of correlation is the square root of the coefficient of determination.
False
In regression, the predictor variable is called the dependent variable.
False
In regression, the variable that is being predicted is usually referred to as the independent variable.
False
In the simple regression model, y = 21 − 5x, if the coefficient of determination is 0.81, we can say that the coefficient of correlation between y and x is 0.90.
False
One of the assumptions of simple regression analysis is that the error terms are exponentially distributed
False
Prediction intervals get narrower as we extrapolate outside the range of the data.
False
Regression output from Excel software directly shows the regression equation.
False
In simple regression analysis the error terms are assumed to be independent and normally distributed with zero mean and constant variance.
True
A t-test is used to determine whether the coefficients of the regression model are significantly different from zero.
True
Correlation is a measure of the degree of linear relationship between two variables.
True
For the regression line, y = 21 − 5x, 21 is the y-intercept of the line.
True
Given x, a 95% prediction interval for a single value of y is always wider than a 95% confidence interval for the average value of y.
True
The coefficient of determination is the proportion of variability of the dependent variable (y) accounted for or explained by the independent variable (x).
True
The difference between the actual y value and the predicted y value found using a regression equation is called the residual.
True
The process of constructing a mathematical model or function that can be used to predict or determine one variable by another variable is called regression analysis.
True
To determine whether the overall regression model is significant, the F-test is used.
True
