If the correlation between body weight and annual income were high and positive, we could conclude that:
High incomes cause people to eat more food
Low incomes cause people to eat less food
High income people tend to spend a greater proportion of their income on food than low income people, on average
High income people tend to be heavier than low income people, on average
High income cause people to gain weight
A study found a correlation of r=-0.61 between the sex of a worker and his or her income. You conclude that:
An arithmetic mistake was made; this is not a possible value of r
This non sense because r makes no sense here
The correlation of -0.61 is not meaningful here because the relationship between sex and income is likely nonlinear
Women earn more than men on average
Women earn less than men on average
The correlation coefficient provides:
A measure of the extent to which changes in one variable cause changes in another variable
A measure of the strength of the linear association between two categorical variables
A measure of the strength of the association (not necessarily linear) between two categorical variables
A measure of the strength of the linear association between two quantitative variables
A measure of the strength of the linear association between a quantitative variable and a categorical variable
The Y intercept (bo) represent the:
Predicted value of Y when X=0
Change in Y per unit change in X
Predicted value of Y
Variation around the line of regression
The slope (b1) represents:
Predicted value of Y when X=0
The estimated average change in Y per unit change in X
The predicted value of Y
Variation around the line of regression
The least squares method (OLS) minimizes which of the following?
SSR
SSE
SST
All of the above
The standard error the estimate is a measure of :
Total variation of the Y variable
The variation around the regression line
Explained variation
The variation of the X variable
The coefficient of determination tells us:
That the coefficient of correlation is larger than one
Whether r has any significance
That we should not partition the total variation
The proportion of total variation that is explained
The residuals represent:
The difference between the actual Y values and the mean of Y
The difference between the actual Y values an the predicted Y values
The square root of the slope
The predicted value of Y for the average X value
If the plot of residuals is fan shaped, which assumption is violated?
Normality of error
Homoscedasticity
Independence of errors
No assumptions are violated, the graph should resemble a fan
The strength of the linear relationship between two numerical variables may be measured by the:
Scatter diagram
Correlation coefficient
Slope
Y intercept
In a simple linear regression problem, r and b1
May have opposite signs
Must have the same sign
Must have opposite signs
Are equal
Assuming a linear relationship between X and Y, if the coefficient of correlation ( r ) equals -.30:
There is no correlation
The slope (b1) is negative
Variable X is larger than variable Y
The variance of X is negative