Winnings = 4850 Hours + 178,000
Winnings = 6300 Hours + 169,000
Winnings = 31,200 Hours + 14,550
Winnings = 32,300 Hours + 7750
Winnings = 42,000 Hours - 52,400
I
II
III
I and III
None of the above are valid conclusions.
None of these could result in the given residual plot.
The line overestimates the data.
The line underestimates the data.
It is not appropriate to fit a line to these data since there is clearly no correlation between the variables.
The data are not related.
None of these
The earnings gained after 12 years are approximately 5.8759.
The earnings gained after 12 years are approximately 356,345.
The earnings will increase by 0.464 thousand dollars each year.
The original investment was $307.90
None of these is valid.
The heart disease rate per 100,000 people has been dropping about 3.627 per year.
The baseline heart disease rate is 7386.87.
The regression line explains 96.28% of the variation in heart disease death rates over the years.
The regression line explains 98.12% of the variation in heart disease death rates over the years.
The heart disease death rate will be zero in the year 2036.
The higher the correlation coefficient, the steeper the line of best fit.
The correlation coefficient has the same sign as the y-intercept of the least squares regression line.
A low correlation coefficient indicates a weak relationship between the two variables.
Two sets of bivariate data can have approximately equal correlation coefficients but very different scatterplots.
All of these are true.
None of these.