Another woman has lean body mass 45 kilograms. What is her predicted metabolic rate? Use the regression line ŷ = 201.2 + 24.026x and give your answer in cal/day to 2 decimal places.
Contingency Table
A contingency table can be defined as the visual representation of the relationship between two or more categorical variables that can be evaluated and registered. It is a categorical version of the scatterplot, which is used to investigate the linear relationship between two variables. A contingency table is indeed a type of frequency distribution table that displays two variables at the same time.
Binomial Distribution
Binomial is an algebraic expression of the sum or the difference of two terms. Before knowing about binomial distribution, we must know about the binomial theorem.
| Mass | 36.4 | 54.5 | 48.6 | 42.0 | 50.6 | 42.0 | 40.3 | 33.1 | 42.4 | 34.5 | 51.1 | 41.2 |
| Rate | 996 | 1423 | 1396 | 1418 | 1502 | 1256 | 1189 | 913 | 1124 | 1052 | 1347 | 1204 |
Another woman has lean body mass 45 kilograms. What is her predicted metabolic rate? Use the regression line ŷ = 201.2 + 24.026x and give your answer in cal/day to 2 decimal places.
Now add the following two new data points to your
- Point A is a high outlier in the y direction and point B is a high outlier in the x direction and a low outlier in the y direction.
- Point A is a low outlier in the x direction and point B is a low outlier in the x direction and a high outlier in the y direction.
- Point A is a high outlier in the x direction and point B is a high outlier in the y direction.
- Point A is a high outlier in the y direction and point B is a high outlier in the x and y directions.
Take a look at three least-squares regression lines on your plot:
(1) the regression line for the original 12 data values
(2) the regression line for the original data plus Point A,
(3) the regression line for the original data plus Point B.
Compare the second two lines with the first one to determine which point (A or B) is more influential for the regression line. Select the best choice from the options below:
- The two points are equally influential for the regression line, because adding them changes the regression line considerably.
- Point B is the more influential for the regression line, because it is an outlier in the x direction. It pulls the line down, making it less steep.
- Point A is the more influential for the regression line, because it is an outlier in the y direction. It pulls the line parallel and up.
- Point B is the more influential for the regression line, because it is a low outlier in the y direction. It makes the line less steep.
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