For the Metric Variable of number of adults in household (ADULTCT), the descriptive statistics is below:
The metric variable of number of adults in household show N=51451 with missing value of 136. The values for the mean, median and mode for this statistics are 3.64, 3.00, and 2. The std. error of mean is minute compared to the std. error of mean for the URBRUR data. The sum is the total values of all subjects without missing data. It is also important to look at the skewness and kurtosis. The most important piece to consider, according to Dr. Matt, in a video demonstration of central tendency (Laureate Education, 2016d), was how the further we get away from 0 for the skewedness, the further we deviate from normal distribution. The skewness of this distribution table is 2.196, which is not a perfect distribution. Therefore, this distribution, because it has a positive skewness, has a long right tail. Kurtosis allows for the degree to which research observation gather around a central socket ((Laureate Education, 2016d). For example, positive kurtosis of 12.3, relative to a normal distribution (i.e. which supposed to be 0 or around 0), shows how the research observation are more geared toward the central point of the distribution; the tail is thinner (not thicker), until otherwise.
Laureate Education (Producer). (2016d). Descriptive statistics [Video file]. Baltimore, MD: Author.
| Statistics | ||
| ADULTCT: Number of adults in household | ||
| N | Valid | 51451 |
| Missing | 136 | |
| Mean | 3.64 | |
| Std. Error of Mean | .011 | |
| Median | 3.00 | |
| Mode | 2 | |
| Std. Deviation | 2.466 | |
| Variance | 6.080 | |
| Skewness | 2.196 | |
| Std. Error of Skewness | .011 | |
| Kurtosis | 12.307 | |
| Std. Error of Kurtosis | .022 | |
| Range | 53 | |
| Minimum | 1 | |
| Maximum | 54 | |
| Sum | 187527 |
| ADULTCT: Number of adults in household | |||||
| Frequency | Percent | Valid Percent | Cumulative Percent | ||
| Valid | 1 | 6572 | 12.7 | 12.8 | 12.8 |
| 2 | 14659 | 28.4 | 28.5 | 41.3 | |
| 3 | 8976 | 17.4 | 17.4 | 58.7 | |
| 4 | 7058 | 13.7 | 13.7 | 72.4 | |
| 5 | 4979 | 9.7 | 9.7 | 82.1 | |
| 6 | 3296 | 6.4 | 6.4 | 88.5 | |
| 7 | 2177 | 4.2 | 4.2 | 92.7 | |
| 8 | 1484 | 2.9 | 2.9 | 95.6 | |
| 9 | 871 | 1.7 | 1.7 | 97.3 | |
| 10 | 537 | 1.0 | 1.0 | 98.4 | |
| 11 | 243 | .5 | .5 | 98.8 | |
| 12 | 198 | .4 | .4 | 99.2 | |
| 13 | 107 | .2 | .2 | 99.4 | |
| 14 | 68 | .1 | .1 | 99.6 | |
| 15 | 76 | .1 | .1 | 99.7 | |
| 16 | 36 | .1 | .1 | 99.8 | |
| 17 | 15 | .0 | .0 | 99.8 | |
| 18 | 29 | .1 | .1 | 99.9 | |
| 19 | 8 | .0 | .0 | 99.9 | |
| 20 | 28 | .1 | .1 | 99.9 | |
| 21 | 9 | .0 | .0 | 100.0 | |
| 22 | 8 | .0 | .0 | 100.0 | |
| 23 | 2 | .0 | .0 | 100.0 | |
| 24 | 1 | .0 | .0 | 100.0 | |
| 25 | 1 | .0 | .0 | 100.0 | |
| 26 | 1 | .0 | .0 | 100.0 | |
| 28 | 1 | .0 | .0 | 100.0 | |
| 29 | 1 | .0 | .0 | 100.0 | |
| 30 | 4 | .0 | .0 | 100.0 | |
| 31 | 1 | .0 | .0 | 100.0 | |
| 32 | 1 | .0 | .0 | 100.0 | |
| 33 | 1 | .0 | .0 | 100.0 | |
| 35 | 1 | .0 | .0 | 100.0 | |
| 41 | 1 | .0 | .0 | 100.0 | |
| 54 | 1 | .0 | .0 | 100.0 | |
| Total | 51451 | 99.7 | 100.0 | ||
| Missing | Missing | 136 | .3 | ||
| Total | 51587 | 100.0 |
