Hi Juncheng,
You're not alone in the confusion! This is a very common question because the labels MCAR, MAR, and MNAR are not intuitive. Based on your description, it sounds like your 7 observations are indeed MCAR. As long as there isn't anything peculiar about these 7 sites that relates to your outcome of interest, then you should be okay with the MCAR assumption.
Also, here's my attempt to clarify the differences between all these missing data mechanisms (this is oversimplified, so I definitely recommend more reading to get full clarity):
MCAR: Missing data are a random subset of what you would've observed if you had zero missing.
MAR: Missing data are not random (totally counterintuitive given the "MAR" label--this leads to much of the confusion!). There's a systematic relation between the missing observations and factors you measured.
MNAR: Missing data are systematically related to factors you didn't measure (and you might not even know about).
I made an illustration that attempts to show these differences:
The first data set shows you the responses to three hypothetical questions from a survey. The image shows what your data would look like if you didn't have any missing values. To the right, the data shows a pattern of missing values (in orange) in the outcome (Y) that are missing completely at random. Next over, you can see the MAR mechanism; that is, the missing values tend to be related to the Age and Sex variables such that mostly younger males failed to provide an answer to the outcome. Lastly, you can see the MCAR pattern, where the missing values are not related to Age nor Sex (they're spread out across younger/older males/females)--however, let's say we failed to collect data on income and if we had observed those unmeasured data, we would see a relation between the values that are missing and the unobserved variable (notice it's the low income folks that didn't respond).
Craig Enders has an excellent book titled "Applied Missing Data Analysis" so I encourage you to check it out if you want to learn more about these issues.
Best,
~Laura
Laura C-S