Simple STEP BY STEP METHOD TO NORMALIZE TABLES TO 3NF
STEP 1:
Ask the following question:
DOES THE TABLE IN QUESTION HAVE ANY REPEATING GROUPS?
NO:
IT IS IN 1NF.
YES:
IT IS UNNORMALIZED, SO TO PUT IT IN 1NF, REMOVE THE REPEATING GROUPS.
STEP 2:
DOES THE PRIMARY KEY CONTAIN ONLY ONE COLUMN?
YES- THEN THE TABLE IS ALREADY IN 2NF.
NO- ASK THE FOLLOWING QUESTION:
o DO ANY OF THE COLUMNS THAT ARE NOT KEYS HAVE THEIR VALUES DETERMINED BY ONLY SOME, AND NOT ALL, OF THE COLUMNS THAT MAKE UP THE KEY?
YES- THEN THE TABLE IS NOT IN 2NF.
To put it in 2NF, remove any columns that are dependent upon only a portion of the key, and create separate or separate tables.
NO- THEN THE TABLE IS IN 2NF.
STEP 3:
ARE ALL OF THE COLUMNS IN THE TABLE DETERMINED ONLY BY CANDIDATE KEYS?
YES- THEN THE TABLE IS IN 3NF.
NO- REMOVE ANY COLUMNS THAT ARE NOT FULLY DETERMINED BY CANDIDATE KEYS, AND AGAIN CREATE ADDITIONAL TABLE(S) THAT CONTAIN THOSE COLUMNS.
CONSIDER THE FOLLOWING TABLE:
Assumptions: Assume a student only has one advisor, and can only take a course once.
Note that in this example, the primary key consists of 2 columns STUDENTID and COURSECODE.
STUDENT (STUDENTID, STUDENTLASTNAME, STUDENTFIRSTNAME, ADVISORID, ADVISORLASTNAME, ADVISORFIRSTNAME, GRADE, COURSECODE)
SAMPLE TABLE:
10 Smith Mary 100 Jones Sally A
B
C
A CIT150
CIT160
CIT170
CIT180
20 Adams William 200 Johnson Charles A
B CIT150
CIT160
30 Davis Robert 100 Jones Sally B
A CIT150
CIT160
Consider the above table.
STEP 1:
Does it have any repeating groups?
YES. Notice that student 10 has 4 grades corresponding to 4 courses that she took. These are repeating groups. Therefore, the table is unnormalized, because it isn't in 1NF.
So, to put it in 1NF, we must REMOVE the repeating groups. One way to do that is as follows:
10 Smith Mary 100 Jones Sally A CIT150
10 Smith Mary 100 Jones Sally B CIT160
10 Smith Mary 100 Jones Sally C CIT170
10 Smith Mary 100 Jones Sally A CIT180
20 Adams William 200 Johnson Charles A CIT150
20 Adams William 200 Johnson Charles B CIT160
30 Davis Robert 100 Jones Sally B CIT150
30 Davis Robert 100 Jones Sally A CIT160
It is now in 1NF.
STEP 2:
DOES THE PRIMARY KEY CONTAIN ONLY ONE COLUMN?
o No, it consists of 2 columns, so we need to ask an additional question:
DO ANY OF THE COLUMNS THAT ARE NOT KEYS HAVE THEIR VALUES DETERMINED BY ONLY SOME, AND NOT ALL, OF THE COLUMNS THAT MAKE UP THE KEY?
The answer is YES. Let's look at the columns that are determined by only a portion of the key:
The easiest way to express this is by using the determinant expression
A B
STUDENTID STUDENTLASTNAME, STUDENTFIRSTNAME,
STUDENTFIRSTNAME, ADVISORID, ADVISORLASTNAME, ADVISORFIRSTNAME
ADVISORID ADVISORLASTNAME,ADVISORFIRSTNAME
STUDENTID, COURSECODE GRADE
In this case, the ONLY column that is determined by the entire key is GRADE.
That means there is some real work to do here. Every other non-key column violates the 2NF condition.
Remember that in redesigning a set of tables, there are usually many different ways to create a set of tables that are normalized.
(b) Each GRADE_REPORT record i s identified with one STUDENT record and one SECTION record.
If the data requested is to fetch child keys, the DBEngine requests for metadata from Index Engine. The metadata contains the child key list. The data is then displayed to
As what was articulated in question #1, factors are describing words and letters. There is no possibility of average in this scenario because the grades are not in numbers. So it is a factor and cannot be calculated as we do to
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No, the value of 4.1620049 was nonsignificant based on the level of significance for df2 which is 5.99.
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From my table of results I have found the formula to be 2n2 + 2n + 1
4. Grades – a letter, number, or other symbol indicating the relative quality of a student's work in a course, examination, or special assignment as perceived by the student. 5. Ethnicity – American Indian/Native Alaskan, Asian, Black, Hispanic, Native Hawaiian/Pacific Islander, Multiracial and White. 6.
InterpretationThis Component matrix indicates how each item in the analysis correlates with each of the five retained factors. Negative and positive correlation carry the same weight. This chart gives you an idea how the items correlate with factors.
There are two histograms, showing information on GPA, and showing information on final grade. Histograms are commonly used with interval or ratio level data (Corty, 2007). The data in the GPA is distributed and slightly skewed to the right, which means it has a positive skew and has a peaked distribution. The final histogram also has a leptokurtic frequency distribution, but is skewed to the left meaning this has a negative skew.
Here, R is a relation, and each "term" is a pair AV, where A in turn is an attribute of R and V is either a domain variable or a constant. For example EMP (empno: 100, ename: 'Ajay') is a membership condition (which evaluates to true if and only if there exists an EMP tuple having empno=100 and ename = 'Ajay') .
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The above kind of errors is the errors committed by the developer who is working on the spreadsheet design. These kinds of errors are normally seen in the working section present in the model. They are classified into three different types namely alteration, omission and deletion.