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What is the difference between the null hypothesis and an alternative hypothesis?
In statistics the null hypothesis is usually the one that asserts that the data come from some defined distribution. The alternative hypotheses may simply be that they do not, or it may be that they come from some other, defined distribution.
Why does a chi square test work?
A very superficial argument goes like this: You have a null hypothesis under which your variable has some distribution. On the basis of this distribution you expect certain values (frequencies) in certain intervals. The intervals may be numeric or categoric. But what you observe are different values. You could look at the differences between your observed and expected values but then, in total, they would all cancel out. So you look at their squares. Also, an observed value of 15 where you expected 10 (difference = 5) is, relatively speaking, much bigger than an observed value of 1005 where you were expecting 1000 (diff still = 5). So you divide by the expected value. Thus, for each interval you have (O-E)2/E. You add all these together and that is your chi-square test statistic. Call it C. If your data are consistent with the null hypothesis, then the observed values will be close to the expected values so that the absolute value of (O-E) and therefore its square will be small. So under the null hypothesis, the test statistic will be small. If C is small, the likelihood is that the observations are consistent with the null hypothesis. And in that case you accept the null hypothesis. As C gets larger, the chance of observing that large a value (or larger) when the null hypothesis is true decreases. Finally, for really large values of C, the chances of getting that big a value (or bigger), still under the null hypothesis, are so smaller than some pre-determined limit that you set - for example less than 5% for 95% confidence or 1% for 99% confidence etc. At that stage you decide that there is so little chance that the data are cnsistent with the null hypothesis that you must reject it and accept the alternative. Rather than calculate the probability of observing a value of C or larger, you would look up tables of critical values of C at the 5%, 1% etc levels. Finally, a word about degrees of freedom. If the data are classified one-way into n categories, the sum of the n expected values and the n observed values is the same. So, once you have n-1 of these the nth is determined. So you only have n-1 degrees of freedom. Similar arguments apply to 2-way, 3-way etc classifications. For more detail I suggest you get hold of a decent textbook. Actually
Is the null hypothesis considered correct until proven otherwise?
No. The null hypothesis is not considered correct. It is an assumption, and hypothesis testing is a consistent meand of determining whether the data is sufficiently strong to say that it may be untrue. The data either supports the alternative hypothesis or it fails to reject it. See examples in links. Also note this quote from Wikipedia: "Statistical hypothesis testing is used to make a decision about whether the data contradicts the null hypothesis: this is called significance testing. A null hypothesis is never proven by such methods, as the absence of evidence against the null hypothesis does not establish it."
What is a Maybe?
In programming, a Maybe is a data type that represents the possibility of a value being present or absent. It is used to handle scenarios where a value may or may not exist, and provides a way to transparently handle null or undefined values. A Maybe can be thought of as a container that either holds a value or indicates its absence.
What are constants and how are they are different from varibles?
Constants are numerical values which are fixed but which may or may not be known. Variables are also numerical but may take a range of values. If a variable appears in an equation or inequality), it may be possible to solve the equation to ascertain the value of the variable.
What is difference between null value and not null value?
In databases: Null Value: Represents the absence of a value or an unknown value. It indicates that the data is missing or not applicable. Not Null Value: Indicates that a field contains a valid, defined value. It means the data is present and has been explicitly set.
Explain what is meant by repetition of information and inability to represent information Explain why each of these properties may indicate a bad relational database design?
Repetition of Information is a condition in a relational database where the values of one attribute are determined by the values of another attribute in the same relation, and both values are repeated throughout the relation. This is a bad relational database design because it increases the storage required for the relation and it makes updating the relation more difficult. - Inability to represent information is a condition where a relationship exists among only a proper subset of the attributes in a relation. This is bad relational database design because all the unrelated attributes must be filled with null values otherwise a tuple without the unrelated information cannot be inserted into the relation. Regards, Jose Deleep. S
When inserting character values into a table is case sensitivity important?
It depends on the database's collation settings. For case-insensitive databases, the case of the inserted values may not matter. In case-sensitive databases, the values are considered distinct based on their case, so 'John' and 'john' would be treated as different entries. It's best to know the collation settings of your database to handle character casing appropriately.
What is the meaning of Null Null?
In mathematics, null mean zero value.-----------In mathematics, the null set is the set without any members that is contained in every set.In computing, null is used in some languages to mean 'no value'-in particular, not zero, not true and not false. (See, for example, SQL or PHP.)In statistics, the word 'null' is most often used in the term 'null hypothesis'. Usually a null hypothesis is a statement claiming that a population parameter of interest equals a certain value. Then the alternative hypothesis might be that the parameter assumes values other than that given value.Perhaps it may be said that the word null has various meanings, depending on context.
What is Null scan?
A Null scan is a stealthy port scanning technique in which the packet header is crafted to have minimal or null values, such as setting flags or options to zero. This type of scan is used to evade detection by firewall or intrusion detection systems that may flag more typical scanning activities. Null scans attempt to gather information about a target system's open ports by observing how the system responds or does not respond to the null packets.
How do you move a database?
well it depends what kind of database is it ? you may move it by copying and pasting the files of the database. or you may use Database importer and shifter for desired database you want to move.
Define the Domain term in the context of Relational Model?
Definition: The domain of a database attribute is the set of all allowable values that attribute may assume.Examples: A field for gender may have the domain {male, female, unknown} where those three values are the only permitted entries in that column.
What is a field length check in a database?
Most database field types have an inherent data length limit, however the business rules of the application may require a lower maximum limit on the field. For example, an integer field may hold positive and negative values in the hundreds of thousands or more, but if the field is designed to hold the price of something, it makes no logical sense to permit values that high if it is for items in a supermarket.
How does select operation perform in RDBMS?
Data is contained in tables. Tables have fields (columns) that hold rows of data. Here is an example of the field in a table: >>invoke $d001.vssdata.lastactv;-- Definition of table \BEAST.$D001.VSSDATA.LASTACTV-- Definition current at 15:46:25 - 06/25/07(STYLE_NBR INT NO DEFAULT NOT NULL, ITEM_NBR INT NO DEFAULT NOT NULL, SHOP_NBR SMALLINT NO DEFAULT NOT NULL, COLOR_NBR SMALLINT NO DEFAULT NOT NULL, SIZE_NBR SMALLINT NO DEFAULT NOT NULL, LAST_ACTIVITY_DATE DATETIME YEAR TO DAY NO DEFAULT NOT NULL, REGULAR_EOP_STOCK_UNITS INT NO DEFAULT NOT NULL, REGULAR_EOP_STOCK_RETAIL NUMERIC( 18, 2) NO DEFAULT NOT NULL, REGULAR_EOP_STOCK_COST NUMERIC( 18, 2) NO DEFAULT NOT NULL, REDLINE_EOP_STOCK_UNITS INT NO DEFAULT NOT NULL, REDLINE_EOP_STOCK_RETAIL NUMERIC( 18, 2) NO DEFAULT NOT NULL, REDLINE_EOP_STOCK_COST NUMERIC( 18, 2) NO DEFAULT NOT NULL, MODEL_INVENTORY INT NO DEFAULT NOT NULL, PCT_STK_NUMERATOR SMALLINT NO DEFAULT NOT NULL, PCT_STK_DENOMINATOR SMALLINT NO DEFAULT NOT NULL, LOAD_STYLE INT NO DEFAULT NOT NULL, INTRANSIT_UNITS INT NO DEFAULT NOT NULL, OWNER_NBR SMALLINT NO DEFAULT NOT NULL)>>CODE EOF LAST MODIFIED OWNER RWEP PExt SE Say you wanted to know the stock cost between May 1 and May 31. You would query that table for that data. select style_number, regular_eop_stock_cost, owner_nbr from $data.vssdata.lastactv where last_activity_date between "05-01-07" and "05-31-07"; Depending on how large the table is, how fragmented, and last update stats, it could return the data quickly or it could take an hour or more. The date format in the query depends on the database.
What is a empty or null set?
A null or empty set is a set that does not contain any elements.
What is Schema and instances?
In DBMS,Schema is the overall Design of the Database.Instance is the information stored in the Database at a particular moment.In programming,you declare a variable which corresponds to "Schema".But its values changes as and when required which corresponds to "Instance". Google about levels of Database Abstraction. Physical Schema describes database design at physical level while a logical schema describes the database design at the logical level.A database may also have several schemas at the view level, sometimes called subschemas, that describe different views of the database.
What is schema instance?
In DBMS,Schema is the overall Design of the Database.Instance is the information stored in the Database at a particular moment.In programming,you declare a variable which corresponds to "Schema".But its values changes as and when required which corresponds to "Instance". Google about levels of Database Abstraction. Physical Schema describes database design at physical level while a logical schema describes the database design at the logical level.A database may also have several schemas at the view level, sometimes called subschemas, that describe different views of the database.
