1. You can also use a query in a join just as you would a table. 2. Joins are to queries what relationships are to tables: an indication of how data in two sources can be combined based on data values they have in common. C�m ơn b�c nhiều!!! 1. You can also use a query in a join just as you would a table => You can also use a query in a join in a similar manner as you would use a table in a join. In other words, you can join queries the same way as you join tables (and can also join both) 2. Joins are to queries what relationships are to tables: an indication of how data in two sources can be combined based on data values they have in common. In a relational database, relationships enable you to prevent redundant data. For example, if you are designing a database that will track information about books, you might have a table called Titles that stores information about each book, such as the book’s title, date of publication, and publisher. There is also information you might want to store about the publisher, such as the publisher's phone number, address, and zip code. If you were to store all of this information in the Titles table, the publisher’s phone number would be duplicated for each title that the publisher prints. To prevent duplication, you set up a separate table called Publishers and put a pointer in Titles table to refer to data in the Publishers table. This means that there is a relationship between Titles table and the Publishers table. Without going into much technical details, there are three types of relationship between tables: ◦One-To-Many Relationships ◦Many-To-Many Relationships ◦One-To-One Relationships Joins are to queries what relationships are to tables => basically means that in terms of data values, joins serve a similar function between queries as relationships do between tables. Relationships enable you to prevent redundant data between tables. Joins enable you to combine the duplicate record in each query into one. Together, they are tools to combine data from different sources based on data values they have in common. When you include multiple data sources in a query, you use joins to limit the records that you want to see, based on how the data sources are related to each other. You also use joins to combine records from both data sources, so that each pair of records from the sources becomes one record in the query results. By default, a join is automatically created if there is already a relationship between two data sources that you use in a query. A join is also created if there are fields that clearly correspond to each other. You can delete an automatically created join. When a lagged explanatory variable is used in a model, this represents a situation where the analyst thinks that the explanatory variable might have a statistical relationship with the response, but they believe that there may be a "lag" in the relationship. This could occur when the explanatory variable has a causal effect on the response variable, but the causal effect occurs gradually, and manifests in changes to the response later in time. When a lagged response variable is used in a model, this represents a kind of proxy for auto-correlation in the response variable, and the remaining explanatory variables are then included to see if there is any remaining statistical relationship between these variables and the response, after the effect of auto-correlation are removed. Both of these situations can occur in a wide variety of econometric settings, since variables in those settings are commonly auto-correlated, and they also often have causal effects on each other that manifest gradually over time. In terms of when to include these kinds of terms in models, that is a complicated judgment relating to underlying theoretical considerations and diagnostic analysis of the data. Putting aside theoretical issues, you can look for auto-correlation in regression residuals and you can also look for lagged correlation between explanatory variables and residuals, so this allows you to see if an existing fitted model might benefit from the addition of a lagged model term. A dependent variable that is lagged in time. For example, if Yt is the dependent variable, then Yt-1 will be a lagged dependent variable with a lag of one period. Lagged values are used in Dynamic Regression modeling. They are also used in ARIMA modeling where it is assumed that the forecast of the next period depends on past values of the same series. Previous Entry Key Performance Indicators (KPIs) Next Entry Lead Time |
