Sara Da Silva Ramos
Biographic Data
| ID | 10800524 |
|---|---|
| NAME | Sara Da Silva Ramos |
| GIVEN NAMES | Sara Da Silva |
| FAMILY NAME | Ramos |
| SIGNATURE | RAMOS S S |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2012 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Discriminant Function Analysis
Discriminant function analysis (DFA) is a data‐reduction technique used to make decisions about group membership, typically in naturally occurring groups. It can answer theoretical questions but has proved especially useful in applied research. In this chapter we provide an overview of DFA, describe two types of DFA investigation, its three main procedures, and discuss DFA's potential and its limitations. We then explain the steps needed for inte…
Discriminant Function Analysis
Discriminant function analysis (DFA) is a data‐reduction technique used to make decisions about naturally occurring group membership. It can answer theoretical questions but has proved especially useful in applied research. In this entry we begin with an overview of DFA, describing the two types of DFA investigation and its three main procedures, followed by a summary of DFA's potential and its limitations. We then explain the steps needed for in…
No prominent works on this page.
Discriminant Function Analysis
Discriminant function analysis (DFA) is a data‐reduction technique used to make decisions about naturally occurring group membership. It can answer theoretical questions but has proved especially useful in applied research. In this entry we begin with an overview of DFA, describing the two types of DFA investigation and its three main procedures, followed by a summary of DFA's potential and its limitations. We then explain the steps needed for in…
Discriminant Function Analysis
Discriminant function analysis (DFA) is a data‐reduction technique used to make decisions about group membership, typically in naturally occurring groups. It can answer theoretical questions but has proved especially useful in applied research. In this chapter we provide an overview of DFA, describe two types of DFA investigation, its three main procedures, and discuss DFA's potential and its limitations. We then explain the steps needed for inte…
Artificial Intelligence (2 works) · Computer Science (2 works) · Data mining (2 works) · Discriminant (2 works) · Discriminant function analysis (2 works) · Function (biology) (2 works) · Linear discriminant analysis (2 works) · Machine learning (2 works) · Mathematics (2 works) · Natural language processing (2 works)