Answer:
C) cluster analysis
Explanation:
Regression analysis. The regression analysis determines the relationship between the two variables. Thus, one of these quantities (X) is given in advance(dependent) and is not random. The second value (U) is the independent and random number. The randomness of the second quantity can be explained for two reasons. First: Measuring the random number U, which depends on the number X, is associated with certain errors; second: The value of U may depend on other uncontrollable factors, in addition to being dependent on the value of the corresponding X value. In this case, we need to talk about the distribution of the random variable U against each value of the X variable. The main purpose of the regression analysis is to build a mathematical model that takes into account the factors affecting the physical process using experimental data and evaluating its accuracy. The least squares method is used for statistical estimation of the mathematical model's suitability to experimental data.
Discriminant analysis is a method used in statistics, pattern recognition, and machine learning to find a linear combination of attributes that define or distinguish two or more classes or events. The resulting combination can be used as a linear classifier or more often to reduce the size before classifying. LDA is closely related to variance analysis (ANOVA) and regression analysis, which try to express a dependent variable as a linear combination of other properties or dimensions. However, while variance analysis uses qualitative independent variables and a continuous dependent variable, discriminant analysis has continuous independent variables and a qualitative dependent variable.
Cluster analysis or clustering is a problem of grouping a number of objects. In this problem, objects must be in some way more similar to those in other groups to accommodate the same clusters (clusters). One of the main problems with data transmission is a common technique used in statistical data analysis. It is also used in machine learning, pattern recognition, image analysis, data retrieval, bioinformatics, data compression and computer graphics.
One-way analysis of variance (ANOVA) is used to calculate the significance of the difference between three and more independent means in a normally distributed series. ANOVA compares the arithmetic means of three or more groups alone; ANOVA result is also significant when at least one of these comparisons is significant. To measure the significance it will have the relation to the regression analysis that's why there will be dependent and independent variables as well.
