Advance Quantitative Analytical Methods PDF

ADVANCE QUANTITATIVE ANALYTICAL METHODS DR. EMMANUEL JAY D. DIMAL, RN, RM LPT CORRELATION Uses statistical analysis to yield results that describe the relationship of two variables. The results, however, are incapable of establishing causal relationship. ANALYSIS OF VARIANCE (ANO...

ADVANCE QUANTITATIVE ANALYTICAL METHODS DR. EMMANUEL JAY D. DIMAL, RN, RM LPT CORRELATION Uses statistical analysis to yield results that describe the relationship of two variables. The results, however, are incapable of establishing causal relationship. ANALYSIS OF VARIANCE (ANOVA) The results of this statistical analysis are sued to determine if the difference in the means or averages of two categories of data are statistically significant. Example- if the mean of the grades of a student attending tutorial lessons is significantly different from the mean of the grades of a student not attending tutorial lessons. REGRESSION  Has some similarities with correlation, in that, it also shows the nature of relationship of variables, but gives more extensive results than that of correlation. aside from indicating the presence of relationship between two variables, it determines whether a variable is capable of predicting the strength of the relation between the treatment (independent variable) and the outcome (dependent variable). Just like correlation, regression is incapable of establishing cause-effect relationship. Example- if reviewing with music (treatment variable) is a statistically significant predictor of the extent of the concept learning (outcome variable) of a person, STATISTICAL METHODS STATISTICS Is a term that pertain to acts of collecting and analyzing numerical data. Doing statistics then means performing some arithmetric procedures like addition, division, subtraction, multiplication and other mathemathical calculation. Statistical Methodologies  Descriptive Statistics- the describes a certain aspect of a data set by making, you calculate the mean, medium, mode and standard deviation. It tells about the placement or position of one data item in relation to the other data, the extent of the distribution or spreading out of data and whether they are correlations or regressions between or among variables. This kind of statistics does not tell anything about the population.  Inferential statistics- is not as simple as the descriptive statistics. This does not focus itself only on the features of the category of set, but on the characteristics of the sample that are also true for the population from you have drawn the sample.  Inferential statistics is a branch of statistics that focuses on conclusions, generalizations, predictions, interpretation, hypotheses, and the like. There are a lot of hypotheses testing in this method of statistics that require you to perform complex and advanced mathematical operatuions. Types of Statistical Data Analysis Univariate Analysis- analysis of variable Bivariate Analysis- analysis of two variables (independent and dependent variables) Multivariate analysis- analysis of multiple relations between multiple variable Statistical Methods of Bivariate Analysis  Correlation or Covariation (correlated variation)- describes the relationship between two variables and also test the strength or significance of their linear relation. This is relationship that makes both variables getting the same high score or one getting a higher score and the other one, a lower score.  Covariance- is the statistical term to measure to extent of the change in the relationship of two random variables. Random variables are data with varied values like those ones in the interval level or scale ( strongly disagree, neutral, agree, strongly agree) whose values depend on the arbitrariness or subjectivity of the respondent.  Cross-Tabulation-is also called crosstab or students-contingency table that follows the format of a matrix that is made up of lines of numbers, symbols and other expressions. Similar to one type of graph called table, matrix arrange data in rows and column. By displaying the frequency and percentage distribution of data, a crosstab explains the reason behind the relationship of two variables and the effect of one variable on the other variable.