Group 2
Ekta shah
Roll no-2013013
Bivariate:
Bivariate data involves other data
they are as follows
·
Involving of tow variables
·
It deals with causes or
relationships
·
The major purpose of Bivariate
analysis is to explain
·
Analysis of two variables
simultaneously
·
Correlations
·
Comparisons, relationships, causes, explanations
·
Tables where one variable is
contingent on the values of the other variable.
·
independent and dependent variables
Bivariate Correlations:
The Bivariate Correlations procedure
computes Pearson's correlation coefficient, suppose X, and Y with their
significance levels. Correlations measure how variables or rank orders are
related. Before calculating a correlation coefficient, screen your data for
outliers and evidence of a linear relationship. Pearson's correlation
coefficient is a measure of linear association. Two variables can be perfectly
related, but if the relationship is not linear, Pearson's correlation
coefficient is not an appropriate statistic for measuring their association.
EXAMPLE: When we conduct a study that examines the
relationship between two variables.
Suppose we conducted a study to see if there were a bivariate
relationship between the height and weight of high school students. Since we are
working with two variables height and weight, we would be working with
bivariate data.
Bivariate
analysis:
It is one of the simplest forms of the statistical
analysis. It involves the analysis of two variables often denoted as X, Y,
for the purpose of determining the empirical relationship between them.
Bivariate analysis can be helpful in testing simple hypotheses of association
and causality. Bivariate analysis can be contrasted with univariate analysis in
which only one variable is analyzed. Bivariate analysis is a simple (two
variable) special case of multivariate analysis (where multiple relations
between multiple variables are examined simultaneously).
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