In statistics, bivariate data is data on each of two variables, where each value of one of the variables is paired with a value of the other variable. Typically it would be of interest to investigate the possible association between the two variables. The association can be studied via a tabular or graphical display, or via sample statistics which might be used for inference. The method used to investigate the association would depend on the level of measurement of the variable. This association that involves exactly two variables can be termed a bivariate correlation, or bivariate association. For two quantitative variables (interval or ratio in level of measurement) a scatterplot can be used and a correlation coefficient or regression model can be used to quantify the association. For two qualitative variables (nominal or ordinal in level of measurement) a contingency table can be used to view the data, and a measure of association or a test of independence could be used. If the variables are quantitative, the pairs of values of these two variables are often represented as individual points in a plane using a scatter plot. This is done so that the relationship (if any) between the variables is easily seen. For example, bivariate data on a scatter plot could be used to study the relationship between stride length and length of legs.In a bivariate correlation, outliers can be incredibly problematic when they involve both extreme scores on both variables. The best way to look for these outliers is to look at the scatterplots and see if any data points stand out between the variables. (Wikipedia).
Statistics: Ch 3 Bivariate Data (1 of 55) What is Bivariate Data?
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From playlist THE "WHAT IS" PLAYLIST
Correlation Coefficient (1 of 2: Overview)
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From playlist Bivariate Data Analysis
Introduction to Bivariate Data (1 of 2: Dependent & independent variables)
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From playlist Descriptive Statistics & Bivariate Data Analysis
Correlation Coefficient (2 of 2: Evaluating with a calculator)
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From playlist Bivariate Data Analysis
Statistics: Ch 3 Bivariate Data (10 of 25) Positive and Negative Correlation
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From playlist STATISTICS CH 3 BIVARIATE DATA
Understanding bivariate and univariate data
From playlist Integrated Algebra Regents
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From playlist Bivariate Data Analysis
Statistics: Ch 3 Bivariate Data (23 of 25) Linear Regression: Method 2: Ex.
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From playlist STATISTICS CH 3 BIVARIATE DATA
08 Data Analytics: Correlation
Lecture on bivariate statistics and correlation.
From playlist Data Analytics and Geostatistics
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From playlist NEW ANTS #4) Synchronization
Statistics: Ch 3 Bivariate Data (5 of 25) 2 Quantitative Data Sets: Ex.
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From playlist STATISTICS CH 3 BIVARIATE DATA
Bivariate relationship linearity, strength and direction | AP Statistics | Khan Academy
Describe a bivariate relationship's linearity, strength, and direction. View more lessons or practice this subject at http://www.khanacademy.org/math/ap-statistics/bivariate-data-ap/scatterplots-correlation/v/bivariate-relationship-linearity-strength-and-direction?utm_source=youtube&utm_m
From playlist Exploring bivariate numerical data | AP Statistics | Khan Academy
OCR MEI Statistics Minor A: PMCC: 08 Bivariate Normal Distribution
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From playlist OCR MEI Statistics Minor A: PMCC
From playlist STAT 200 Lectures (OER)
Types Of Data | Statistics & Probability | Maths | FuseSchool
CREDITS Animation & Design: Waldi Apollis Narration: Lucy Billings Script: Lucy Billings Hi, I’m Lucy and in this video, we are going to look at the different types of data that exist and how it can be classified. Starting with data collection... If data is collected by or for the compa
From playlist MATHS
Statistics: Ch 3 Bivariate Data (9 of 25) Dependent and Independent Variable
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From playlist STATISTICS CH 3 BIVARIATE DATA
05c Machine Learning: Feature Selection
Lecture on methods for feature selection for machine learning workflows. Follow along with the demonstration workflows in Python: o. Feature Selection / Ranking: https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/SubsurfaceDataAnalytics_Feature_Ranking.ipynb Subsurface Mach
From playlist Machine Learning