ANSWERThree categories of data analysis include univariate analysis, bivariate analysis, and multivariate analysis. Show Univarate AnalysisUnivariate analysis is the simplest form of data analysis where the data being analyzed contains only one variable. Since it's a single variable it doesn’t deal with causes or relationships. The main purpose of univariate analysis is to describe the data and find patterns that exist within it. You can think of the variable as a category that your data falls into. One example of a variable in univariate analysis might be "age". Another might be "height". Univariate analysis would not look at these two variables at the same time, nor would it look at the relationship between them. Some ways you can describe patterns found in univariate data include looking at mean, mode, median, range, variance, maximum, minimum, quartiles, and standard deviation. Additionally, some ways you may display univariate data include frequency distribution tables, bar charts, histograms, frequency polygons, and pie charts. Bivarate AnalysisBivariate analysis is used to find out if there is a relationship between two different variables. Something as simple as creating a scatterplot by plotting one variable against another on a Cartesian plane (think X and Y axis) can sometimes give you a picture of what the data is trying to tell you. If the data seems to fit a line or curve then there is a relationship or correlation between the two variables. For example, one might choose to plot caloric intake versus weight. Multivariate AnalysisMultivariate analysis is the analysis of three or more variables. There are many ways to perform multivariate analysis depending on your goals. Some of these methods include:
When it comes to the level of analysis in statistics, there are three different analysis techniques that exist. These are –
The selection of the data analysis technique is dependent on the number of variables, types of data and focus of the statistical inquiry. The following section describes the three different levels of data analysis – Univariate analysis Univariate analysis is the most basic form of statistical data analysis technique. When the data contains only one variable and doesn’t deal with a causes or effect relationships then a Univariate analysis technique is used. Here is one example of Univariate analysis- In a survey of a class room, the researcher may be looking to count the number of boys and girls. In this instance, the data would simply reflect the number, i.e. a single variable and its quantity as per the below table. The key objective of Univariate analysis is to simply describe the data to find patterns within the data. This is be done by looking into the mean, median, mode, dispersion, variance, range, standard deviation etc. Univariate analysis is conducted through several ways which are mostly descriptive in nature – •Frequency Distribution Tables •Histograms •Frequency Polygons •Pie Charts •Bar Charts Bivariate analysis Bivariate analysis is slightly more analytical than Univariate analysis. When the data set contains two variables and researchers aim to undertake comparisons between the two data set then Bivariate analysis is the right type of analysis technique. Here is one simple example of bivariate analysis – In a survey of a classroom, the researcher may be looking to analysis the ratio of students who scored above 85% corresponding to their genders. In this case, there are two variables – gender = X (independent variable) and result = Y (dependent variable). A Bivariate analysis is will measure the correlations between the two variables. Bivariate analysis is conducted using – •Correlation coefficients •Regression analysis Multivariate analysis Multivariate analysis is a more complex form of statistical analysis technique and used when there are more than two variables in the data set. Here is an example of multivariate analysis – A doctor has collected data on cholesterol, blood pressure, and weight. She also collected data on the eating habits of the subjects (e.g., how many ounces of red meat, fish, dairy products, and chocolate consumed per week). She wants to investigate the relationship between the three measures of health and eating habits? In this instance, a multivariate analysis would be required to understand the relationship of each variable with each other. Commonly used multivariate analysis technique include – •Factor Analysis •Cluster Analysis •Variance Analysis •Discriminant Analysis •Multidimensional Scaling •Principal Component Analysis Redundancy Analysis
What is the difference between regression and multiple regression?Linear regression is one of the most common techniques of regression analysis. Multiple regression is a broader class of regressions that encompasses linear and nonlinear regressions with multiple explanatory variables. Regression as a tool helps pool data together to help people and companies make informed decisions.
What is bivariate regression?Bivariate Regression: Bivariate regression is a simple linear regression model which is used to predict one variable (referred to as the outcome, criterion, or dependent variable) from one other variable (referred to as the predictor or independent variable).
What is the difference between multiple and multivariate regression?To summarise multiple refers to more than one predictor variables but multivariate refers to more than one dependent variables.
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What is the main difference between multiple regression and bivariate regression?
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