Time series variables and Insights

Time series variables and Insights

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• Discrete variables - Discrete data is information that can only take certain values. Discrete data refers to individual and countable items (discrete variables). Countable, Point in time data (Bank balance). Looks like clusters, points. The number of customers who bought different items. The number of computers in each department. The number of items you buy at the grocery store each week
• Continuous Variables - Continuous data is data that can take any value. Takes any measured value within a specific range. Height, weight, temperature and length are all examples of continuous data. Some continuous data will change over time. Looks like line graphs, continuous.
• Univariate analysis is the simplest form of data analysis where the data being analyzed contains only one variable
• Bivariate data – This type of data involves two different variables. The analysis of this type of data deals with causes and relationships and the analysis is done to find out the relationship among the two variables.
• Multivariate analysis is the analysis of three or more variables.
• Multiple linear regression
• Multiple logistic regression
• Multivariate analysis of variance (MANOVA)
• Factor analysis
• Cluster analysis
• The aim of multivariate analysis is to find patterns and correlations between several variables simultaneously
• Simple regression pertains to one dependent variable and one independent variable
• Multiple regression (aka multivariable regression) pertains to one dependent variable and multiple independent variables
• Multivariate regression pertains to multiple dependent variables and multiple independent variables
• A stationary (time) series is one whose statistical properties such as the mean, variance and autocorrelation are all constant over time. Hence, a non-stationary series is one whose statistical properties change over time.

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