After checking the stationarity, the next step is to select the appropriate model for our data to predict future observations in order to enhance things. For this purpose, a number of models are available. We will discuss the ARIMA model. ARIMA (Auto Regressive Integrated Moving Average) model, which comprises three parts AR, I, and MA,. […]
Time series analysis is a method of examining a set of data points collected over a period of time. Time series analysis implies analysts recording data points at constant intervals over an established span rather than just periodically or arbitrarily. It is simple to attain using the ts() method and certain parameters. Time series use
Stationarity is a primary principle in time series analysis. A time series is considered to be stationary if its statistical aspects, such as mean, variance, and autocorrelation, remain constant through time. To elucidate, the time series does not reflect trends, seasonality, or other regular patterns that alternate with time. Stationarity is essential in time series
It is a big obstacle to deal with missing values during data analysis. Missing values can occur for a number of reasons like skipped response, incomplete data entry, lost files, data malfunctions, etc. These missing values have consequences like biased analysis, model instability, incorrect inference, and loss of information, and they mostly reduce the statistical
ANCOVA is a statistical term that is used to compare the means of a dependent variable among two or more groups and co-variates. It combines the ANOVA and regression as it contains at least one categorical (factors) and one continuous (covariates) independent variable. Co-variates, known as confounding variables, are continuous independent variables in the model
