Which model is most appropriate for the data set?
If the data lies on a straight line, or seems to lie approximately along a straight line, a linear model may be best. If the data is non-linear, we often consider an exponential or logarithmic model, though other models, such as quadratic models, may also be considered.
How to choose a model for data?
How to Choose a Machine Learning Model – Some Guidelines
- Collect data.
- Check for anomalies, missing data and clean the data.
- Perform statistical analysis and initial visualization.
- Build models.
- Check the accuracy.
- Present the results.
Which is the best model to model the data?
If the data lies on a straight line, or seems to lie approximately along a straight line, a linear model may be best. If the data is non-linear, we often consider an exponential or logarithmic model, though other models, such as quadratic models, may also be considered.
Which is the best representative measure of a continuous variable?
If continuous variable follows normal distribution, mean is the representative measure while for non-normal data, median is considered as the most appropriate representative measure of the data set. Similarly in the categorical data, proportion (percentage) while for the ranking/ordinal data, mean ranks are our representative measure.
Which is the best method for data analysis?
Two main statistical methods are used in data analysis: descriptive statistics, which summarizes data using indexes such as mean and median and another is inferential statistics, which draw conclusions from data using statistical tests such as student’s t-test.
When to use regression or t-test in data analysis?
Selection of statistical test depends upon our aim and objective of the study. Suppose our objective is to find out the predictors of the outcome variable, then regression analysis is used while to compare the means between two independent samples, unpaired samples t-test is used. Type and distribution of the data used