Regression Analysis in Inferential Statistics
Regression analysis is a statistical technique used to understand the relationship between variables. It quantifies how a dependent variable changes with respect to one or more independent variables. There are several types of regressions, including simple linear, multiple linear, logistic, and ordinal regression.
Types of regression
- Simple Linear Regression: This is the most basic form of regression, involving two variables: one independent variable and one dependent variable. It assumes that there is a linear relationship between the two variables.
- Multiple Linear Regression: This type of regression involves more than one independent variable. It is used when there are multiple factors that may influence the dependent variable.
- Logistic Regression: Unlike linear regression, logistic regression is used when the dependent variable is binary (i.e., it has only two possible outcomes). It models the probability of the dependent variable belonging to a particular category.
- Ordinal Regression: This type of regression is used when the dependent variable is ordinal, meaning it has ordered categories. It models the probability of the dependent variable falling into a particular category or higher.
What is Inferential Statistics?
In the world of data analysis, statistics plays a big role in helping us understand patterns and insights from raw data. Descriptive statistics help us summarize and describe data, while inferential statistics take us a step further by letting us make predictions and decisions about a larger group based on a smaller sample.
In this article, we’ll dive into inferential statistics, looking at why it’s important, how it works, and where it’s used.
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