Degrees of freedom Central limit theorem Parameters & test statistics Estimation Hypothesis testing Statistical tests Choosing the right test Assumptions for hypothesis testing Correlation Regression analysis t tests ANOVAs Chi-square Effect size Model selection Reporting statistics in APA Interesting topics Pa...
A“variable” in algebra really just means one thing—an unknown value. However, in statistics, you’ll come acrossdozens of types of variables.In most cases, the word still means that you’re dealing with something that’s unknown, but—unlike in algebra—that unknown isn’t always a numb...
In statistics, the sum of squares is used to calculate thevarianceandstandard deviationsof a data set, which are in turn used in regression analysis. Analysts and investors can use these techniques to make better decisions about their investments. Keep in mind, though that using it means you'r...
The laws of statistics imply that accurate measurements and assessments can be made about a population by using a sample.Analysis of variance (ANOVA), linearregression, and more advanced modeling techniques are valid because of thelaw of large numbersand thecentral limit theorem. ...
Finding patterns, trends, and correlations in data sets is the aim of statistical analysis. This is accomplished through the use of numerous statistical techniques and methodologies, including regression analysis, hypothesis testing, and descriptive statistics. These results can be utilized to anticipate...
In inferential statistics, linear regression is the most often employed type of regression. The dependent variable’s response to a unit change in the independent variable is examined through linear regression. These are a few crucial equations for regression analysis using inferential statistics:...
Linear regression modelOne of the most promising applications of the methodology of imprecise probabilities in statistics is the reliable analysis of interval data (or more generally coarsened data). As soon as one refrains from making strong, often unjustified assumptions on the coarsening process, ...
Augustin, Statistical modeling under partial identifi- cation: Distinguishing three types of identification regions in regression analysis with interval data, International Journal of Approximate Reason- ing 56 (2015) 224-248.G. Schollmeyer, T. Augustin. Statistical modeling un- der partial ...
and inferential statistics, which uses those properties to test hypotheses and draw conclusions. Descriptive statistics include mean (average), variance,skewness, andkurtosis. Inferential statistics include linear regression analysis, analysis of variance (ANOVA), logit/Probit models, and null hypothesis te...
Regression This is the model that is used the most in statistical analysis.Use itwhen you want to decipher patterns in large sets of data and when there's a linear relationship between the inputs. This method works by figuring out a formula, which represents the relationship between all the...