Most stats applications are expensive, annoying to install and difficult to use.
People need good quality educational materials to help them understand statistical data analysis and interpret the results.
We provide an online no-code statistical analysis application where instructors and their students can perform statistical models in a couple of clicks anytime anywhere.
We provide the just-in-time stat education: Users can consume good quality of online materials such as videos, blogs and articles, and apply their learnings immediately on the platform.
MagicStat - an online no-code statistical analysis platform
MagicStat allows users to run statistical models within a couple of clicks in a code-free and user-friendly graphical interface environment anytime, anywhere. This way, they can focus what their obtained results MEANT, rather than how to get them in the first place.
Our Statistical Models
CORRELATION
Pearson correlation: A widely-used parametric test that measures the strength and direction of the relationship between linearly related variables and is the appropriate correlation analysis when two measured variables are normally distributed.
Spearman’s correlation: A non-parametric test that is used to measure the degree of association between two variables. It is the appropriate correlation analysis when the variables are measured on a scale that is at least ordinal.
Kendall correlation: A non-parametric test that measures the strength of dependence between two variables.
CHI-SQUARE
Chi-Square Goodness-of-Fit Test: Used to determine whether sample data are consistent with a hypothesized distribution when you have one categorical variable from a single population.
Chi-Square Goodness Test for Independence: Used to determine whether there is a significant association between two categorical variables from a single population.
t-TEST
Independent Samples t-test: Parametric method that compares the means of two independent groups in order to determine whether there is statistical evidence that the associated population means are significantly different.
Paired Samples t-test: Used to determine whether there is statistical evidence that the mean difference between paired observations on a particular outcome is significantly different from zero.
One Sample t-test: A parametric test that determines whether the sample mean is statistically different from a known or hypothesized population mean.
REGRESSION
Logistic Regression (Logit): Predictive analysis used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables.
Linear Regression: Used to summarize and study relationships between two continuous (quantitative) variables.
ANOVA
One-Way Between Subjects ANOVA (One-Way Non-repeated Measures ANOVA): Used to determine whether there are any statistically significant differences between the means of two or more independent (unrelated) groups.
Two-Way Between Subjects ANOVA (Factorial Non-repeated Measures ANOVA): Used to determine whether there are any statistically significant differences between the means of two or more independent (unrelated) groups of the given two factors.
One-Way Within Subjects ANOVA (One-Way Repeated Measures ANOVA): Used to compare three or more groups means where the participants are the same in each group and one factor is repeatedly tested.
Two-Way Within Subjects ANOVA (Factorial Repeated Measures ANOVA): There are two within-subjects factors that are repeatedly tested and used to compare three or more group means where the participants are the same in each group.
Two-Way Mixed ANOVA (Factorial Between Subjects and Within Subjects ANOVA): There are a between-subjects factor and a within-subjects factor which is used to compare three or more group means of two factors where the participants are the same in each group.