**MagicStat**, 1.0.9, Copyright © 2019 Merjek, Inc.

**We turn ***data* into *information*

**We turn ***questions* into *answers*

**What is MagicStat?**

#### MagicStat is like all the other stats software packages out there, except, you know, *better*. We help you explore and analyze your data in a minute. Yes, it’s like Magic.

**Ingredients of MagicStat**

- Magic
- Other stuff

#### (if you must know) MagicStat is built on Python and Django.

**Our vision**

#### We believe that if you have a research question, you shouldn’t need years of statistics education to answer it. Let us help you.

**Benefits of using MagicStat**

*Cost-effective data analysis:* MagicStat is a **free** data analysis tool. That’s why you’re here, right?
*No deep statistics knowledge needed:* You do the stuff you’re good at; let us do the stuff we’re good at.
*Easy access via any web browser, anytime, anywhere:* Chrome, FireFox, Safari, and Microsoft Edge.
*No coding needed:* Our interface is entirely pointy-clicky, so no need to code any commands like those other packages.
*Rich visual analytics integration:* Ooooh, pretty!
*Data import/export support:* We let you import and export your data in well-known file formats on our system:
- Comma-separated (.csv)
- Microsoft Excel (.xls, .xlsx)
- More data types coming soon...

**The current supported statistical models**

**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 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 Independance:** Used to determine whether there is a significant association between two categorical variables from a single population.
**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
**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
**One-Way Between Subjects ANOVA:** Used to determine whether there are any statistically significant differences between the means of two or more independent (unrelated) groups.
**More Models Coming Soon**

**Data support**

#### At the moment, we support structured data up to 500 variables and 1,000,000 data points.

**Data Privacy**

#### Your data is just that: __your__ data. We __never__ use the data you upload for any purpose other than your analysis. In fact, we destroy any data you upload after you leave the site. Yeah, we take this seriously.