OpenLLM-France/wikimedia huggingface dataset:
# Statistics Ground Zero/Significance
## Significance

In statistical testing we deal in probabilities. To ask our research question in a statistically testable way is to ask
    If the null hypothesis is true, how likely is it that I would observe the data that I have collected?
Put slightly more technically
    The p value represents the probability of seeing data this extreme if the null hypothesis were true
We set a threshold, most commonly 99% or 95%, meaning that we acknowledge that we might be misled into rejecting the null hypothesis 1% or 5% of the time respectively. P must fall below this threshold for us to reject the null hypothesis. That is to say p must be less than 0.01 or less than 0.05 (the inverse of 99% and 95% expressed as decimals).
This value, the p value, is said to determine whether the outcome of a test is significant or not. If the outcome is significant then the null hypothesis is rejected.

### Choosing a test

Very often people find step three above - choosing the correct test - the most difficult, but if we know what we want to do and something about the nature of our data it is not so very difficult. The following table covers a surprisingly large number of common cases.
| Question | Measure of Dependent Variable | Two Variables or Groups | More than Two Variables or Groups | Parametric | Non-parametric |
| Is there an association? | Nominal | Yes | | | Chi-square |
| Is there an association? | Ordinal | Two | | | Spearman's correlation coefficient (with an indication of strength) |
| Is there an association? | Scalar | Two | | Pearson's correlation coefficient (with an indication of strength) | |
| Are the means or medians the same? | Scalar | Two | | Student's T-test | Mann-Whitney U-test |
| Are the means or medians the same? | Scalar | | More than two | Analysis of Variance (ANOVA) | Kruskal-Wallis |
| Can I predict one from another? | Scalar | Two | More than two independent | Regression or multiple regression | |

### One or two tails?

When we formulate our hypothesis involving the comparison of values for a parameter or statistic, we choose whether to ask the question in one of two ways. We might simply ask are the values different or we might ask is one value smaller (or greater) than the other. In the first case we will determine the outcome using a two tailed test and in the second case, using a one tailed test.

## Notes

* ↑ This is not true: it is possible to test the assocation of more than two nominal variables but the design is complicated

## Contents

1 Introduction
2 Statistical Measure
3 Parametric and Non-parametric Methods
4 Descriptive Statistics
5 Inferential Statistics: hypothesis testing
6 Degrees of freedom
7 Significance
8 Association
9 Comparing groups or variables
10 Regression
# Statistics Ground Zero/Regression
## Regression

Regression analysis is the process of building a model of the relationship between variables in the form of mathematical equations. The general purpose is to explain how one variable, the dependent variable, is systematically related to the values of one or more independent variables. The independent variable is so called because we imagine its value varying freely across its range while the dependent variable is dependent upon the values taken by the independent. The mathematical function is expressed in terms of a number of parameters that are the coefficients of the equation, and the values of the independent variable. The coefficients are numeric constants by which variable values in the equation are multiplied or which are added to a variable value to determine the unknown. A simple example is the equation for the line
$y=mx+b$
Here, by convention, x and y are the variables of interest in our data with y the unknown or dependent variable and x the known or independent variable. The constant m is slope of the line and b is the y-intercept - the value where the line cross the y axis. So, m and b are the coefficients of the equation.
If we can build a robust regression model of known data then we can use the equation to predict the values for unobserved cases. Regression also involves the estmation of the strength of the association between the dependent and independent variables, most often through the computation of the correlation coefficient which as noted above is itself part of a linear model of the data. The correlation coefficient is squared in reporting a regression analysis and we call it, perhaps rather obviously, R squared.
Models are often only approximately like the observed data. Most often some error in the data will mean that no mathematical function produces exactly the data observed and only that data. Therefor we are explicitly involved in estimation and our models involve recognition of error. It is for this reason that the equation for the line is often given as
${\hat {y}}=mx+b\pm \epsilon$
Where ε quantifies the error in our data.

### Linear regression

In linear regression the model consists of linear equations. A linear equation is an equation involving only constants or single variable values multiplied by a constant. The variable values in a linear equation must be of the first power, that is they cannot involving raising a value to a power other than one - they cannot for example be squared or cubed. Any value raised to the power of one is just the original value: x$^1$=x.
To carry out a linear regression, first make a scatter plot of your data.
Let us look again at the following measurements of baby height against age:
| Age (months | Height (cm) |
| 0 | 53.0 |
| 3 | 59.5 |
| 6 | 66.0 |
| 9 | 71.5 |
| 12 | 76.0 |
| 18 | 85.0 |
| 24 | 90.0 |
This data can be visualised in this scatterplot:

## Contents

1 Introduction
2 Statistical Measure
3 Parametric and Non-parametric Methods
4 Descriptive Statistics
5 Inferential Statistics: hypothesis testing
6 Degrees of freedom
7 Significance
8 Association
9 Comparing groups or variables
10 Regression
# Statistics Ground Zero/Parametric and Non-parametric Methods
## Parametric and Non-parametric Methods

Before looking at some statistics, we should take note of this important distinction in statistical testing. It becomes crucial when we discuss inference below, but I introduce it here because of the relevance of descriptive statistics.
The terms parametric and non-parametric refer to statistical methods. Parametric methods make assumptions about your data set - specifically about how values are distributed. Non-parametric methods make relatively few assumptions about the data. As a consequence, parametric methods have more information to draw on in reasoning about data than non-parametric methods. If parametric methods are available they are more powerful; non-parametric methods (they are often referred to as conservative) are less powerful.
The assumptions are about the parameters of the dataset (hence the name). These parameters cover the location of the values; the dispersion of the values across the metric; the shape of the frequency distribution of the values, that is to say the central tendency, range, variance, skewness and kurtosis.
It is common to use a Gaussian or normal distribution as a reference point for these parameters and to describe other distributions where they deviate from this.
Before you can analyse your data, you will need to determine if the variables of interest have normally distributed scores, or at least close to normally distributed, and thus whether to use parametric or non-parametric methods.
If necessary you can sometimes transform a variable so that the values are normally distributed but I will not cover this here - this kind of transformation is outside the scope of an emergency guide.

### Checking data for normality

You can check whether data are normally distributed using a Q-Q plot.
A Q-Q plot plots the quantiles of one data set against another - usually against a known distribution. For present purposes then you plot your data against a normally distributed variable. If the variables are both normally distributed then the points should converge strongly around the line x=y. You can also check for normality using a Kolmogorov-Smirnov Test. This is a non-parametric test where the null hypothesis is that your data represent a normally distributed random variable and so if the result of this test is not significant you can assume your data are normally distributed.

## Contents

1 Introduction
2 Statistical Measure
3 Parametric and Non-parametric Methods
4 Descriptive Statistics
5 Inferential Statistics: hypothesis testing
6 Degrees of freedom
7 Significance
8 Association
9 Comparing groups or variables
10 Regression
# Statistics Ground Zero/Inferential Statistics: hypothesis testing
## Inferential Statistics: hypothesis testing

Inferential statistics draws conclusions about a population on the basis of data from a sample. In general we set out to
* Generate a research hypothesis
* Cast this as a statistical hypothesis
* Decide which test statistic can test the hypothesis and compute the statistic
* Determine the significance of the statistic by examining its p value
* Evaluate the statistical hypothesis
A statistical hypothesis is tested by first constructing the null hypothesis. Typically the null hypothesis is of the form there is no difference between these variables or groups or there is no association between these variables, one does not affect the value of the other. The alternative hypothesis is that there is an association or difference. We hope that if we design our study carefully and formulate the hypotheses carefully then rejection of the null hypothesis will compel us to accept the alternative.

## Contents

1 Introduction
2 Statistical Measure
3 Parametric and Non-parametric Methods
4 Descriptive Statistics
5 Inferential Statistics: hypothesis testing
6 Degrees of freedom
7 Significance
8 Association
9 Comparing groups or variables
10 Regression
# Statistics Ground Zero/Degrees of freedom
## Degrees of freedom

This is a good point to introduce the idea of degrees of freedom (df). This notion causes some anxiety but there is no reason for this in practical circumstances where good statistical software will compute the degrees of freedom for you.
Let us consider an example: to compute the variance I first sum the square deviations from the mean. The mean is a parameter: it is a characteristic of the variable under examination as a whole and is part of describing the overall distribution of values. If you know all the parameters you can accurately describe the data. The more parameters you know, that is to say the more you fix, the fewer samples fit this model of the data. If you know only the mean, there will be many possible sets of data that are consistent with this model but if you know the mean and the standard deviation, fewer possible sets of data fit this model.
So in computing the variance I had first to calculate the mean. When I have calculated the mean, I could vary any of the scores in the data except for one. If I leave one score unexamined it can always be calculated accurately from the rest of the data and the mean itself. Maybe an example can make this clearer.
I take the ages of a class of students and find the mean. If I fix the mean, how many of the other scores (there are N of them remember) could still vary? The answer is N-1. There are N-1 independent pieces of information that could vary while the mean is known. These are the degrees of freedom. One piece of information cannot vary because its value is fully determined by the parameter (in this case the mean) and the other scores. Each parameter that is fixed during our computations constitutes the loss of a degree of freedom.
If we imagine starting with a small number of data points and then fixing a relatively large number of parameters as we compute some statistic, we see that as more degrees of freedom are lost, fewer and fewer different situations are accounted for by our model since fewer and fewer pieces of information could in principle be different from what is actually observed.
So, the interest, to put it very informally, in our data is determined by the degrees of freedom: if there is nothing that can vary once our parameter is fixed (because we have so very few data points - maybe just one) then there is nothing to investigate. Degrees of freedom can be seen as linking sample size to explanatory power.

## Contents

1 Introduction
2 Statistical Measure
3 Parametric and Non-parametric Methods
4 Descriptive Statistics
5 Inferential Statistics: hypothesis testing
6 Degrees of freedom
7 Significance
8 Association
9 Comparing groups or variables
10 Regression
# US Income Tax

    AMENDMENT XVI. The Congress shall have power to lay and collect taxes on incomes, from whatever source derived, without apportionment among the several states, and without regard to any census or enumeration.
This book covers federal income tax in the United States. It approaches the tax system from a legal perspective, not an accounting perspective. Rather than show how to calculate tax liability, it shows which factors lead to tax liability, and how different types of transactions are treated by the Internal Revenue Code.

## Contents

* Introduction
* Income
* Gains and Losses
* Capital Gains and Losses
* Business Deductions
* Personal Deductions
* Tax Liability
* Tax Credits
# US Internet Law/Preface

This is an attempt to build a Wikibook textbook on Internet law. Lots of work needs to be done, but here is a start.
Internet Law: Table of Contents · Preface · Introduction · Defamation · Defamation - General · Section 230 · Copyright · Copyright - General · Secondary Liability · Fair Use · DMCA · DMCA Safe Harbor · DMCA Anti-Circumvention · Trademark · Domain Names · Content Regulation · Online Anonymity · Communications Decency Act · Online Contracts · The Hold Harmless Clause in User Agreements · Clickwrap Agreements · UCITA · Privacy · ECPA · SCA ·
# US Internet Law/Trademark

A Trademark is a word, phrase, device, color or sound that specifically identifies certain goods or services to the exclusion of all others of the same or similar goods or services identified by the trademark. The Trademark Act of 1946 (the “Lanham Act”) (15 U.S.C. §§ 1051-1127) was enacted to ensure that consumers are able to rely on marks in exercising their preferences, by prohibiting competitors from using marks in a manner that confuses consumers about the source of a product or service. A trademark could be applied to
* a brand of software used on or sold over the Internet, such as Apache for the web server or Linux for the operating system.
* a particular service available over the Internet, such as the Yahoo web portal, the eBay resale service, or the Amazon book store.
Internet Law: Table of Contents · Preface · Introduction · Defamation · Defamation - General · Section 230 · Copyright · Copyright - General · Secondary Liability · Fair Use · DMCA · DMCA Safe Harbor · DMCA Anti-Circumvention · Trademark · Domain Names · Content Regulation · Online Anonymity · Communications Decency Act · Online Contracts · The Hold Harmless Clause in User Agreements · Clickwrap Agreements · UCITA · Privacy · ECPA · SCA ·
# US Internet Law/Secondary Liability

Secondary liability can consist of contributory or vicarious liability.
Contributory Infringement - a person with knowledge of the infringing activity induces causes or materially contributes to the conduct of another to infringe.
Vicarious Liability - a person or entity has the right and ability to control the activities of the person directly infringing and receives financial benefit from those activities – even if the entity or person is unaware of the activity.
Internet Law: Table of Contents · Preface · Introduction · Defamation · Defamation - General · Section 230 · Copyright · Copyright - General · Secondary Liability · Fair Use · DMCA · DMCA Safe Harbor · DMCA Anti-Circumvention · Trademark · Domain Names · Content Regulation · Online Anonymity · Communications Decency Act · Online Contracts · The Hold Harmless Clause in User Agreements · Clickwrap Agreements · UCITA · Privacy · ECPA · SCA ·
# US Tort Law

* Welcome to the Wikibook on
* US TORT LAW
* This book is about United States Tort Law, its history, development and current state. The book is designed to complement the Wikiversity course of the same name, but can be read on its own. Each chapter begins with an outline of the topic because an understanding of the principles of tort law is necessary to place the topic in context. Where appropriate each chapter will highlight relevant case law and statutes, which may include links to other resources within the world of Wikimedia. Amongst the supplementary material there is also a glossary of legal terms.
* Part 1: Topics
* Overview  (2017-04-17)
* Introduction  (2017-04-17)
* Causes of Action  (2017-04-17)
* Ordinary Person of Reasonable Prudence  (2017-04-17)
* Wrongful Death  (2017-04-17)
* Part 2: Appendices
* Bibliography  (2017-04-17)
* Glossary  (2017-04-17)
* Style Guide and Authors  (2017-04-17)
* This book is dual licensed under the GFDL and Creative Commons Attribution-ShareAlike 3.0 Unported License.
# US Tort Law/Causes of Action

In this chapter of the Torts Casebook, we look at Brown v. Kendall and the concept of a Cause of Action.

### Sources

* Neil Wehneman's podcast on this topic:
* The Brown v. Kendall case:
# US Internet Law/UCITA

The Uniform Computer Information Transactions Act (UCITA)is the most complete attempt so far to bring standardization to the law of computer information transactions. In July, 1999, the National Conference of Commissioners on Uniform State Laws “NCCUSL”, after 10 years of work, approved the UCITA provisions and recommended that UCITA be enacted in all 50 states.
In the past, the American Law Institute “ALI” had worked with NCCUSL to study and draft new uniform state laws, however in the case of UCITA, ALI pulled out of the project because of concerns about the proposed act’s scope and its overall coherence and clarity as well as its treatment of customer rights. In fact ALI members had called for “fundamental revisions” to the proposed Act.
Prior to UCITA, and in states where it or other laws have not been adopted, the Uniform Commercial Code "UCC", still governs software sales. The UCC was developed in the 1940s and 1950s to standardize laws for the sale of goods across the U.S.
States must vote whether or not to adopt UCITA without change, with change or not at all. Two states have adopted UCITA with some modifications; the first was Maryland in 2000 and the second, Virginia in 2001. In subsequent years, not one other state has adopted UCITA. In fact four states have passed anti-UCITA laws (Iowa, North Carolina, West Virginia and Vermont). Massachusetts may be the next to pass what has come to be known as “bomb-shelter” legislation, preventing vendors from applying UCITA laws within the state.
UCITA is a proposed uniform law that would create new rules for software licensing, online access and other transactions in computer information. UCITA was originally a joint project of the American Law Institute (ALI) and the National Conference of Commissioners on Uniform State Laws. Both organizations have withdrawn support.
Internet Law: Table of Contents · Preface · Introduction · Defamation · Defamation - General · Section 230 · Copyright · Copyright - General · Secondary Liability · Fair Use · DMCA · DMCA Safe Harbor · DMCA Anti-Circumvention · Trademark · Domain Names · Content Regulation · Online Anonymity · Communications Decency Act · Online Contracts · The Hold Harmless Clause in User Agreements · Clickwrap Agreements · UCITA · Privacy · ECPA · SCA ·
# Structural Biochemistry/Functional Categories of Eukaryotic Organelles

All the different membranous organelles perform different tasks in the cell (animal cells and plant cells). However, they also communicate with each other though ions and lipids. In addition, some of the organelles have membrane that are connected. The others are related by transfer of membrane segments. As we know, structures determine functions in a lot of ways in nature. We can categorize eukaryotic organelles into four categories according to their general functions.

### Manufacturer

In this first category, nucleus, ribosomes, rough ER, smooth ER and golgi apparatus are included. They can perform this task because of they have metabolically active membranes. Some of them can also perform transportation of the products. Below is some examples of the manufacturing task these organelles do.
* Nucleus--DNA and RNA synthesis
* Ribosome--Polypeptide(protein) synthesis
* Rough ER--Membrane protein synthesis, secretory proteins synthesis and hydrolytic enzymes synthesis; formation of transport vesicles
* Smooth ER--Lipid synthesis; detoxification and carbohydrate metabolism in liver cells
* Golgi apparatus--Modificaiton, temporary storage and transportation of macromolecules such as proteins; formation of lysosomes and transport vesicles

### Waste disposer

In this category, lysosomes, peroxisomes and vacuoles are included. They are all consist of single membrane sac that can be used to collect materials and/or break down unwanted/harmful materials.
* Lysosomes--Digestion of bacteria, damaged organelle
* Peroxisomes--breakdown of H$_2$O$_2$ by-product
* Vacuoles--Digestion of waste products; collect excess water from the cell

### Energy provider

In this category, chloroplasts and mitochondria are included. They both have metabolically active membranes and intermembrane compartments that can convert energy units to provide to the cells.
* Chloroplast--Conversion of light energy to chemical energy in plant cells
* Mitochondria--Conversion of chemical energy of food to chemical energy of ATP

### Support, Movement and Communication between cells

In this last category, cytoskeleton(such as cilia, flagella and centrioles in animal cells), cell walls(in plants, fungi and some protists), extracellular matrix(in animal cells) and cell junctions are included. They are all responsible for the keeping cells in shape.
* Cytoskeleton--Maintenance of cell shape; movement of organelles within cells; cell movement; mechanical transmission of signals from exterior of cell to interior
* Cell walls--Maintenance of cell shape and skeletal support; surface protection; binding of cells in tissue
* Extracellular matrix--Binding of cells in tissues; surface protection; regulation of cellular activities
* Cell junctions--Communication between cells; binding of cells in tissues