OpenLLM-France/wikimedia huggingface dataset:
# Modern Benoni

Knight c3 to support the central pawn advance of e4.

## Theory table

For explanation of theory tables, see theory table and for notation, see algebraic notation. .
1. d4 Nf6 2. c4 c5 3. d5 e6 4.Nc3
| | 4 | 5 | 6 | 7 | 8 |
| Modern Benoni | Nc3 exd5 | cxd5 d6 | e4 g6 | Nf3 Bg7 | Be2 O-O | = |
| Czech Benoni | ... d6 | e4 Be7 | g3 O-O | Bg2 Ne8 | Nge2 Nd7 | += |
When contributing to this Wikibook, please follow the Conventions for organization.

## References

* Modern Chess Openings 15th ed, 2008. Nick de Firmian. Random House, New York. ISBN 0-8129-3682-5.
# Old Indian Defence
### 2...d6

This is the so-called "Old Indian," and it can either transpose into a KID or follow less popular lines with a strategy similar to the KID, such as the Tartakower-Indian.

## Theory table

For explanation of theory tables, see theory table and for notation, see algebraic notation. .
1. d4 Nf6 2. c4 d6
| | 3 | 4 | 5 | 6 |
| | Nc3 e5 | Nf3 Nbd7 | e4 g6 | Be2 Bg7 | +/= |
| King's Indian Defence | Nf3 g6 | Nc3 Bg7 | e4 O-O | | +/= |
| Tartakower-Indian | ... Bg4 | Nc3 Nbd7 | | | +/= |
When contributing to this Wikibook, please follow the Conventions for organization.

## References

* Kasparov, Garry, & Keene, Raymond 1989 Batsford chess openings 2. ISBN 0-8050-3409-9.
* This Chess related article is a stub. Please assist Wikibooks by expanding it.
# Old Indian
### 3. Nc3
## Theory table

For explanation of theory tables, see theory table and for notation, see algebraic notation. .
1. d4 Nf6 2. c4 d6 3. Nc3
| 3 |
| ... Nbd7 | Nf3 | +/= |
| ... e5 | Nf3 | +/= |
| ... g6 | e4 | +/= |
When contributing to this Wikibook, please follow the Conventions for organization.

## References

* Kasparov, Garry, & Keene, Raymond 1989 Batsford chess openings 2. ISBN 0-8050-3409-9.
* This Chess related article is a stub. Please assist Wikibooks by expanding it.
# Chess Opening Theory/1. d4/1...Nf6/2. c4/2...e5/3. dxe5
## Theory table

For explanation of theory tables, see theory table and for notation, see algebraic notation.
1.d4 Nf6 2.c4 e5 3.dxe5
| | 3 | 4 | 5 | 6 | 7 |
| Fajarowicz gambit | ... Ne4 | Nf3 Nc6 | a3 d6 | Qc2 Bf5 | Nc3 Nxf2 | += |
| Adler variation | ... Ng4 | Nf3 Bc5 | e3 Nc6 | Be2 O-O | O-O Re8 | += |
| Rubinstein variation | ... ... | Bf4 Nc6 | Nf3 Bb4+ | Nbd2 Qe7 | e3 Ngxe5 | += |
| | ... ... | ... ... | ... ... | Nc3 Bxc3+ | bxc3 Qe7 | += |
| | ... ... | ... g5 | Bg3 Bg7 | Nf3 Nc6 | Nc3 Ngxe5 | += |
| Alekhine variation | ... ... | e4 Nxe5 | f4 Nec6 | a3 a5 | Be3 Na6 | += |
# Biostatistics with R/Import
## Why R for biostatistics?

R is superior to common statistical packages such as SPSS, SAS and MINITAB because it is
* powerful
* available for many platforms (Mac OS X, Windows, Linux etc.)
* programmable
* non-commercial
* extensively documented

## Obtaining R/Installation

You may refer to R FAQ

## Data Import

The format of data set available in Wiley's website are CSV, Excel, MINITAB, SAS and SPSS. Although you can import the data saved in Excel, SAS and SPSS into R using the foreign package, you should download the data in CSV format. It is because CSV is the easiest one to process in R.
For example, you would like to import the "Large Data set" data file. The downloaded data file (LDS_C02_NCBIRTH800.csv) , assuming stored in the directory "/desktop",can be imported into R as a data.frame called "largedataset" using following syntax:
if you prefer to choose the data file using the standard "point-and-click" GUI way, you may use the function file.choose, i.e.
Now, you should imported the data from the CSV to a data frame called "largedataset". You may try to look inside the data frame by calling its name
You can access the variable (in computer lingo, column) "sex" inside the largedataset dataframe by
For example, you want to count the frequency of sex
You can attach the data frame so that you can call the variable directly

## Basic data management

R is designed to be a analysis system instead of a integrated environment such as SPSS. Unlike SPSS, R doesn't have a spreadsheet-like environment for data input. Usually data are entered using different software (e.g. database, spreadsheet software such as OO.o Calc) and then imported to R as described above. For quick one-off calculations, you can do the data entry in R. For example, if you want to calculate the mean age of ten patients (30,31,32,34,35,36,37,30,40,45) you can enter the data into R using the c function.
You may call the newly created object pt_age by its name...
...and then calculate the mean age of the ten patients.
# Biostatistics with R/Multiple Regression and Correlation
## Summary of Formulars with R

| Formular Number | Name | Formular | Formular with R |
| 10.2.1 | Representation of the multiple linear regression equation | Example | Example |
| 10.2.2 | Representation of the multiple linear regression equation with two independent variables | Example | Example |
| 10.2.3 | Random deviation of a point from a plane when there are two independent variables | Example | Example |
| 10.3.1 | Sum-of-squared residuals | Example | Example |
| 10.4.1 | Sum-of-squares equation | Example | Example |
| 10.4.2 | Coefficient of multiple determination | Example | Example |
| 10.4.3 | t statistic for testing hypotheses about b i | Example | Example |
| 10.5.1 | Estimation equation for multiple linear regression | Example | Example |
| 10.5.2 | Confidence interval for the mean of Y for a given X | Example | Example |
| 10.5.3 | Prediction interval for Y for a given X | Example | Example |
| 10.6.1 | Multiple correlation model | Example | Example |
| 10.6.2 | Multiple correlation coefficient | Example | Example |
| 10.6.3 | F statistic for testing the multiple correlation coefficient | Example | Example |
| 10.6.4–10.6.6 | Partial correlation between two variables (1 and 2) after controlling for a third (3) | Example | Example |
| 10.6.7 | t statistic for testing hypotheses about partial correlation coefficients | Example | Example |
| Symbol Key | | | |
# Biostatistics with R/Analysis of Variance
## Summary of Formulars with R

| Formular Number | Name | Formular | Formular with R |
| 8.2.1 | One-way ANOVA model | Example | Example |
| 8.2.2 | Total sum-of-squares | Example | Example |
| 8.2.3 | Within-group sum-of-squares | Example | Example |
| 8.2.4 | Among-group sum-of-squares | Example | Example |
| 8.2.5 | Within-group variance | Example | Example |
| 8.2.6 | Among-group variance I | Example | Example |
| 8.2.9 | Tukey’s HSD (equal sample sizes) | Example | Example |
| 8.2.10 | Tukey’s HSD (unequal sample sizes) | Example | Example |
| 8.3.1 | Two-way ANOVA model | Example | Example |
| 8.3.2 | Sum-of-squares representation | Example | Example |
| 8.3.3 | Sum-of-squares total | Example | Example |
| 8.3.4 | Sum-of-squares block | Example | Example |
| 8.3.5 | Sum-of-squares treatments | Example | Example |
| 8.3.6 | Sum-of-squares error | Example | Example |
| 8.4.1 | Fixed-effects, additive single-factor, repeated-measures ANOVA model | Example | Example |
| 8.4.2 | Fixed-effects, additive two-factor, repeated-measures ANOVA model | Example | Example |
| 8.5.1 | Two-factor completely randomized fixed-effects factorial model | Example | Example |
| 8.5.2 | Probabilistic representation of a | Example | Example |
| 8.5.3 | Sum-of-squares total I | Example | Example |
| 8.5.4 | Sum-of-squares total II | Example | Example |
| 8.5.5 | Sum-of-squares treatment partition | Example | Example |
| Symbol Key | | | |
# Biostatistics with R/Biostatistics with R authors
## License

The text of this book is released under the terms of the Creative Commons Attribution-ShareAlike 3.0 and GNU Free Documentation License. The particular version of that license that is being used can be found at:
    Wikibooks:Creative Commons Attribution-ShareAlike 3.0 Unported License
    Wikibooks:GNU Free Documentation License
Images used in this document are available under various licenses. Clicking on the image will take you to a description page where the licensing information is displayed.

## Authors
### List

* Hanjin Deviasse
** First authorship: Sep, 2015
** Contributions:
* Chainsawriot
** First authorship:2011
** Contributions: 1 page
# Biostatistics with R/Estimation
## Summary of Formulars with R

| Formular Number | Name | Formular | Formular with R |
| 6.2.1 | Expression of an interval estimate | estimator ± (reliability coefficient)× standard error of the estimator | Example |
| 6.2.2 | Interval estimate for $\mu$ when $\sigma$ is known | ${\bar {X}}\pm z_{(1-\alpha /2)}\sigma _{\bar {x}}$ | Example |
| 6.3.1 | t-transformation | $t={\frac {{\bar {x}}-\mu }{s/{\sqrt {n}}}}$ | Example |
| 6.3.2 | Interval estimate for $\mu$ when $\sigma$ is unknown | ${\bar {X}}\pm t_{(1-\alpha /2)}{\frac {s}{\sqrt {n}}}$ | Example |
| 6.4.1 | Interval estimate for the difference between two population means when $\sigma _{1}$ and $\sigma _{2}$ are known | $({\bar {x}}_{1}-{\bar {x}}_{2})\pm z_{(1-\alpha /2)}{\sqrt {{\frac {\sigma _{1}^{2}}{n_{1}}}+{\frac {\sigma _{2}^{2}}{n_{2}}}}}$ | Example |
| 6.4.2 | Pooled variance estimate | $s_{p}^{2}={\frac {(n_{1}-1)s_{1}^{2}+(n_{2}-1)s_{2}^{2}}{n_{1}+n_{2}-2}}$ | Example |
| 6.4.3 | Standard error of estimate | $s_{({\bar {X}}_{1}-{\bar {X}}_{2})}={\sqrt {{\frac {s_{p}^{2}}{n_{1}}}+{\frac {s_{p}^{2}}{n_{1}}}}}$ | Example |
| 6.4.4 | Interval estimate for the difference between two population means when s 1 is unknown | $({\bar {x}}_{1}-{\bar {x}}_{2})\pm t_{(1-\alpha /2)}{\sqrt {{\frac {s_{p}^{2}}{n_{1}}}+{\frac {s_{p}^{2}}{n_{1}}}}}$ | Example |
| 6.4.5 | Cochran’s correction for reliability coefficient when variances are not equal | $t'_{(1-\alpha /2)}={\frac {w_{1}t_{1}+w_{2}t_{2}}{w_{1}+w_{2}}}$ | Example |
| 6.4.6 | Interval estimate using Cochran’s correction for t | $({\bar {x}}_{1}-{\bar {x}}_{2})\pm t'_{(1-\alpha /2)}{\sqrt {{\frac {s_{p}^{2}}{n_{1}}}+{\frac {s_{p}^{2}}{n_{1}}}}}$ | Example |
| 6.5.1 | Interval estimate for a population proportion | $$ | Example |
| 6.6.1 | Interval estimate for the difference between two population proportions | $$ | Example |
| 6.7.1–6.7.3 | Sample size determination when sampling with replacement | $$ | Example |
| 6.7.4–6.7.5 | Sample size determination when sampling without replacement | $$ | Example |
| 6.8.1 | Sample size determination for proportions when sampling with replacement | $$ | Example |
| 6.8.2 | Sample size determination for proportions when sampling without replacement | $$ | Example |
| 6.9.1 | Interval estimate for s 2 | $$ | Example |
| 6.9.2 | Interval estimate for s | $$ | Example |
| 6.10.1 | Interval estimate for the ratio of two variances | $$ | Example |
| 6.10.2 | Relationship among F ratios | $$ | Example |
| Symbol Key | | | |
# Biostatistics with R/Hypothesis Testing
## Summary of Formulars with R

| Formular Number | Name | Formular | Formular with R |
| 7.1.1, 7.1.2, 7.2.1 | z-transformation (using either $\mu$ or $\mu _{0}$ ) | $z={\frac {{\bar {x}}-\mu }{\sigma /{\sqrt {n}}}}$ | Example |
| 7.2.2 | t-transformation | $t={\frac {{\bar {x}}-\mu _{0}}{s/{\sqrt {n}}}}$ | Example |
| 7.2.3 | Test statistic when sampling from a population that is not normally distributed | $z={\frac {{\bar {x}}-\mu _{0}}{s/{\sqrt {n}}}}$ | Example |
| 7.3.1 | Test statistic when sampling from normally distributed populations:population variances known | $z={\frac {({\bar {x}}_{1}-{\bar {x}}_{2})-(\mu _{1}-\mu _{2})_{0}}{\sqrt {{\frac {\sigma _{1}^{2}}{n_{1}}}+{\frac {\sigma _{2}^{2}}{n_{2}}}}}}$ | Example |
| 7.3.2 | Test statistic when sampling from normally distributed populations:population variances unknown and equal | Example | Example |
| 7.3.3, 7.3.4 | Test statistic when sampling from normally distributed populations: population variances unknown and unequal | Example | Example |
| 7.3.5 | Sampling from populations that are not normally distributed | Example | Example |
| 7.4.1 | Test statistic for paired differences when the population variance is unknown | Example | Example |
| 7.4.2 | Test statistic for paired differences when the population variance is known | Example | Example |
| 7.5.1 | Test statistic for a single population proportion | Example | Example |
| 7.6.1, 7.6.2 | Test statistic for the difference between two population proportions | Example | Example |
| 7.7.1 | Test statistic for a single population variance | Example | Example |
| 7.8.1 | Variance ratio | Example | Example |
| 7.9.1, 7.9.2 | Upper and lower critical values for � x | Example | Example |
| 7.10.1, 7.10.2 | Critical value for determining sample size to control type II errors | Example | Example |
| 7.10.3 | Sample size to control type II errors | Example | Example |
| 5.5.3 | Continuity correction when x > np | Example | Example |
| 5.6.1 | z-transformation for difference between two proportions | Example | Example |
| Symbol Key | | | |
# Biostatistics with R/Introduction to Biostatistics

REVIEW EXERCISES
1. Explain what is meant by descriptive statistics.
2. Explain what is meant by inferential statistics.
3. Define: (a) Statistics (b)Biostatistics (c) Variable (d)Quantitative variable (e) Qualitative variable (f)Random variable (g) Population (h)Finite population (i) Infinite population (j)Sample (k) Discrete variable (l)Continuous variable (m) Simple random sample (n)Sampling with replacement (o) Sampling without replacement
4. Define the word measurement.
5. List, describe, and compare the four measurement scales.
6. For each of the following variables, indicate whether it is quantitative or qualitative and specify the measurement scale that is employed when taking measurements on each: (a) Class standing of the members of this class relative to each other (b) Admitting diagnosis of patients admitted to a mental health clinic (c) Weights of babies born in a hospital during a year (d) Gender of babies born in a hospital during a year (e) Range of motion of elbow joint of students enrolled in a university health sciences curriculum (f) Under-arm temperature of day-old infants born in a hospital
7. For each of the following situations, answer questions a through e: (a) What is the sample in the study? (b) What is the population? (c) What is the variable of interest? (d) How many measurements were used in calculating the reported results? (e) What measurement scale was used? Situation A. A study of 300 households in a small southern town revealed that 20 percent had at least one school-age child present. Situation B. A study of 250 patients admitted to a hospital during the past year revealed that, on the average, the patients lived 15 miles from the hospital.
8. Consider the two situations given in Exercise 7. For Situation A describe how you would use a stratified random sample to collect the data. For Situation B describe how you would use systematic sampling of patient records to collect the data.
# Biostatistics with R/Descriptive Statistics
## Summary For Formular with R

| Formula Number | Name | Formula | Formula with R |
| 2.3.1 | Class interval width using Sturges’s Rule | $w={\frac {R}{k}}$ | Example |
| 2.4.1 | Mean of a population | $\mu ={\sum _{i=1}^{n}{x_{i}} \over N}$ | Example |
| 2.4.2 | Skewness | $Skewness={\frac {{\sqrt {n}}\sum _{i=1}^{n}\left(x_{i}-{\overline {x}}\right)^{3}}{(\sum _{i=1}^{n}\left(x_{i}-{\overline {x}}\right)^{2})^{\frac {3}{2}}}}={\frac {{\sqrt {n}}\sum _{i=1}^{n}\left(x_{i}-{\overline {x}}\right)^{3}}{(n-1){\sqrt {n-1}}s^{3}}}$ | Example |
| 2.4.2 | Mean of a sample | ${\bar {x}}={\sum _{i=1}^{n}{x_{i}} \over N}$ | Example |
| 2.5.1 | Range | $R=x_{L}-x_{s}$ | Example |
| 2.5.2 | Sample variance | $s^{2}={\frac {1}{n-1}}\sum _{i=1}^{n}\left(x_{i}-{\overline {x}}\right)^{2}$ | Example |
| 2.5.3 | Population variance | $\sigma ^{2}={\frac {1}{N}}\sum _{i=1}^{N}\left(x_{i}-\mu \right)^{2}$ | Example |
| 2.5.4 | Standard deviation | $s={\sqrt {s^{2}}}={\sqrt {{\frac {1}{n-1}}\sum _{i=1}^{n}\left(x_{i}-{\overline {x}}\right)^{2}}}$ | Example |
| 2.5.5 | Coefficient of variation | $C.V.={\frac {s}{\bar {x}}}$ | Example |
| 2.5.6 | Quartile location in ordered array | $Q_{1}={\frac {1}{4}}(n+1)$ | Example |
| 2.5.7 | Interquartile range | $IQR=Q_{3}-Q_{1}$ | Example |
| 2.5.8 | Kurtosis | $Kurtosis={\frac {\sum _{i=1}^{n}\left(x_{i}-{\overline {x}}\right)^{4}}{(\sum _{i=1}^{n}\left(x_{i}-{\overline {x}}\right)^{2})^{2}}}-3={\frac {n\sum _{i=1}^{n}\left(x_{i}-{\overline {x}}\right)^{4}}{(n-1)^{2}s^{4}}}$ | Example |
| Symbol Key | $C.V.$= coefficient of variation $IQR$ = Interquartile range $k$ = number of class intervals $m$ = population mean $N$ = population size $n$ = sample size $(n-1)$=degrees of freedom $Q_{1}$ = first quartile $Q_{2}$= second quartile = median $Q_{3}$= third quartile $R$ =range $s$ =standard deviation $s^{2}$= sample variance $\sigma ^{2}$= population variance $x_{i}$=$i^{t}h$ data observation $x_{L}$= largest data point $x_{S}$=smallest data point ${\bar {x}}$= sample mean $w$=class width | $C.V.$= coefficient of variation $IQR$ = Interquartile range $k$ = number of class intervals $m$ = population mean $N$ = population size $n$ = sample size $(n-1)$=degrees of freedom $Q_{1}$ = first quartile $Q_{2}$= second quartile = median $Q_{3}$= third quartile $R$ =range $s$ =standard deviation $s^{2}$= sample variance $\sigma ^{2}$= population variance $x_{i}$=$i^{t}h$ data observation $x_{L}$= largest data point $x_{S}$=smallest data point ${\bar {x}}$= sample mean $w$=class width | Example |
# Big Idea Physics/Big Ideas
## About Big Ideas

The basis of this book is the Idea. This is an important concept to take note of. It could be a definition, it could be an equation, it could be a principle, it could be a law, or it could be an important concept.
Ideas build up to form a Big Idea: an Idea that represents the heart of the topic. A Big Idea represents the topic fully.
When scientists studied the world, they had their own ideas. When many ideas were placed together, they formed theories, fields of physics, even branches of science themselves. This is what motivates this book: ideas and concepts. Ideas and Big Ideas help the reader build up their concepts like physicists did before: through concepts and principles.

## Who This Book is For

This book is aimed at middle school and high school students. It does not go too much in equations requiring much trigonometry or calculus, but rather, it focuses on the concepts in physics.

## About This Book

Big Idea Physics is about the concept and the principle. Physics cannot be where it is today if not for ideas. Big Idea Physics wants to show its readers where physics came from: ideas.
# Big Idea Physics/Motion
## Intro

A large part of physics is the study of how things move. In this section, the motion that you experience in everyday life will be explained.

## Position

In order to describe how things move, we first must describe where they are. Coordinates are anyway to tell where a particle is. You can have a line, for example, where the coordinates might be described by the Real number line. You could also talk about two-dimensional coordinates, x and y, that tell you where things are on a rectangle.

## Displacement

Displacement is the change in the position of something.
For example, say we have the real number line with an ant at 0. If the ant moves to the 2, its displacement would be 2.
What if the ant moves from 0 to 2 to 5 and then to 2 again? Its displacement over the whole series of actions would still be 2, for displacement only measures the change in position after a series of actions. Its distance, however, would be 6.
# Bikol/Adjectives

Adjectives are words that describe a noun. Some English examples are: "happy", "tired", "beautiful", "young" and "fresh".
Most of adjectives in Bikol starts with the prefix ma-, like magayon which means beautiful, its rootword is gayon with means beauty.
The order of an adjective and a noun is interchangeable, it can be an adjective first then followed by a noun or a noun first before an adjective.
But remember:
* you must add the connector word na if the first word (maybe an adjective or noun) ends with a consonant (especially if the word ends in -n)
* if the suffix -ng if the first word (maybe an adjective or noun) ends with a vowel before being succeeded by the second word.
For example:
aking magayon o magayon na aki both mean beautiful child. Magayon means beautiful and aki means child.
An adjective can be used as a predicate in a sentence. Most of the time, Bikol sentences start with the predicate before the subject, but you can also put the subject before the predicate.
For Example:
Maugma si Juan. (John is happy.) Pobre si Juan pero maugma. (John is poor but happy.)

## Kinds of Adjectives
### Adjectives of Quality

1. Sarong maisog na babayi si Maria Makiling.
* Maria Makiling is a brave woman.
2. Mainiton kaidtong Sabado.
* It was hot last Saturday.
3. 'Honesto si Jose.
* Jose is an honest man.
4. Sarong dakulang syudad an Manila.
* Manila is a big city.
5. Sarong magayon na burak an Sampaguita.
* Jasmine is a beautiful flower.
5. Mabata an kwarto.
* The room is smelly.
** mabaho - smelly
** kwarto - room
6. Mahamot an pahamot.
* The perfume smells good.
** mahamot - fragrant
** pahamot - perfume

### Adjectives of Quantity

1. Dakul na kakanon an natada.
* There are many food left.
** dakul - many
** kakanon - Food
** natada - left

#### Adjectives of Number

1. Dakulon na tawo an nag-atendir sa pasale.
* There were many people that went to the concert/show.
** dakul - many
** tawo - people
** nag-atendir - visited
** pasale - show

### Demonstrative Adjectives

1. Sakuya an kabayong ini.
* This horse is mine.
** kabayo - horse
** ini - this/these
** sakuya - mine
2. Saimo an kabayong iyan.
* That horse is yours.
** iyan - that
** saimo - yours
3. Masiram an mga mansanas na ini.
* These apples are delicious.
** masiram - delicious
** mga mansanas - apples
** ini - these/this

### Useful Adjectives

| Word | Antonym |
| marhay - good | maraot - bad |
| tama - right | sala - wrong |
| mayaman - rich | tios - poor |
| magayon - beautiful | makanos - ugly |
| malinig - clean | maati - dirty |
| mainit - hot | malipot - cold |
| magabat - heavy | magian - light |
| swerte - lucky | malas - unlucky |
| aki - young | gurang - old |
| dakul - many | diit - few |
# Big Idea Physics
## Preface

* Physics
* Big Ideas

## Contents

* Units Measurement, The SI and Conversion
* Motion Displacement, Velocity, Speed, Acceleration and Projectile Motion
* Force Newton's Laws, Friction and Drag Force
* Work Energy and Power
* Energy Kinetic, Potential and Conservation
* Momentum Center of Mass, Linear, Angular, Collision, Inelastic and Elastic
* Torque Circular Motion and Angular Momentum
* Gravity Laws and Orbits
* Fluids Density, Pressure and Buoyancy
* Waves Types, Interference, Standing Waves and Sound
* Thermodynamics Temperature, Heat, Laws and Entropy
* Gases Ideal Gases, Pressure, Temperature and Speed
* Electricity Charge, Fields, Flux and Potential
* Magnetism Fields, Electromagnetism and Electromagnetic Waves
* Electronics Capacitance, Current, Resistance and Circuits
* Optics Light, Interference and Diffraction
# Bikol/About Bikol

Bicol Region is composed of six provinces: Albay, Camarines Norte, Camarines Sur, Sorsogon and the island-provinces of Catanduanes and Masbate.
# Bikol/Affirmation

Here are the ways to use the Bikol word for 'yes' to affirm every statement said. The Bikol word 'iyo' (ee-YAW) literally means 'yes'.
1) as an expression of feeling surprised.
2) as a form of polite insisting. Example: Maghugas ka nin mga pinggan, iyo? Do the dishes, okay?
3) 'iyo na' is an expression used to mean 'already done'.
4) 'iyong iyo' is an expression used to mean 'over and over again'. Example: Iyong iyo na sana an piggigibo niya. He is doing the same thing over and over again.
5) 'iyo' can be used together with the particles 'ngani' and 'baga'. Example: iyo ngani iyo baga
# Big Idea Physics/Physics
## What Physics Is

The dictionary defines physics as "the branch of science concerned with the nature and properties of matter and energy. The subject matter of physics, distinguished from that of chemistry and biology, includes mechanics, heat, light and other radiation, sound, electricity, magnetism, and the structure of atoms."
Physics is the study of the world we live in.