# data[].attributes.body
1. A cell phone - cell phones
2. A book - books
3. A notebook - notebooks
4. Tennis
5. A pillow - pillows
6. A chair - chairs
7. A table - tables
8. A bed - beds
9. A pencil - pencils
10.  A computer - computers
11. Eartphones
12. Glasses
13. A backpack - backpaks
14. A box - boxes
15. Wardrobe

# data[].attributes.body
1.  A shoes
2. A box - boxes
3. A brush - brushes
4. A knife - knives
5. A chair - chairs
6. A toy - toys
7. A TV - tvs
8. A Pillow - Pillows
9. A pencil - Pencils
10. A cell phone -cell phones
11. Earphones
12. A notebook - notebooks
13. A Photo -  Photos
14. A Blanket - Blankets
15. A table - tables

# data[].attributes.body
1.Book-Books
2.Window-Windows
3.Desk-Desks
4.Bench-Benches
5.Brush-Brushes
6.Watch-Watches
7.Bed-Beds
8.Shoes
9.Mirrors
10.notebooks
11.A computer-Computers
12.A clothes
13.Sheets
14.Headphones
15.Dog-Dogs

# data[].attributes.body
1.     Dog-Dogs

2.     Cat- Cats

3.     Apple-Apples

4.     Car-Cars

5.     Bus- Buses

6.     Box-Boxes

7.    Candy-Candies

8.     Tomato-Tomatoes

9.     Toy-Toys

10.   Stone-Stone

11.   Brush- Brushes

12.   Knife- Knives

13.   Tooth-Teeth

14.   Mause-Mice

15.   Chair- Chairs

# data[].attributes.body
1.- Bed - beds

2.- A Book - books

3.- A Pillow - pillows

4.- A Table - tables

5.- A Photograph - photographs

6.- A backpack - backpacks

7.- A telephone -telephones

8.- A door - doors

9.- A computer - computers

10.- A mirror - mirrors

11.- Tennis

12.- A lamp- lamps

13.- A shirt - shirts

14.- Socks

15.- Jeans

# data[].attributes.body
1.- bed - beds
2.- Teddy - Teddies
3.- Jacket - jackets
4.- A chair - chairs
5.- A door - doors
6.- A pillow - pillows
7.- Tennis
8.- A tv - tvs
9.- A notebook - notebooks
10.- A cellphone - cell phones
11.- A desk - desks
12.- A laptop - laptops
13.- socks
14.- headphones
15 A mouse pad - mouse pads

# data[].attributes.body
1. A cell phone -cell phones

2. A book - Books

3. A TV - tvs

4. A  table - tables

5. A printer - printers

6. A bookcase - bookcases

7. A notebook - notebooks

8. A pencil - Pencils

9. Earphones

10. Shoes

11.  A paper - Papers

12. Sunglsses

13. A Bed - Beds

14. A Pillow - Pillows

15. A Watch - Watches

# data[].attributes.body
1. A kiss - kisses
2. A baby - babies
3.  A  life - lives
4. A day - days
5. A bird - birds
6. A lady - ladies
7. A tray - trays
8. A boy - boys
9. A wife - wives
10. A laptop - laptops
11. A class - classes
12. A phone - phones
13. A desk - desks
14 A pencil case - pencil cases
15. A urgency - urgencies

# data[].attributes.body
1.Bed - Beds
2. Chair - Chairs
3. Wardrobe - Wardrobes
4. Shirt - Shirts
5. Tv - Tvs
6. Notebook - Notebooks
7. Charger - Chargers
8. Towel - Towels
9. Cream - Creams
10. Soap -Soaps
11. Brush - Brushes
12. Dumbbell - Dumbbells
13. Key - Keys
14. Backpack - Backpacks
15. Wallet - Wallets

# data[].attributes.body
1.- a notebook - notebooks
2.- a laptop- laptops
3.- a bed - beds
4.- a dog - dogs
5.- a cat - cats
6.- a door - doors
7.- a table- tables
8.- a backpack - backpacks
9.- a candy - candies
10.- Earphones
11.- a book -books
12.- a camera - camera
13.- a tv - tvs
14.- a knife - knives
15.- a chair - chairs

# data[].attributes.body
1. A calculator - calculators
2. A watch - watches
3. A photo - photos
4. A cap - caps
5. A notebook - notebooks
6. A book - books
7. A backpack - backpacks
8. A pencil - pencils
9. A toy - toys
10. A horn - horns
11. A phone - phones
12. A coat - coats
13. A spotlight - spotlights
14. A mirror - mirrors
15. A curtain - curtains

# data[].attributes.body
1.- a mask - masks
2.- a pencil - pencils
3.- a flower - flower
4.- a wife - wives
5.- a workbook - workbooks
6.- a school - schools
7.- a picture - pictures
8.- a sunglasses - sunglasses
9.- an airphones - airphones
10.- a scissors - scissors
11.- a bottle - bottles
12.- a song - songs
13.- a number - numbers
14.- a mouse - mouses
15.- a key - keys

# data[].attributes.body
1.-A apple--apples
2.-A room-rooms
3.-A truck-trucks
4.-A church-churches
5.-A kiss- kisses
6.- A toy-toys
7.- A day- days
8.-A baby-babies
9.-A zero-zeroes
10.- A fly-flies
11.-A bus- buses
12.-A hero- heroes
13.- A life-lives
14.- A library- libraries
15.- A book- books

# data[].attributes.body
1.A bed - beds
2.A pillow - pillows
3.A workbook - workbooks
4.A notebook - notebooks
5.A pencil - penciles
6.A door - doors
7.A poster - posters
8.A computer - computers
9.A cell phone - cell phones
10.Headphones
11.Shoes
12.Clothes
13.Wardrobe - wardrobes
14.A table - tables
15.A puppy - puppies

# data[].attributes.body
1.- A cell phone- cell phones
2.- A book- books
3.- A notebook- notebooks
4.- A paper- papers
5.- A pencil- pencils
6.- A computer- computers
7.- A Tv- Tv's
8.- Shows
9.- A bed- veda
10.- A table- tables
11.- A couch- couchs
12.- A chair- chairs
13.- A candy- candies
14.- A toy- toys
15.- A pillow- pillows

# data[].attributes.body
1.-a notebook-notebooks
2.-one watches-two watches
3.-a computer- two computers
4.- pencil- pencils
5.-  color- colors
6.- chair-chairs
7.-a perfume -perfumes
8.-portrait-portraits
9.-a glass-glasses
10.-shirt-shirts
11.-a shoe-shoes
12.-a rule-rules
13.-a stuffed-stuffed animals
14.-flip-flops
15.-one stack- two stacks

# data[].attributes.body
1.- table - tables
2.- shoe - shoes
3.- computer - computers
4.- bed - beds
5.- pillow - pellows
6.- mirrow - mirrows
7.- phone - phones
8.- chair - chairs
9.- pencil - pencils
10.- book - books
11.- hair comb - hair combs
12.-cap - caps
13.- sweater - sweaters
14.-  notebook - notebooks
15.- body cream - body creams

# data[].attributes.body
1. A blouse - blouses
2.A shoe- shoes
3. A phone - phones
4. brush - brushes
5. fish - fishes
6. cross- crosses
7. happy - happies
8. party - parties
9. hungry - hungries
10. apencil - pencils
11. book - book
12. lamp - lamps
13. elephant - elephants
14. a cat - ctas
15. a dog - dogs

# data[].attributes.body
1.dog - dogs
2. car- cars
3. rule - rules
4. shoe-shoes
5.book-books
6.candy-candies
7.dummy-dummies
8.fairy-fairies
9.toy-toys
10.day-days
11.pencil-pencils
12.elf-elves
13.loaf-loaves
14.wife-wives
15.shirt-shirts

# data[].attributes.body
1. A Phone - phones
2. A Book - Books
3. A Pencil - pencils
4. A Computer- computers
5. A cat - cats
6. A car - cars
7. A mouse - mouses
8. A Hero - Heroes
9. A library - libraries
10. A Life - Lives
11. A sweater -  Sweaters
12. A shoe - Shoes
13. A Bird -  Birds
14. A dog -  Dogs
15. A Snake - Snakes

# data[].attributes.body
1._Car: cars

2._House: houses

3._Window: windows

4._Boy: boys

5._Desk: desks
6._Box: boxes

7._Wish: wishes

8._Bush: bushes

9._Class: classes

10._Ash: ashes
11._Half: halves

12._Knife: knives

13._Life: lives

14._Thief: thieves

15._Wife: wives

# data[].attributes.body
1. A car - cars
2. A book - books
3. A phone - phones
4. A beach - beaches
5. A class - classes
6. A fly - flies
7. A baby - babies
8. A library - libraries
9. A life - lives
10.A half - halves
11. A elf - elves
12. A hero - heroes
13. A zero - zeroes
14. A day - days
15. A quay - quays

# data[].attributes.body
1. Bed - Beds
2. Cover - Covers
3. Dresser - Dressers
4. Curtain - Curtains
5.  Lam - Lamps
6. Phone - Phones
7. Wardrobe - Wardrobes
8. Mirror - Mirrors
9. Pillow - Pillows
10. Laptop - Laptops
11. Book - Books
12. Charger - Chargers
13. Shoe - Shoes
14. Table - Tables
15.Chair - Chairs

# data[].attributes.body
1. Wolf - Wolves
2. Life - Lives
3. woman - women
4. man - men
5. child - children
6. tooth - teeth
7. foot - feet
8. person - people
9. leaf - leaves
10. mouse - mice
11. goose - geese
12. half - halves
13. bus - buses
14. wish - wishes
15. pitch - pitches

# data[].attributes.body
List
1. Cat - cats
2. Apple - apples
3. Bird - birds
4. Truck - trucks
5. Car - cars
6. Boy - boys
7. Box - boxes
8. Class - classes
9. Match - matches
10. Family - families
11. City - cities
12. Fly - flies
13. Hero - heroes
14. Zero - zeroes
15. Life - lives

# data[].attributes.body
List singular and Plurals
1. A book - books
2. A phone - phones
3. A quiz - quizzes
4. A lady - ladies
5. A boat - boats
6. A wolf - wolves
7. A mouse - mice
8. A corpse - corpses
9. A buzzard - buzzards
10. A goat - goats
11. A Raspberry - raspberrys
12. A Candy - candies
13. A beaver - beavers
14. A cicada - cicadas
15. A moth a moths

# data[].attributes.body
1. a Bed- Beds
2. a cup- cups
3. shoe- shoes
4.racket- rackets
5.bag-bags
6. table-tables
7.mirror-mirrors
8.dictionary- dictionaries
9. brush-brushes
10.camera- cameras
11.box-boxes
12.computer-computers
13. rose-roses
14.book-books
15.spoon-spoons