A standard multiplication table:
1 2 3 4 5 6 7 8 9 10
1 1 2 3 4 5 6 7 8 9 10
2 2 4 6 8 10 12 14 16 18 20
3 3 6 9 12 15 18 21 24 27 30
4 4 8 12 16 20 24 28 32 36 40
5 5 10 15 20 25 30 35 40 45 50
6 6 12 18 24 30 36 42 48 54 60
7 7 14 21 28 35 42 49 56 63 70
8 8 16 24 32 40 48 56 64 72 80
9 9 18 27 36 45 54 63 72 81 90
10 10 20 30 40 50 60 70 80 90 100
A B C D E F G
1 User name E-mail
=CONCATENATE(A2,"@stern.nyu.edu")
2 edm376 edm376@stern.nyu.edu
3 bmf352 bmf352@stern.nyu.edu
4 sg1892 sg1892@stern.nyu.edu
5 tys311 tys311@stern.nyu.edu
6 ldb366 ldb366@stern.nyu.edu
7 wc677 wc677@stern.nyu.edu
8 wck327 wck327@stern.nyu.edu
9 tl408 tl408@stern.nyu.edu
10 jr1918 jr1918@stern.nyu.edu
11 jsr366 jsr366@stern.nyu.edu
A B C D E F G
1 Name Address Age
2 Steve 72 Andover Dr. 11 =COUNT(C2:C34)
3 Yuri 1133 Nicholas Av. 14 Count 33 =COUNTA(A2:A34)
4 David 110 Arlington Street 11 Counta 33 =COUNTIF(C2:C34,12)
5 Andy 38 Devonshire Road 11 Countif 5
6 Dan 38 Devonshire Road 14
7 Blaine 59 Village Road 11
8 Justin 59 Village Road 11
9 Rob 39 Jackson Drive 15
10 Russell 39 Jackson Dr 14
11 Justin 16 Crestwood Rd. 17
12 Tristan 75 Peak Avenue 11
13 Marty 101 Captains Walk 16
14 Jason 73 Minuteman Dr. 13
15 Joe 73 Minuteman Dr. 11
16 Philip 61 Prospect Avenue 11
17 Evan 281 Burnt Plains Road 11
18 Scott 281 Burnt Plains Road 14
19 Ivan 11 Riverview Terrace 12
20 John 41 Fairwood Ave 11
21 Kevin 381 Ocean Avenue 12
22 Robert 57 Wilson Street 11
23 Brian 20 Judith Drive 15
24 Zack 275 Rock La 11
25 Jon 275 Rock Lane 13
26 Adam 50 Live Oaks Rd 12
27 Sean 14 Lafayette Street 11
28 Matt 110 Kings Highway 11
29 Neil 125 Seemans Lane #11 11
30 Lord 4 Villa Rosa Terr 16
31 Kevin 23 Ellis St 17
32 David 660 Merwin Av 12
33 Greg 34 Elgin Road 13
34 Cliff 34 Elgin Road 12
H I
1
2
=COUNT(C2:C34)
3
=COUNTA(A2:A34)
4
=COUNTIF(C2:C34,12)
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
1 2.718282
2 7.389056
3 3
Axles Bearings Bolts 4
4 4 9 7
5 7 10 7
6 8 11 11
A B C D E F G H I
1 Exam Score Grade
=IF(C2>$D$13,"A",IF(C2>$D$14,"B","C"))
2 Pam Anderson 4 C
3 William Basie 88 B
4 Amelia Earhart 76 C
5 Albert Einstein 73 C
6 Mark Fydrich 79 C
7 Christopher Guest 85 B
8 Holly Jackson 100 A
9 Jeane Kirkpatrick 82 B
10 Bart Simpson 67 C
11 James Traficant 94 A
12
13 Cutoff for A 90
14 Cutoff for B 80
86 4.454347
2.718282 1
20.08554 3
1.386294 4
10
7
9
27 27
2 30
A B C D E F G H I
1 2x2 example
2 matrix 1 matrix 2
3 1 3 2 0
4 7 2 0 2
5 =MMULT({1,3;7,2},{2,0;0,2})
6 mmult the hard way using sumproducts
7 2 6 2 6 2 6
8 14 4 14 4 14 4
9
10
11 3x3 example
12
13 1 3 2 0 1 3
14 4 1 9 5 2 7
15 0 2 5 7 1 6
16
17 mmult the hard way
18 29 9 36 29 9 36
19 68 15 73 68 15 73
20 45 9 44 45 9 44
21
22
23 2x4 example
24 1 3 0 1
25 4 1 5 2
26 0 2
27
28 mmult
=MMULT(A24:B26,E24:F25)
29 15 7 =1*0 + 3*5 =1*1 + 3*2
30 5 6 =4*0 + 1*5 =4*1 + 1*2
31 10 4 =0*0 + 2*5 =0*1 + 2*2
A B C D E
1 Z values
=NORMSDIST(A2)
2 -1.96 0.0250
=NORMSDIST(A3)
3 -1 0.1587
=NORMSDIST(A4)
4 0 0.5000
=NORMSDIST(A5)
5 1.645 0.9500
Rate 0.08
Nper 20 ($59,777.15)
Pmt 500
FV 0
16 4
-16 #NUM!
16 4
A B C D E F G H
1 Order # Revenue Salesperson
2 7002-3556 $ 91,919 Vishal
3 7002-3557 $ 23,967 Mike
=SUMIF($C$2:$C$21,E4,$B$2:$B$21)
4 7002-3558 $ 257,641 Carmen Carmen $ 637,367
=SUMIF($C$2:$C$21,E5,$B$2:$B$21)
5 7002-3559 $ 86,196 Harry Harry $ 434,636
=SUMIF($C$2:$C$21,E6,$B$2:$B$21)
6 7002-3560 $ 134,663 Vishal Mike $ 291,566
=SUMIF($C$2:$C$21,E7,$B$2:$B$21)
7 7002-3561 $ 78,266 Vishal Vishal $ 459,741
8 7002-3562 $ 102,697 Harry
9 7002-3563 $ 80,447 Vishal
10 7002-3564 $ 74,446 Vishal
11 7002-3565 $ 125,529 Carmen
12 7002-3566 $ 104,885 Harry
13 7002-3567 $ 95,316 Carmen
14 7002-3568 $ 93,995 Mike
15 7002-3569 $ 63,062 Mike
16 7002-3570 $ 84,530 Harry
17 7002-3571 $ 21,900 Mike
18 7002-3572 $ 56,328 Harry
19 7002-3573 $ 88,642 Mike
20 7002-3574 $ 66,191 Carmen
21 7002-3575 $ 92,690 Carmen
I J
1
2
3
MIF($C$2:$C$21,E4,$B$2:$B$21)
4
MIF($C$2:$C$21,E5,$B$2:$B$21)
5
MIF($C$2:$C$21,E6,$B$2:$B$21)
6
MIF($C$2:$C$21,E7,$B$2:$B$21)
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
3 4 2 7
8 6 6 7
1 9 5 3
156
1 2 3
1
2
3
A B C D E F G H
1 School Cost
=VLOOKUP(A2,A5:J31,6,0)
2 NYU $ 31,746
3
4 School Accept % Enroll % GMAT GPA Cost Minority pct Non-U.S. pct
5 Stanford 8.8% 78.4% 718 3.58 $ 31,002 24.6% 30.9%
6 Harvard 11.6% 87.3% 703 3.58 $ 30,050 18.6% 37.2%
7 Penn 15.5% 70.2% 703 3.57 $ 31,218 19.0% 43.1%
8 MIT 18.1% 69.9% 703 3.50 $ 31,200 19.0% 35.5%
9 Northwestern 16.6% 61.8% 700 3.45 $ 30,255 17.5% 30.8%
10 Duke 20.1% 52.8% 690 3.43 $ 30,323 20.6% 32.7%
11 Chicago 28.4% 59.7% 695 3.33 $ 30,596 18.2% 33.2%
12 Columbia 13.5% 68.7% 705 3.40 $ 31,912 19.3% 26.5%
13 Dartmouth 18.8% 53.3% 690 3.40 $ 30,250 17.2% 29.9%
14 Berkeley 14.5% 50.3% 688 3.43 $ 21,242 19.4% 30.2%
15 Michigan 19.6% 58.5% 676 3.34 $ 30,686 20.5% 31.2%
16 Virginia 16.6% 51.0% 681 3.40 $ 27,283 15.1% 30.1%
17 NYU 22.3% 48.2% 689 3.40 $ 31,746 19.0% 30.2%
18 Yale 20.0% 56.7% 686 3.50 $ 29,055 17.3% 38.9%
19 UCLA 17.4% 48.5% 691 3.60 $ 22,490 21.6% 24.0%
20 Cornell 25.8% 51.2% 669 3.32 $ 30,455 22.6% 25.6%
21 North Carolina 23.8% 44.2% 674 3.30 $ 25,525 16.2% 30.4%
22 Carnegie-Mellon 28.2% 60.1% 652 3.25 $ 28,452 21.3% 38.3%
23 Texas 30.0% 52.4% 680 3.34 $ 21,200 12.3% 26.7%
24 USC 29.0% 43.4% 670 3.30 $ 30,082 32.3% 23.8%
25 Indiana 24.7% 49.8% 651 3.35 $ 20,696 14.3% 34.5%
26 Emory 31.5% 46.7% 651 3.30 $ 28,012 10.7% 29.9%
27 Rochester 32.7% 42.2% 637 3.33 $ 28,620 10.3% 54.2%
28 Georgetown 20.8% 46.2% 662 3.35 $ 28,440 10.8% 38.7%
29 Michigan State 22.3% 55.0% 641 3.36 $ 16,955 13.6% 37.9%
30 Ohio State 29.0% 41.4% 645 3.38 $ 22,151 15.5% 29.2%
31 Minnesota 34.4% 40.6% 645 3.33 $ 20,352 8.6% 29.1%
I J
1
2
3
4 Female % Salary
5 38.7% $ 124,740
6 34.2% $ 121,979
7 29.3% $ 121,200
8 26.5% $ 118,381
9 27.5% $ 114,664
10 34.8% $ 115,444
11 23.6% $ 115,331
12 36.5% $ 117,989
13 27.6% $ 121,692
14 30.4% $ 111,321
15 27.6% $ 117,498
16 29.1% $ 112,706
17 34.1% $ 112,900
18 27.5% $ 112,514
19 28.6% $ 103,364
20 27.1% $ 116,588
21 30.6% $ 109,420
22 26.8% $ 111,211
23 22.7% $ 106,859
24 28.7% $ 95,213
25 20.7% $ 99,732
26 23.4% $ 104,417
27 25.2% $ 103,466
28 28.1% $ 99,528
29 25.2% $ 88,746
30 36.7% $ 99,598
31 27.9% $ 91,140