session 2-mathematical text count

7/25/2019 Session 2-Mathematical Text Count

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Mathematical

SUM1 2 3

AVERAGE5 8 6.5

RAND0.393835208

RANDBETWEEN7

ROUND2.35556 2.36

ROUNDDOWN2.35556 2.355

ROUNDUP2.35556 2.3556

SIGN-4 -1

SUBTOTAL2 4

MAX1 2 2

MIN1 2 1

RANK

STDDEV

OVAR

ORREL


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Text

!"#$ S&'#$() *+*

"$,&$& /"#$ )&'#$()

L& /"

R#(& $()

M#

L$ 21

S)&& /"#$ M$ )&'#$()

L"'

U'

T'# R,):'";) &


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Count

"$& ") $"& ,"$& & "$ $') #$ ''

"$,"$#"$:,'#&'#

"$)

' # ," #$ #&


4/38

Name

A# M, 1200 L' D'#; A# M,

D' ' 1000 ?""" L$ D' '

B'#&$ S') 300 S#$(' R" B'#&$ S')P&"$ M$$#$( 500 @B S&'& P&"$ M$$#$(

Q-1 The table below contains the fctitious addresses o column.


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#ddress

1200 L' D'#;

1000 ?""" L$

300 S#$(' R"500 @B S&'&

w movie stars. Using text unctions extract each !ersons name to one column a


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nd each !erson"s street address to another


7/38

Q-$ %ou are given the ollowing table&

i' #dd two more columns namel( Name-)ower and *urname-)ower. U

ii' #dd a column named +ullName. Using a unction !o!ulate it with t

No Name *urname *uburb Name - )ower

1 ROGER WILSON B'#(&"$ '"('

2 MAR DRISOLL S"& M"'$ '

3 KATE =U B$( &4 !ULIE GREGOR A),"& V /#

5 PETER ?ARRISON T''("$ &'

6 ?AROLD SUNS?INE S$)#$ '"

7 OSAR RENN M""$ P"$) "),'

8 MELINDA WRILL B$( #$

9 =RED !AKSON B'#(&"$ '

10 MAR LEWIS '#)&) ?#) '

11 KAT?ERINE SMIT? E$;"' ?#(&) &'#$

12 !UNE GREGSON ?#'( ?#(&) /$

13 AUGUSTE SMT?E I;$" ()&14 ?ARR !ONES D$#) ''

15 WILBUR !O?NSON S$)#$ #'

16 DONALD KENDALL M""'""' "$

17 S?ELL LEWIS ='$&' G )

18 SAMANT?A MARTIN T T' ?# )$&

19 LOUISE WATSONIA W&)"$# "#)

20 MARTIN POLLARD S$)#$ '$


8/38

e a text unction to convert u!!ercase to lowercase rom Name and *urname

he ull name o the !eo!le in the table.

*urname - )ower +ullName

#)"$ ROGER WILSON

'#)," MAR DRISOLL

KATE =U('("' !ULIE GREGOR

''#)"$ PETER ?ARRISON

)$)#$ ?AROLD SUNS?INE

'$$ OSAR RENN

'# MELINDA WRILL

/,)"$ =RED !AKSON

#) MAR LEWIS

)#& KAT?ERINE SMIT?

('()"$ !UNE GREGSON

)& AUGUSTE SMT?E/"$) ?ARR !ONES

/"$)"$ WILBUR !O?NSON

$ DONALD KENDALL

#) S?ELL LEWIS

'$ SAMANT?A MARTIN

&)"$# LOUISE WATSONIA

"' MARTIN POLLARD


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columns


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Column 1 Column 2 Column 3 Column 4

"$ 1 1

"$ 2 0.787282 1

"$ 3 0.401848 -0.24825 1

"$ 4 -0.0709 -0.037006 -0.056429 1


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R;$ E


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Q-, The table below gives the x!ense data or Max ental /ro!erties.

0i' Count the total number o !ro!erties under Max ental.

0ii' Com!ute the number o !ro!erties being managed b( acob and that hav

0iii' 2hat is the maximum and minimum /roft amount or the entire data

0iv' /re!are the correlation matrix or the our variables in the data

Max Rental Properties

Name-Manager State City Property Revenue

Billy Texas Dallas Triplex 154,276

Billy Texas Houston Triplex 57,024

Billy Texas Houston Triplex 69,466

Billy Texas Dallas Triplex 91,463

Billy Texas Fort Worth Triplex 94,936

Billy Texas Dallas part!ent 356,040


Billy Texas Fort Worth part!ent 90,305


Billy Texas Houston part!ent 261,005

Billy Texas Houston part!ent 92,621

Billy Texas Fort Worth Triplex 394,276

Billy Texas Fort Worth part!ent 116,122

"in#er Flori$a Ta!pa Triplex 91,224"in#er "eor#ia %a&annah part!ent 76,032

"in#er Flori$a Daytona Triplex 121,19'

"in#er "eor#ia tlanta Triplex 1'1,9''

"in#er Flori$a Daytona Triplex 110,772

"in#er "eor#ia (a)on part!ent 12',563

"in#er "eor#ia tlanta part!ent 96,76'

"in#er Flori$a Ta!pa Triplex 110,772

"in#er "eor#ia tlanta part!ent 432,900


"in#er Flori$a Ta!pa part!ent 217,504"in#er "eor#ia (a)on part!ent 12',563

"in#er Flori$a Daytona Triplex 121,19'


"in#er Flori$a Ta!pa part!ent 12',563

"in#er "eor#ia tlanta Triplex 121,19'


*a)o+ Texas Dallas Tonho!e 94,936


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*a)o+ Texas Fort Worth Tonho!e 69,466

*a)o+ Texas Dallas part!ent 519,4'0

*a)o+ Texas Houston Duplex 113,924

*a)o+ Texas Fort Worth Duplex 154,276

*a)o+ Texas Dallas Duplex 91,23'

*a)o+ Texas Houston Tonho!e 91,463

*a)o+ Texas Dallas Duplex 90,305

*a)o+ Texas Fort Worth Duplex 92,621

*a)o+ Texas Houston Tonho!e 154,276

*a)o+ Texas Houston Duplex 1,447,1'6

*a)o+ Texas Fort Worth Tonho!e 113,924

*a)o+ Texas Houston Duplex 94,936

-athy "eor#ia tlanta part!ent 1,205,9''

-athy "eor#ia tlanta part!ent 32',563

%te&e Flori$a Ta!pa Tonho!e 54,120

%te&e "eor#ia tlanta Tonho!e 39,600

%te&e Flori$a Daytona Duplex 77,1'4

%te&e Flori$a Daytona Duplex 76,219

%te&e "eor#ia tlanta Duplex 79,114


%te&e "eor#ia tlanta Triplex 71,676

%te&e "eor#ia (a)on Duplex 75,254

%te&e Flori$a Daytona Tonho!e 52,140


%te&e "eor#ia tlanta Duplex 57,'''

%te&e "eor#ia tlanta Duplex 79,114%te&e Flori$a Daytona Tonho!e 52,140

%te&e "eor#ia (a)on Duplex 76,219

%te&e "eor#ia tlanta Tonho!e 51,4'0

%te&e Flori$a Daytona Duplex 75,254

%te&e Flori$a Ta!pa Duplex 79,114

%te&e Flori$a Ta!pa Tonho!e 51,4'0

%te&e Flori$a Daytona Duplex 77,1'4

%te&e Flori$a Daytona Tonho!e 52,'00

%te&e "eor#ia (a)on part!ent 296,700

%te&e "eor#ia (a)on Tonho!e 52,'00%te&e "eor#ia tlanta Duplex 57,'''


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34

a vacanc( rate o more than 11

564174 08$6793'

Expenses Profit Vacancy Rate

143,477 10,799 64.3/

54,743 2,2'1 59.2/

66,6'7 2,779 41.2/

72,256 19,207 35.6/

77,'4' 17,0'9 52.9/

259,909 96,131 25.5/

93,417 20,506 25.5/

70,43' 19,'67 25.5/

45,619 1,901 24.7/

221,'54 39,151 23.3/

'2,433 10,1'' 1'.7/

366,677 27,599 15.4/

'4,769 31,353 13.4/

66,594 24,630 65.0/ 75,272 760 62.3/

112,714 ',4'4 45.0/

205,646 23,65' 43.2/

94,156 16,616 39.'/

119,564 ',999 39.'/

70,641 26,127 34.5/

125,172 14,400 49.'/

367,965 64,935 24.7/

127,714 36,490 24.7/

1'4,'7' 32,626 23.3/ 119,564 ',999 23.3/

112,714 ',4'4 23.3/

125,172 14,400 23.3/

119,564 ',999 15.4/

112,714 ',4'4 15.4/

70,959 717 13.4/

77,'4' 17,0'9 57.6/


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66,6'7 2,779 57.3/

1,443,556 924,076 39.'/

93,417 20,506 35.6/

143,477 10,799 52.2/

90,326 912 31.5/

72,256 19,207 27.6/

70,43' 19,'67 26.5/

104,662 12,041 25.5/

143,477 10,799 24.7/

975,545 471,641 23.3/

93,417 20,506 1'.7/

77,'4' 17,0'9 13.4/

657,''0 54',10' 43.2/

305,564 22,999 39.'/

44,37' 9,742 54.3/

3',016 1,5'4 61.2/

'7,21' 10,034 34.4/

60,213 16,006 75.4/

64,'73 14,240 43.5/

3',016 1,5'4 56.3/

70,959 717 23.4/

5',69' 16,556 35.6/

41,191 10,949 35.6/

44,37' 9,742 37.7/

55,572 2,316 24.5/

64,'73 14,240 27.6/ 41,191 10,949 27.6/

60,213 16,006 26.5/

5',172 6,692 26.5/

5',69' 16,556 25.5/

64,'73 14,240 25.5/

40,154 11,326 25.5/

6',694 ',490 25.5/

59,664 6,'64 25.5/

216,591 '0,109 23.3/

46,992 5,'0' 1'.7/ 55,572 2,316 1'.7/


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SUMMAR OUTPUT

Regression Statistics

M R 0.825945

R S' 0.682186

A/)& R S' 0.657739

S&$' E''"' 8.576867O)';"$) 15

ANOVA

df SS MS F gnicance F

R('))#"$ 1 2052.723 2052.723 27.90441 0.000148

R)# 13 956.3145 73.56265

T"& 14 3009.037

Coecient andard Err t Stat P-value o!er "#$%&&er "#$

I$&',& 27.17916 -0.514527 0.615519 -72.70141 44.7326X V'# 1 4.21696 0.798294 5.282463 0.000148 2.49235 5.94157

RESIDUAL OUTPUT

'(servation redicted Residuals

1 129.3922 -6.392242

2 142.0431 0.956877

3 163.1279 -11.127924 120.9583 -0.958321

5 125.1753 -0.175282

6 129.3922 -3.392242

7 133.6092 11.3908

8 137.8262 15.17384

9 116.7414 10.25864

10 133.6092 7.390798

11 125.1753 3.158052

12 123.0668 -0.733468

13 120.9583 -4.62498814 118.8498 -8.516508

15 116.7414 -12.40803


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o!er "#*+ &&er "#*+$

-72.70141 44.73262.49235 5.94157

30 32 34 36 38 40 42

-20

0

20

: ;ariable 1 esidual /lot

: ;ariable 1

esiduals

30 32 34 36 38 40 42 44

0

100

200

: ;ariable 1 )ine +it /

: ;ariable 1

%


18/38

44

lot

P'#,&


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SUMMAR OUTPUT


M R 0.825945

R S' 0.682186

A/)& R 0.657739

S&$' E 8.576867O)';" 15

ANOVA

df SS MS F gnicance F

R('))#"$ 1 2052.723 2052.723 27.90441 0.000148

R)# 13 956.3145 73.56265

T"& 14 3009.037

Coecient andard Err t Stat P-value o!er "#$%&&er "#$o!er "#*+

I$&',& -13.98441 27.17916 -0.514527 0.615519 -72.70141 44.7326 -72.70141R#$ I$ 4.21696 0.798294 5.282463 0.000148 2.49235 5.94157 2.49235


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&&er "#*+$

44.73265.94157


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Q-6. The table below gives the data o 2heat /roduction.

(i) Wat is te expecte! arvest for "##$%

(ii) Wat is te value of R-S&uare%

'ear usels of Weat(onnes)

1993 34 123 E"$1994 37 143

1995 42 152

1996 32 120 "' 2008 -

1997 33 125

199' 34 126 R S'

1999 35 145

2000 36 153

2001 31 127

2002 35 141

2003 33 12'2004 32.5 122

2005 32 116

2006 31.5 110

2007 31 104

200' 36

Rainfall(*nces)


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F 4.2169602464145 X -13.9844065838878

)) F 137.8262

0.682186


23/38

SUMMAR OUTPUT


M R 0.9527350382

R S' 0.907704053

A/)& R S' 0.8896950877

S&$' E''"' 2853.8093082O)';"$) 50

ANOVA

df SS MS F

R('))#"$ 8 3283941418.241 410492677.28 50.4029

R)# 41 333913330.259 8144227.5673

T"& 49 3617854748.5

Coecients Standard Error t Stat P-value

I$&',& 75655.726216 9799.337333403 7.7204941153 1.61E-009X V'# 1 -1815.65536 234.1435138449 -7.7544550785 1.45E-009

X V'# 2 -754.70949432 447.2508749353 -1.6874410686 0.099113

X V'# 3 -211.2924865 41.5990276463 -5.0792650322 8.69E-006

X V'# 4 2.9989437513 2.8151170335 1.0652998492 0.292974

X V'# 5 0.3563938357 10.0025861058 0.0356301692 0.97175

X V'# 6 -76.079416992 1994.348900364 -0.0381474961 0.969755

X V'# 7 581.72537377 183.5889293582 3.1686299158 0.002893

X V'# 8 956.40187019 403.4626819961 2.370484094 0.022549

RESIDUAL OUTPUT

'(servation Predicted ) Residuals

1 44213.588904 -1547.588904234

2 64503.882214 3956.117785744

3 47112.70487 3845.295130483

4 38085.925245 729.0747548318

5 57216.611365 3804.388635045

6 58139.916627 -1146.9166273877 63280.839588 5314.160411836

8 58194.129226 -205.1292258152

9 52391.113194 -4613.113193843

10 52409.150729 -1548.150728663

11 70126.477206 -2912.477206155

12 46962.204912 613.7950883657

13 56495.950633 -260.9506331749

14 47185.411736 780.5882641421


24/38

15 49929.549064 -949.5490640921

16 53797.140577 -3620.140576572

17 37999.973286 3538.026714114

18 41397.87783 2335.122170312

19 48502.715079 -1921.715078715

20 68192.90999 2352.090010082

21 64298.42123 1102.57877021622 50791.872907 -2200.872907006

23 59878.542884 -2590.542884411

24 35026.403666 2763.5963337

25 49645.367616 -2778.367616032

26 46114.536994 -2460.536994379

27 52328.750058 -2635.750058434

28 54554.759292 1806.240707943

29 60519.755217 3211.244783021

30 65943.187508 4434.812491839

31 42829.923882 678.076118163232 56253.59077 -220.5907701199

33 49986.036657 -3437.036657283

34 49294.800835 -3609.800835291

35 48802.927575 -814.9275749994

36 42227.004065 594.9959351718

37 51094.019006 -925.0190063724

38 51618.538594 -905.5385939043

39 56779.185545 -1078.185545351

40 48957.433657 -4332.433657252

41 47051.453813 -1019.453812627

42 45631.281844 -2017.281843719

43 45784.748757 4258.251242553

44 55617.907693 1015.092307482

45 55593.073928 -3489.073927806

46 60587.806509 645.1934912166

47 57728.641912 349.3580883089

48 34040.658484 3948.341515589

49 53308.9377 -1214.937700164

50 50827.359126 2379.640873641


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gnicance F

8.79E-019

o!er "#$%&&er "#$o!er "#*+ &&er "#*+$

55865.56 95445.89 55865.56 95445.89-2288.518 -1342.793 -2288.518 -1342.793

-1657.951 148.532 -1657.951 148.532

-295.3034 -127.2815 -295.3034 -127.2815

-2.6863 8.684188 -2.6863 8.684188

-19.84424 20.55703 -19.84424 20.55703

-4103.749 3951.59 -4103.749 3951.59

210.96 952.4907 210.96 952.4907

141.5925 1771.211 141.5925 1771.211

6 8 10 12

-10000

0

10000

: ;ariable 1 es

: ;aria

esiduals

4.0 5.0

-10000

0

10000

: ;ariable

esiduals

-100

100

: ;a

esiduals

esi


26/38


27/38

14 16 18 20 22

dual /lot

ble 16.0 7.0 8.0 9.0 10.0 11.0

esidual /lot

: ;ariable $20.0 40.0 60.0 80.0 100.0 120.0

00

0

00

iable , esidual /lot

: ;ariable ,0 100 200 300 400 500 600 700 800 900

-10000

0

10000


: ;ariable 6

uals

150.0 200.0 250.0 300.0 350.0 400.0 450.0 500.0

-10000

0

10000


: ;ariable 5

esiduals

0.600.801.001.201.401.601.802.002.202.402.60

-10000

0

10000


: ;ariable 3

esiduals

15.0 20.0 25.0 30.0 35.0 4

-10000

0

10000


: ;ariable 9

esiduals

2.0 3.0 4.0 5.0 6.0 7

-10000

0

10000

: ;ariable 4 esidual

: ;ariable 4

esiduals

6 8 10 12 14 16

0

50>000

100>000

: ;ariable 1 )in

: ;ariable 1

%

4.0 5.0 6.0 7.

0

50>000

100>000

: ;ariable

: ;ari

%

20.0

0

50>000

100>000

: ;a

% 1

%


28/38


29/38

.0

.0 8.0 9.0

lot

18 20 22

+it /lot

P'#,&

.0 8.0 9.0 10.0 11.0

$ )ine +it /lot

P'#,&

ble $40.0 60.0 80.0 100.0 120.0

riable , )ine +it /lot

P'#,&

: ;ariable ,0 100 200 300 400 500 600 700 800 900

0

0>000

0>000

: ;ariable 6 )ine +it /lot

P'#,&

: ;ariable 6150.0200.0250.0300.0350.0400.0450.0500.0

0

50>000

100>000: ;ariable 5 )ine +it /lot

P'#,&

: ;ariable 5

%

0.50 1.00 1.50 2.00 2.50 3.00

0

50>000

100>000


P'#,&

: ;ariable 3

%

15.0 20.0 25.0 30.0 35.0 40.0

0

50>000

100>000


P'#,&

: ;ariable 9

%

2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0

0

50>000

100>000

: ;ariable 4 )ine +it /

: ;ariable 4

%


30/38


31/38

lot

P'#,&


32/38


33/38

Q-5 The Table below gives *tate an


34/38

T000 #'&)> & '#"' &" 1 '> ')#$&) "$

W#& - H " & ""$ && #) #&

'# - ;#"$& ,'# ''> "',# '> '"'> $ ((';& ))& ' 100>000 "

D",&"') - J ",&"') ' 100>000 ')#$&)>


35/38

sing Correlation and egression unctionalit( o xcel (ou have to inter!ret the d

T' D&) U$#;')#& U$" I$,"

1.81 22.0 5.0 42>666

1.63 27.3 6.7 68>460

1.69 25.1 5.5 50>958

1.96 18.8 5.1 38>8151.21 29.6 7.2 61>021

1.14 35.6 4.9 56>993

0.86 35.6 5.7 68>595

1.23 27.5 4.8 57>989

1.56 25.8 6.2 47>778

1.46 27.5 6.2 50>861

1.33 29.1 3.9 67>214

1.60 24.0 4.9 47>576

1.16 29.9 6.5 56>235

1.26 22.9 5.9 47>9661.42 24.3 4.1 48>980

1.38 29.6 4.4 50>177

1.80 19.7 6.4 41>538

2.17 20.3 4.6 43>733

1.22 25.4 5.4 46>581

1.09 35.2 4.4 70>545

0.76 38.1 5.3 65>401

1.04 24.7 8.4 48>591

0.88 31.5 5.4 57>288

2.04 19.4 6.9 37>790

1.43 25.0 6.1 46>867

2.45 27.1 4.5 43>654

1.32 27.1 3.3 49>693

1.68 21.9 6.7 56>361

0.96 33.3 3.8 63>731

0.95 34.4 5.5 70>378

1.54 24.7 4.2 43>508

0.97 31.9 5.4 56>033

1.62 26.1 6.3 46>549

1.42 26.9 3.2 45>685

1.14 24.1 6.5 47>988

1.58 22.2 3.8 42>822

1.31 28.1 6.4 50>169

1.37 26.3 5.4 50>713

0.80 30.0 7.8 55>701

2.09 23.7 6.9 44>625

1.62 25.1 3.0 46>032

1.70 22.9 6.4 43>614


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1.38 25.3 4.9 50>043

1.11 29.1 3.4 56>633

0.86 32.1 4.8 52>104

1.25 33.7 4.0 61>233

1.00 30.7 5.3 58>078

2.10 17.1 4.3 37>989

1.27 25.7 4.7 52>0941.60 23.6 3.1 53>207


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ata.


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session 2-mathematical text count

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