Instatpioneer.netserv.chula.ac.th/~tdumrong/2301286/instat_20Mar2017.pdf · การนําเสนอข้อมูลด้วยตารางและกราฟ ... LOG

เอกสารเผยแพรเพอการศกษารวมฉลอง 100 ป

การวเคราะหขอมลทางสถต

ดวยโปรแกรมสาเรจรป

Instat

รองศาสตราจารย ดารงค ทพยโยธา

ภาควชาคณตศาสตรและวทยาการคอมพวเตอร

คณะวทยาศาสตร จฬาลงกรณมหาวทยาลย

การคานวณคาสถตเบองตนการนาเสนอขอมลดวยตารางและกราฟการหาชวงความเชอม นของคาพารามเตอรการทดสอบสมมตฐานการหาสมประสทธการถดถอยและสหสมพนธการวเคราะหความแปรปรวน

รองศาสตรจารย ดารงค ทพยโยธาภาควชาคณตศาสตรและวทยาการคอมพวเตอร คณะวทยาศาสตร จฬาลงกรณมหาวทยาลย

Download Instat at http://www.rdg.ac.uk/ssc

Statistical Software Instat

การวเคราะหขอมลทางสถตดวยโปรแกรมสาเรจรป

Instat Functions

CALculate

CAL

CALculate LHS = RHS

CAL LHS = RHS

LHS = RHS

X1

?

+, - , *, /, ^?

, , , ,

SQR

EXP

LN

LOG

SQR(X) = X

EXP(X) = Xe

LN(X) = n X

LOG(X) = og(X)

PI

SIN

COS

TAN

PI =

ACS

ASN

ATNACS = arccos, ASN = arcsin

ATN = arctan

ABS

INT

SGN

ABS(X) = X

INT(X) = [ X ]

SGN(X) = signum(X)

RND(1)

RND(X)

RND(1) 1

RND(X) X

FAC

COM

PER

FAC(n) = n!

COM(r, n) = )!rn(!r!n

PER(r, n) = !r!n

ACC ACC 5

ACC n

n

Instathttp://www.rdg.ac.uk/ssc

Download data : http://pioneer.netserv.chula.ac.th/~tdumrong/instatdata

Instat

1 . . 2552

Instat . - - :

, 2551.

160

1. ( ) . 2. - - . I .

.

005.55

ISBN 978-974-03-2371-6

10330

. 0-2218-7000-3 0-2255-4441

. 0-2218-9881 0-2254-9495

. . . 0-5526-0162-5 0-5526-0165

. . . 0-4421-6131-4 0-4421-6135

. . . 0-3839-4855-9 0-3839-3239

. . . 0-3739-3023 0-3739-3023

(CHAMCHURI SQUARE) 4 . 0-2160-5300-1 0-2160-5304

Call Center 0-2255-4433 http://www.chulabook.com

email : [email protected]

. . . 0-5391-7020-4 0-5391-7025

. . . 0-5526-7010 0-5521-6388

. . . 0-7567-3648-51 0-7567-3652

. . 0-7521-8115 0-7521-8115

( ) . 43/1 . 0-2538-2573 0-2539-7091

14 . 0-2218-9889-90 0-2254-9495

. 0-2218-3557, 0-2218-3563

http://www.cuprint.chula.ac.th

Instat

Instat

1 2

Instat

-

-

-

-

-

-

Instat

1.

Instat

Instat

Download Website

http://www.rdg.ac.uk/ssc

http://www.rdg.ac.uk/ssc/software/instat/instat.html

Download

Website

http://pioneer.netserv.chula.ac.th/~tdumrong/instatdata

Window in Instat

Window

Window Current Worksheet

1. *.wor

2. Graphs, Statistics

3. F11

4. F2 Window

Current Worksheet Window Commands and

Output

Window Commands and Output

1.

2. Output *.out

3.

4. Copy

Word

5. Output File

Edit View/Edit Text Open

Output File *.out

6.

Window Graphs

1. *.igt

2. Load File Load graph

Graphics Load graph

3.

4. Word

Copy

Word Paste

Window Table

1. *.itb

2. Window graph


3. Load File Load Table

Statistics Tables Load Table

4. Save File Save

( Window Table )

1 1 - 24

1.1 Instat 1

1.2 2

1.3 2 5

1.4 Two data Columns 6

1.5 Factor 7

1.6 9

2 25 - 48

2.1 Statistics Summary Describe 26

2.2 Statistics Summary Column Statistics 32

2.3 Statistics Summary Group 34

2.4 Statistics Tables Frequency 36

2.5 Statistics Tables Summary 38

2.6 Statistics Tables General 40

2.7 Graphics 42

3 49 - 68

3.1 49

3.2 55

3.3 65

4 69 - 96

4.1 0H : = 0 69

4.2 0H : 1 = 2 2 73

4.3 0H : D = 0d 2 82

4.4 0H : 2 = 20 87

4.5 0H : 21 = 2

2 90

4.6 0H : p = 0p 93

4.7 0H : 1p - 2p = 0p ( 1p 2p ) 95

5 97 - 112

5.1 97

5.2 105

6 113 - 132

6.1 113

6.2 %100)1(

122

6.3 0H : = 0 125

6.4 0H : = 0 128

7 133 - 146

7.1

(One-Way ANOVA, Simple-Factor ANOVA) 133

7.2

(Randomized Complete Block Designs, Mutiple-Factor ANOVA) 139

1 Window Commands and Output 147 - 148

2 Instat macro programing Instat 149 - 150

3 Instat Log File 151 - 152

4 Word Excel Instat 153 - 154

1

1.1 Instat Instat

1. Icon

Start / All Programs

2. Instat Logo Instat

1 2

Instat

Instat

1. Instat+ for Windows Instat

2. Command menu bar

3. Current Worksheet Window Current Worksheet

4. UNTITLED.WOR

5. Commands and Output Window

F2 Window Current Worksheet


6.

7. Instat X1, X2, X3, ...

8. Instat id, sex, age, ...

9. 1, 2, 3, ... 10.

1.2

1. Instat X1 x

2. 5 2, 3, 7, 5, 12

3. example001.wor

4.

1 3

1. 1 x

2.

2

2, 3, 7, 5, 12

Enter

X1 Instat

x

3. 3

File

4. Save Save Worksheet As

1. Excel

2. copy

Word Excel

4

1 4

5. 4 example001 ( wor )

5 example001.wor

6. 6 Statistics

7. 7 Summary Describe

Descriptive Statistics

1.

Instat

directory

InstatWorking

2. directory

directory

1 5

8. 8

X1

Available data

9. OK Window Commands and Output

1.3 2 1 : 12, 15, 23, 34, 42, 55 X1(sample1)

2 : 18, 29, 44, 35, 65, 72, 84, 89 X2(sample2)

2

1. X1(sample1) X2(sample2)

1.

(*.out)

2. 35

3. Copy

Word

4. DES


1 6

X1, X2 Instat

sample1, sample2

2. X2(data) X1-F(code)

X1-F Data Factor ( Instat

Factor )

X2 Instat

code

data

1.4 Two data Columns 1. Instat


2. 1

sample1 sample 2

1 7

1.5 Factor 1. Instat Window Current Worksheet

2. 1 code data

code 1 1

2 2 data

2 X1 Factor

Factor -F

3. Manage Column Properties Factor

1 8

4. Factor Make or modify a factor column

1. Data column X1(code)

2. Number of levels (

1.6)

5. OK

1 X1 X1-F Factor

1. 2

2. 2 columns Two data columns

3.

Data columns and Two Factor levels

1. Number of levels Instat

2. Attach to existing lebel column

Labels

3. Attach to new lebel column

lebel

4. Generate new label column from

data lebel

5.

Factor Option Make

factor into an ordinary variable

factor

1 9

1.6

Instat

1. ........................

2.

3. ................................

4.

5.

6. ...........................................

7. ..........................

8. ............................

Instat

(file name) Instat (Instat variable name)

(User variable label) (missing value)

(value label Factor)

example4.wor

1. Instat X1

id

2. Instat X2

sex

-9

1 Male 2 Female

1 10

3. Instat X3

age

-99

4. Instat X4

educ

-9

1 Under_gr 2 Graduate 3 Post_gr 4 Doctor

5. Instat X5

status

-9

1 Single 2 Married 3 Widow 4 Divorced

6. Instat X6

income

2

-9999

7. Instat X7

grade

2

8. Instat X8

bonus

2

-999

1.

2. 1 2

-9

1 11

3.

-99

4. 1

2

3

4

-9

5. 1

2

3

4

-9

6.

-999

7.

8.

-999

1. 1

2.

3. 37

4.

5.

6. 5500

7. 3.78

8. 11000


1. Instat X1, X2, .... , X8 column Current WorkSheet

2. id, sex, age, educ, status, income, grade, bonus

1

2

4

1 12

3.

4. (Missing value)

5. (Value label)

1. Instat

Instat File New Worksheet ( Ctrl + N)

2. Window Current Worksheet

3. 1 id, sex, age, educ, status, income, grade, bonus

4. 50

id sex age educ status income grade bonus1 1 37 2 4 5500 3.78 110002 2 29 3 1 4100 3.89 123003 2 48 1 2 5400 3.67 216004 1 -99 1 2 -999 2.78 199985 2 33 2 -9 -999 3.00 299976 2 45 3 4 8300 3.45 166007 2 38 1 4 7700 3.89 77008 2 23 3 1 3900 3.67 117009 1 34 2 4 4500 2.56 9000

10 1 50 2 2 6700 2.69 670011 2 43 2 2 4700 3.56 1880012 2 37 3 2 3900 3.00 390013 1 24 2 1 3300 2.45 990014 1 46 2 2 4900 2.45 1470015 1 32 1 1 4000 3.87 800016 1 42 2 3 6600 3.67 1320017 1 38 4 2 8000 3.23 32000

1 13

18 2 41 2 3 7000 3.45 2100019 2 -99 1 -9 2000 3.21 200020 1 54 2 2 7400 3.00 2220021 2 32 3 -9 6200 2.56 2480022 1 43 1 2 4700 2.45 1880023 2 22 1 1 3400 3.78 340024 1 40 2 2 5900 2.67 1770025 1 37 4 -9 7500 3.45 2250026 1 28 1 1 3100 2.78 930027 1 44 3 2 6800 2.56 1360028 1 56 2 2 6400 2.78 1920029 1 35 3 1 5800 3.33 580030 2 42 1 2 3900 2.56 1170031 1 21 2 1 4700 2.67 1410032 1 39 2 2 5900 2.89 1770033 1 45 1 2 4900 2.56 490034 1 31 1 2 3100 3.23 930035 1 51 2 3 5400 3.01 540036 1 23 3 1 6300 2.77 1260037 1 40 3 2 7100 2.89 2130038 1 47 2 3 6600 2.77 1980036 1 53 2 2 7200 2.31 2160040 2 27 2 1 1700 2.67 510041 1 29 4 1 5000 2.89 1500042 1 40 3 2 6000 3.67 1800043 2 30 1 1 3000 2.56 1200044 2 53 2 2 4700 3.00 940045 1 31 1 1 2800 2.74 560046 1 45 2 2 5700 2.67 2280047 1 22 2 4 4300 3.07 430048 2 34 1 1 3900 2.56 780049 2 33 3 2 6700 2.12 2010050 1 54 2 2 4800 2.66 19200

50 data_001.wor

1 14

5. Missing value

Manage Resize Worksheet

6. Resize Worksheet Worksheet sizes

1. 1 -9

2. 2 -99

3. 3 -999

7. OK

1. Missing value -9 *

2. Missing value -99 **

3. Missing value -999 ***

1 15

Value label sex, educ, status

8. Labels ( 1)

Label

9. Label

Column L1 Male, Female

Column L2 Under_gr, Graduate, Post_gr, Doctor

Column L3

Single, Married, Widow, Divorce

1 16

Label sex

10.

Manage Column Properties Factor

11. Factor

Make or modify a factor column

12. 1.

2.

Data column

3. Instat

Option : Number of levels = 2

1. Number of levels Instat

2. Attach to existing lebel column

Labels

3. Attach to new lebel column

lebel

4. Generate new label column from

data lebel

5.

Factor Option Make

factor into an ordinary variable

factor

1 17

13. 1. Option : Attach to existing label column

2. L1 Label

3. Label Column L1 Male

Female

14. OK

1. 1 Male 2 Female

2. X2 -F Factor value label

Label educ


1 18


1. X4(educ)

2. Instat Option : Number of levels = 4

3. Option : Attach to existing label column

4. L2 Label column L2

5. Label Column L2 Under_gr, Graduate, Post_gr, Doctor

17. OK

1. 1 Under_gr 2 Graduate

3 Post_gr 4 Doctor


1 19

Label status



1. X5(status)

2. Instat Option : Number of levels = 4

3. Option : Attach to existing label column

4. L3 Label column L3

5. Label Column L3 Single, Married, Widow, Divorce

1 20

20. OK

1. 1 Single 2 Married

3 Widow 4 Divorce


1. Label


Make or Modify a factor column

2. Label Data column

3. Option : Make factor into an ordinary variable

2. (ordinary variable) Factor

Option : Detach label column

1 21

21. Manage Column Properties Format

22. Format Format data

X6(income) 2

1. X6

2. 0.00

3. Required format

0.00

23. OK

income 2

1 22

24. Manage Column Properties Format

Format Format data

X7(grade) X8(bonus) 2

1. X7, X8

2. 0.00

3. Required format 0.00

25. OK

grade bonus 2

Format 0.00 grade 5 3

Format 0.00 grade 5 3.00

1 23

26. Manage Worksheet Information

27. Worksheet Information

1. 2. directory

3. Label 3 4. 8

28. Option Columns

1. Column X1, X2, X3, ...

2.

Name id, sex, age, ...

3.

Vaiable Label

Factor Label

1 24

1. Window Current Worksheet File

2. Save Save as data_002

3. Save

1. File Open Worksheet

2. Open Worksheet Open file for input

3. Open

1.

Instat

directory

InstatWorking

2. directory

directory

2

1 2 Instat

1. Statistics Summary Describe

1 3

Stem and Leaf, Boxplot, Cumulative frequency

2. Statistics Summary Column Statistics

3. Statistics Summary Group

4. Statistics Tables Frequency 1 2

5. Statistics Tables Summary

6. Statistics Tables General

7. Graphics Plot

8. Graphics Boxplot Box Plot

9. Graphics Histogram Histogram

10. Graphics Frequency Chart

11. Graphics Stem and Leaf

2 26

2.1 Statistics Summary Describe Statistics Summary Describe

1. example005.wor Window Current Worksheet


3. Describe Descriptive Statistics

1. X3

2. Instat

2 27

4. OK Window Commands and Output

1.

2. DES X3 Instat

DES X3 Window Commands and Output

3. age

4.

No. of observations 50 50

No. not missing 48 48 2

Minimum 21

Maximum 56

Range 35 = max - min

Mean 37.938

Std. deviation 9.5526

Std. deviation 1n

)xx( 2i

n

1 i

Statistics Summary Describe

(Additional statistics) 1 3

Stem and Leaf, Boxplot, Cumulative frequency

2 28

Descriptives Statistics

4. (Additional statistics)

3. Descriptive Statistics

1. Additional statistics

2. Additional statistics

Standard error, Median, Quartiles

5.

1. Percentiles

2. Percentiles

45P 90P 45 90 ( comma 45, 90)

Instat

2 29

6. 1. Proportions

2. Proportions

25 25

3. 25

4.

7.

1. 4 Precision 4

2. Kg, mm, inch Unit

3.

4. Graphics

Check box

Stem and Leaf, Boxplot

Cumulative frequency

2 30

8. OK

1. Title

2. Instat Window Commands and Output


4. Additional statistics 1 3

5. 45 90

6. 25

7. 25

12.5% 48

8.

4

STEM & LEAF

2 31

Stem and Leaf

1. = 21 = 56 48 ( Missing data )

2. 5 : 6 6 56 1

3. 20 - 24 6 21, 22, 22, 23, 23, 24

25 - 29 10 - 6 = 4 27, 28, 29, 29

:

35 - 39 7 35, 37, 37, 37, 38, 38, 39

:

50 - 54 7 - 1 = 6 50, 51, 53, 53, 54, 54

55 - 59 1 56

Box Plot

1. = 21

2. 1. = 31

3. = 38

4. 3. = 45

5. = 56

6.

Cumulative frequency

1. Copy Word

CTRL+C

Word Paste

2.

2 32

2.2 Statistics Summary Column Statistics Statistics Summary Column Statistics

1.

example005.wor

Window

Current Worksheet

2. Statistics Summary Column Statistics

3. Column Statistics Column Statistics

1. X3

2. Count, Mean, St. dev, Median Percentiles 50

4.

OK

2 33

5. Column Statistics

1. By factor

2. X4(educ)

1.

Factor

2. For factor

4. OK

1. For factor 2. X4(educ) 3. Under_gr

OK

2 34

2.3 Statistics Summary Group Statistics Summary Group


2. Statistics Summary Group

3. Group Grouped Frequency Distribution

1. X3

2. Display percentages

2 35

4.

OK

1. Window Commands and Output File Save Save as

Save Commands and Output As

( .out ) Save

2.

Window Current Worksheet Window Commands and Output

Edit View/Edit Text Open

Open Text File for Editing Open

2 36

2.4 Statistics Tables Frequency Statistics Tables Frequency 1 2

1.

example005.wor

Window

Current Worksheet

2. Statistics Tables Frequency

3. Frequency Frequency Tables

1. X4

4. OK

1. Window ( )

2. File Save Save as ( .itb ) Save

3.

3.1 Current Worksheet Statistics Tables Load Table

3.2 Window Table ( ) File Load Table

4. ( )

2 37

2

5. 3.

Frequency Tables

1. X2, X4 Factors

6. OK 2

2 sex, educ, status

X2, X4, X5 Factors

2 38

2.5 Statistics Tables Summary Statistics Tables Summary


2. Statistics Tables Summary

3. Summary Summary Tables

1. X5 status

2. X3 age

3. Means, Medians, St. Deviations

2 39

4. OK

X5, X4 Factors

OK

2 40

2.6 Statistics Tables General Statistics Tables General

1.

example005.wor

Window

Current Worksheet

2. Statistics Tables General

3. General General tables

1. X2, X5

2 sex, status

2. X6 income

3. Averages

4. Add Statistic

5.

Means(4;0;;X6;)

Add Statistic

3., 4. 5.

2 41

4. OK

(Male Single) = 4375.0

(Female Single) = 3333.3

(All, Single) = 3928.6

X2, X5, X4 Factors

(Male, Single, Under_gr) = 3300

2 42

2.7 Graphics2.7.1 Graphics Plot

Graphics Plot

1.

example005.wor

Window

Current Worksheet

2. Graphics Plot

3. Plot Plot

1. X6 income Y

2. X3 age X

4. OK

2 43

2.7.2 Graphics Boxplot

5. Graphics Boxplot

6. Boxplot BoxPlot

1. X3 Data to be ploted

7. OK

1. Window Save

( )

File Save Save as

Save Graph As ( .igt ) Save

2.

2.1 Window Current Worksheet Window Commands and Output

Graphics Load Graph Open

2.2 Window ( )

File Load Graph

Graph Open

3. ( )

2 44

2.7.3 Graphics Histogram

5. Graphics Histogram

6. Histogram Histogram

1. X3 Variable to plot age

2.

3. 5

7. OK

2 45

8. Graphics Histogram Histogram Histogram

1. X3 Variable to plot age

2. 10

9. OK

1. Options

2.

2 46

2.7.4 Graphics Frequency Chart

10.

Graphics Frequency Chart

11.

Frequency Chart Frequency Chart

1. X4 Variable to plot educ

2. Bar

(Chart type) 4 (Bar) (Hbar)

(Pie) (Line)

12. OK

(Pie)

2 47

13. Graphics Frequency Chart Frequency Chart

Frequency Chart

1. X4(educ) educ

2. Option : Cluster by

3. Option : Cluster by

Factor X2(sex)

X2(sex)

14. OK

2 48

2.7.5 Graphics Stem and Leaf

15. Graphics Stem and Leaf

16. Stem and Leaf Stem and Leaf

1. X3 Data to be displayed age

17. OK

1. = 21 = 56 48 ( Missing data )

2. 5 : 6 6 56 1

3. 20 - 24 6 21, 22, 22, 23, 23, 24

25 - 29 10 - 6 = 4 27, 28, 29, 29

:

35 - 39 7 35, 37, 37, 37, 38, 38, 39

:

50 - 54 7 - 1 = 6 50, 51, 53, 53, 54, 54

55 - 59 1 56

3

3.1 %100)1(

1. 2

n x

%100)1( x - 2

zn

x + 2

zn

2. 2

n x 2s

2.1 n 30 %100)1(

x - 2

zns x +

2z

ns

2.2 n 30 %100)1(

x - 2

tns x +

2t

ns = n - 1

3. 2

n 30 x

%100)1( x - 2

zn

x + 2

zn

4. 2

n 30 x 2s

2 2s

%100)1( x - 2

zn

x + 2

zn

3 50

%100)1( 2

2

2

2s)1n( 2 2

2 1

2s)1n( = n - 1

3.1.1 2 = 1600

829.5 790.6 829.9 780.0 750.7 810.2 717.8 786.3 835.8 739.0770.1 722.8 804.4 786.9 732.5 823.7 726.6 725.6 799.8 801.4765.5 720.5 811.0 829.2 810.1

25 780

95%

2 = 1600

n = 25 x = 780

95%

x - 025.0zn

x + 025.0zn

780 - 1.9625

40 780 + 1.9625

40

764.32 795.68

95% (764.32, 795.68)

%100)1( Instat

1.

1

x 25

example6.wor

2. Statistics Simple Models Normal, One Sample

3 51

3. Normal, One Sample

Simple Models - Normal Distribution, One Sample

1. Single data column

2. 5

3. z, t F 6

- t

- z ( s )

- 2

4.

3 52

4. Simple Models - Normal Distribution, One Sample


2. X1 X1(x)

3. Mean, known variance (z-interval)

4. Mean, known variance (z-interval) St. Dev Variance

5. 95%

5.

OK

95% (764.32, 795.68)

1. ZInt 'x' ; sde 40 Window Commands and Output

2. 95% = 41

ZInt 'x' ; sde 41 Window Commands and Output

3 53

4.



2. X1 X1(x)

3. Mean (t-interval)

4. 95%

5.

OK

95% (763.48, 796.51)

1. TINt 'x' Window Commands and Output

2. 8 ACC 8 Window

Commands and Output

3 54

95% (763.48, 796.51)

x - 2

tns x +

2t

ns = n - 1

= 0.05, 2 = 0.025, = 25 - 1 = 24, 24 , 0.025t = 2.064, x = 780, s = 40.002

95% x - 2

tns x +

2t

ns

780 - 2.06425002.40 780 + 2.064

25002.40

780 - 16.5128256 780 + 16.5128256

763.4871744 796.5128256

%100)1( 4.


1. Single data column 2. X1

3. Variance (F-interval) 4. 95%

5.

OK

3 55

95% (31.234, 55.649) 95% (975.58, 3096.8)

95% (975.58, 3096.8)

2

2

2s)1n( 2 2

21

2s)1n( = n - 1

n = 25, = 0.05, 2 = 0.025, = 24, 2025.0 = 39.364, 2

975.0 = 12.401, 2s = 1600.2

95% 2 2

2

2s)1n( 2 2

21

2s)1n(

364.39)2.1600)(24( 2 401.12

)2.1600)(24( 975.6 2 3096.9

3.23.2.1 %100)1( 1 - 2

21. 1n 30 2n 30 1.1 2 2

1 22

%100)1( 1 - 2

( 1x - 2x ) - 2

z2

22

1

21

nn 1 - 2 ( 1x - 2x ) + 2

z2

22

1

21

nn

1.2. 2 21 2

2 2

1s , 22s 2

1 , 22 2

1s , 22s

%100)1( 1 - 2

( 1x - 2x ) - 2

z2

22

1

21

ns

ns 1 - 2 ( 1x - 2x ) +

2z

2

22

1

21

ns

ns

2. 1n 30 2n 30 2.1 2 2

1 22

%100)1( 1 - 2

( 1x - 2x ) - 2

z2

22

1

21

nn 1 - 2 ( 1x - 2x ) + 2

z2

22

1

21

nn

2.2 2 21 2

2 2

1s 22s

2.2.1 21 = 2

2 %100)1( 1 - 2

( 1x - 2x ) - 2t Ps

21 n1

n1 1 - 2 ( 1x - 2x ) +

2t Ps

21 n1

n1

3 56

2Ps = 2nn

s)1n(s)1n(

21

222

211 = 1n + 2n - 2

2.2.2 21 2

2 %100)1( 1 - 2

( 1x - 2x ) - 2

t2

22

1

21

ns

ns

1 - 2 ( 1x - 2x ) + 2

t2

22

1

21

ns

ns

=

)1n(1)n

s()1n(

1)ns

(

)ns

ns

(

22

2

22

12

1

21

22

22

1

21

3.2.1 2

1n = 9 1

61.36 57.76 71.94 61.77 58.66 71.61 71.52 58.67 62.77

2n = 16 2

56.92 58.30 67.48 53.96 62.00 59.61 52.02 61.6064.83 58.55 52.53 64.74 55.51 66.18 55.51 54.18

95% 1 - 2

Instat

1.

2 data1, data2

example7.wor

2. Statistics Simple Models Normal, Two Samples

3 57

3. Normal, Two Samples

Simple Models - Normal Distribution, Two Samples

1.

1.1 Two data columns 2

1.2 Data columns and two factor levels 2

1 (Factor variable)

1

3. 7

3. z, t F 6

- t

- z

- F

4.

3 58

5.

5.1 2

5.2 2

4. Simple Models - Normal Distribution, Two Samples

1. Two data columns

2. Data Column X1(data1)

2nd Data Column X2(data2)

3. Means (t-interval)

4. 95%

5. 2

3 59

5. OK

21 2

2

95% 1 - 2 (-0.02916, 10.052)

21 = 2

2

4.

1. Two data columns




4. 95%

5. 2

3 60

5.

OK

21 = 2

2

95% 1 - 2 (0.38758, 9.6357)

%100)1(

22

21

s

s

2f1 2

2

21 2

2

21

s

s

2 1f

1 1 = 1n - 1, 2 = 2n - 1

4. 1. Two data columns



3. Variances (F-interval)

4. 95%

3 61

5. OK

95% (0.44819, 5.8796)

95% 22

21

22

21

s

s

2f1 2

2

21 2

2

21

s

s

2 1f

1

1 = 1n - 1 = 9 - 1 = 8 2 = 2n - 1 = 16 - 1 = 15

15) (8, 0.025,f = 3.2, 15) (8, 0.975,f = 8) (15, 0.025,f

1 = 10.41 2

1s = 35.853, 22s = 25.008

95% 22

21

008.25853.35

2.31 2

2

21 008.25

853.35)1.4

1(1

0.4482 22

21 5.8796

3 62

3.2.2 %100)1( D

2 ( )

%100)1(

D 2 ( )

n 1 2

1. 2.

1x 1y

2x 2y

3x 3y

: :

nx ny

1. id = ix - iy , i = 1, 2, ... , n

2. d

3. ds

1. n 30 %100)1( D

d - 2

znd D d +

2z

nd

2. n 30

%100)1( D

d - 2

tn

sd D d + 2

tn

sd = n - 1

%100)1( D

3.2.2 10

1 2 10

1 (test1) 2 (test2)1 76 812 60 523 85 874 58 705 91 866 75 777 82 908 64 639 79 85

10 88 83

3 63

id = i1test - i2test , i = 1, 2, ... , 10

98% D

Instat

1.

2 test1, test2

example9.wor

2. Statistics Simple Models Normal, Two Samples

3. Normal, Two Sample


3 64

1. Two data columns

2. Data Column X1(test1)

2nd Data Column X2(test2)


4. 95%

5.

4. OK

95% D (-6.1644, 2.9644)

8

1. Window Commands and Output

2. ACC 8

ACC n n , n = 1, 2, 3, 4, ... , 10

3. ACC 5

3 65

3.33.3.1 %100)1( p

%100)1( p p - 2

z nqp p p +

2z n

qp p = nx

p = 35

example005.wor 50 95% p

1. example005.wor

2.

Statistics Simple Models Proportion, One Sample

3. Proportion, One Sample

One proportion - binomial model

1. Layout Single data column

2. X3 X3(age)

3. Option Less than 35

3 66

4. Option Simple Normal (symmetrical)

5. 95%

3. OK

1. n = 48

2. 35 x = 19

3. p = 4819 = 0.396

4. nqp = 0.071

5. 95% p (0.257, 0.534)

3.3.2 %100)1( 1p - 2p

%100)1( 1p - 2p

( 1p - 2p ) - 2

z2

22111

nqp

nqp 1p - 2p ( 1p - 2p ) +

2z

222

111

nqp

nqp

1p = 11

nx , 2p =

22

nx

1p = 35

2p = 35

example005.wor 50 95% 1p - 2p

1. example005.wor

3 67

2. Statistics Simple Models Proportions, Two Samples

3. Proportions, Two Samples

Two proportions - binomial model

1. Layout Data column and two factor levels

2. X3 X3(age)

3. X2 X2(sex)

4. 1st Factor Value Female

5. 2nd Factor Value Male

6. Option Less than 35

7. 95%

3 68

3. OK

1. 1n = 17 2n = 31

2. 35 1x = 9

35 2x = 10

3. 1p = 35 = 179 = 0.529

2p = 35 = 3110 = 0.323

4. 1p - 2p = 3110 - 17

9 = 0.207

5.2

22111

nqp

nqp = 0.147

6. 95% 1p - 2p (-0.082, 0.496)

2p 1p Instat 1p - 2p

1p 2p Instat 2p - 1p

4

4.1 0H : = 0

1. 0H : = 0

1H : 0

2.

3. n x 2s

4. Z t

5. z t 4.

6.

6.1 Z 2

z 2

z Z 2

z Z 2

z

6.2 t 2

t 2

t t 2

t t 2

t

7.

7.1 Z z 2

z z 2

z 0H

7.2 t t 2

t t 2

t 0H

4. ( )

1. 2 Z =

n

x 0

2. 2

2.1 n 30 s Z =

ns

x 0

4 70

2.2 n 30 t =

ns

x 0

3. 2

n 30 Z =

n

x 0

4. 2

n 30 s Z =

ns

x 0

Instat

1. 0H : = 0

1H : 0

2.

3. n x 2s

4. t Z

5. t =

ns

x 0 t = n - 1

z =

n

x 0 z

6. t

Sig. (2-tailed) t

Sig. (2-tailed)

= 2 t t

= 2 P( t t )

z Sig. (2-tailed) = 2 P( Z z )

7. 2

1 t 2

t

t 2

t t 2

t 0H

z 2

z

z 2

z z 2

z 0H

2 Sig. (2-tailed)

Sig. (2-tailed) 0H

4 71

1

1H : 0 1H : 0

Z z z

Z z Z z

t t t

t t t t

Sig. (2-tailed)

t 0, Sig. (2-tailed) 2

0H

z 0, Sig. (2-tailed) 2

0H


0H


0H

4.1.1 50

50 12

41, 42, 47, 41, 54, 26, 26, 65, 34, 49, 29, 50 0.05

1. 0H : = 50

1H : 50

2. = 0.05

3.

x

example10.wor

example10.wor

4. n 30 t

4.1 Statistics Simple Models Normal, One Sample

4 72

4.2 Normal, One Sample



2. X1 X1(x)

3. Mean (t-interval)

4. Significance Test

5. 0H : = 50 50 Value

6. 2-tailed Significant

4.3 OK

4 73

5. t = -2.33 = 116. Sig. (2-tailed) t

Instat Sig. (2-tailed) = 0.0400 7.

1. t = -2.329 110.025,t = 2.201 1H : 50 t -2.201 t 2.201 0H

2. Sig. (2-tailed) = 0.05 Sig. (2-tailed) = 0.04 0.05 = 0H

50 0.05

4.2 0H 1 = 2

1. 0H : 1 = 2 1H : 1 2

2. 3. 1n 1 1x , 2

1s 2n 2 2x , 2

2s 4. Z t 5. z t 4. 6.

6.1 Z

2z

2z Z

2z Z

2z

6.2 t

2t

2t t

2t t

2t

7. 7.1 Z z

2z z

2z 0H

7.2 t t

2t t

2t 0H

4 74

0H : 1 = 2 2

1. 1n 30 2n 30

1.1 2 21 2

2

Z =

2

22

1

21

2121

nn

)()xx(

1.2. 2 21 2

2

21s 2

1 21s

22s 2

2 22s

Z =

2

22

1

21

2121

nn

)()xx(

2. 1n 30 2n 30

2.1 2 21 2

2

Z =

2

22

1

21

2121

nn

)()xx(

2.2 2 21 2

2

21s 2

2s

2.2.1 21 = 2

2 t =

21p

2121

n1

n1s

)()xx(

2ps = 2nn

s)1n(s)1n(21

222

211 = 1n + 2n - 2

2.2.2 21 2

2 t =

2

22

1

21

2121

ns

ns

)()xx(

=

)1n(1)n

s()1n(

1)ns

(

)ns

ns

(

22

2

22

12

1

21

22

22

1

21

21 = 2

2 4.4

Instat

1. 0H : 1 = 2

1H : 1 2

2.

4 75

3. 1n 1, 2n 2

1x 2x 21s 2

2s

4. t

21 = 2

2 t =

21p

2121

n1

n1s

)()xx(

2ps =

2nns)1n(s)1n(

21

222

211 = 1n + 2n - 2

21 2

2 t =

2

22

1

21

2121

ns

ns

)()xx(

=

)1n(1)n

s()1n(

1)ns

(

)ns

ns

(

22

2

22

12

1

21

22

22

1

21

5. t t

6. Sig. (2-tailed) t

Sig. (2-tailed)

= 2 t t

= 2 P( t t )

7. 2

1 t 2

t

t 2

t t 2

t 0H

2 Sig. (2-tailed)

Sig. (2-tailed) 0H

1

1H : 1 - 2 0 1H : 1 - 2 0

Z z z

Z z Z z

t t t

t t t t

Sig. (2-tailed)


0H


0H

4 76

4.2.1 2

1 1

2 2

1 16 ( )

161 162 183 174 164 184 185 183 184 184185 174 194 175 174 198

2 18 ( )

181 184 182 183 183 184 184 185 183 183184 185 183 184 184 172 195 183

0.05

2

1. 0H : 1 = 2

1H : 1 2

2. = 0.05

3.

w1

1

w2

2

( example11.wor)

4. 2 30

t

4.1 Statistics Simple Models Normal, Two Samples

4 77

4.2 Normal, Two Samples


1. Two data columns

2. X1(w1) X2(w2)




6. 0H : 1 = 2

0H : 1 - 2 = 0 0 Value

7. t 2

4.3

OK

4 78

21 2

2

5. t =

2

22

1

21

2121

ns

ns

)()xx( =

)1n(1)

ns

()1n(

1)ns

(

)ns

ns

(

2

2

2

22

1

2

1

21

2

2

22

1

21

Instat t = -2.15 = 19.4

6. Instat Sig. (2-tailed) = 0.0446

7.

1. t

19,025.0t = 2.093 20,025.0t = 2.086 19.4,025.0t = 2.1

1H : 1 2

t -2.1 t 2.1 0H

2. Sig. (2-tailed)

1H : 1 2

= 0.05 Sig. (2-tailed)

Sig. (2-tailed) = 0.0446 0.05 = 0H

0.05

t 2

4 79

4.2.2 2

1n = 9 1

61.36 57.76 71.94 61.77 58.66 71.61 71.52 58.67 62.77

2n = 16 2

56.92 58.30 67.48 53.96 62.00 59.61 52.02 61.6064.83 58.55 52.53 64.74 55.51 66.18 55.51 54.18

10 0.05

2

1. 0H : 1 - 2 = 10

1H : 1 - 2 10

2. = 0.05

3. example7.wor


4. 2 30

t




4 80

1. Two data columns

2. X1(data1) X2(data2)




6. 0H : 1 - 2 = 10

10 Value

7. t 2

4.3 OK

4 81

21 = 2

2

5. t =

21p

2121

n1

n1s

)()xx(

2ps = 2nn

s)1n(s)1n(21

222

211 = 1n + 2n - 2

Instat t = -2.33

= 23

6. Sig. (2-tailed) t

Instat Sig. (2-tailed) = 0.0357

7.

1. t

23,025.0t = 2.069

1H : 1 - 2 10

t -2.069 t 2.069

t = -2.33 0H

2. Sig. (2-tailed)

1H : 1 - 2 10

= 0.05 Sig. (2-tailed)

Sig. (2-tailed) = 0.0357 0.05 = 0H

10 0.05

4 82

4.3 0H : D = 0d 2 2

0H : D = 0d

1. 0H : D = 0d

1H : D 0d

2.

3. n 1 2 ( )

1. 2.

1x 1y

2x 2y

3x 3y

: :

nx ny

1. id = ix - iy , i = 1, 2, ... , n

2. d

3. ds

4. Z t

n 30 Z =

ns

ddd

0

n 30

t =

ns

ddd

0 = n - 1

5.

6.

6.1 Z 2

z 2

z Z 2

z Z 2

z

6.2 t 2

t 2

t t 2

t t 2

t

7.

7.1 Z z 2

z z 2

z 0H

7.2 t t 2

t t 2

t 0H

4 83

Instat

1. 0H : D = 0d

1H : D 0d

2.

3. n d ds

4. t Z

5. t

t =

ns

ddd

0 = n - 1 z =

ns

ddd

0

6. t Sig. (2-tailed) t

Sig. (2-tailed) = 2 t

t

= 2 P( t t )

Z Sig. (2-tailed) = 2 P( Z z )

7. 2

1. t z

t t 2

t t 2

t 0H

Z z 2

z z 2

z 0H

2. Sig. (2-tailed)

Sig. (2-tailed) 0H

1

1H : D 0d 1H : D 0d

Z z z

Z z Z z

t t t

t t t t

Sig. (2-tailed)


0H


0H


0H


0H

4 84

4.3.1 5

2 2

1 2 3 4 5

2.0 2.0 2.3 2.1 2.4

2.2 1.9 2.5 2.3 2.4

0.05

Instat

1. 0H : D = 0

1H : D 0

2. = 0.05

3.


xray

chem

Save example12.wor

4. 30 t




4 85

1. Two data columns

2. X1(xray) X2(chem)



5.

6.

0H : D = 0 0 Value


4.3 OK

4 86

5. t = 1.581139 = 4

6. Sig. (2-tailed) t Sig. (2-tailed) = 0.1890

7.

1. 4,025.0t = 2.776 t -2.776 t 2.776

t = 1.581 0H

2. Sig. (2-tailed)

Sig. (2-tailed) = 0.1890 0.05 = 0H

0.05

0H : D = 0.1

1H : D 0.1

Value 0.1

7.

1. 4,025.0t = 2.776 t -2.776 t 2.776

t = -3.16 0H

2. Sig. (2-tailed)

Sig. (2-tailed) = 0.0341 0.05 = 0H

0.01 0.05

4 87

4.4 0H : 2 = 20

1. 0H : 2 = 20

1H : 2 20

2.

3. n 2s

4.

5. 2 = 20

2s)1n(

Sig. (1-tailed) Sig. (2-tailed)

Instat

1. 20 2s Sig. (1-tailed) = P( 2 2 )

Sig. (2-tailed) = 2 P( 2 2 )

2. 2s 20 Sig. (1-tailed) = 2 P( 2 2 )

Sig. (2-tailed) = 2 P( 2 2 )

6. 2

21 2

2

= n - 1

2 2

21 2 2

2

7. 1. 2

2 2

21 2 2

2 0H

2. Sig. (2-tailed)

Sig. (2-tailed) 0H

1

1H : 2 20 1H : 2 2

0

21

2

2 21

2 2

Sig. (1-tailed)

20 2s Sig. (1-tailed)

0H

20 2s Sig. (1-tailed)

0H

4 88

4.4.1

0.81

10

5.25 3.76 5.36 3.67 6.05 3.89 3.39 6.12 6.49 6.03

2 = 0.81 2 0.81

0.05

1.

0H : 2 = 0.81

1H : 2 0.81

2. = 0.05

3.


x

Save example60.wor

4.

4.1

Statistics Simple Models

Normal, One Samples

4.2 Normal, One Samples


4 89


2. X1(x)

3. Variance (F-interval)


5. 0H : 2 = 0.81

0H : = 0.9

0.9 Value


4.3 OK

5. 2 = 20

2s)1n( 2 = 16.001

6. 29,025.0 = 19.023, 2

9,975.0 = 2.7 = 10 - 1 = 9

2 2.7 2 19.023

Sig. (2-tailed) Instat

20 = 0.81 1.4401 = 2s

Sig. (2-tailed) = 2 P( 2 16.001) = 2(0.066860708097003) = 0.1337

7. 1.

2 0H

2. Sig. (2-tailed)

Sig. (2-tailed) = 0.1337 0.05 = 0H

0.81 0.05

4 90

4.5 0H : 21 = 2

2

Instat

1 2

( 21s 2

2s )

1. 0H : 21 = 2

2

1H : 21 2

2

2.

3. 21s , 2

2s

4. F

5. f = 22

21

s

s ( 2

1s 22s )

21s 2

2s f 1

Sig. (2-tailed) = 2 P(F f )

Sig. (1-tailed) = P(F f )

6.

1 = 1n - 1 2 = 2n - 1

21

f 2

f

F 21

f F 2

f

7. 1.

f 21

f f 2

f 0H

2. Sig. (2-tailed)

Sig. (2-tailed) 0H

1 ( 21s 2

2s )

1H : 21 2

2

f

F f

Sig. (1-tailed) Sig. (1-tailed) 0H

4 91

4.5.1 5 1

1.024 0.972 1.004 0.986 1.015

6 2

1.017 0.991 1.018 0.975 0.983 1.018

0H : 21 = 2

2 1H : 21 2

2 0.1

1. 0H : 21 = 2

2

1H : 21 2

2

2. = 0.1

3.


x Save

example61.wor

4. F




4 92

1. Two data columns

2. X1(data1) X2(data2)

3. Variances (F-interval)


5. 0H : 21 = 2

2

22

21 = 1 ( testing variance ratio)

1 Value


4.3 OK

5. f = 22

21

s

s = 1.1629

6. 1 = 1n - 1 = 4 2 = 2n - 1 = 5

5),(4,0.05f = 5.199 5),(4,0.95f = 4),(5,0.05f

1 = 26.61 = 0.1597444

F 0.1597444 F 5.199

7. 1. f

f 0H

2. Sig. (2-tailed)

Sig. (2-tailed) = 0.8518 0.1 0H

2 0.1

4 93

4.6 0H : p = 0p

1. 0H : p = 0p

1H : p 0p

2.

3. n n 30 p

4. Z

5. z

Sig. (2-tailed) = 2 P( Z z )

6. 2

z 2

z Z 2

z Z 2

z

7. 2

1 z 2

z z 2

z 0H

2 Sig. (2-tailed)

Sig. (2-tailed) 0H

1

1H : p 0p 1H : p 0p

Z z z

Z z Z z

Sig. (2-tailed)


0H


0H

4.6.1 p = 35

example005.wor 50 p = 0.5 0.05

1. 0H : p = 0.5

1H : p 0.5

2. = 0.05

3.

4. Z Instat

4.1 example005.wor

4.2 Statistics Simple Models Proportion, One Sample

4.3 Proportion, One Sample One proportion - binomial model

4 94

1. Layout

Single data column

2. X3

X3(age)

3. Option Less than

35

4. Option Simple

Normal (symmetrical)

5.

Significance Test

6. 0H : p = 0.5 0.5 Value


4.4 OK

5. Instat 1. 1 66

2. z = -1.44

3. Sig. (2-tailed) = 0.1489

6. 025.0z = -1.96 025.0z = 1.96 Z -1.96 Z 1.96

7. 1 z = -1.44 0H

2 Sig. (2-tailed) = 0.1489 0.05 = 0H

p = 0.5 = 0.05

4 95

4.7 0H : 1p - 2p = 0p ( 1p 2p )

1. 0H : 1p - 2p = 0p ( 1p 2p )

1H : 1p - 2p 0p

2.

3. 1n 1 1p

2n 2 2p

4. Z

5. z Sig. (2-tailed) = 2 P( Z z )

6. 2

z 2

z Z 2

z Z 2

z

7. 1 z 2

z z 2

z 0H

2 Sig. (2-tailed)

Sig. (2-tailed) 0H

1

1H : 1p - 2p 0p 1H : 1p - 2p 0p

Z z z

Z z Z z

Sig. (2-tailed)


0H


0H

1p = 35

2p = 35

example005.wor 50 1p - 2p = 0

1p - 2p 0 0.05

1. 0H : 1p - 2p = 0

1H : 1p - 2p 0

2. = 0.05

3. ( example005.wor)

4. Z Instat

4.1 example005.wor

4.2 Statistics Simple Models Proportions, Two Samples

4.3 Proportions, Two Samples

Two proportions - binomial model

4 96

1. Layout

Data column and

two factor levels

2. X3

3. X2

4. 1st Factor Value

Female

5. 2nd Factor Value

Male

6. Option Less than

35

7.

Significance Test

8. 0H : 1p - 2p = 0

0 Value

9.

2-tailed Significant

4.4 OK

5.

1.

1 68

2. z = 1.40

3. Sig. (2-tailed) = 0.1603

6. 025.0z = -1.96 025.0z = 1.96 Z -1.96 Z 1.96

7. 1 z = 1.40 0H

2 Sig. (2-tailed) = 0.1603 0.05 = 0H

1p - 2p = 0 = 0.05

5

5.1

1. 0H :

1H :

2.

3. io , i = 1, 2, 3, ... , k

4.

5. ie

2 = k

1i i

2ii

e)eo(

6. 2

= k - - 1

2 2

7.

2 2 0H

5 98

5.1.1 3 240

x 3

example14.wor

0.05

1. 0H :

1H :

2. = 0.05

3. io

example14.wor 1o = 24, 2o = 98, 3o = 95, 4o = 23

4.

5. ie

x = 0, 1, 2, 3 ie

X io X P(X = x) ie

0 24 081

81 (240) = 30

1 98 183

83 (240) = 90

2 95 283

83 (240) = 90

3 23 381

81 (240) = 30

240 240

2 = 4

1i i

2ii

e)eo( = 30

)3024( 2 + 90

)9098( 2 + 90

)9095( 2 + 30

)3023( 2

= 1.2 + 0.7111 + 0.2778 + 1.6333 = 3.8222

6. = 3

205.0 = 7.815 2 7.815

7.

2 7.815 2

0H

Instat

1. 0H :

1H :

5 99

2.

3. io

4.

5. ie

2 = k

1i i

2ii

e)eo(

Sig. (1-tailed) = P( 2 2 )

6. 2

= k - - 1

2 2

7. 2 1. 2 2 0H

2. Sig. (1-tailed) 0H

5.1.1 3 240

x 3

example14.wor

0.05

Instat

1. 0H :

1H :

2. = 0.05

3. io

1 2

2 3

:

240 3 example14.wor

4.

5. 5.1 ie

0, 1, 2, 3 81 , 8

3 , 83 , 8

1 240

0 1e = 81 (240) = 30

1 2e = 83 (240) = 90

2 3e = 83 (240) = 90

3 4e = 81 (240) = 30

5 100

5.2 example14.wor

( )

example14.wor

Statistics Summary Group

5.3 io , i = 1, 2 , 3, 4

Statistics Summary Group

5.4 Group

1. X1

5.5 OK

io , i = 1, 2 , 3, 4

1o = 24, 2o = 98, 3o = 95, 4o = 23

5 101

5.6 ( X2(o))

( X3(e))

1.

2. X2(o), X3(e)

5.7 2 Sig. (1-tailed) 2

Statistics Simple Models Goodness of Fit

5.8 Goodness of Fit Googness of Fit

1. X2(o)

2. X3(e)

3.

ie

Number of estimated parameters 0

0 Number of estimated parameters

5 102

5.9

OK

Instat

io ie

1 24 30

2 98 90

3 95 90

4 23 30

240 240

1. 2 = k

1i i

2ii

e)eo( = 3.8 = 3

2. Sig. (1-tailed) = 0.2813

6. = 3

205.0 = 7.815 2 7.815

7. 1. 2 7.815 0H

2. Sig. (1-tailed) = 0.281312 0.05 0H

0.05

5 103

o e

5.1.2 2 X, Y

4

1 : 2 : 3 : 4 = 1 : 3 : 3 : 1

800

1 77 2 338 3 262 4 123

0.05

1. 0H :

1H :

2. = 0.05

3. 1o = 77, 2o = 338, 3o = 262, 4o = 123

1 : 2 : 3 : 4 = 1 : 3 : 3 : 1 N = 800

1e = 81 (800) = 100, 2e = 8

3 (800) = 300, 3e = 83 (800) = 300, 4e = 8

1 (800) = 100

3.1 o

e

3.2 Statistics Simple Models Goodness of Fit

5 104

3.3 Goodness of Fit Goodness of Fit

1. X2(o)

2. X3(e)

3.

ie

Number of estimated parameters 0

3.4

OK

Instat

io ie

1 77 100

2 338 300

3 262 300

4 123 100

800 800

1. 2 = 4

1i i

2ii

e)eo( = 20.2 = 3

2. Sig. (1-tailed) = 0.0002

5 105

6. = 3

205.0 = 7.815 2 7.815

7.

1. 2 = 20.2 7.815 0H

2. Sig. (1-tailed) = 0.0002 0.05 0H

0.05

5.2 2

1. 0H : A B

1H : A B

2.

3. ijo

4.

5. ije

ijo ije = NCR ji i = 1, 2, 3, ... , r j = 1, 2, 3, ... , c

1A 2A 3A cA

1B

2B

:

:

rB

11o

21o

:

:

1ro

12o

22o

:

:

2ro

13o

23o

:

:

3ro

...

...

...

c1o

c2o

:

:

rco

1R

2R

:

:

rR

1C 2C 3C ... cC N

ije 5 N 50

2 = r

1i

c

1j ij

2ijij

e)eo(

= (r - 1)(c - 1)

6. 2 = (r - 1)(c - 1) 2 2

7. 2 2

2 2 0H

5 106

Instat

1. 0H : A B

1H : A B

2.

3. ijo

4.

5. ije

2 = r

1i

c

1j ij

2ijij

e)eo(

2 = Sig. (1-tailed) = P( 2 2 )

6. 2 = (r - 1)(c - 1)

2 2

7. 2 1. 2 2 0H

2. Sig. (1-tailed) 0H

5.2.1 example15.wor

3

2

0.05

Instat

1. 0H :

1H :

2. = 0.05

3. ijo

4.

5. 5.1 example15.wor

5 107

X1(ID)

X2(area) Factor

1 = (east) 2 = (west)

X3(religion) Factor

1 = (protestant), 2 = (catholic) 3 = (jewish)

5.2 area, religion Statistics Tables Frequency

5.3 Frequency Frequency Tables

1. X2, X3

2 Factor

2. Column Factor 1

5.4 OK

5 108

5.5 Window Current Worksheet

X4, X5, X6 Table 1

5.6 ije

Statistics Simple Models Chi-square Test

5.7 Chi-square Test Chi-square Test

1. Layout of data Multiple Data Columns

2.

X4, X5, X6

5 109

5.8 OK

Instat

182 215 203

201.6 210.6 187.8600

154 136 110

134.4 140.4 125.2400

336 351 313 1000

1. ( ) ije ( )

2. 2 = 8.1 = 2

3. Sig. (1-tailed) = 0.0177

6. 205.0 = 5.991 = 2

2 5.991

7. 1 2 = 8.1 5.991 0H

2 Sig. (1-tailed) = 0.0177 0.05 0H

0.05

5 110

5.2.2

3

25 45 32

175 455 168

0.05

1.

0H :

1H :

2. = 0.05

3.

X1, X2, X3

3.1 ije

Statistics Simple Models Chi-square Test

5 111

3.3 Chi-square Test Chi-square Test

1. Layout of data Multiple Data Column

2. X1, X2, X3

5.4 OK

5 112

Instat

1. ( ) ije ( )

25 45 32

22.7 56.7 22.7102

175 455 168

177.3 443.3 177.3798

200 500 200 900

2. 2 = 7.3 = 2

3. Sig. (1-tailed) = 0.0258

6. 205.0 = 5.991 = 2

2 5.991

7. 1 2 = 7.3 5.991 0H

2 Sig. (1-tailed) = 0.0258 0.05 0H

0.05

6

2

(simple linear regression) (correlation)

(simple linear regression) 1

1

y = a + bx n y = a + b n x y = a + b n x n y = a + bx

(Simple correlation)

2

y = a + bx n y = a + b n x y = a + b n x n y = a + bx

y = a 2x + bx + c

y = a + 1b 1x + 2b 2x + ... + nb nx

6.1 X Y X, Y x|Y = + x

(regression coefficients) (intercept)

x|Y = + x y = a + bx

( correlation )

r

6 114

1. -1 1

2. X Y

3. = 0 X Y

4. 0 X Y X Y

5. 0 X Y X Y

6. b r

7. b Y X

1. 2.

1x 1y

2x 2y

: :

nx ny

a b y = a + bx r

n

1iix ,

n

1iiy ,

n

1iiiyx ,

n

1i

2ix ,

n

1i

2iy

1 b = 2n

1ii

n

1i

2i

n

1ii

n

1ii

n

1iii

xxn

yxyxn

a = y - b x

r = 2n

1ii

n

1i

2i

2n

1ii

n

1i

2i

n

1ii

n

1ii

n

1iii

yynxxn

yxyxn

2 b = xxxy

SS

r = yyxx

xySS

S

xxS = n

1i

2i )xx( =

n

1i

2ix - n

x2n

1ii

, yyS = n

1i

2i )yy( =

n

1i

2iy - n

y2n

1ii

xyS = n

1iii )yy)(xx( =

n

1iiiy x - n

y xn

1ii

n

1ii

6 115

x 1.5 1.8 2.4 3.0 3.5 3.9 4.4 4.8 5.0

y 4.8 5.7 7.0 8.3 10.9 12.4 13.1 13.6 15.3

y = a + bx r

x y 2x xy 2y

1.5 4.8 2.250 7.200 23.040

1.8 5.7 3.240 10.260 32.490

2.4 7.0 5.760 16.800 49.000

3.0 8.3 9.000 24.900 68.890

3.5 10.9 12.250 38.150 118.810

3.9 12.4 15.210 48.360 153.760

4.4 13.1 19.360 57.640 171.610

4.8 13.6 23.040 65.280 184.960

5.0 15.3 25.000 76.500 234.090

i9

1ix = 30.3 i

9

1iy = 91.1 2

i9

1ix = 115.11 ii

9

1iyx = 345.09 2

i9

1iy = 1036.65

x = 93.30 = 3.366667 y = 9

1.91 = 10.122222

b = 29

1ii

9

1i

2i

9

1ii

9

1ii

9

1iii

xx(9)

yxyx)9(

= 2)3.30()11.115)(9()1.91)(3.30()09.345)(9( = 2.93028

a = 10.122222 - 2.93028(3.366667) = 0.256947

y = 0.256947 + 2.93028 x

r = 2n

1ii

n

1i

2i

2n

1ii

n

1i

2i

n

1ii

n

1ii

n

1iii

yynxxn

yxyxn

= 22 )1.91(5)(9)(1036.6)3.30()(9)(115.11

)1.91)(3.30()09.345)(9( = 0.991089

6 116

2 xxS = 9

1i

2ix - 9

x29

1ii

= 115.11 - 9)3.30( 2

= 13.1

yyS = 9

1i

2iy - 9

y29

1ii

= 1036.65 - 9)1.91( 2

= 114.515556

xyS = 9

1iii yx - 9

yx9

1ii

9

1ii

= 345.09 - 9)1.91)(3.30( = 38.386667

b = xxxy

SS

= 1.13386667.38 = 2.93028

r = yyxx

xySS

S =

515556.1141.13386667.38 = 0.991089

6 117

Instat

1. x y

Window Current Worksheet Save example16.wor

2. Graphics Plot

3. Plot

1. X2 (Y-Variable(s))

2. X1 (X-Variable)

6 118

4. OK Window

1. Graph1 Instat Save

Graphics Load Graph

2. Plot

3.

Pointer

6 119

1. X

2. ( X : Independent variable or Explanatory variable)

3. Automatic scale

4. X 1

5. X 6

6. X 0.5

7. gridlines

Y ( Y : Dependent variable or Response variable)

6 120

Instat

1. x y


Save example16.wor

example16.wor

2. Statistics Regression Simple

3. Simple Simple Linear Regression

1. X2 (Response variable Dependent variable)

2. X1 (Explanatory variable Independent variable)

Option Plots

6 121

4. OK

y = a + bx

y = a + bx

x|Y = + x

1. (Y-intercept) a = 0.2569

2. (slope) b = 2.93

Fitted equation y = 0.2569 + 2.93 x

3. B = b = 0.1489

= n - 2 = 9 - 2 = 7

4. 95% (2.5783, 3.2823)

5. r

r b

r = squareR = 9823.0 = 0.9446

r = 0.9446

R Square = 0.9823 y = a + bx

2R 1 y = a + bx 2R 0 y = a + bx

6 122

2R = 0.1

y = a + bx y 10 %2R = 0.98226

y = a + bx y 98.226%

r b

1. r = 0.9446 X, Y

X Y X Y

2. b = 2.93

X 1 Y 2.93

X 1 Y 2.93

6.2 %100)1(

%100)1(

b - 2

tXXSS b +

2t

XXSS = n - 2

%100)1(

a - 2

t s xx

n

1i

2i

nS

x a +

2t s

xx

n

1i

2i

nS

x = n - 2

s = 2nSSE SSE = yyS - b xyS

6.2.1 (

) ( 0.01 )

4.3 126

4.5 121

5.9 116

5.6 118

6.1 114

5.2 118

3.8 132

2.1 141

7.5 108

6 123

1. b y = a + bx

2. a ( Y) y = a + bx

3. 95%

4. 95% x|Y = + x

5. r

1. 2

rain

air

Save example17.wor

2. Statistics Regression Simple with groups

3. Simple with groups Comparison of regression

1. X2 (Response variable Dependent variable)

2. X1 (Explanatory variable Independent variable)

6 124

4. OK

6 125

Instat

1. b = -6.324

2. a = 153.18

3. 95% -7.511 -5.137

4. 95% 147 159.4

5. r = - squaredR = - 9578.0 = -0.9787

( r b )

6.3 0H : = 0 2

1. 0H : = 0

1H : 0

2.

3. n r

4. t

5. t = 2r12nr = n - 2

Sig. (2-tailed) t

Sig. (2-tailed) = 2 P(t t )

6. 2

t 2

t t 2

t t 2

t

7.

1

t 2

t t 2

t 0H

2 Sig t

t 2

t t 2

t t 2

t

P(t t ) 2

2 P(t t )

Sig. (2-tailed)

Sig. (2-tailed) 0H

= 0 t = 2r12nr t =

)SS(b

xx

6 126

1

1H : 0 1H : 0

- 2n,t 2n,t

t - 2n,t t 2n,t

Sig. (2-tailed)

t 0

Sig. (2-tailed) 2

0H

t 0

Sig. (2-tailed) 2

0H

6.2.1

0.05

1. 0H : = 0

1H : 0

2. = 0.05

3. Instat

example17.wor ( 116)

4. t

5. Instat REGRESSION COEFFICIENTS

REGRESSION COEFFICIENTS

1. t = -12.600 = n - 2 = 9 - 2 = 7

2. Sig. (2-tailed) = P(t t ) = 0.0000

Sig. (2-tailed) = 0.0000045792

6. - 7,025.0t = -2.365, 7,025.0t = 2.365 t -2.365 t 2.365

7. 1 t 0H

2 Sig. (2-tailed) = 0.0000 0.05 0H

0.05

6 127

0H : = 0 ANOVA

2

1. 0H : = 0

1H : 0

2.

3.

4.

5. ANOVA

yyS , xxS , xyS ANOVA

SST = 2i

n

1iy - n

)y( 2i

n

1i = yyS

SSR = b xyS = 2b xxS

SSE = SST - SSR

ANOVA TABLE

SOV SS DF MS F Sig

Regression SSR 1 MSR f = MSEMSR P(F f )

Residual SSE n - 2 MSE

Total SST n - 1

6. )2,1(,f 1 = 1 , 2 = n - 2

F )2,1(,f

7. 1 f )2,1(,f 0H

2 Sig. (1-tailed) 0H

6.2.1

0.05

1. 0H : = 0

1H : 0

2. = 0.05

3. Instat

6 128

example17.wor ( 116)

4. F

5. Instat ANOVA for regression of air on rain

ANOVA for regression of air

1. f = 158.77 1v = 1, 2v = 7

2. Sig. (1-tailed) = P(F f ) = 0.0000

Sig. (1-tailed) = 0.0000045792

6. )7,1(,05.0f = 5.59 F 5.59

7. 1 f = 158.768 5.59 0H

2 Sig. (1-tailed) = 0.0000 0.05 0H

0.05

6.4 0H : = 0

y = 2 + 3x x 1 y 3

= 0

0 = 0

1. 0H : = 0

1H : 0

2.

3. n b, s, xxS

4. t

5. t =

xxSsb = n - 2

Sig. (2-tailed) = 2 P(t t )

6 129

6. 2

t 2

t t 2

t t 2

t

7. 1

t 2

t t 2

t 0H


0 = 0 = 0

0 = 0 t = 2r12nr t =

xxSSb

1

1H : 0 1H : 0

2n,t 2n,t

t 2n,t t 2n,t

Sig. (2-tailed)

t 0

Sig. (2-tailed) 2

0H

t 0

Sig. (2-tailed) 2

0H

6.2.1 (y)

(x) x|Y = + x = 0

0.05

1. 0H : = 0

1H : 0

2.

3. n b, s, xxS

4.

5. Instat REGRESSION COEFFICIENTS

6 130

REGRESSION COEFFICIENTS

1. t = 2r12nr = -12.600 = n - 2 = 9 - 2 = 7

2. Sig. (2-tailed) = P(t t ) = 0.0000

Sig. (2-tailed) = 0.0000045792

6. 7,025.0t = -2.365 7,025.0t = 2.365

t -2.365 t 2.365

7. 1 t = -12.6 0H

2 Sig. (2-tailed) = 0.0000 0.05 0H

x|Y = + x 0 0.05

0 0

1. 0H : = 0

1H : 0

2.

3. n

4.

5. t =

xx

0

Ss

b = n - 2

Sig. (2-tailed) = 2 P( t t )

6. 2

t 2

t t 2

t t 2

t

7. 1 t 2

t

t 2

t t 2

t 0H


1

1H : 0 1H : 0

2n,t 2n,t

t 2n,t t 2n,t

Sig. (2-tailed)

t 0

Sig. (2-tailed) 2

0H

t 0

Sig. (2-tailed) 2

0H

6 131

x 1.5 1.8 2.4 3.0 3.5 3.9 4.4 4.8 5.0

y 4.8 5.7 7.0 8.3 10.9 12.4 13.1 13.6 15.3

x|Y = + x

= 2.5 2.5 0.05

1. 0H : = 2.5

1H : 2.5

2. = 0.05

3. n

4. x

y Window Current Worksheet

Save example16.wor

4.1 Statistics Regression Simple

4.2 Simple Simple Linear Regression

6 132

1. X2 (Response variable dependent variable)

2. X1 (Explanatory variable independent variable)


4. 0H : = 2.5

Value 2.5

4.3 OK

1. b = 2.93

2. Standard error xxSs = 0.1489

3. slope = 2.5 Simple Linear Regression

4. t = 2.89

5. Sig. (2-tailed) = 2 P(t t ) = 0.0233

5. t = 2.89 = 7 Sig. (2-tailed) = 0.0233

6. 7,025.0t = -2.365 7,025.0t = 2.365

t -2.365 t 2.365

7. 1 t 0H

2 Sig. (2-tailed) = 0.0233 0.05 0H

= 2.5 0.05

7

2

7.1

(One-Way ANOVA, Simple-Factor ANOVA) 1n , 2n , ... , kn 1, 2, ... , k k

(Treatments)

5

6

( %)

1 2 3 4 5

551 595 639 417 563

457 580 615 449 631

450 508 511 517 522

731 583 573 438 613

499 633 648 415 656

632 517 677 555 679

7 134

k

1 , 2 , ... , k 2

0H : 1 = 2 = ... = k

1H : 2

(Treatment)

1 2 ... j ... k

1 11x 12x ... j1x ... k1x

2 21x 22x ... j2x ... k2x

: : : : :

i 1ix 2ix ... ijx ... ikx

: : : : :

jjnx

22nx

11nx ... kknx

1n 2n jn kn

1.T 2.T j.T k.T ..T

1.x 2.x j.x k.x ..x

Treatment

N = = 1n + 2n + ... + kn ijx = i j

j.T = j

j.x = j

..T = ..x = NT.. =

3

1. (SST : TOTAL SUM OF SQUARE, Total Sum Square)

SST = k

1j

jn

1i

2ijx - N

T2..

2. Treatment (SSTR : TREATMENT SUM OF SQUARE, Between-

Groups Sum Square) SSTR = k

1j j

2j.

nT

- NT2

..

3. Treatment (SSE : ERROR SUM OF SQUARE, Within-Groups Sum Square, Residual Sum Square) SSE = SST - SSTR

SST, SSTR, SSE (ANalysis Of VAriance : ANOVA)

7 135

ANOVA

(SOV (SS) (DF) (MS)

f

(Treatment)SSTR k - 1 MSTR =

1kSSA f =

MSEMSTR

(Error)SSE N - k MSE = kN

SSE

(Total) SST N - 1

Sig. (1-tailed) f

1 = k - 1, 2 = N - k

Sig. (1-tailed) = P(F f )

1. 0H : 1 = 2 = 3 = ... = k

1H : 1 2 3 ... k ( 2 )

2.

3.

4. F

5. f ANOVA

6. f 1 = k - 1, 2 = N - k F f

7. 1 f f 0H


7.1.1 5 ( 125)

1 , 2 , 3 , 4 , 5 1, 2, 3, 4, 5

1 , 2 , 3 , 4 , 5 0.05

1. 0H : 1 = 2 = 3 = 4 = 5

1H : 1 2 3 4 5 ( 2 )

2. = 0.05

3.

4. F

5. F ANOVA

7 136

N = 30 SST = x2ij

6

1i

5

1j - N

T2.. = 2551 + 2457 + ... + 2679 - 30

168542 = 209376.8

SSTR = 6T2

j.5

1j - N

T2.. = 6

36642791366334163320 22222 - 30

168542 = 85356.467

SSE = 209376.8 - 85356.467 = 124021.33

ANOVA

(SOV) (SS) (DF) (MS)

f

(Treatment)SSTR = 85356.467 k - 1 = 4 21339.117 4.30

(Error)SSE = 124020.33 N - k = 25 4960.8133

(Total) SST = 209376.8 N - 1 = 29

6. 05.0f = 2.76 1 = 4, 2 = 25 F 2.76

7. f = 4.30 2.76 0H

Instat

1. 0H : 1 = 2 = 3 = ... = k

1H : 1 2 3 ... k ( 2 )

2.

3.

4. F

5. F ( ANOVA )

5.1 f

5.2 Sig. (1-tailed) F

6. 6.1 f 1 = k - 1, 2 = N - k F f

6.2 Sig. (1-tailed)

7. 1 f f 0H


Instat 7.1.1

1. 0H : 1 = 2 = 3 = 4 = 5

1H : 1 2 3 4 5 ( 2 )

2. = 0.05

7 137

3.

3.1

type trement

weight

example19.wor

trement

Factor column

1. X1 ordinary variable

2. X1-F Factor column

X1(type) Factor column

3.5

3.2

Manage Column Properties Factor

3.3 Factor


1. X1(type)

Factor column

2. X1(type) 5

7 138

3.4 OK

X1

Ordinary variable

Factor column X1-F

3.5

Statistics Analysis of Variance

One-way

3.6 One-way

OneWay Analysis of Variance

1. X2(weight)

2. treatment

X1(type)

3.

3.7 OK

7 139

ANOVA

SOV SS DF MS f Sig. (1-tailed)

Treatment(weight) 85356.467 4 21339.117 4.3 0.009

Error 124020.33 25 4960.8133

Total 209376.8 29

4. Sig f 5. ANOVA f = 4.3 Sig. (1-tailed) = 0.0096. 1 = 4, 2 = 25 05.0f = 2.76

F 2.76 7. 1 f = 4.3 2.76 0H

2 Sig.(1-tailed) = 0.009 0.05 0H 0.05

7.2-

Randomized Block Design

(Block) (Treatment)

4 5

7 140

1 2 3 4

1 44 38 47 36

2 46 40 52 43

3 34 36 44 32

4 43 38 46 33

5 38 42 49 39

1

2 (Factor)

1 Treatment 1 Block 1

(Treatment)

1 2 ... j ... k

1 11x 12x ... j1x ... k1x .1T .1x

2 21x 22x ... j2x ... k2x .2T .2x

3 31x 32x ... j3x ... k3x .3T .3x

: : : : : : :

i 1ix 2ix ... ijx ... ikx .iT .ix

: : : : : : :

b 1bx 2bx ... bjx ... bkx .bT .bx

1.T 2.T ... j.T ... k.T ..T

1.x 2.x ... j.x ... k.x ..x

N

ijx j i j = 1, 2, ... , k i = 1, 2, ... , b

j.x = j ( thj treatment mean)

..x = (grand mean overall mean)

.ix = i ( thi block mean)

j.T = j ( thj treatment total)

.iT = i ( thi block total)

..T = (grand total overall total)

7 141

4

1. (SST : TOTAL SUM OF SQUARE, Total Sum Square)

SST = 2..ij

b

1i

k

1j)xx( =

k

1j

b

1i

2ijx -

NT 2

..

2. Treatment (SSTR : TREATMENT SUM OF SQUARE, Between-

Treatment Sum Square) SSTR = 2..j.

k

1j)xx(b = b

Tk

1j

2j.

- N

T 2..

3. Block (SSBL : BLOCK SUM OF SQUARE, Between-Groups Sum

Square) SSBL = )xx(k 2..i.

b

1i= k

Tb

1i

2.i

- N

T 2..

4. Treatment Block

(SSE : ERROR SUM OF SQUARE, Within-Groups Sum Square, Residual Sum Square)

SSE = SST - SSTR - SSBL

ANOVA

(SOV)(SS) (DF) (MS)

f

(Treatment)SSTR k - 1 MSTR =

1kSSTR treatmentf

= MSE

MSTR

(Block)SSBL b - 1 MSBL = 1b

SSBL blockf = MSE

MSBL

(Error)SSE (b - 1)(k - 1) MSE = )1k)(1b(

SSE

(Total) SST N - 1

1. (Treatment)

0H : 1. = 2. = 3. = ... = k.

1H : 1. 2. 3. ... k. ( 2 )

(Block)

0H : .1 = .2 = .3 = ... = .b

1H : .1 .2 .3 ... .b ( 2 )

2.

7 142

3.

4. F

5. F ANOVA

Treatment Sig. (1-tailed) = P(F treatmentf )

Block Sig. (1-tailed) = P(F blockf )

6. 6.1 Treatment f

1 = k - 1, 2 = (b - 1)(k - 1) F f

6.2 Block f

1 = b - 1, 2 = (b - 1)(k - 1) F f

7. 7.1 Treatment

7.1.1 treatmentf f Treatment 0H

7.1.2 Sig. (1-tailed) treatmentf 0H

7.2 Block

7.2.1 blockf f Block 0H

7.2.2 Sig. (1-tailed) blockf 0H

7.2.1 4

5

1 2 3 4

1 44 38 47 36

2 46 40 52 43

3 34 36 44 32

4 43 38 46 33

5 38 42 49 39

0.05 4

0.05 5

1.

( Treatment)

0H : 1. = 2. = 3. = 4.

1H : 1. 2. 3. 4. ( 1 )

( Block)

0H : .1 = .2 = .3 = .4 = .5

1H : .1 .2 .3 .4 .5 ( 1 )

2. = 0.05

7 143


3.1

X1(man)

X2(machine)

X3(time)

Save example20.wor

trement Block

Factor column

1. X1 X2

ordinary variable

2. X1-F X2-F

Factor column

X1(man) X2(machine) Factor column

3.8

3.2 Manage Column Properties Factor

3.3 Factor Make or modify a factor column

1. X1(man) Factor column

2. X1(man) 5

7 144

3.4 OK X1(man)

Factor column (X1-F)

3.5 Manage Column Properties Factor

3.6 Factor


1. X2(machine)

Factor column

2.

X2(machine) 4

7 145

3.7 OK X2(machine)

Factor column (X2-F)

3.8

Statistics Analysis of Variance

Orthogonal

3.9 Orthogonal

Analysis of Variance for Orthogonal Designs

1. X3(time)

2. treatment block

X1, X2

3.10 OK

7 146

Instat

SSTR(machine) = 338.8 MSTR(machine) = 112.93

SSBL(man) = 161.5 MSBL(man) = 40.375

SSE = 73.7 MSE = 6.1417 SST = 574.0

4. F

5. ANOVA

ANOVA

SOV SS DF MS f Sig. (1-tailed)

machine 338.8 3 122.93 treatmentf = 18.4 0.000

man 161.5 4 40.375 blockf = 6.6 0.005

Error 73.7 12 6.1417

Total 574.0 19

6.

(machine) 12),(3,0.05f = 3.94 F 3.94

(man) 12),(4,0.05f = 3.26 F 3.26

7.

(Treatment : var name = machine)

1. treatmentf = 18.4 3.49 0H

2. Sig. (1-tailed) = P(F treatmentf ) = 0.000

Sig. (1-tailed) = 0.000 0.05 0H

4 0.05

(Block : var name = man)

1. blockf = 6.6 3.26 0H

2. Sig. (1-tailed) = P(F blockf ) = 0.005

Sig. (1-tailed) = 0.005 0.05 0H

5 0.05

1


Instat


1.

1.

1. X1(x) 5 2, 3, 7, 5, 12

2. DES X1 X1

3.

Statistics Summary Describe X1(x)

2.

1. X1, X2, X3

2. CHIsquare X1-X3 X1, X2, X3 2 ( 102)

2.

1. ?

2. X1, X2 Window Current Worksheet

1148

3.

1. X1, X2 ( X3, X4 )

2. calc X3 = 2*X1 X3

3. calc X4 = X1+X2 X4

4. Manage Calculations

1. 25/4 OK

2. 1.

3.

4. Option Further functions

Probability Comb com com(r, n) = )!rn(!r!n

Npr npr npr(k) P(Z k)

Summary Sum sum sum(X1) X1

Count cou cou(X1) X1

2

Instat macro programing Instat

Instat

Instat macro

1. Window macro


1.2 Edit View/Edit Macro New

1.3 New Window Editing Commands

2. macro

2.1 DES X1

2.2 ?SUM(X1)

3. Macro

3.1 Window Editing Commands

3.2 File Save File Save As

3.3 Save Save Text File As

3.4 macro1 ( macro file *.ins)

3.5 Save

Editing Commands

Editing macro1.ins

2150

4. Macro

4.1 X1 example001.wor

4.2 Window Commands and Output

4.3 @macro1 Enter


1. @macro1 Enter

2. DES X1

3. ?SUM(X1)

5. Macro

Submit Run Macro


5.2 Submit Run Macro

5.3 Run Macro

Run Macro

5.4 macro1

5.5 OK

6. Macro


6.2 Edit View/Edit Macro Open

6.3 Open Edit macro

6.4 marco1

6.5 OK Window Editing Commands

macro1.ins

Save

3

Instat Log File

Instat Instat

Log File

1. Instat

1.1 Instat Window Current Worksheet

1.2 Edit Command Logging Start ( F9)

1.3 Start Start Logging Commands to a File

1.4 Log1

(

Log file Log1)

1.5 OK

1.6 OK Window Commands and Output LOG @Log1.log

Instat Log1.log


2. example001.wor

3.



3152

2. Instat Log File


2.2 Edit Command Logging Stop ( Shift+F9)

2.3 Stop Window Commands and Output LOG

Log1.log

3. Log1.log


3.2 Edit Command Logging View Log File ( Ctrl+F9)

3.3 View Log File Open Log File

3.4 Log1 Open

3.5 Open Window Viewing log1.log

1. Log File log1.log

2. example001.wor

3. DES X1


1. Log File

2. Copy Log File macro

3. Log File

Edit View/Edit Text Open ( Shift+F8)

*.log

4

Word Excel Instat

Word Excel Instat

1. Word Instat

1.1 Word

1.2 Copy


1

1.4 Paste Copy Word

1. Word

2. Word Instat

1. ?SUM(X1) X1(a)

2. ?MEAN(X2) X2(b)

3. ?SDE(X3) X3(c)

4154

2. Excel Instat

2.1 Excel

2.2 Copy


1

2.4 Paste Copy Excel

1. test1, test2, test3

2. Excel Instat

1. ?SUM(X1) X1(test1)

2. ?MEAN(X2) X2(test2)

3. ?SDE(X3) X3(test3)

Instat Functions

MIN

MAX

SUM

MEAN

SDE

SSQ

COU

MIN(X) = X

MAX(X) = X

SUM(X) = X

MEAN(X) = X

SDE(X) =

X

SSQ(X) =

X

COU(X) = X

K1 = LHS.

K2 = LHS.

Constants

K1 - K9

X1 Current Worksheet

NPR

NDE

Normal distribution

NPR(k) = P(Z k)

NDE(p) = k

P(Z k) = p

TPR

TDE

Student's t distribution

TPR(k, v) = P(t k) df = v

TDE(p, v) = k

P(t k) = p df = v

CPR

CDE

Chi-square distribution

CPR(k, v) = P( 2 k) df = v

CDE(p, v) = k

P( 2 k) = p df = v

FPR

FDE

F distribution

FPR(k, v1, v2) = P(F k)

FDE(p) = k P(F k) = p

df = (v1, v2)

โปรแกรม Instat สามารถ Download ไดจาก Website http://www.rdg.ac.uk/ssc

ตามเง�อนไขท�ไดรบอนญาต เชนใชงานสวนตว หนงสอเลมน�ทาใหผอานสามารถ

ใชโปรแกรม Instat ไดโดยงาย เพราะวาทกข �นตอนของการทางานดวยคาส �งของ

Instat จะมภาพประกอบการทางาน มการอางองทฤษฎทางสถต และ สตรท�ใช

เน�อหาภายในเลมประกอบดวย

♦ การสรางแฟมขอมล

♦ การคานวณคาสถตเบ�องตน เชน Mean, Median

♦ การแจกแจงความถ� หาคาเปอรเซนตไทล

♦ การนาเสนอขอมลในรปแบบตาราง

♦ การเขยนกราฟ เชน Stem and Leaf, Boxplot

♦ การหาชวงความเช�อม �นของคาพารามเตอร

♦ การทดสอบสมมตฐาน คาเฉล�ย ความแปรปรวน

♦ การทดสอบภาวะสารปสนทด

♦ การทดสอบความเปนอสระ

♦ การเขยนกราฟ แผนภาพการกระจาย

♦ การหาสมการเสนถดถอย (regression line)

♦ สมประสทธ� สหสมพนธ (Correlation)

♦ การวเคราะหความแปรปรวน (ANOVA)

จดจาหนายโดยศนยหนงสอแหงจฬาลงกรณมหาวทยาลย

ถนนพญาไท เขตปทมวน กรงเทพฯ 10330

ศาลาพระเก�ยว โทร. 0-2218-7000-3 โทรสาร 0-2255-4441

สยามสแควร โทร. 0-2218-9881 โทรสาร 0-2254-9495

ม.นเรศวร จ.พษณโลก โทร. 0-5526-0162-5 โทรสาร 0-5526-0165

ม.บรพา จ.ชลบร โทร. 0-3839-4855-9 โทรสาร 0-3839-3239

ม.เทคโนโลยสรนาร จ.นครราชสมา โทร. 0-4421-6131-4 โทรสาร 0-4421-6135

โรงเรยนนายรอย จปร. จ.นครนายก โทร. 0-3739-3023 โทรสาร 0-3739-3023

จตรสจามจร (CHAMCHURI SQUARE) ช �น 4 โทร 0-2160-5300-1 โทรสาร 0-2160-5304

Call Center 0-2255-4433 http://www.chulabook.com email : [email protected]

Documents

Instatpioneer.netserv.chula.ac.th/~tdumrong/2301286/instat_20Mar2017.pdf · การนําเสนอข้อมูลด้วยตารางและกราฟ ... LOG