SlideShare una empresa de Scribd logo
1 de 3
Statistical Inference
1. P(X = x) = n
Cx px
(1 – p)n – x
2. P(X=x) = e λ−
!x
x
λ
3. P(X = x) = !x!...x!x
N!
n21
1
1
x
P x 2
2
x
P x 3
3
x
P . . . . xn
nP
4. λ = np 5. Z =
σ
µ−x
6. P = 7. Z =
n
x
/σ
µ−
8. t =
ns
x
/
µ−
9. σ2
= -
10. s = ( )
1
/
22
−
Σ−Σ
n
nxx
11. S.E(x) =
12. Var(x) = – 13. μ =
14. x = 15. x ± Zα/2
16. x ± t 2
α
n
S
17. W1- α = - Wα
18. M1-α = n1(n1 + n2 +1) – Mα 19. E = Zα/2
20. Z =
)2/()/(
)()(
2
21
2
1
2121
nn
xx
σσ
µµ
+
−−−
21. t =
21
2121
11
)()(
nn
Sp
xx
+
−−− µµ
22. Sp =
2
)1()1(
21
2
22
2
11
−+
−+−
nn
SnSn
23. t =
2
2
2
1
2
1
2121 )()(
n
Sn
S
xx
+
−−− µµ
24.
11 2
2
2
2
2
1
2
1
2
1
2
2
2
2
1
2
1
−






+
−










+
=∆
n
n
S
n
n
S
n
S
n
S
25. ( )21 xx − + Z 2
α
2
2
2
1
2
1
nn
σσ
+
26. ( )21 xx − + t 2
α
2
2
2
1
2
1
n
S
n
S
+ 27. p + Z 2
x
n
pp )1( −
28. t =
nsd
d
/
29. Sd =
1
/)( 22
−
Σ−Σ
n
ndd
30. d = 31. d.f = n – 1
32. d.f = n1 + n2 – 2 33. d.f = (r – 1) (c – 1)
34. Z =
n
pp
pp
)1( −
−
35. Z =
)11)(1(
)()(
21
2121
nn
PP
PPPP
PP +−
−−−
36. PP =
21
21
nn
xx
+
+
37. X2
= 2
1
σ
−n
x s2
38. F = 2
2
2
1
S
S
39. (p1–p2)+Z 2
α
2
22
1
1 )1()1(
n
pp
n
pp −
+
−
40. X2
= }/){( 2
EEo −Σ 41. E =
42. d.f = (k - 1, n - k) 43. RP = 2
wS
xw
xxΣ
Σ
44. Sxx = nxx /)( 22
Σ−Σ 45. Sxy = nyxyx /))(( ΣΣ−Σ
46. Syy = nyy /)( 22
Σ−Σ 47. b1 =
Sxx
Sxy
48. b0 = y - b1 1x 49. r = Sxy / yyxx SS
50. t =
SxxSe
b
/
1
51. Se =
2−n
SSE
52. t =
2
1 2
−
−
n
r
r
53. β1 ± t 2
α
Sxx
Se
54. SST = nxx /)( 22
Σ−Σ 55. SSE = SST – SSTR
56. SSTR = ∑ – 57. r12 = 2
2
2
2
2
1
2
1
2121
)()( xxnxxn
xxxxn
Σ−ΣΣ−Σ
ΣΣ−Σ
58. MSE = 59. MSTR =
60. F-Ratio = 61. R1.23 = 2
23
132312
2
13
2
12
1
2
r
rrrrr
−
−+
62. r12 = 63. SSR =
64. SST = Syy 65. d.f = (n1 – 1) , (n2 – 1)
66.
( )( )2
23
2
13
231312
3.12
11 rr
rrr
r
−−
−
= 67. F =
68. SSR = b0Σy + b1 Σx1y + b2 Σx2y -
69. rs = 1 -
70. H = ×Σ - 3(n + 1) 71. X ± A2 R
72. C ± 3 Sc 73. Sc =
74. P ± 3 75. UCLR = D4 R
76. LCLR = D3 R 77. P(X=x) = n-1
Cx-1 × px
× (1 – p)n-x
78. P = 79. σ =

Más contenido relacionado

La actualidad más candente

[DL輪読会]Deep Learning 第16章 深層学習のための構造化確率モデル
[DL輪読会]Deep Learning 第16章 深層学習のための構造化確率モデル[DL輪読会]Deep Learning 第16章 深層学習のための構造化確率モデル
[DL輪読会]Deep Learning 第16章 深層学習のための構造化確率モデルDeep Learning JP
 
第1回 Rプログラミングを始めよう
第1回 Rプログラミングを始めよう第1回 Rプログラミングを始めよう
第1回 Rプログラミングを始めようWataru Shito
 
Prml4.4 ラプラス近似~ベイズロジスティック回帰
Prml4.4 ラプラス近似~ベイズロジスティック回帰Prml4.4 ラプラス近似~ベイズロジスティック回帰
Prml4.4 ラプラス近似~ベイズロジスティック回帰Yuki Matsubara
 
PRML輪読#8
PRML輪読#8PRML輪読#8
PRML輪読#8matsuolab
 
好みの日本酒を呑みたい! 〜さけのわデータで探す自分好みの酒〜
好みの日本酒を呑みたい! 〜さけのわデータで探す自分好みの酒〜好みの日本酒を呑みたい! 〜さけのわデータで探す自分好みの酒〜
好みの日本酒を呑みたい! 〜さけのわデータで探す自分好みの酒〜Takashi Kitano
 
Tabela derivadas e integrais 1
Tabela derivadas e integrais 1Tabela derivadas e integrais 1
Tabela derivadas e integrais 1José Milton
 
統計的学習の基礎_3章
統計的学習の基礎_3章統計的学習の基礎_3章
統計的学習の基礎_3章Shoichi Taguchi
 
[PRML勉強会資料] パターン認識と機械学習 第3章 線形回帰モデル (章頭-3.1.5)(p.135-145)
[PRML勉強会資料] パターン認識と機械学習 第3章 線形回帰モデル (章頭-3.1.5)(p.135-145)[PRML勉強会資料] パターン認識と機械学習 第3章 線形回帰モデル (章頭-3.1.5)(p.135-145)
[PRML勉強会資料] パターン認識と機械学習 第3章 線形回帰モデル (章頭-3.1.5)(p.135-145)Itaru Otomaru
 
PRML上巻勉強会 at 東京大学 資料 第4章4.3.1 〜 4.5.2
PRML上巻勉強会 at 東京大学 資料 第4章4.3.1 〜 4.5.2PRML上巻勉強会 at 東京大学 資料 第4章4.3.1 〜 4.5.2
PRML上巻勉強会 at 東京大学 資料 第4章4.3.1 〜 4.5.2Hiroyuki Kato
 
東京都市大学 データ解析入門 4 スパース性と圧縮センシング1
東京都市大学 データ解析入門 4 スパース性と圧縮センシング1東京都市大学 データ解析入門 4 スパース性と圧縮センシング1
東京都市大学 データ解析入門 4 スパース性と圧縮センシング1hirokazutanaka
 
PRML第6章「カーネル法」
PRML第6章「カーネル法」PRML第6章「カーネル法」
PRML第6章「カーネル法」Keisuke Sugawara
 
31350052 introductory-mathematical-analysis-textbook-solution-manual
31350052 introductory-mathematical-analysis-textbook-solution-manual31350052 introductory-mathematical-analysis-textbook-solution-manual
31350052 introductory-mathematical-analysis-textbook-solution-manualMahrukh Khalid
 
If on the average the rain falls on twelve days in every thirty days.docx
If on the average  the rain falls on twelve days in every thirty days.docxIf on the average  the rain falls on twelve days in every thirty days.docx
If on the average the rain falls on twelve days in every thirty days.docxNadeem Uddin
 

La actualidad más candente (15)

[DL輪読会]Deep Learning 第16章 深層学習のための構造化確率モデル
[DL輪読会]Deep Learning 第16章 深層学習のための構造化確率モデル[DL輪読会]Deep Learning 第16章 深層学習のための構造化確率モデル
[DL輪読会]Deep Learning 第16章 深層学習のための構造化確率モデル
 
第1回 Rプログラミングを始めよう
第1回 Rプログラミングを始めよう第1回 Rプログラミングを始めよう
第1回 Rプログラミングを始めよう
 
Prml4.4 ラプラス近似~ベイズロジスティック回帰
Prml4.4 ラプラス近似~ベイズロジスティック回帰Prml4.4 ラプラス近似~ベイズロジスティック回帰
Prml4.4 ラプラス近似~ベイズロジスティック回帰
 
PRML輪読#8
PRML輪読#8PRML輪読#8
PRML輪読#8
 
好みの日本酒を呑みたい! 〜さけのわデータで探す自分好みの酒〜
好みの日本酒を呑みたい! 〜さけのわデータで探す自分好みの酒〜好みの日本酒を呑みたい! 〜さけのわデータで探す自分好みの酒〜
好みの日本酒を呑みたい! 〜さけのわデータで探す自分好みの酒〜
 
Tabela derivadas e integrais 1
Tabela derivadas e integrais 1Tabela derivadas e integrais 1
Tabela derivadas e integrais 1
 
統計的学習の基礎_3章
統計的学習の基礎_3章統計的学習の基礎_3章
統計的学習の基礎_3章
 
[PRML勉強会資料] パターン認識と機械学習 第3章 線形回帰モデル (章頭-3.1.5)(p.135-145)
[PRML勉強会資料] パターン認識と機械学習 第3章 線形回帰モデル (章頭-3.1.5)(p.135-145)[PRML勉強会資料] パターン認識と機械学習 第3章 線形回帰モデル (章頭-3.1.5)(p.135-145)
[PRML勉強会資料] パターン認識と機械学習 第3章 線形回帰モデル (章頭-3.1.5)(p.135-145)
 
PRML上巻勉強会 at 東京大学 資料 第4章4.3.1 〜 4.5.2
PRML上巻勉強会 at 東京大学 資料 第4章4.3.1 〜 4.5.2PRML上巻勉強会 at 東京大学 資料 第4章4.3.1 〜 4.5.2
PRML上巻勉強会 at 東京大学 資料 第4章4.3.1 〜 4.5.2
 
東京都市大学 データ解析入門 4 スパース性と圧縮センシング1
東京都市大学 データ解析入門 4 スパース性と圧縮センシング1東京都市大学 データ解析入門 4 スパース性と圧縮センシング1
東京都市大学 データ解析入門 4 スパース性と圧縮センシング1
 
PRML第6章「カーネル法」
PRML第6章「カーネル法」PRML第6章「カーネル法」
PRML第6章「カーネル法」
 
31350052 introductory-mathematical-analysis-textbook-solution-manual
31350052 introductory-mathematical-analysis-textbook-solution-manual31350052 introductory-mathematical-analysis-textbook-solution-manual
31350052 introductory-mathematical-analysis-textbook-solution-manual
 
Hazrat Syeda Sauda R.A.
Hazrat Syeda Sauda R.A.Hazrat Syeda Sauda R.A.
Hazrat Syeda Sauda R.A.
 
If on the average the rain falls on twelve days in every thirty days.docx
If on the average  the rain falls on twelve days in every thirty days.docxIf on the average  the rain falls on twelve days in every thirty days.docx
If on the average the rain falls on twelve days in every thirty days.docx
 
Integral table
Integral tableIntegral table
Integral table
 

Destacado

Formulas statistics
Formulas statisticsFormulas statistics
Formulas statisticsPrashi_Jain
 
Statistic formulas
Statistic formulasStatistic formulas
Statistic formulasbrchudu1
 
Descriptive v inferential
Descriptive v inferentialDescriptive v inferential
Descriptive v inferentialKen Plummer
 
Basic Concepts of Inferential statistics
Basic Concepts of Inferential statisticsBasic Concepts of Inferential statistics
Basic Concepts of Inferential statisticsStatistics Consultation
 
Research Questions and Hypotheses
Research Questions and HypothesesResearch Questions and Hypotheses
Research Questions and Hypotheseswtidwell
 
Inferential statistics powerpoint
Inferential statistics powerpointInferential statistics powerpoint
Inferential statistics powerpointkellula
 
1a difference between inferential and descriptive statistics (explanation)
1a difference between inferential and descriptive statistics (explanation)1a difference between inferential and descriptive statistics (explanation)
1a difference between inferential and descriptive statistics (explanation)Ken Plummer
 
Basic Descriptive Statistics
Basic Descriptive StatisticsBasic Descriptive Statistics
Basic Descriptive Statisticssikojp
 
Inferential statistics.ppt
Inferential statistics.pptInferential statistics.ppt
Inferential statistics.pptNursing Path
 
Descriptive Statistics
Descriptive StatisticsDescriptive Statistics
Descriptive Statisticsguest290abe
 
descriptive and inferential statistics
descriptive and inferential statisticsdescriptive and inferential statistics
descriptive and inferential statisticsMona Sajid
 

Destacado (14)

Formulas statistics
Formulas statisticsFormulas statistics
Formulas statistics
 
Statistic formulas
Statistic formulasStatistic formulas
Statistic formulas
 
Descriptive v inferential
Descriptive v inferentialDescriptive v inferential
Descriptive v inferential
 
Basic Concepts of Inferential statistics
Basic Concepts of Inferential statisticsBasic Concepts of Inferential statistics
Basic Concepts of Inferential statistics
 
Research Questions and Hypotheses
Research Questions and HypothesesResearch Questions and Hypotheses
Research Questions and Hypotheses
 
Inferential statistics powerpoint
Inferential statistics powerpointInferential statistics powerpoint
Inferential statistics powerpoint
 
1a difference between inferential and descriptive statistics (explanation)
1a difference between inferential and descriptive statistics (explanation)1a difference between inferential and descriptive statistics (explanation)
1a difference between inferential and descriptive statistics (explanation)
 
Basic Descriptive Statistics
Basic Descriptive StatisticsBasic Descriptive Statistics
Basic Descriptive Statistics
 
Inferential statistics.ppt
Inferential statistics.pptInferential statistics.ppt
Inferential statistics.ppt
 
Descriptive Statistics
Descriptive StatisticsDescriptive Statistics
Descriptive Statistics
 
descriptive and inferential statistics
descriptive and inferential statisticsdescriptive and inferential statistics
descriptive and inferential statistics
 
Hypothesis
HypothesisHypothesis
Hypothesis
 
Hypothesis Testing
Hypothesis TestingHypothesis Testing
Hypothesis Testing
 
Types of hypotheses
Types of hypothesesTypes of hypotheses
Types of hypotheses
 

Similar a Statistical inference formulasheet

Statistics formulaee
Statistics formulaeeStatistics formulaee
Statistics formulaeeSumit Satam
 
أبحاث فى الرياضيات الهندسية
أبحاث فى الرياضيات الهندسيةأبحاث فى الرياضيات الهندسية
أبحاث فى الرياضيات الهندسيةYassin Balja
 
resposta do capitulo 15
resposta do capitulo 15resposta do capitulo 15
resposta do capitulo 15silvio_sas
 
合同数問題と保型形式
合同数問題と保型形式合同数問題と保型形式
合同数問題と保型形式Junpei Tsuji
 
Techniques of integration
Techniques of integrationTechniques of integration
Techniques of integrationmusadoto
 
University of manchester mathematical formula tables
University of manchester mathematical formula tablesUniversity of manchester mathematical formula tables
University of manchester mathematical formula tablesGaurav Vasani
 
A note on variational inference for the univariate Gaussian
A note on variational inference for the univariate GaussianA note on variational inference for the univariate Gaussian
A note on variational inference for the univariate GaussianTomonari Masada
 
Mathematical formula tables
Mathematical formula tablesMathematical formula tables
Mathematical formula tablesSaravana Selvan
 
Solution Manual : Chapter - 06 Application of the Definite Integral in Geomet...
Solution Manual : Chapter - 06 Application of the Definite Integral in Geomet...Solution Manual : Chapter - 06 Application of the Definite Integral in Geomet...
Solution Manual : Chapter - 06 Application of the Definite Integral in Geomet...Hareem Aslam
 
Copia de derivadas tablas
Copia de derivadas tablasCopia de derivadas tablas
Copia de derivadas tablasGeral Delgado
 
T.I.M.E. JEE Advanced 2013 Solution Paper1
T.I.M.E. JEE Advanced 2013 Solution Paper1T.I.M.E. JEE Advanced 2013 Solution Paper1
T.I.M.E. JEE Advanced 2013 Solution Paper1askiitians
 
Notes - Probability, Statistics and Data Visualization.pdf
Notes - Probability, Statistics and Data Visualization.pdfNotes - Probability, Statistics and Data Visualization.pdf
Notes - Probability, Statistics and Data Visualization.pdfAmaanAnsari49
 

Similar a Statistical inference formulasheet (20)

Statistics formulaee
Statistics formulaeeStatistics formulaee
Statistics formulaee
 
أبحاث فى الرياضيات الهندسية
أبحاث فى الرياضيات الهندسيةأبحاث فى الرياضيات الهندسية
أبحاث فى الرياضيات الهندسية
 
resposta do capitulo 15
resposta do capitulo 15resposta do capitulo 15
resposta do capitulo 15
 
合同数問題と保型形式
合同数問題と保型形式合同数問題と保型形式
合同数問題と保型形式
 
Sk7 ph
Sk7 phSk7 ph
Sk7 ph
 
Sk7 ph
Sk7 phSk7 ph
Sk7 ph
 
Techniques of integration
Techniques of integrationTechniques of integration
Techniques of integration
 
University of manchester mathematical formula tables
University of manchester mathematical formula tablesUniversity of manchester mathematical formula tables
University of manchester mathematical formula tables
 
MA8353 TPDE
MA8353 TPDEMA8353 TPDE
MA8353 TPDE
 
A note on variational inference for the univariate Gaussian
A note on variational inference for the univariate GaussianA note on variational inference for the univariate Gaussian
A note on variational inference for the univariate Gaussian
 
Mathematical formula tables
Mathematical formula tablesMathematical formula tables
Mathematical formula tables
 
Deber10
Deber10Deber10
Deber10
 
Calculo integral - Larson
Calculo integral - LarsonCalculo integral - Larson
Calculo integral - Larson
 
Solution Manual : Chapter - 06 Application of the Definite Integral in Geomet...
Solution Manual : Chapter - 06 Application of the Definite Integral in Geomet...Solution Manual : Chapter - 06 Application of the Definite Integral in Geomet...
Solution Manual : Chapter - 06 Application of the Definite Integral in Geomet...
 
Basic m4-2-chapter1
Basic m4-2-chapter1Basic m4-2-chapter1
Basic m4-2-chapter1
 
Copia de derivadas tablas
Copia de derivadas tablasCopia de derivadas tablas
Copia de derivadas tablas
 
List of formulae
List of formulaeList of formulae
List of formulae
 
T.I.M.E. JEE Advanced 2013 Solution Paper1
T.I.M.E. JEE Advanced 2013 Solution Paper1T.I.M.E. JEE Advanced 2013 Solution Paper1
T.I.M.E. JEE Advanced 2013 Solution Paper1
 
Notes - Probability, Statistics and Data Visualization.pdf
Notes - Probability, Statistics and Data Visualization.pdfNotes - Probability, Statistics and Data Visualization.pdf
Notes - Probability, Statistics and Data Visualization.pdf
 
Guia edo todas
Guia edo todasGuia edo todas
Guia edo todas
 

Más de Pimsat University (20)

Entrepreneurship Chap 13
Entrepreneurship Chap 13Entrepreneurship Chap 13
Entrepreneurship Chap 13
 
Entrepreneurship Chap 14
Entrepreneurship Chap 14Entrepreneurship Chap 14
Entrepreneurship Chap 14
 
Entrepreneurship Chap 12
Entrepreneurship Chap 12Entrepreneurship Chap 12
Entrepreneurship Chap 12
 
Entrepreneurship Chap 11
Entrepreneurship Chap 11Entrepreneurship Chap 11
Entrepreneurship Chap 11
 
Entrepreneurship Chap 10
Entrepreneurship Chap 10Entrepreneurship Chap 10
Entrepreneurship Chap 10
 
Entrepreneurship Chap 9
Entrepreneurship Chap 9Entrepreneurship Chap 9
Entrepreneurship Chap 9
 
Entrepreneurship Chap 8
Entrepreneurship Chap 8Entrepreneurship Chap 8
Entrepreneurship Chap 8
 
Entrepreneurship Chap 7
Entrepreneurship Chap 7Entrepreneurship Chap 7
Entrepreneurship Chap 7
 
Entrepreneurship Chap 6
Entrepreneurship Chap 6Entrepreneurship Chap 6
Entrepreneurship Chap 6
 
Entrepreneurship Chap 5
Entrepreneurship Chap 5Entrepreneurship Chap 5
Entrepreneurship Chap 5
 
Entrepreneurship Chap 4
Entrepreneurship Chap 4Entrepreneurship Chap 4
Entrepreneurship Chap 4
 
Entrepreneurship Chap 3
Entrepreneurship Chap 3Entrepreneurship Chap 3
Entrepreneurship Chap 3
 
Entrepreneurship Chap 2
Entrepreneurship Chap 2Entrepreneurship Chap 2
Entrepreneurship Chap 2
 
Entrepreneurship Chap 1
Entrepreneurship Chap 1Entrepreneurship Chap 1
Entrepreneurship Chap 1
 
Bailment and pledge
Bailment and pledgeBailment and pledge
Bailment and pledge
 
Chapter 1 contract
Chapter 1 contractChapter 1 contract
Chapter 1 contract
 
Contract offer and accpetance
Contract offer and accpetanceContract offer and accpetance
Contract offer and accpetance
 
Contract of agency
Contract of agencyContract of agency
Contract of agency
 
Consideratio n
Consideratio nConsideratio n
Consideratio n
 
Partnership
PartnershipPartnership
Partnership
 

Último

2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...Martijn de Jong
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024Rafal Los
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsMaria Levchenko
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slidespraypatel2
 
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptxFactors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptxKatpro Technologies
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountPuma Security, LLC
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdfhans926745
 
Unblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen FramesUnblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen FramesSinan KOZAK
 
Top 5 Benefits OF Using Muvi Live Paywall For Live Streams
Top 5 Benefits OF Using Muvi Live Paywall For Live StreamsTop 5 Benefits OF Using Muvi Live Paywall For Live Streams
Top 5 Benefits OF Using Muvi Live Paywall For Live StreamsRoshan Dwivedi
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024The Digital Insurer
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationSafe Software
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonetsnaman860154
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreternaman860154
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Servicegiselly40
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)Gabriella Davis
 
Developing An App To Navigate The Roads of Brazil
Developing An App To Navigate The Roads of BrazilDeveloping An App To Navigate The Roads of Brazil
Developing An App To Navigate The Roads of BrazilV3cube
 
A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024Results
 
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...gurkirankumar98700
 
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Enterprise Knowledge
 
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure serviceWhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure servicePooja Nehwal
 

Último (20)

2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slides
 
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptxFactors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path Mount
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf
 
Unblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen FramesUnblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen Frames
 
Top 5 Benefits OF Using Muvi Live Paywall For Live Streams
Top 5 Benefits OF Using Muvi Live Paywall For Live StreamsTop 5 Benefits OF Using Muvi Live Paywall For Live Streams
Top 5 Benefits OF Using Muvi Live Paywall For Live Streams
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreter
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
Developing An App To Navigate The Roads of Brazil
Developing An App To Navigate The Roads of BrazilDeveloping An App To Navigate The Roads of Brazil
Developing An App To Navigate The Roads of Brazil
 
A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024
 
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
 
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
 
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure serviceWhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
 

Statistical inference formulasheet

  • 1. Statistical Inference 1. P(X = x) = n Cx px (1 – p)n – x 2. P(X=x) = e λ− !x x λ 3. P(X = x) = !x!...x!x N! n21 1 1 x P x 2 2 x P x 3 3 x P . . . . xn nP 4. λ = np 5. Z = σ µ−x 6. P = 7. Z = n x /σ µ− 8. t = ns x / µ− 9. σ2 = - 10. s = ( ) 1 / 22 − Σ−Σ n nxx 11. S.E(x) = 12. Var(x) = – 13. μ = 14. x = 15. x ± Zα/2 16. x ± t 2 α n S 17. W1- α = - Wα 18. M1-α = n1(n1 + n2 +1) – Mα 19. E = Zα/2 20. Z = )2/()/( )()( 2 21 2 1 2121 nn xx σσ µµ + −−− 21. t = 21 2121 11 )()( nn Sp xx + −−− µµ 22. Sp = 2 )1()1( 21 2 22 2 11 −+ −+− nn SnSn 23. t = 2 2 2 1 2 1 2121 )()( n Sn S xx + −−− µµ 24. 11 2 2 2 2 2 1 2 1 2 1 2 2 2 2 1 2 1 −       + −           + =∆ n n S n n S n S n S 25. ( )21 xx − + Z 2 α 2 2 2 1 2 1 nn σσ + 26. ( )21 xx − + t 2 α 2 2 2 1 2 1 n S n S + 27. p + Z 2 x n pp )1( − 28. t = nsd d / 29. Sd = 1 /)( 22 − Σ−Σ n ndd 30. d = 31. d.f = n – 1 32. d.f = n1 + n2 – 2 33. d.f = (r – 1) (c – 1)
  • 2. 34. Z = n pp pp )1( − − 35. Z = )11)(1( )()( 21 2121 nn PP PPPP PP +− −−− 36. PP = 21 21 nn xx + + 37. X2 = 2 1 σ −n x s2 38. F = 2 2 2 1 S S 39. (p1–p2)+Z 2 α 2 22 1 1 )1()1( n pp n pp − + − 40. X2 = }/){( 2 EEo −Σ 41. E = 42. d.f = (k - 1, n - k) 43. RP = 2 wS xw xxΣ Σ 44. Sxx = nxx /)( 22 Σ−Σ 45. Sxy = nyxyx /))(( ΣΣ−Σ 46. Syy = nyy /)( 22 Σ−Σ 47. b1 = Sxx Sxy 48. b0 = y - b1 1x 49. r = Sxy / yyxx SS 50. t = SxxSe b / 1 51. Se = 2−n SSE 52. t = 2 1 2 − − n r r 53. β1 ± t 2 α Sxx Se 54. SST = nxx /)( 22 Σ−Σ 55. SSE = SST – SSTR 56. SSTR = ∑ – 57. r12 = 2 2 2 2 2 1 2 1 2121 )()( xxnxxn xxxxn Σ−ΣΣ−Σ ΣΣ−Σ 58. MSE = 59. MSTR = 60. F-Ratio = 61. R1.23 = 2 23 132312 2 13 2 12 1 2 r rrrrr − −+ 62. r12 = 63. SSR = 64. SST = Syy 65. d.f = (n1 – 1) , (n2 – 1) 66. ( )( )2 23 2 13 231312 3.12 11 rr rrr r −− − = 67. F = 68. SSR = b0Σy + b1 Σx1y + b2 Σx2y - 69. rs = 1 - 70. H = ×Σ - 3(n + 1) 71. X ± A2 R
  • 3. 72. C ± 3 Sc 73. Sc = 74. P ± 3 75. UCLR = D4 R 76. LCLR = D3 R 77. P(X=x) = n-1 Cx-1 × px × (1 – p)n-x 78. P = 79. σ =