SlideShare a Scribd company logo
1 of 14
Applied Statistics and Mathematics in
       Economics & Business
             BS1501
           Tutorial 3
       Pairach Piboonrungroj
               (Champ)

           me@Pairach.com
1. A Yogurt manufacturer

                           x0 − µ 
(a)
      P ( x > x0 ) = P z >
                                  
                            σx   
      x0 = 570, µ = 500, σ x = 33
                     570 − 500 
P ( x > 570) = P z >           
                        33     
              = P ( Z > 2.12) = 0.017
1. A Yogurt manufacturer

(b)                         x0 − µ 
       P ( x > x0 ) = P z >
                                   
                             σx   
     σ    33                        ~ ~ N ( µ = 500, σ ~ = 8.82)
σx =    =    = 8.82                 x                  x
      n   14

                       490 − 500       510 − 500 
 P (490 < x < 510) = P           <Z <               
                       8.82               8.82 
                   = P(−1.13 < Z < 1.13) = 1 − 2 P ( z > 1.13)
                       = 1 − 2(0.12924) = 0.740
Z = 1.13
Z = -1.13
2. Women shift-workers

(a)                               P(1 − P) = (0.60)(0.40) = 0.04
        E ( p ) = 0.60
            ˆ              σp =
                            ˆ
                                     n           150

                 ˆ               0.60 − 0.60     0.70 − 0.60 
       P (0.60 < P < 0.70) = P (             <Z<             
                                    0.04            0.04     
           P(0 < Z < 2.5) = 0.5 − 0.0062 = 0.4938
(b)       ˆ ≥ 0.50) = P Z > 0.50 − 0.60 
       P( P                             
                               0.04     
      P ( Z > −2.5) = 1 − P ( Z > 2.5) = 1 − 0.0062 = 0.9938
3. A Building Society
                                                                 s
                                        C.I. for µ = x ± Z α *
                                                          2      n
                                 s
(a) 90% C.I. for µ = x ± 1.645 *
                                  n
                                          8500
                      = 15200 ± 1.645 *
                                            30
                       12667.16 < µ < 17752.84

(b)                                s                          8500
      99 % C.I. for µ = x ± 2.57 *      = 15200 ± 2.57 *
                                    n                            30
                             11211.67 < µ < 19188.33
4. Voters from the electorate
(a)
       180 indicated their preference for a particular
       candidate (x)

        400 sample voters (n)

                    ˆ  x
                    P=
                       n
                    ˆ  180
                    P=     = 0.45
                       400
4. Voters from the electorate

(b)                              ˆ     ˆ
                                 P(1 - P)
                   ˆ
      C.I. for P = P ± Z α
                         2          n

                                   ˆ     ˆ
                                   P(1 - P)
                  ˆ
95 % C.I. for P = P ± Z 0.05
                             2        n
                              0.45 * 0.55
                = 0.45 ± 1.96
                                 400
                                          =   0.45 ± 0.049

       0.401 < P < 0.499
4. Voters from the electorate

(c)                              ˆ     ˆ
                                 P(1 - P)
                   ˆ
      C.I. for P = P ± Z α
                         2          n

                                   ˆ     ˆ
                                   P(1 - P)
                  ˆ
99 % C.I. for P = P ± Z 0.01
                             2        n
                              0.45 * 0.55
                = 0.45 ± 2.57             =   0.45 ± 0.064
                                 400


       0.386 < P < 0.514
5. Lifetime of light bulbs

(a)     n = 100,    x = 1570,    s = 120

H 0 : µ = 1600        α = 0.05 ⇒       Reject H0 if z < -1.96 or if Z>+1.96
                                                      ⇒


H 1 : µ ≠ 1600        α = 0.01         Reject H0 if z < -2.58 or if Z>+2.58

   x−µ            1570 − 1600
z=              =             = −2.5               < -1.96 BUT > -2.58
   s               120
     n                  100
               ∴Reject H0 at 5% level of significance

 i.e.    There is sufficient evidence at 5% level of significance to reject the
 hypothesis that the mean lifetime of bulbs has changed, but not at the 1% level.
5. Lifetime of light bulbs

 (b)n = 100, x = 1570, s = 120               x − µ 1570 − 1600
                                          z=       =           = −2.5
           H 0 : µ = 1600                     s      120
                                                 n       100
            H1 : µ < 1600
      α = 0.05          ⇒         Reject H0      if z < -1.64 (one tailed)
                                  Reject H0      if z < -2.33 (one
      α = 0.01          ⇒
                        tailed)

∴Reject H0 5% and 1% levels of significance.
 The first alternative hypothesis would seem to be a fairly weak one
 compared to the second one,
 given that it would be of more interest to know whether the mean lifetime
 of the population of bulbs has deteriorated, rather than simply change.
6. Rent for student room in
                Cardiff

(a)  H 0 : µ = 220                          x − µ0           230 − 220
                                       z=               =
        H 1 : µ ≠ 220                        s    n          35   40
  Reject H0 if Z<-1.96 or if Z>1.96.
                                                          =1.81
 Thus we do not reject H0, i.e. the claim of
 £220 per month cannot be rejected.

(b)                                     s                          35
         C.I. for   µ = x ± Z 0.025 *         = 230 ± 1.96 *
                                         n                   40
                                             219.15 < µ < 240.85
      Do not reject H0 since £220 is in the above interval.
7. A bank manager & her customers


  (a)                                  20
         H 0 : p = 0.30           p=
                                   ˆ      = 0.20
                                     100
         H 1 : p ≠ 0.30                   0.30 * 0.70
                                 σp =
                                  ˆ                    = 0.045
                                              100
           ˆ
           P − P0 0.20 − 0.30
        Z=       =            = −2.22 < −1.96
             σP
              ˆ      0.045
  (b)
         Therefore, we reject at 95% level of confidence.

        Reject H0 if Z<-2.57.
        Thus do not reject H0 at 1% level of significance.
Thank You for Your Attention
• To download this slide (PPT & PDF)
  visit
  www.pairach.com/teaching


 • Any further question?
   Please email to
   me@pairach.com

More Related Content

What's hot

CalculusStudyGuide
CalculusStudyGuideCalculusStudyGuide
CalculusStudyGuide
Mo Elkhatib
 
Computational mathematic
Computational mathematicComputational mathematic
Computational mathematic
Abdul Khaliq
 

What's hot (17)

Proximal Splitting and Optimal Transport
Proximal Splitting and Optimal TransportProximal Splitting and Optimal Transport
Proximal Splitting and Optimal Transport
 
Low Complexity Regularization of Inverse Problems - Course #2 Recovery Guaran...
Low Complexity Regularization of Inverse Problems - Course #2 Recovery Guaran...Low Complexity Regularization of Inverse Problems - Course #2 Recovery Guaran...
Low Complexity Regularization of Inverse Problems - Course #2 Recovery Guaran...
 
Topic5
Topic5Topic5
Topic5
 
Low Complexity Regularization of Inverse Problems - Course #3 Proximal Splitt...
Low Complexity Regularization of Inverse Problems - Course #3 Proximal Splitt...Low Complexity Regularization of Inverse Problems - Course #3 Proximal Splitt...
Low Complexity Regularization of Inverse Problems - Course #3 Proximal Splitt...
 
Mesh Processing Course : Multiresolution
Mesh Processing Course : MultiresolutionMesh Processing Course : Multiresolution
Mesh Processing Course : Multiresolution
 
Mesh Processing Course : Active Contours
Mesh Processing Course : Active ContoursMesh Processing Course : Active Contours
Mesh Processing Course : Active Contours
 
CalculusStudyGuide
CalculusStudyGuideCalculusStudyGuide
CalculusStudyGuide
 
Solutions Manual for College Algebra Concepts Through Functions 3rd Edition b...
Solutions Manual for College Algebra Concepts Through Functions 3rd Edition b...Solutions Manual for College Algebra Concepts Through Functions 3rd Edition b...
Solutions Manual for College Algebra Concepts Through Functions 3rd Edition b...
 
Low Complexity Regularization of Inverse Problems - Course #1 Inverse Problems
Low Complexity Regularization of Inverse Problems - Course #1 Inverse ProblemsLow Complexity Regularization of Inverse Problems - Course #1 Inverse Problems
Low Complexity Regularization of Inverse Problems - Course #1 Inverse Problems
 
Geodesic Method in Computer Vision and Graphics
Geodesic Method in Computer Vision and GraphicsGeodesic Method in Computer Vision and Graphics
Geodesic Method in Computer Vision and Graphics
 
Vibrational Rotational Spectrum of HCl and DCl
Vibrational Rotational Spectrum of HCl and DClVibrational Rotational Spectrum of HCl and DCl
Vibrational Rotational Spectrum of HCl and DCl
 
Signal Processing Course : Convex Optimization
Signal Processing Course : Convex OptimizationSignal Processing Course : Convex Optimization
Signal Processing Course : Convex Optimization
 
Linear Programming Problems : Dr. Purnima Pandit
Linear Programming Problems : Dr. Purnima PanditLinear Programming Problems : Dr. Purnima Pandit
Linear Programming Problems : Dr. Purnima Pandit
 
Computational mathematic
Computational mathematicComputational mathematic
Computational mathematic
 
Linreg
LinregLinreg
Linreg
 
numericai matmatic matlab uygulamalar ali abdullah
numericai matmatic  matlab  uygulamalar ali abdullahnumericai matmatic  matlab  uygulamalar ali abdullah
numericai matmatic matlab uygulamalar ali abdullah
 
2014 st josephs geelong spec maths
2014 st josephs geelong spec maths2014 st josephs geelong spec maths
2014 st josephs geelong spec maths
 

Similar to BS1501 tutorial 3_final

Statistics Presentation week 6
Statistics Presentation week 6Statistics Presentation week 6
Statistics Presentation week 6
krookroo
 
curve fitting lecture slides February 24
curve fitting lecture slides February 24curve fitting lecture slides February 24
curve fitting lecture slides February 24
TaiwoD
 
Jam 2006 Test Papers Mathematical Statistics
Jam 2006 Test Papers Mathematical StatisticsJam 2006 Test Papers Mathematical Statistics
Jam 2006 Test Papers Mathematical Statistics
ashu29
 
Matematika - Graph Of Cosinus Function
Matematika - Graph Of Cosinus FunctionMatematika - Graph Of Cosinus Function
Matematika - Graph Of Cosinus Function
Ramadhani Sardiman
 

Similar to BS1501 tutorial 3_final (20)

Cat 2007 solutions
Cat 2007 solutionsCat 2007 solutions
Cat 2007 solutions
 
Ch12p
Ch12pCh12p
Ch12p
 
Statistical controls for qc
Statistical controls for qcStatistical controls for qc
Statistical controls for qc
 
Statistical Tools for the Quality Control Laboratory and Validation Studies
Statistical Tools for the Quality Control Laboratory and Validation StudiesStatistical Tools for the Quality Control Laboratory and Validation Studies
Statistical Tools for the Quality Control Laboratory and Validation Studies
 
Normal Distribution of data (Continous data)
Normal Distribution of data (Continous data)Normal Distribution of data (Continous data)
Normal Distribution of data (Continous data)
 
Acceptable pins
Acceptable pins Acceptable pins
Acceptable pins
 
Statistics Presentation week 6
Statistics Presentation week 6Statistics Presentation week 6
Statistics Presentation week 6
 
L. Jonke - A Twisted Look on Kappa-Minkowski: U(1) Gauge Theory
L. Jonke - A Twisted Look on Kappa-Minkowski: U(1) Gauge TheoryL. Jonke - A Twisted Look on Kappa-Minkowski: U(1) Gauge Theory
L. Jonke - A Twisted Look on Kappa-Minkowski: U(1) Gauge Theory
 
S7 sp
S7 spS7 sp
S7 sp
 
Section1 stochastic
Section1 stochasticSection1 stochastic
Section1 stochastic
 
Formulario Trigonometria
Formulario TrigonometriaFormulario Trigonometria
Formulario Trigonometria
 
curve fitting lecture slides February 24
curve fitting lecture slides February 24curve fitting lecture slides February 24
curve fitting lecture slides February 24
 
Presentation on Regression Analysis
Presentation on Regression AnalysisPresentation on Regression Analysis
Presentation on Regression Analysis
 
Jam 2006 Test Papers Mathematical Statistics
Jam 2006 Test Papers Mathematical StatisticsJam 2006 Test Papers Mathematical Statistics
Jam 2006 Test Papers Mathematical Statistics
 
Matematika - Graph Of Cosinus Function
Matematika - Graph Of Cosinus FunctionMatematika - Graph Of Cosinus Function
Matematika - Graph Of Cosinus Function
 
Maths04
Maths04Maths04
Maths04
 
Normal probability distribution
Normal probability distributionNormal probability distribution
Normal probability distribution
 
jacobi method, gauss siedel for solving linear equations
jacobi method, gauss siedel for solving linear equationsjacobi method, gauss siedel for solving linear equations
jacobi method, gauss siedel for solving linear equations
 
Normal lecture
Normal lectureNormal lecture
Normal lecture
 
Ch09s
Ch09sCh09s
Ch09s
 

Recently uploaded

The basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptxThe basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptx
heathfieldcps1
 
1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi  6.pdf1029-Danh muc Sach Giao Khoa khoi  6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdf
QucHHunhnh
 
1029 - Danh muc Sach Giao Khoa 10 . pdf
1029 -  Danh muc Sach Giao Khoa 10 . pdf1029 -  Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdf
QucHHunhnh
 

Recently uploaded (20)

Mixin Classes in Odoo 17 How to Extend Models Using Mixin Classes
Mixin Classes in Odoo 17  How to Extend Models Using Mixin ClassesMixin Classes in Odoo 17  How to Extend Models Using Mixin Classes
Mixin Classes in Odoo 17 How to Extend Models Using Mixin Classes
 
Understanding Accommodations and Modifications
Understanding  Accommodations and ModificationsUnderstanding  Accommodations and Modifications
Understanding Accommodations and Modifications
 
Magic bus Group work1and 2 (Team 3).pptx
Magic bus Group work1and 2 (Team 3).pptxMagic bus Group work1and 2 (Team 3).pptx
Magic bus Group work1and 2 (Team 3).pptx
 
Unit-IV- Pharma. Marketing Channels.pptx
Unit-IV- Pharma. Marketing Channels.pptxUnit-IV- Pharma. Marketing Channels.pptx
Unit-IV- Pharma. Marketing Channels.pptx
 
SKILL OF INTRODUCING THE LESSON MICRO SKILLS.pptx
SKILL OF INTRODUCING THE LESSON MICRO SKILLS.pptxSKILL OF INTRODUCING THE LESSON MICRO SKILLS.pptx
SKILL OF INTRODUCING THE LESSON MICRO SKILLS.pptx
 
On National Teacher Day, meet the 2024-25 Kenan Fellows
On National Teacher Day, meet the 2024-25 Kenan FellowsOn National Teacher Day, meet the 2024-25 Kenan Fellows
On National Teacher Day, meet the 2024-25 Kenan Fellows
 
How to Give a Domain for a Field in Odoo 17
How to Give a Domain for a Field in Odoo 17How to Give a Domain for a Field in Odoo 17
How to Give a Domain for a Field in Odoo 17
 
Unit-V; Pricing (Pharma Marketing Management).pptx
Unit-V; Pricing (Pharma Marketing Management).pptxUnit-V; Pricing (Pharma Marketing Management).pptx
Unit-V; Pricing (Pharma Marketing Management).pptx
 
TỔNG ÔN TẬP THI VÀO LỚP 10 MÔN TIẾNG ANH NĂM HỌC 2023 - 2024 CÓ ĐÁP ÁN (NGỮ Â...
TỔNG ÔN TẬP THI VÀO LỚP 10 MÔN TIẾNG ANH NĂM HỌC 2023 - 2024 CÓ ĐÁP ÁN (NGỮ Â...TỔNG ÔN TẬP THI VÀO LỚP 10 MÔN TIẾNG ANH NĂM HỌC 2023 - 2024 CÓ ĐÁP ÁN (NGỮ Â...
TỔNG ÔN TẬP THI VÀO LỚP 10 MÔN TIẾNG ANH NĂM HỌC 2023 - 2024 CÓ ĐÁP ÁN (NGỮ Â...
 
ICT role in 21st century education and it's challenges.
ICT role in 21st century education and it's challenges.ICT role in 21st century education and it's challenges.
ICT role in 21st century education and it's challenges.
 
SOC 101 Demonstration of Learning Presentation
SOC 101 Demonstration of Learning PresentationSOC 101 Demonstration of Learning Presentation
SOC 101 Demonstration of Learning Presentation
 
The basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptxThe basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptx
 
Asian American Pacific Islander Month DDSD 2024.pptx
Asian American Pacific Islander Month DDSD 2024.pptxAsian American Pacific Islander Month DDSD 2024.pptx
Asian American Pacific Islander Month DDSD 2024.pptx
 
1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi  6.pdf1029-Danh muc Sach Giao Khoa khoi  6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdf
 
Application orientated numerical on hev.ppt
Application orientated numerical on hev.pptApplication orientated numerical on hev.ppt
Application orientated numerical on hev.ppt
 
Key note speaker Neum_Admir Softic_ENG.pdf
Key note speaker Neum_Admir Softic_ENG.pdfKey note speaker Neum_Admir Softic_ENG.pdf
Key note speaker Neum_Admir Softic_ENG.pdf
 
Food safety_Challenges food safety laboratories_.pdf
Food safety_Challenges food safety laboratories_.pdfFood safety_Challenges food safety laboratories_.pdf
Food safety_Challenges food safety laboratories_.pdf
 
1029 - Danh muc Sach Giao Khoa 10 . pdf
1029 -  Danh muc Sach Giao Khoa 10 . pdf1029 -  Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdf
 
ComPTIA Overview | Comptia Security+ Book SY0-701
ComPTIA Overview | Comptia Security+ Book SY0-701ComPTIA Overview | Comptia Security+ Book SY0-701
ComPTIA Overview | Comptia Security+ Book SY0-701
 
Accessible Digital Futures project (20/03/2024)
Accessible Digital Futures project (20/03/2024)Accessible Digital Futures project (20/03/2024)
Accessible Digital Futures project (20/03/2024)
 

BS1501 tutorial 3_final

  • 1. Applied Statistics and Mathematics in Economics & Business BS1501 Tutorial 3 Pairach Piboonrungroj (Champ) me@Pairach.com
  • 2. 1. A Yogurt manufacturer  x0 − µ  (a) P ( x > x0 ) = P z >    σx   x0 = 570, µ = 500, σ x = 33  570 − 500  P ( x > 570) = P z >   33  = P ( Z > 2.12) = 0.017
  • 3. 1. A Yogurt manufacturer (b)  x0 − µ  P ( x > x0 ) = P z >    σx   σ 33 ~ ~ N ( µ = 500, σ ~ = 8.82) σx = = = 8.82 x x n 14  490 − 500 510 − 500  P (490 < x < 510) = P <Z <   8.82 8.82  = P(−1.13 < Z < 1.13) = 1 − 2 P ( z > 1.13) = 1 − 2(0.12924) = 0.740
  • 4. Z = 1.13 Z = -1.13
  • 5. 2. Women shift-workers (a) P(1 − P) = (0.60)(0.40) = 0.04 E ( p ) = 0.60 ˆ σp = ˆ n 150 ˆ  0.60 − 0.60 0.70 − 0.60  P (0.60 < P < 0.70) = P ( <Z<   0.04 0.04  P(0 < Z < 2.5) = 0.5 − 0.0062 = 0.4938 (b) ˆ ≥ 0.50) = P Z > 0.50 − 0.60  P( P    0.04  P ( Z > −2.5) = 1 − P ( Z > 2.5) = 1 − 0.0062 = 0.9938
  • 6. 3. A Building Society s C.I. for µ = x ± Z α * 2 n s (a) 90% C.I. for µ = x ± 1.645 * n 8500 = 15200 ± 1.645 * 30 12667.16 < µ < 17752.84 (b) s 8500 99 % C.I. for µ = x ± 2.57 * = 15200 ± 2.57 * n 30 11211.67 < µ < 19188.33
  • 7. 4. Voters from the electorate (a) 180 indicated their preference for a particular candidate (x) 400 sample voters (n) ˆ x P= n ˆ 180 P= = 0.45 400
  • 8. 4. Voters from the electorate (b) ˆ ˆ P(1 - P) ˆ C.I. for P = P ± Z α 2 n ˆ ˆ P(1 - P) ˆ 95 % C.I. for P = P ± Z 0.05 2 n 0.45 * 0.55 = 0.45 ± 1.96 400 = 0.45 ± 0.049 0.401 < P < 0.499
  • 9. 4. Voters from the electorate (c) ˆ ˆ P(1 - P) ˆ C.I. for P = P ± Z α 2 n ˆ ˆ P(1 - P) ˆ 99 % C.I. for P = P ± Z 0.01 2 n 0.45 * 0.55 = 0.45 ± 2.57 = 0.45 ± 0.064 400 0.386 < P < 0.514
  • 10. 5. Lifetime of light bulbs (a) n = 100, x = 1570, s = 120 H 0 : µ = 1600 α = 0.05 ⇒ Reject H0 if z < -1.96 or if Z>+1.96 ⇒ H 1 : µ ≠ 1600 α = 0.01 Reject H0 if z < -2.58 or if Z>+2.58 x−µ 1570 − 1600 z= = = −2.5 < -1.96 BUT > -2.58 s 120 n 100 ∴Reject H0 at 5% level of significance i.e. There is sufficient evidence at 5% level of significance to reject the hypothesis that the mean lifetime of bulbs has changed, but not at the 1% level.
  • 11. 5. Lifetime of light bulbs (b)n = 100, x = 1570, s = 120 x − µ 1570 − 1600 z= = = −2.5 H 0 : µ = 1600 s 120 n 100 H1 : µ < 1600 α = 0.05 ⇒ Reject H0 if z < -1.64 (one tailed) Reject H0 if z < -2.33 (one α = 0.01 ⇒ tailed) ∴Reject H0 5% and 1% levels of significance. The first alternative hypothesis would seem to be a fairly weak one compared to the second one, given that it would be of more interest to know whether the mean lifetime of the population of bulbs has deteriorated, rather than simply change.
  • 12. 6. Rent for student room in Cardiff (a)  H 0 : µ = 220 x − µ0 230 − 220  z= =  H 1 : µ ≠ 220 s n 35 40 Reject H0 if Z<-1.96 or if Z>1.96. =1.81 Thus we do not reject H0, i.e. the claim of £220 per month cannot be rejected. (b) s 35 C.I. for µ = x ± Z 0.025 * = 230 ± 1.96 * n 40 219.15 < µ < 240.85 Do not reject H0 since £220 is in the above interval.
  • 13. 7. A bank manager & her customers (a) 20  H 0 : p = 0.30 p= ˆ = 0.20  100  H 1 : p ≠ 0.30 0.30 * 0.70 σp = ˆ = 0.045 100 ˆ P − P0 0.20 − 0.30 Z= = = −2.22 < −1.96 σP ˆ 0.045 (b) Therefore, we reject at 95% level of confidence. Reject H0 if Z<-2.57. Thus do not reject H0 at 1% level of significance.
  • 14. Thank You for Your Attention • To download this slide (PPT & PDF) visit www.pairach.com/teaching • Any further question? Please email to me@pairach.com