SlideShare a Scribd company logo
1 of 23
Multiple Causes of Food Insecurity:
  Multiple Regression Analysis


          Reference: Gujarati (2004)
THE THREE-VARIABLE MODEL
THE THREE-VARIABLE MODEL
THE THREE-VARIABLE MODEL
Maternal education and community
  characteristics as indicators of
  nutritional status of children –
            application
    of multivariate regression



           Nutritional Status of Children   5
Main results

•   Step 1: Estimating the coefficients of the model(Table 10.2,10.3).
•   Step 2: Examining how good the model predicts(Table 10.4,10.5).
•   Step 3: Hypotheses testing.
•   Tests about the equation(Table 10.6,10.7).
•   Tests about individual coefficients(Table 10.8,10.9).
•   Part and partial correlation coefficients.
•   Step 4: Checking for violations of regression assumptions.
•   Checking normality of the errors(Table 10.10,Figure 10.1,10.2).
•   Checking for homogeneity of variance of the residuals(Figure
    10.3,10.4).

                            Nutritional Status of Children               6
Empirical analysis




    Nutritional Status of Children   7
Empirical analysis




  Data description and methodology



     Nutritional Status of Children   8
Table 10.1 Means and standard deviations of
                      variables: Malawi sample
Variables                             Mean                  Standard deviation

BFEEDNEW                               1.57                       0.49

DIARRHEA                               0.16                       0.37

CALREQ                                 0.32                       0.47

ATTCLINI                               1.32                       0.59

AGEMNTH                               27.29                       16.28

CLINFEED                               2.29                       4.57

DRINKDST                               1.98                       1.39

EDUCSPOUS                              2.23                       1.30

LATERINE                               0.40                       0.49

PXFD                                   9.00                       9.85




                           Nutritional Status of Children                        9
Table 10.2 Determinants of weight for age Z-scores
Variables                           Model 1                                            Model 2

                     Coefficients              Std. error               Coefficients             Std. error

Constant                -3.03                    0.504                     -2.93                   0.492

EDUCSPOUS               0.173                    0.058                     0.156                   0.057

ATTCLINI                0.439                    0.144                     0.439                   0.142

DRINKDST               -0.143                    0.051                    -0.147                   0.05

LATERINE                0.134                    0.152                     0.152                   0.148

PXFD                    0.009                    0.008

AGEMNTH                -0.084                    0.021                    -0.088                   0.021

AGESQ                   0.001                    0.0003                    0.001                  0.0003

CLINFEED                0.717                    0.181                     0.699                   0.179

DIARRHEA               -0.608                    0.201                    -0.620                   0.198

BFEEDNEW                0.664                    0.226                     0.625                   0.224

CALREQ                                                                     0.566                   0.212

HEALTDST               -0.094                    0.071                     -0.06                   0.072


                                       Nutritional Status of Children                                         10
Table 10.3 Determinants of height for age Z-scores
Variables                           Model 1                                           Model 2

                     Coefficients               Std. error             Coefficients             Std. error

Constant               -2.159                     0.677                  -2.564                   0.685

EDUCSPOUS               0.048                     0.072                   0.106                   0.074

ATTCLINI                0.514                     0.18                    0.402                   0.177

DRINKDST               -0.024                     0.064                  -0.018                   0.062

LATERINE                0.072                     0.192                   0.114                   0.185

PXFD                    0.014                     0.01

AGEMNTH                -0.091                     0.034                  -0.094                   0.034

AGESQ                   0.001                    0.0004                  0.0009                  0.0004

CLINFEED                0.686                     0.227                   0.761                   0.224

DIARRHEA               -0.829                     0.261                  -0.932                   0.260

BFEEDNEW                0.149                     0.305                   0.174                   0.299

INSECURE                                                                  0.252                   0.085

HEALTDST               -0.106                     0.092                  -0.127                   0.091


                                      Nutritional Status of Children                                         11
Equations to predict the weight for age Z-scores
                  for model 1




                  Nutritional Status of Children   12
Table 10.4 Summary of the model for determinants of
              weight for age Z-scores



                                                        Adjusted R2   Std. error of
     Models     R                    R2
                                                                      the estimate


       1       0.488              0.238                   0.203           1.09

       2       0.506              0.256                   0.222          1.079




                       Nutritional Status of Children                                 13
Table 10.5 Summary of the model for determinants of
              height for age Z-scores


                                                       Adjusted R2   Std. error of
    Models     R                   R2
                                                                     the estimate

              0.418             0.174                     0.13          1.285
      1


      2       0.448             0.201                    0.157          1.265




                      Nutritional Status of Children                                 14
Table 10.6 Analysis of variance table for weight for
                   age Z-scores

                       Sum of
Models                                      df           Mean Square      F     Sig
                      Squares
  I      Regression   89.139                11                  8.104   6.797   0

         Residual     284.936              239                  1.192

         Total        374.075              250

  II     Regression   95.757                11                  8.705   7.475   0

         Residual     278.318              239                  1.165

         Total        374.075              250




                               Nutritional Status of Children                         15
Table 10.7 Analysis of variance table for height for age
                        Z-scores

                        Sum of
 Models                                      df           Mean Square      F     Sig
                       Squares

   I      Regression   70.948                11                  6.45    3.901   0.00


          Residual     335.649              203                  1.653

          Total        406.597              214

   II     Regression   81.525                11                  7.411   4.628   0.00

          Residual     325.072              203                  1.601

          Total        406.597              214




                                Nutritional Status of Children                          16
Table 10.8 Tests of individual coefficients for determinants of
                       weight for age Z-scores
Variables                     Model 1                                         Model 2
                   t-stat                  P value                   t-stat             P value
Constant           -6.016                   0.000                   -5.956               0.000

EDUCSPOUS          2.987*                   0.003                    2.70*               0.007

ATTCLINI           3.047*                   0.003                   3.102*               0.002

DRINKDST           -2.817*                  0.005                   -2.938*              0.004

LATERINE           0.882                    0.378                    1.03                0.304

PXFD               1.185                    0.237

AGEMNTH            -4.041*                  0.000                   -4.282*              0.00

AGESQ              3.096*                   0.002                   3.386*               0.001

CLINFEED           3.966*                   0.000                   3.911*               0.00

DIARRHEA           -3.018*                  0.003                   -3.126*              0.002

BFEEDNEW           2.935*                   0.004                   2.791*               0.006

CALREQ                                                              2.668*               0.008

HEALTDST            -1.32                   0.188                   -0.851               0.396

                  Note: * denotes at 1 per centof Children
                               Nutritional Status level of significance.                          17
Table 10.9 Tests of individual coefficients for determinants of
                       height for age Z-scores
Variables                                    Model 1                                                 Model 2
                                t-stat                     P value                      t-stat                     P value
Constant                        -3.187                      0.002                       -3.745                     0.000

EDUCSPOUS                       0.661                       0.509                       1.437                      0.152

ATTCLINI                        2.863*                      0.005                      2.273**                     0.024

DRINKDST                        -0.37                       0.712                       -0.286                     0.775

LATERINE                        0.375                       0.708                       0.615                      0.539

PXFD                            1.479                       0.141

AGEMNTH                        -2.642*                      0.009                       -2.78*                     0.006

AGESQ                          1.949**                      0.053                      2.022**                     0.044


CLINFEED                        3.018*                      0.003                      3.394*                      0.001


DIARRHEA                       -3.171*                      0.002                      -3.584*                      0.00


BFEEDNEW                        0.489                       0.625                       0.580                      0.563

INSECURE
                                                                                       2.977*                      0.003

HEALTDST                        -1.15                       0.252                       -1.395                     0.164

            Note: * denotes at 1 per cent level of significance, ** denotes at 5 per cent level of significance.
                                             Nutritional Status of Children                                                  18
Table 10.10 Part and partial correlation coefficients for weight
                   for age and height for age
Variables                    Weight for age                                      Height for age
               Part correlation         Partial correlation        Part correlation         Partial correlation
EDUCSPOUS           0.169                       0.19                    0.042                     0.046

ATTCLINI            0.172                      0.193                    0.183                     0.197

DRINKDST            -0.159                    -0.179                    -0.024                    -0.026

LATERINE             0.05                      0.057                    0.024                     0.026

PXFD                0.067                      0.076                    0.094                     0.103

AGEMNTH             -0.228                    -0.253                    -0.168                    -0.182

AGESQ               0.175                      0.196                    0.124                     0.136


CLINFEED            0.224                      0.248                    0.192                     0.207


DIARRHEA            -0.17                     -0.192                    -0.202                    -0.217


BFEEDNEW            0.166                      0.187                    0.031                     0.034

HEALTDST            -0.075                    -0.085                    -0.073                     -0.08


                                  Nutritional Status of Children                                             19
Figure 10.1 Histogram of standardized residuals of
                  weight for age




                  Nutritional Status of Children     20
Figure 10.2 Normal P-P plot of regression standardized
                      residuals




                    Nutritional Status of Children   21
Figure 10.3 Residuals plotted against predicted values
                  for weight for age




                    Nutritional Status of Children   22
Figure 10.4 Residuals plotted against predicted values
                  for height for age




                    Nutritional Status of Children   23

More Related Content

Viewers also liked

Multiple Regression worked example (July 2014 updated)
Multiple Regression worked example (July 2014 updated)Multiple Regression worked example (July 2014 updated)
Multiple Regression worked example (July 2014 updated)Michael Ling
 
econometrics project PG1 2015-16
econometrics project PG1 2015-16econometrics project PG1 2015-16
econometrics project PG1 2015-16Sayantan Baidya
 
The Barings Bank Collapse
The Barings Bank CollapseThe Barings Bank Collapse
The Barings Bank CollapseUday Tharar
 
Regression analysis
Regression analysisRegression analysis
Regression analysissaba khan
 
Regression Analysis
Regression AnalysisRegression Analysis
Regression Analysisnadiazaheer
 
Multiple regression presentation
Multiple regression presentationMultiple regression presentation
Multiple regression presentationCarlo Magno
 
Presentation On Regression
Presentation On RegressionPresentation On Regression
Presentation On Regressionalok tiwari
 
Regression analysis
Regression analysisRegression analysis
Regression analysisRavi shankar
 
Regression analysis ppt
Regression analysis pptRegression analysis ppt
Regression analysis pptElkana Rorio
 
Topic17 regression spss
Topic17 regression spssTopic17 regression spss
Topic17 regression spssSizwan Ahammed
 
Week6 partb projecthelp
Week6 partb projecthelpWeek6 partb projecthelp
Week6 partb projecthelpBrent Heard
 
Statistical and Predictive Modelling
Statistical and Predictive ModellingStatistical and Predictive Modelling
Statistical and Predictive ModellingJMP software from SAS
 
Week 6 project part b
Week 6 project part bWeek 6 project part b
Week 6 project part bmariesm2012
 
Statistical Modeling - Cereal Data Project
Statistical Modeling - Cereal Data Project  Statistical Modeling - Cereal Data Project
Statistical Modeling - Cereal Data Project Hubert Lo
 
Brandie and Michaella's Project
Brandie and Michaella's ProjectBrandie and Michaella's Project
Brandie and Michaella's ProjectLacilia0024
 
Session 8 c 8 c rambaldi slides (kz)
Session 8 c 8 c   rambaldi slides (kz)Session 8 c 8 c   rambaldi slides (kz)
Session 8 c 8 c rambaldi slides (kz)IARIW 2014
 
Micro-econometrics Marriage and Wages
Micro-econometrics Marriage and WagesMicro-econometrics Marriage and Wages
Micro-econometrics Marriage and WagesXuefeng Xing
 
Linear Regression Ordinary Least Squares Distributed Calculation Example
Linear Regression Ordinary Least Squares Distributed Calculation ExampleLinear Regression Ordinary Least Squares Distributed Calculation Example
Linear Regression Ordinary Least Squares Distributed Calculation ExampleMarjan Sterjev
 
Mutiple linear regression project
Mutiple linear regression projectMutiple linear regression project
Mutiple linear regression projectJAPAN SHAH
 

Viewers also liked (20)

Multiple Regression worked example (July 2014 updated)
Multiple Regression worked example (July 2014 updated)Multiple Regression worked example (July 2014 updated)
Multiple Regression worked example (July 2014 updated)
 
econometrics project PG1 2015-16
econometrics project PG1 2015-16econometrics project PG1 2015-16
econometrics project PG1 2015-16
 
The Barings Bank Collapse
The Barings Bank CollapseThe Barings Bank Collapse
The Barings Bank Collapse
 
Regression analysis
Regression analysisRegression analysis
Regression analysis
 
Regression
RegressionRegression
Regression
 
Regression Analysis
Regression AnalysisRegression Analysis
Regression Analysis
 
Multiple regression presentation
Multiple regression presentationMultiple regression presentation
Multiple regression presentation
 
Presentation On Regression
Presentation On RegressionPresentation On Regression
Presentation On Regression
 
Regression analysis
Regression analysisRegression analysis
Regression analysis
 
Regression analysis ppt
Regression analysis pptRegression analysis ppt
Regression analysis ppt
 
Topic17 regression spss
Topic17 regression spssTopic17 regression spss
Topic17 regression spss
 
Week6 partb projecthelp
Week6 partb projecthelpWeek6 partb projecthelp
Week6 partb projecthelp
 
Statistical and Predictive Modelling
Statistical and Predictive ModellingStatistical and Predictive Modelling
Statistical and Predictive Modelling
 
Week 6 project part b
Week 6 project part bWeek 6 project part b
Week 6 project part b
 
Statistical Modeling - Cereal Data Project
Statistical Modeling - Cereal Data Project  Statistical Modeling - Cereal Data Project
Statistical Modeling - Cereal Data Project
 
Brandie and Michaella's Project
Brandie and Michaella's ProjectBrandie and Michaella's Project
Brandie and Michaella's Project
 
Session 8 c 8 c rambaldi slides (kz)
Session 8 c 8 c   rambaldi slides (kz)Session 8 c 8 c   rambaldi slides (kz)
Session 8 c 8 c rambaldi slides (kz)
 
Micro-econometrics Marriage and Wages
Micro-econometrics Marriage and WagesMicro-econometrics Marriage and Wages
Micro-econometrics Marriage and Wages
 
Linear Regression Ordinary Least Squares Distributed Calculation Example
Linear Regression Ordinary Least Squares Distributed Calculation ExampleLinear Regression Ordinary Least Squares Distributed Calculation Example
Linear Regression Ordinary Least Squares Distributed Calculation Example
 
Mutiple linear regression project
Mutiple linear regression projectMutiple linear regression project
Mutiple linear regression project
 

Similar to Topic 18 multiple regression

Calculation of beta
Calculation of betaCalculation of beta
Calculation of betaiipmff2
 
SPICE MODEL of PBYR10100 (Standard Model) in SPICE PARK
SPICE MODEL of PBYR10100 (Standard Model) in SPICE PARKSPICE MODEL of PBYR10100 (Standard Model) in SPICE PARK
SPICE MODEL of PBYR10100 (Standard Model) in SPICE PARKTsuyoshi Horigome
 
Ch 06 financial management notes
Ch 06 financial management notesCh 06 financial management notes
Ch 06 financial management notesBabasab Patil
 
WCCI 2008 Tutorial on Computational Intelligence and Games, part 2 of 3
WCCI 2008 Tutorial on Computational Intelligence and Games, part 2 of 3WCCI 2008 Tutorial on Computational Intelligence and Games, part 2 of 3
WCCI 2008 Tutorial on Computational Intelligence and Games, part 2 of 3togelius
 
2.1 2.4 presentation
2.1 2.4 presentation2.1 2.4 presentation
2.1 2.4 presentationdinomtruck
 
Final Project Report
Final Project ReportFinal Project Report
Final Project Reportbutest
 
SPICE MODEL of LR6AG_RL=10(Ohm) in SPICE PARK
SPICE MODEL of LR6AG_RL=10(Ohm) in SPICE PARKSPICE MODEL of LR6AG_RL=10(Ohm) in SPICE PARK
SPICE MODEL of LR6AG_RL=10(Ohm) in SPICE PARKTsuyoshi Horigome
 
SPICE MODEL of ZR6DC_RL=3.3(Ohm) in SPICE PARK
SPICE MODEL of ZR6DC_RL=3.3(Ohm) in SPICE PARKSPICE MODEL of ZR6DC_RL=3.3(Ohm) in SPICE PARK
SPICE MODEL of ZR6DC_RL=3.3(Ohm) in SPICE PARKTsuyoshi Horigome
 
Cross-State Air Pollution rule trading and generation fuels
Cross-State Air Pollution rule trading and generation fuelsCross-State Air Pollution rule trading and generation fuels
Cross-State Air Pollution rule trading and generation fuelscarriesisto
 
SPICE MODEL of FML-G14S (Professional Model) in SPICE PARK
SPICE MODEL of FML-G14S (Professional Model) in SPICE PARKSPICE MODEL of FML-G14S (Professional Model) in SPICE PARK
SPICE MODEL of FML-G14S (Professional Model) in SPICE PARKTsuyoshi Horigome
 
SPICE MODEL of 1SS377 (Standard Model) in SPICE PARK
SPICE MODEL of 1SS377 (Standard Model) in SPICE PARKSPICE MODEL of 1SS377 (Standard Model) in SPICE PARK
SPICE MODEL of 1SS377 (Standard Model) in SPICE PARKTsuyoshi Horigome
 
Dsc Presentation (Aocs)
Dsc Presentation (Aocs)Dsc Presentation (Aocs)
Dsc Presentation (Aocs)dwinetzky
 
Interpretation of Cell Tower Readings Measured by TERM Cell / TCIL vs. Safe l...
Interpretation of Cell Tower Readings Measured by TERM Cell / TCIL vs. Safe l...Interpretation of Cell Tower Readings Measured by TERM Cell / TCIL vs. Safe l...
Interpretation of Cell Tower Readings Measured by TERM Cell / TCIL vs. Safe l...Neha Kumar
 

Similar to Topic 18 multiple regression (14)

Calculation of beta
Calculation of betaCalculation of beta
Calculation of beta
 
SPICE MODEL of PBYR10100 (Standard Model) in SPICE PARK
SPICE MODEL of PBYR10100 (Standard Model) in SPICE PARKSPICE MODEL of PBYR10100 (Standard Model) in SPICE PARK
SPICE MODEL of PBYR10100 (Standard Model) in SPICE PARK
 
Ch 06 financial management notes
Ch 06 financial management notesCh 06 financial management notes
Ch 06 financial management notes
 
Sabrina olivares
Sabrina olivaresSabrina olivares
Sabrina olivares
 
WCCI 2008 Tutorial on Computational Intelligence and Games, part 2 of 3
WCCI 2008 Tutorial on Computational Intelligence and Games, part 2 of 3WCCI 2008 Tutorial on Computational Intelligence and Games, part 2 of 3
WCCI 2008 Tutorial on Computational Intelligence and Games, part 2 of 3
 
2.1 2.4 presentation
2.1 2.4 presentation2.1 2.4 presentation
2.1 2.4 presentation
 
Final Project Report
Final Project ReportFinal Project Report
Final Project Report
 
SPICE MODEL of LR6AG_RL=10(Ohm) in SPICE PARK
SPICE MODEL of LR6AG_RL=10(Ohm) in SPICE PARKSPICE MODEL of LR6AG_RL=10(Ohm) in SPICE PARK
SPICE MODEL of LR6AG_RL=10(Ohm) in SPICE PARK
 
SPICE MODEL of ZR6DC_RL=3.3(Ohm) in SPICE PARK
SPICE MODEL of ZR6DC_RL=3.3(Ohm) in SPICE PARKSPICE MODEL of ZR6DC_RL=3.3(Ohm) in SPICE PARK
SPICE MODEL of ZR6DC_RL=3.3(Ohm) in SPICE PARK
 
Cross-State Air Pollution rule trading and generation fuels
Cross-State Air Pollution rule trading and generation fuelsCross-State Air Pollution rule trading and generation fuels
Cross-State Air Pollution rule trading and generation fuels
 
SPICE MODEL of FML-G14S (Professional Model) in SPICE PARK
SPICE MODEL of FML-G14S (Professional Model) in SPICE PARKSPICE MODEL of FML-G14S (Professional Model) in SPICE PARK
SPICE MODEL of FML-G14S (Professional Model) in SPICE PARK
 
SPICE MODEL of 1SS377 (Standard Model) in SPICE PARK
SPICE MODEL of 1SS377 (Standard Model) in SPICE PARKSPICE MODEL of 1SS377 (Standard Model) in SPICE PARK
SPICE MODEL of 1SS377 (Standard Model) in SPICE PARK
 
Dsc Presentation (Aocs)
Dsc Presentation (Aocs)Dsc Presentation (Aocs)
Dsc Presentation (Aocs)
 
Interpretation of Cell Tower Readings Measured by TERM Cell / TCIL vs. Safe l...
Interpretation of Cell Tower Readings Measured by TERM Cell / TCIL vs. Safe l...Interpretation of Cell Tower Readings Measured by TERM Cell / TCIL vs. Safe l...
Interpretation of Cell Tower Readings Measured by TERM Cell / TCIL vs. Safe l...
 

More from Sizwan Ahammed

Topic 21 evidence on diet diversity
Topic 21 evidence on diet diversityTopic 21 evidence on diet diversity
Topic 21 evidence on diet diversitySizwan Ahammed
 
Topic 21 diet diversity
Topic 21 diet diversityTopic 21 diet diversity
Topic 21 diet diversitySizwan Ahammed
 
Topic 21 diet diversity stata
Topic 21  diet diversity stataTopic 21  diet diversity stata
Topic 21 diet diversity stataSizwan Ahammed
 
Topic 20 anthropomeric indicators
Topic 20 anthropomeric indicatorsTopic 20 anthropomeric indicators
Topic 20 anthropomeric indicatorsSizwan Ahammed
 
Topic 20 anthro meaurement
Topic 20 anthro meaurementTopic 20 anthro meaurement
Topic 20 anthro meaurementSizwan Ahammed
 
Topic 20 anthro meaurement230312
Topic 20 anthro meaurement230312Topic 20 anthro meaurement230312
Topic 20 anthro meaurement230312Sizwan Ahammed
 
Topic 19 inequality stata
Topic 19 inequality stataTopic 19 inequality stata
Topic 19 inequality stataSizwan Ahammed
 
Topic 15 correlation spss
Topic 15 correlation spssTopic 15 correlation spss
Topic 15 correlation spssSizwan Ahammed
 
Topic 14 maternal education
Topic 14 maternal educationTopic 14 maternal education
Topic 14 maternal educationSizwan Ahammed
 
Topic 13 con pattern spss
Topic 13 con pattern spssTopic 13 con pattern spss
Topic 13 con pattern spssSizwan Ahammed
 
Topic 12 gender technology interface
Topic 12 gender technology interfaceTopic 12 gender technology interface
Topic 12 gender technology interfaceSizwan Ahammed
 
Topic 11 commercialization
Topic 11 commercializationTopic 11 commercialization
Topic 11 commercializationSizwan Ahammed
 

More from Sizwan Ahammed (20)

Topic 21 evidence on diet diversity
Topic 21 evidence on diet diversityTopic 21 evidence on diet diversity
Topic 21 evidence on diet diversity
 
Topic 21 diet diversity
Topic 21 diet diversityTopic 21 diet diversity
Topic 21 diet diversity
 
Topic 21 diet diversity stata
Topic 21  diet diversity stataTopic 21  diet diversity stata
Topic 21 diet diversity stata
 
Topic 20 anthropomeric indicators
Topic 20 anthropomeric indicatorsTopic 20 anthropomeric indicators
Topic 20 anthropomeric indicators
 
Topic 20 anthro meaurement
Topic 20 anthro meaurementTopic 20 anthro meaurement
Topic 20 anthro meaurement
 
Topic 20 anthro meaurement230312
Topic 20 anthro meaurement230312Topic 20 anthro meaurement230312
Topic 20 anthro meaurement230312
 
Topic 20 anthro stata
Topic 20 anthro stataTopic 20 anthro stata
Topic 20 anthro stata
 
Topic 19 inequaltiy
Topic 19 inequaltiyTopic 19 inequaltiy
Topic 19 inequaltiy
 
Topic 19 inequality stata
Topic 19 inequality stataTopic 19 inequality stata
Topic 19 inequality stata
 
Topic 17 regression
Topic 17 regressionTopic 17 regression
Topic 17 regression
 
Topic 16 poverty(ii)
Topic 16 poverty(ii)Topic 16 poverty(ii)
Topic 16 poverty(ii)
 
Topic 16 poverty(i)
Topic 16 poverty(i)Topic 16 poverty(i)
Topic 16 poverty(i)
 
Topic 15 correlation spss
Topic 15 correlation spssTopic 15 correlation spss
Topic 15 correlation spss
 
Topic 15 correlation
Topic 15 correlationTopic 15 correlation
Topic 15 correlation
 
Topic 14 two anova
Topic 14 two anovaTopic 14 two anova
Topic 14 two anova
 
Topic 14 maternal education
Topic 14 maternal educationTopic 14 maternal education
Topic 14 maternal education
 
Topic 13 cons pattern
Topic 13 cons patternTopic 13 cons pattern
Topic 13 cons pattern
 
Topic 13 con pattern spss
Topic 13 con pattern spssTopic 13 con pattern spss
Topic 13 con pattern spss
 
Topic 12 gender technology interface
Topic 12 gender technology interfaceTopic 12 gender technology interface
Topic 12 gender technology interface
 
Topic 11 commercialization
Topic 11 commercializationTopic 11 commercialization
Topic 11 commercialization
 

Recently uploaded

Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...panagenda
 
Emixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native developmentEmixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native developmentPim van der Noll
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxLoriGlavin3
 
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxLoriGlavin3
 
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxThe Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxLoriGlavin3
 
UiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to HeroUiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to HeroUiPathCommunity
 
What is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdfWhat is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdfMounikaPolabathina
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsSergiu Bodiu
 
Modern Roaming for Notes and Nomad – Cheaper Faster Better Stronger
Modern Roaming for Notes and Nomad – Cheaper Faster Better StrongerModern Roaming for Notes and Nomad – Cheaper Faster Better Stronger
Modern Roaming for Notes and Nomad – Cheaper Faster Better Strongerpanagenda
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc
 
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...AliaaTarek5
 
Generative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdfGenerative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdfIngrid Airi González
 
Decarbonising Buildings: Making a net-zero built environment a reality
Decarbonising Buildings: Making a net-zero built environment a realityDecarbonising Buildings: Making a net-zero built environment a reality
Decarbonising Buildings: Making a net-zero built environment a realityIES VE
 
[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality Assurance[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality AssuranceInflectra
 
Moving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdfMoving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdfLoriGlavin3
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024BookNet Canada
 
The State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxThe State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxLoriGlavin3
 
TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024Lonnie McRorey
 
A Framework for Development in the AI Age
A Framework for Development in the AI AgeA Framework for Development in the AI Age
A Framework for Development in the AI AgeCprime
 
Potential of AI (Generative AI) in Business: Learnings and Insights
Potential of AI (Generative AI) in Business: Learnings and InsightsPotential of AI (Generative AI) in Business: Learnings and Insights
Potential of AI (Generative AI) in Business: Learnings and InsightsRavi Sanghani
 

Recently uploaded (20)

Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
 
Emixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native developmentEmixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native development
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
 
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
 
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxThe Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
 
UiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to HeroUiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to Hero
 
What is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdfWhat is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdf
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platforms
 
Modern Roaming for Notes and Nomad – Cheaper Faster Better Stronger
Modern Roaming for Notes and Nomad – Cheaper Faster Better StrongerModern Roaming for Notes and Nomad – Cheaper Faster Better Stronger
Modern Roaming for Notes and Nomad – Cheaper Faster Better Stronger
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
 
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...
 
Generative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdfGenerative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdf
 
Decarbonising Buildings: Making a net-zero built environment a reality
Decarbonising Buildings: Making a net-zero built environment a realityDecarbonising Buildings: Making a net-zero built environment a reality
Decarbonising Buildings: Making a net-zero built environment a reality
 
[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality Assurance[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality Assurance
 
Moving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdfMoving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdf
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
 
The State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxThe State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptx
 
TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024
 
A Framework for Development in the AI Age
A Framework for Development in the AI AgeA Framework for Development in the AI Age
A Framework for Development in the AI Age
 
Potential of AI (Generative AI) in Business: Learnings and Insights
Potential of AI (Generative AI) in Business: Learnings and InsightsPotential of AI (Generative AI) in Business: Learnings and Insights
Potential of AI (Generative AI) in Business: Learnings and Insights
 

Topic 18 multiple regression

  • 1. Multiple Causes of Food Insecurity: Multiple Regression Analysis Reference: Gujarati (2004)
  • 5. Maternal education and community characteristics as indicators of nutritional status of children – application of multivariate regression Nutritional Status of Children 5
  • 6. Main results • Step 1: Estimating the coefficients of the model(Table 10.2,10.3). • Step 2: Examining how good the model predicts(Table 10.4,10.5). • Step 3: Hypotheses testing. • Tests about the equation(Table 10.6,10.7). • Tests about individual coefficients(Table 10.8,10.9). • Part and partial correlation coefficients. • Step 4: Checking for violations of regression assumptions. • Checking normality of the errors(Table 10.10,Figure 10.1,10.2). • Checking for homogeneity of variance of the residuals(Figure 10.3,10.4). Nutritional Status of Children 6
  • 7. Empirical analysis Nutritional Status of Children 7
  • 8. Empirical analysis Data description and methodology Nutritional Status of Children 8
  • 9. Table 10.1 Means and standard deviations of variables: Malawi sample Variables Mean Standard deviation BFEEDNEW 1.57 0.49 DIARRHEA 0.16 0.37 CALREQ 0.32 0.47 ATTCLINI 1.32 0.59 AGEMNTH 27.29 16.28 CLINFEED 2.29 4.57 DRINKDST 1.98 1.39 EDUCSPOUS 2.23 1.30 LATERINE 0.40 0.49 PXFD 9.00 9.85 Nutritional Status of Children 9
  • 10. Table 10.2 Determinants of weight for age Z-scores Variables Model 1 Model 2 Coefficients Std. error Coefficients Std. error Constant -3.03 0.504 -2.93 0.492 EDUCSPOUS 0.173 0.058 0.156 0.057 ATTCLINI 0.439 0.144 0.439 0.142 DRINKDST -0.143 0.051 -0.147 0.05 LATERINE 0.134 0.152 0.152 0.148 PXFD 0.009 0.008 AGEMNTH -0.084 0.021 -0.088 0.021 AGESQ 0.001 0.0003 0.001 0.0003 CLINFEED 0.717 0.181 0.699 0.179 DIARRHEA -0.608 0.201 -0.620 0.198 BFEEDNEW 0.664 0.226 0.625 0.224 CALREQ 0.566 0.212 HEALTDST -0.094 0.071 -0.06 0.072 Nutritional Status of Children 10
  • 11. Table 10.3 Determinants of height for age Z-scores Variables Model 1 Model 2 Coefficients Std. error Coefficients Std. error Constant -2.159 0.677 -2.564 0.685 EDUCSPOUS 0.048 0.072 0.106 0.074 ATTCLINI 0.514 0.18 0.402 0.177 DRINKDST -0.024 0.064 -0.018 0.062 LATERINE 0.072 0.192 0.114 0.185 PXFD 0.014 0.01 AGEMNTH -0.091 0.034 -0.094 0.034 AGESQ 0.001 0.0004 0.0009 0.0004 CLINFEED 0.686 0.227 0.761 0.224 DIARRHEA -0.829 0.261 -0.932 0.260 BFEEDNEW 0.149 0.305 0.174 0.299 INSECURE 0.252 0.085 HEALTDST -0.106 0.092 -0.127 0.091 Nutritional Status of Children 11
  • 12. Equations to predict the weight for age Z-scores for model 1 Nutritional Status of Children 12
  • 13. Table 10.4 Summary of the model for determinants of weight for age Z-scores Adjusted R2 Std. error of Models R R2 the estimate 1 0.488 0.238 0.203 1.09 2 0.506 0.256 0.222 1.079 Nutritional Status of Children 13
  • 14. Table 10.5 Summary of the model for determinants of height for age Z-scores Adjusted R2 Std. error of Models R R2 the estimate 0.418 0.174 0.13 1.285 1 2 0.448 0.201 0.157 1.265 Nutritional Status of Children 14
  • 15. Table 10.6 Analysis of variance table for weight for age Z-scores Sum of Models df Mean Square F Sig Squares I Regression 89.139 11 8.104 6.797 0 Residual 284.936 239 1.192 Total 374.075 250 II Regression 95.757 11 8.705 7.475 0 Residual 278.318 239 1.165 Total 374.075 250 Nutritional Status of Children 15
  • 16. Table 10.7 Analysis of variance table for height for age Z-scores Sum of Models df Mean Square F Sig Squares I Regression 70.948 11 6.45 3.901 0.00 Residual 335.649 203 1.653 Total 406.597 214 II Regression 81.525 11 7.411 4.628 0.00 Residual 325.072 203 1.601 Total 406.597 214 Nutritional Status of Children 16
  • 17. Table 10.8 Tests of individual coefficients for determinants of weight for age Z-scores Variables Model 1 Model 2 t-stat P value t-stat P value Constant -6.016 0.000 -5.956 0.000 EDUCSPOUS 2.987* 0.003 2.70* 0.007 ATTCLINI 3.047* 0.003 3.102* 0.002 DRINKDST -2.817* 0.005 -2.938* 0.004 LATERINE 0.882 0.378 1.03 0.304 PXFD 1.185 0.237 AGEMNTH -4.041* 0.000 -4.282* 0.00 AGESQ 3.096* 0.002 3.386* 0.001 CLINFEED 3.966* 0.000 3.911* 0.00 DIARRHEA -3.018* 0.003 -3.126* 0.002 BFEEDNEW 2.935* 0.004 2.791* 0.006 CALREQ 2.668* 0.008 HEALTDST -1.32 0.188 -0.851 0.396 Note: * denotes at 1 per centof Children Nutritional Status level of significance. 17
  • 18. Table 10.9 Tests of individual coefficients for determinants of height for age Z-scores Variables Model 1 Model 2 t-stat P value t-stat P value Constant -3.187 0.002 -3.745 0.000 EDUCSPOUS 0.661 0.509 1.437 0.152 ATTCLINI 2.863* 0.005 2.273** 0.024 DRINKDST -0.37 0.712 -0.286 0.775 LATERINE 0.375 0.708 0.615 0.539 PXFD 1.479 0.141 AGEMNTH -2.642* 0.009 -2.78* 0.006 AGESQ 1.949** 0.053 2.022** 0.044 CLINFEED 3.018* 0.003 3.394* 0.001 DIARRHEA -3.171* 0.002 -3.584* 0.00 BFEEDNEW 0.489 0.625 0.580 0.563 INSECURE 2.977* 0.003 HEALTDST -1.15 0.252 -1.395 0.164 Note: * denotes at 1 per cent level of significance, ** denotes at 5 per cent level of significance. Nutritional Status of Children 18
  • 19. Table 10.10 Part and partial correlation coefficients for weight for age and height for age Variables Weight for age Height for age Part correlation Partial correlation Part correlation Partial correlation EDUCSPOUS 0.169 0.19 0.042 0.046 ATTCLINI 0.172 0.193 0.183 0.197 DRINKDST -0.159 -0.179 -0.024 -0.026 LATERINE 0.05 0.057 0.024 0.026 PXFD 0.067 0.076 0.094 0.103 AGEMNTH -0.228 -0.253 -0.168 -0.182 AGESQ 0.175 0.196 0.124 0.136 CLINFEED 0.224 0.248 0.192 0.207 DIARRHEA -0.17 -0.192 -0.202 -0.217 BFEEDNEW 0.166 0.187 0.031 0.034 HEALTDST -0.075 -0.085 -0.073 -0.08 Nutritional Status of Children 19
  • 20. Figure 10.1 Histogram of standardized residuals of weight for age Nutritional Status of Children 20
  • 21. Figure 10.2 Normal P-P plot of regression standardized residuals Nutritional Status of Children 21
  • 22. Figure 10.3 Residuals plotted against predicted values for weight for age Nutritional Status of Children 22
  • 23. Figure 10.4 Residuals plotted against predicted values for height for age Nutritional Status of Children 23