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he last two variables are entered into the regression as centered around their mean, Mom_Age_Cntrd and M_WtGain_Cntrd, i.e. Mom_Age_Cntrd is the variable Mom_Age minus the average age of Mom_Age, and simi- larly for M_WtGain_Cntrd. The average age of mothers is 27.2 years and the average weight gain is 30.8 pounds. Dependent Variable: WEIGHT Method: Least Squares Date: 03/11/22 Time: 15:19 Sample: 1 65535 Included observations: 65535 Huber-White-Hinkley (HC1) heteroskedasticity consistent standard errors

he last two variables are entered into the regression as centered around their mean, Mom_Age_Cntrd and M_WtGain_Cntrd, i.e. Mom_Age_Cntrd is the variable Mom_Age minus the average age of Mom_Age, and simi- larly for M_WtGain_Cntrd. The average age of mothers is 27.2 years and the average weight gain is 30.8 pounds. Dependent Variable: WEIGHT Method: Least Squares Date: 03/11/22 Time: 15:19 Sample: 1 65535 Included observations: 65535 Huber-White-Hinkley (HC1) heteroskedasticity consistent standard errors

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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) Is there evidence of a nonlinear relationship between birth weight and
mother’s weight gain?

A dataset contains data on birth weights of 65535 babies born in June 1997 along with variables that are potentially related to birth weights. The sample is restricted to singleton births, with mothers recorded as either black or white, between the ages of 18 and 45, resident in the United States. With the aim to investigate factors influencing the weight of babies at birth, a regression of Weight on a range of variables has been carried out. A description of variables is given below and the estimation results can be found on the following page. Referring to these results, answer the following questions. Keep in mind that two of the regressors, Mom_Age and M_WtGain, are entered in the regression in terms of the deviations from their mean.
Transcribed Image Text:A dataset contains data on birth weights of 65535 babies born in June 1997 along with variables that are potentially related to birth weights. The sample is restricted to singleton births, with mothers recorded as either black or white, between the ages of 18 and 45, resident in the United States. With the aim to investigate factors influencing the weight of babies at birth, a regression of Weight on a range of variables has been carried out. A description of variables is given below and the estimation results can be found on the following page. Referring to these results, answer the following questions. Keep in mind that two of the regressors, Mom_Age and M_WtGain, are entered in the regression in terms of the deviations from their mean.
The last two variables are entered into the regression as centered around their mean, Mom_Age_Cntrd and M_WtGain_Cntrd, i.e. Mom_Age_Cntrd is the variable Mom_Age minus the average age of Mom_Age, and simi- larly for M_WtGain_Cntrd. The average age of mothers is 27.2 years and the average weight gain is 30.8 pounds. Dependent Variable: WEIGHT Method: Least Squares Date: 03/11/22 Time: 15:19 Sample: 1 65535 Included observations: 65535 Huber-White-Hinkley (HC1) heteroskedasticity consistent standard errors and covariance Variable Coefficient Std. Error t-Statistic Prob. 3316.786 104.6528 8.106849 409.1338 0.0000 BOY 4.231952 24.72921 0.0000 MARRIED 66.44096 5.699306 11.65773 0.0000 ED_COL ED_HS ED_SMCOL SMOKE 27.51704 7.816031 3.520590 0.0004 8.680020 6.494597 1.336498 0.1814 24.48548 -200.6533 -1.260510 -200.5243 7.229266 3.386994 0.0007 11.36470 -17.65585 0.0000 CIGSPER 0.812260 -1.551855 0.1207 BLACK 6.468994 -30.99775 0.0000 5.641921 0.461996 12.21205 0.0000 MOM_AGE_CNTRD MOM_AGE_CNTRD^2 -0.514637 M_WTGAIN_CNTRD M_WTGAIN_CNTRD^2 -0.027913 0.064432 -7.987346 0.0000 9.618949 0.198337 48.49802 0.0000 0.008685 -3.214042 0.0013 0.113853 Mean dependent var 0.113691 S.D. dependent var 541.4975 Akaike info criterion R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) Prob(Wald F-statistic) 3347.636 575.1800 15.42675 1.92E+10 Schwarz criterion 15.42856 -505483.1 Hannan-Quinn criter. 15.42731 701.5267 Durbin-Watson stat 1.957147 0.000000 Wald F-statistic 623.6461 0.000000
Transcribed Image Text:The last two variables are entered into the regression as centered around their mean, Mom_Age_Cntrd and M_WtGain_Cntrd, i.e. Mom_Age_Cntrd is the variable Mom_Age minus the average age of Mom_Age, and simi- larly for M_WtGain_Cntrd. The average age of mothers is 27.2 years and the average weight gain is 30.8 pounds. Dependent Variable: WEIGHT Method: Least Squares Date: 03/11/22 Time: 15:19 Sample: 1 65535 Included observations: 65535 Huber-White-Hinkley (HC1) heteroskedasticity consistent standard errors and covariance Variable Coefficient Std. Error t-Statistic Prob. 3316.786 104.6528 8.106849 409.1338 0.0000 BOY 4.231952 24.72921 0.0000 MARRIED 66.44096 5.699306 11.65773 0.0000 ED_COL ED_HS ED_SMCOL SMOKE 27.51704 7.816031 3.520590 0.0004 8.680020 6.494597 1.336498 0.1814 24.48548 -200.6533 -1.260510 -200.5243 7.229266 3.386994 0.0007 11.36470 -17.65585 0.0000 CIGSPER 0.812260 -1.551855 0.1207 BLACK 6.468994 -30.99775 0.0000 5.641921 0.461996 12.21205 0.0000 MOM_AGE_CNTRD MOM_AGE_CNTRD^2 -0.514637 M_WTGAIN_CNTRD M_WTGAIN_CNTRD^2 -0.027913 0.064432 -7.987346 0.0000 9.618949 0.198337 48.49802 0.0000 0.008685 -3.214042 0.0013 0.113853 Mean dependent var 0.113691 S.D. dependent var 541.4975 Akaike info criterion R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) Prob(Wald F-statistic) 3347.636 575.1800 15.42675 1.92E+10 Schwarz criterion 15.42856 -505483.1 Hannan-Quinn criter. 15.42731 701.5267 Durbin-Watson stat 1.957147 0.000000 Wald F-statistic 623.6461 0.000000
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