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1. Interpret the estimates of β1 in the model to the context. 2. Interpret the estimates of β2 in the model to the context.

1. Interpret the estimates of β1 in the model to the context. 2. Interpret the estimates of β2 in the model to the context.

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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1. Interpret the estimates of β1 in the model to the context.

2. Interpret the estimates of β2 in the model to the context.

EXAMPLE 3.3 (Vendor). Detailed interviews were con- ducted with over 1,000 street vendors in the city of Puebla, Mexico, in order to study the factors influencing ven- dors’ incomes. Vendors were defined as individuals work- ing in the street, and included vendors with carts and stands on wheels and excluded beggars, drug dealers, and prostitutes. The researchers collected data on gen- der, age x1, hours worked per day x2, annual earnings y, and education level. A subset of these data (x1, x2 and y) appears in the accompanying table. VENDOR ANNUAL HOURS WORKED NUMBER EARNINGS, y AGE, x1 PER DAY, x2 21 $2841 29 12 53 1876 2934 21 8 60 62 10 184 1552 18 10 263 281 3065 3670 40 11 50 11 354 2005 65 5 401 515 3215 1930 44 17 8 8 2010 3111 2882 633 70 677 20 9. 710 29 9. 800 1683 15 5 914 1817 14 7 997 4066 33 12
Transcribed Image Text:EXAMPLE 3.3 (Vendor). Detailed interviews were con- ducted with over 1,000 street vendors in the city of Puebla, Mexico, in order to study the factors influencing ven- dors’ incomes. Vendors were defined as individuals work- ing in the street, and included vendors with carts and stands on wheels and excluded beggars, drug dealers, and prostitutes. The researchers collected data on gen- der, age x1, hours worked per day x2, annual earnings y, and education level. A subset of these data (x1, x2 and y) appears in the accompanying table. VENDOR ANNUAL HOURS WORKED NUMBER EARNINGS, y AGE, x1 PER DAY, x2 21 $2841 29 12 53 1876 2934 21 8 60 62 10 184 1552 18 10 263 281 3065 3670 40 11 50 11 354 2005 65 5 401 515 3215 1930 44 17 8 8 2010 3111 2882 633 70 677 20 9. 710 29 9. 800 1683 15 5 914 1817 14 7 997 4066 33 12
Consider the first-order model y = Bo + B1x1 + 62x2 + € and assume it is appropriate. Below is the SAS output. The REG Procedure Model: MODEL1 Dependent Variable: earning Number of Observations Read 15 Number of Observations Used 15 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr >F Model 2 5018232 2509116 8.36 0.0053 Error 12 3600196 300016 Corrected Total 14 8618428 Root MSE 547.73748 R-Square 0.5823 Dependent Mean 2577.13333 Adj R-Sq 0.5126 Coeff Var 21.25375 Parameter Estimates Parameter Estimate Standard Error t Value Pr > |t| Variable DF 95% Confidence Limits Intercept 1 -20.35201 652.74532 -0.03 0.9756 -1442.56189 1401.85787 age 1 13.35045 7.67168 1.74 0.1074 -3.36470 30.06559 hour 1 243.71446 63.51174 3.84 0.0024 105.33428 382.09465
Transcribed Image Text:Consider the first-order model y = Bo + B1x1 + 62x2 + € and assume it is appropriate. Below is the SAS output. The REG Procedure Model: MODEL1 Dependent Variable: earning Number of Observations Read 15 Number of Observations Used 15 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr >F Model 2 5018232 2509116 8.36 0.0053 Error 12 3600196 300016 Corrected Total 14 8618428 Root MSE 547.73748 R-Square 0.5823 Dependent Mean 2577.13333 Adj R-Sq 0.5126 Coeff Var 21.25375 Parameter Estimates Parameter Estimate Standard Error t Value Pr > |t| Variable DF 95% Confidence Limits Intercept 1 -20.35201 652.74532 -0.03 0.9756 -1442.56189 1401.85787 age 1 13.35045 7.67168 1.74 0.1074 -3.36470 30.06559 hour 1 243.71446 63.51174 3.84 0.0024 105.33428 382.09465
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