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This exercise refers to the drunk driving panel data regression summarized below. Regression Analysis of the Effect of Drunk Driving Laws on Traffic Deaths Dependent variable: traffic fatility rate (deaths per 10,000). Regressor Beer tax Drinking age 18 Drinking age 19 Drinking age 20 Drinking age Mandatory jail or community service? Average vehicle miles per driver Unemployment rate Real income per capita (logarithm) Years (1) 0.33* (0.015) State Effects? Time effects? (2) (3) (4) -0.75** -0.78*** -0.42 (0.24) (0.38) (0.39) 1982-88 1982-88 yes yes no yes Clustered standard errors? yes yes F-Statistics and p-Values Testing Exclusion of Groups of Variables 429 no no no 0.034 (0.076) -0.024 (0.053) 0.035 -0.112*** (0.051) (0.052) (5) -0.74** (0.33) -0.014 (0.083) -0.084 (0.072) 0.041 0.082 (0.104) (0.119) 0.006 0.018 (0.006) (0.013) -0.072* (0.018) 1.82* (0.65) 1982-88 1982-88 1982-88 yes yes yes 10 72 yes yes yes 3.64 (6) -0.36 (0.39) -0.003 (0.022) 0.039 (0.103) 0.008 (0.003) -0.061* (0.013) 1.95* (0.63) 1982-88 yes yes yes 10.69 (7) -0.93* (0.31) 0.032 (0.109) -0.073 (0.101) -0.111 (0.131) 0.092 (0.162) 0.126 (0.045) -0.094* (0.025) 1.02 (0.63) 1982 & 1988 only yes yes yes 37 68

This exercise refers to the drunk driving panel data regression summarized below. Regression Analysis of the Effect of Drunk Driving Laws on Traffic Deaths Dependent variable: traffic fatility rate (deaths per 10,000). Regressor Beer tax Drinking age 18 Drinking age 19 Drinking age 20 Drinking age Mandatory jail or community service? Average vehicle miles per driver Unemployment rate Real income per capita (logarithm) Years (1) 0.33* (0.015) State Effects? Time effects? (2) (3) (4) -0.75** -0.78*** -0.42 (0.24) (0.38) (0.39) 1982-88 1982-88 yes yes no yes Clustered standard errors? yes yes F-Statistics and p-Values Testing Exclusion of Groups of Variables 429 no no no 0.034 (0.076) -0.024 (0.053) 0.035 -0.112*** (0.051) (0.052) (5) -0.74** (0.33) -0.014 (0.083) -0.084 (0.072) 0.041 0.082 (0.104) (0.119) 0.006 0.018 (0.006) (0.013) -0.072* (0.018) 1.82* (0.65) 1982-88 1982-88 1982-88 yes yes yes 10 72 yes yes yes 3.64 (6) -0.36 (0.39) -0.003 (0.022) 0.039 (0.103) 0.008 (0.003) -0.061* (0.013) 1.95* (0.63) 1982-88 yes yes yes 10.69 (7) -0.93* (0.31) 0.032 (0.109) -0.073 (0.101) -0.111 (0.131) 0.092 (0.162) 0.126 (0.045) -0.094* (0.025) 1.02 (0.63) 1982 & 1988 only yes yes yes 37 68

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
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### Drunk Driving Panel Data Regression Analysis #### Introduction: This exercise provides a regression analysis examining the effect of drunk driving laws on traffic fatalities, focusing on various legislative measures such as beer taxes and minimum legal drinking ages. #### Summary of the Regression Table: - **Dependent Variable**: Traffic fatality rate (deaths per 10,000) - **Regressors**: 1. **Beer Tax**: Coefficient varies across models, with significant negative associations in some models (e.g., column (6): -0.93 with p<0.01). 2. **Drinking Ages (18, 19, 20)**: Influences vary: - Age 19 has a negative and statistically significant coefficient in certain models. 3. **Mandatory Jail or Community Service**: Shows varying influence, but not statistically significant. 4. **Average Vehicle Miles per Driver**: Minimal and generally insignificant effects. 5. **Unemployment Rate**: Appears significant in some models (e.g., column (6): -0.061 with p<0.05). 6. **Real Income per Capita**: Significant in some analyses. - **Estimated Years**: From 1982 to 1988, with specific focus in some models on select years such as 1982 & 1988. - **Model Specifications**: - **State Effects**: Included in models (2) through (7). - **Time Effects**: Evaluated and significant in some analyses. - **Clustered Standard Errors**: Used consistently across models. #### Statistical Tests and Significance: - **F-Statistics and p-Values**: Explore the exclusion of variable groups. - Time effects are significant with p<0.01. - Coefficients relating to drinking age and other economic variables show varied significance. - **R² Values**: Range from 0.094 to 0.995, indicating varying degrees of explained variance. #### Practical Application: - New Jersey's population context is provided as a case study. - Hypothetical scenario: Increasing the beer tax by $1 (in 1988 dollars) and its predicted impact on saving lives, utilizing column (4) results. - **Prediction Exercise**: Users are prompted to calculate and input the predicted number of lives that would be saved based on this regression analysis. ### Conclusion: The analysis
Transcribed Image Text:### Drunk Driving Panel Data Regression Analysis #### Introduction: This exercise provides a regression analysis examining the effect of drunk driving laws on traffic fatalities, focusing on various legislative measures such as beer taxes and minimum legal drinking ages. #### Summary of the Regression Table: - **Dependent Variable**: Traffic fatality rate (deaths per 10,000) - **Regressors**: 1. **Beer Tax**: Coefficient varies across models, with significant negative associations in some models (e.g., column (6): -0.93 with p<0.01). 2. **Drinking Ages (18, 19, 20)**: Influences vary: - Age 19 has a negative and statistically significant coefficient in certain models. 3. **Mandatory Jail or Community Service**: Shows varying influence, but not statistically significant. 4. **Average Vehicle Miles per Driver**: Minimal and generally insignificant effects. 5. **Unemployment Rate**: Appears significant in some models (e.g., column (6): -0.061 with p<0.05). 6. **Real Income per Capita**: Significant in some analyses. - **Estimated Years**: From 1982 to 1988, with specific focus in some models on select years such as 1982 & 1988. - **Model Specifications**: - **State Effects**: Included in models (2) through (7). - **Time Effects**: Evaluated and significant in some analyses. - **Clustered Standard Errors**: Used consistently across models. #### Statistical Tests and Significance: - **F-Statistics and p-Values**: Explore the exclusion of variable groups. - Time effects are significant with p<0.01. - Coefficients relating to drinking age and other economic variables show varied significance. - **R² Values**: Range from 0.094 to 0.995, indicating varying degrees of explained variance. #### Practical Application: - New Jersey's population context is provided as a case study. - Hypothetical scenario: Increasing the beer tax by $1 (in 1988 dollars) and its predicted impact on saving lives, utilizing column (4) results. - **Prediction Exercise**: Users are prompted to calculate and input the predicted number of lives that would be saved based on this regression analysis. ### Conclusion: The analysis
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