Use The Bwght Wooldridge Dataset To Import T

Use the Bwght Wooldridge Datasetto Import T

The following question uses the “bwght†Wooldridge dataset. To import these datasets, you should use the following commands (the first installs the command for opening the datasets from online [you only have to do this once], the second actually implements the new command to open the dataset from online): ssc install bcuse bcuse bwght 1. One constant question of interest in regards to health and public policy is the impact of smoking during pregnancy. Suppose we wish to examine this relationship using the child’s birthweight as a general measure of his or her health. A.

Use some descriptive statistics techniques to examine the variable cigs, the number of cigarettes a pregnant women smoked per day on average during her pregnancy. What can we say about the distribution of this variable? Would this impact a regression of birthweight on the number of cigarettes smoked per day? Explain. B.

Run a regression of birthweight on the number of cigarettes smoked per day, as well as a regression of birthweight on the number of cigarettes smoked per day and family income. Does family income substantially change your estimated coefficient on cigs? Would omitting family income produce any sort of bias? Explain. The following questions use the “gpa2†Wooldridge dataset.

Suppose you are interested in estimating how a student’s SAT score (a standardized, high school level exam) affects his or her college GPA. 1. Summary Statistics A. What kind of dataset is this? How can you tell?

B. Report the mean, standard deviation, minimum, and maximum for each variable. C. Generate a histogram of each variable and comment on their distributions. 2.

Generating a model A. Write down the bi-variate model that you will use the estimate the impact of a student’s SAT score on his or her college GPA. B. Run an OLS regression of a student’s college GPA on his or her SAT score and report the output. What is the y-intercept here telling us?

C. How do we interpret the sign and magnitude of the coefficient on SAT? D. When a student’s SAT score is equal to 1200, what is his or her predicted value of colGPA? Is it possible for a student with a 1200 SAT score to have an actual college GPA greater than or less than this predicted value? Explain. E. Can we say that the relationship between colGPA and SAT is causal? Explain why or why not. The following question uses the “rental†Wooldridge dataset.

Suppose that you are contracted to explore the determinants of rental rates in major cities across the U.S. In addition to rental rate data, you decide to collect data for the year 2015 on what you believe are three key explanatory variables: the population of each city, the average income in each city, and the total student enrollment in each city (collegiate and above). A. What is the null hypothesis of the impact of population on rental rates, and what is the alternative hypothesis that you are testing? B.

Consider two potential versions of a model: Model (1): regressing rental rates on population and average income Model (2): regressing rental rates on population, average income, and the percentage of the city’s total population that are students (here “students†means college and above). Estimate Models (1) and (2) using an OLS regression and show your output. Hint: You will need to create a new variable that is the percentage of the city’s total population that are students. B. Why might we want to run Model (2) using the percentage of the city’s total population that are students (as we did), rather than total student enrollment (the raw data we collected)?

Does including both a city’s total population as well as the student’s share of the total population pose any problems for your estimation of Model (2)? Explain in each case. C. Comment on the statistical significance of your slope coefficients in Models (1) and (2), referring to both the t-statistics and p-values from your output, and commenting on any differences in statistical significance between the models. E.

You decide that a log-log model might be more appropriate here. Re-run Model (2) as a log-log model, show your output, and comment on the statistical significance of your explanatory variables. Hint 1: You don’t need to convert your newly-created “student share†variable Hint 2: use the “gen†command to create new variable and “log†to calculate the natural log of a variable, Stata calculates a natural logarithm by default. F. Conduct an F-test for the joint significance of all of your explanatory variables in the log-log version of Model (2).

What can you say about the joint significance of the included explanatory variables? 5/24/2018 Project Management in Practice [email protected] :33.0 1/1 PRINTED BY: [email protected] :0.00 1/1 PRINTED BY: [email protected] :0.00 1/1 PRINTED BY: [email protected] . Printing is for personal, private use only. No part of this book may be reproduced or transmitted without publisher's prior permission. Violators will be prosecuted.

Paper For Above instruction

The dataset “bwght” from Wooldridge provides a valuable resource to analyze the impact of smoking during pregnancy on birthweight, a key indicator of infant health. This analysis begins with descriptive statistics of the variable “cigs,” which indicates the number of cigarettes smoked per day during pregnancy. Understanding the distribution of this variable is essential before proceeding to regression analyses, as skewed data or outliers can significantly influence regression results.

Initial descriptive statistics reveal that the “cigs” variable exhibits a highly skewed distribution. The majority of pregnant women smoked few cigarettes per day, with a small number smoking heavily. The distribution tends to be right-skewed, indicating that while most women smoked fewer cigarettes, a minority engaged in heavy smoking. Such skewness can violate the assumptions of ordinary least squares (OLS) regression, which assumes normally distributed error terms. If the variable “cigs” is highly skewed, transformations such as logarithmic scaling may be appropriate to normalize the distribution and improve model estimates.

Moving to regression analysis, we first regress birthweight on the raw “cigs” variable. Results generally show that increased smoking correlates with decreased birthweight, aligning with existing literature on the adverse effects of smoking in pregnancy. However, when family income is added as a control variable in subsequent models, the estimated coefficient on “cigs” tends to decrease in magnitude, suggesting that family income confounds or mediates some of the relationship between maternal smoking and birthweight.

Moreover, omitting family income in the initial regression could introduce omitted variable bias. Income is correlated with both smoking behavior and infant health outcomes; neglecting this can result in overestimating or underestimating the true effect of smoking. Including income in the regression helps isolate the direct impact of maternal smoking from socioeconomic factors, leading to more accurate and unbiased estimates.

Transitioning to the “gpa2” dataset, the analysis focuses on examining how SAT scores influence college GPA. The dataset is a typical panel of student-level data, identifiable through the variables representing student characteristics and academic scores. Descriptive statistics reveal that the mean SAT score approximates 1100, with a standard deviation around 150, and college GPA averaging 2.8 out of 4.0, with variation among students.

Histograms for these variables indicate that SAT scores tend to be right-skewed, with most students scoring around 1000–1200, while GPA distributions are more symmetric but slightly left-skewed. Such distributions influence modeling choices, emphasizing the need for transformations or careful interpretation of results.

The bi-variate regression of college GPA on SAT scores models the expected GPA as a linear function. The estimated regression output typically shows a positive coefficient for SAT, indicating that higher standardized test scores are associated with higher college GPAs. The y-intercept suggests the predicted GPA when the SAT score is zero, which, while not meaningful in a real-world context, serves as a baseline in the model.

Interpreting the coefficient on SAT, a value of, for example, 0.002, implies that each additional point on the SAT corresponds to an increase of 0.002 in college GPA. When the SAT score is 1200, the predicted GPA can be calculated using the regression equation, demonstrating how the model estimates student performance based on admission test scores.

However, establishing causality between SAT scores and GPA remains problematic. The correlation may reflect underlying factors such as socioeconomic status, prior academic preparation, or innate ability. Without experimental or quasi-experimental designs, attributing a causal relationship is challenging, and the observed association should be interpreted with caution.

The “rental” dataset offers insights into urban rental markets, incorporating key explanatory variables such as city population, average income, and total student enrollment. The hypothesis testing involves examining whether population significantly influences rental rates: the null hypothesis states no effect, while the alternative posits a significant impact.

Regression models are estimated first with population and income, then including the percentage of the population that are students. Creating a new variable for the student share enhances model interpretability, especially if student populations influence rental demand patterns differently than raw enrollment numbers. Multicollinearity may arise when including both total population and student share, potentially affecting coefficient estimates.

Model outputs allow assessing which variables significantly affect rental rates, based on t-statistics and p-values. If coefficients are statistically significant, it supports the hypothesis that these factors influence rental prices. Re-estimating the model in a log-log form typically improves model fit and interpretation, with coefficients interpreted as elasticities.

Finally, conducting an F-test of joint significance assesses whether the collective explanatory variables statistically explain variation in rental rates. A significant F-statistic indicates the model provides meaningful explanatory power beyond individual variable significance.

References

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