ECON603 Introduction to Econometrics

Assignment, Semester 2, 2020

Y_i=β1+β2 X_2i+u_i
Y_i=β∗1+β∗2i+β∗3 X_3i+u_i
Y_i=β1+β2 X_2i+β3 X_3i+β4 X_4i+u_i
Y_i=儿童死亡率（CM），婴儿和5岁以下儿童的死亡（每1000个活产）。
X_2i=女性识字率（FLR），15岁及以上能够读写的成年女性的百分比。
X_3i=人均国民生产总值（PGNP）。
X_4i=总生育率（TFR），即妇女在其一生中所生或可能生的子女总数，如果妇女受人口中按年龄分列的生育率的影响。

（4分）

（5分）

（5分）

（5分）

（5分）

（2分）

（4分）

（4分）

i） Y_i=α+βX_i+u u i
ii）Y_i=α+βln⁡（X_i）+u_i

（8分）

（5分）

（5分）

（5分）

（5分）

QUESTION 4 (20 Marks)
Use the data in the worksheet entitled “Question 4” in “Assignment 2_data.xlsx” for this question. The data contains the following information collected for 680 university students in the United States:
stndfnl = the standardized final exam score
atndrte = the percentage of lectures attended
fresh = 1 if in 1st year of university; and 0 otherwise
second = 1 if in 2nd year of university; and 0 otherwise
priGPA = prior cumulative GPA (grade point average)
ACT= State high school graduation achievement test score

To determine the effects of attending lectures on final exam performance, first estimate a model relating the standardized final exam score (stndfnl) to the percentage of lectures attended (atndrte). Include the binary variables fresh and second as explanatory variables. Interpret the estimated coefficients and discuss their statistical significance:
(10 marks)

As proxy variables for student ability, add to the regression priGPA and ACT. Now, what is the effect of atndrte? Discuss why and how the effects differ from that in (a).
(5 marks)

To test for a nonlinear effect of atndrte, add its squared term to the regression equation in (b). What do you conclude?
(5 marks)

QUESTION 5 (18 Marks)
Use the data in the worksheet entitled “Question 5” in “Assignment 2_data.xlsx” for this question. The data contains the following information collected for 654 youths on the following variables:
fev = stands for forced expiratory volume, the volume of air (in litres) that can be forced out after taking a deep breath, an important measure of pulmonary function. The objective of this exercise is to find out the impact of age, height, weight and smoking habits on fev.
smoke = smoker coded as 1; non-smoker coded as 0
age = in years
ht = height in inches
sex = coded 1 for male and 0 for female

Develop a suitable regression model for the exercise, i.e. find out the impact of age, height, gender and smoking habits on fev.
(5 marks)

What is the expected effect of each explanatory variable on fev? Do the regression results support your expectation?
(5 marks)

Which of the explanatory variables, or regressors, are individually statistically significant, say, at the 5% level? What are the estimated p values?
(5 marks)

Would you reject the hypothesis that the slope coefficients of all the regressors are statistically insignificant? How would you interpret the R^2 value?
(3 marks)

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