Download ?the ?data ?file ?CHICK6.csv ?from ?Assignment ?1. [2] Load the CHICK6.csv data into R and rename it so that it includes your own name or initials (for example, we ?might ?name ?our
Assignment 1
[1] Download the data file CHICK6.csv from Assignment 1.
[2] Load the CHICK6.csv data into R and rename it so that it includes your own name or initials (for example,
we might name our dataset JunfuCHICK6 ).
[3] Generate the mean and the standard deviation for the variables Y, PC, PB, and YD from the CHICK6.csv
data set.
[4] You are going to run a regression (in [5]) where Y is the dependent variable, and PC, PB, and YD are
the independent variables. Discuss whether you think the independent variable PB will have a negative or
positive eect on the dependent variable. In your decision, try to use as much economic theory as you can —
theory is what motivates what variables are included in a model and what sign we anticipate for the model’s
estimates.
[5] Estimate the model in R and present the results.
[6] Interpret the results for the coefficient PB from your model. Make sure to include whether or not the
result aligned with your expectations.
Econ 4650, Spring 2019 Assignment 1
Introduction
Welcome to Assignment 1!
It is suggested you do the following before attempting this assignment*
• Read the appropriate textbook chapters (Chapter 1 & Chapter 2 in the Custom Text; Chapter 17 & Chapter 1 in the full text)
• Read the Econ_4650_Sprin_2019_Intro_to_R.pdf in Module 1.a • Install the software R • Read OrdinaryLeastSquares pdf in Module 1.b • Take a deep breath!
*Note, You may also watch the Module 1 video tutorials, but this is not required. Also, please note that there have been adjustments to the number of assignments and the structure of the course since the time the videos were made.
Definitions for Annual Consumption of Chicken Data
Y = per capita chicken consumption (in pound) in a given year
PC = the price of chicken (in cents per pound) in a given year
PB = the price of beef (in cents per pound) in a given year
YD = U.S. per capita disposable income (in hundred of dollars) in a given year
Assignment 1
[1] Download the data file CHICK6.csv from Assignment 1.
[2] Load the CHICK6.csv data into R and rename it so that it includes your own name or initials (for example, we might name our dataset JunfuCHICK6 ).
[3] Generate the mean and the standard deviation for the variables Y, PC, PB, and YD from the CHICK6.csv data set.
[4] You are going to run a regression (in [5]) where Y is the dependent variable, and PC, PB, and YD are the independent variables. Discuss whether you think the independent variable PB will have a negative or positive e�ect on the dependent variable. In your decision, try to use as much economic theory as you can – theory is what motivates what variables are included in a model and what sign we anticipate for the model’s estimates.
[5] Estimate the model in R and present the results.
[6] Interpret the results for the coe�cient PB from your model. Make sure to include whether or not the result aligned with your expectations.
1
,
| OBS | YEAR | Y | PC | PB | YD | TEMP | PRP |
| 1 | 1974 | 39.7 | 42.3 | 143.8 | 50.1 | -16 | 107.8 |
| 2 | 1975 | 38.69 | 49.4 | 152.2 | 54.98 | -4 | 134.6 |
| 3 | 1976 | 42.02 | 45.5 | 145.7 | 59.72 | -24 | 134 |
| 4 | 1977 | 42.71 | 45.3 | 145.9 | 65.17 | 16 | 125.4 |
| 5 | 1978 | 44.75 | 49.3 | 178.8 | 72.24 | 5 | 143.6 |
| 6 | 1979 | 48.35 | 50 | 222.4 | 79.67 | 13 | 152.5 |
| 7 | 1980 | 48.47 | 53.5 | 233.6 | 88.22 | 21 | 147.5 |
| 8 | 1981 | 50.37 | 53.8 | 234.7 | 97.65 | 49 | 161.2 |
| 9 | 1982 | 51.52 | 51.5 | 238.4 | 104.26 | 4 | 185.6 |
| 10 | 1983 | 52.55 | 56 | 234.1 | 111.31 | 35 | 179.7 |
| 11 | 1984 | 54.61 | 61.5 | 235.5 | 123.19 | 11 | 171.4 |
| 12 | 1985 | 56.42 | 56.2 | 228.6 | 130.37 | 4 | 170.8 |
| 13 | 1986 | 57.7 | 63.1 | 226.8 | 136.49 | 18 | 188.8 |
| 14 | 1987 | 61.94 | 53.1 | 238.4 | 142.41 | 35 | 199.4 |
| 15 | 1988 | 63.8 | 62.1 | 250.3 | 152.97 | 46 | 194 |
| 16 | 1989 | 66.88 | 64.2 | 265.7 | 162.57 | 32 | 193.5 |
| 17 | 1990 | 70.34 | 60.5 | 281 | 171.31 | 64 | 224.9 |
| 18 | 1991 | 73.26 | 57.7 | 288.3 | 176.09 | 52 | 224.2 |
| 19 | 1992 | 76.39 | 59 | 284.6 | 184.94 | 18 | 209.5 |
| 20 | 1993 | 78.27 | 27.1 | 293.4 | 188.72 | 27 | 209.1 |
| 21 | 1994 | 79.65 | 26.2 | 282.9 | 195.55 | 48 | 209.5 |
| 22 | 1995 | 79.27 | 26.9 | 284.3 | 202.87 | 71 | 206.1 |
| 23 | 1996 | 80.61 | 28 | 280.2 | 210.91 | 36 | 233.7 |
| 24 | 1997 | 83.1 | 33.2 | 279.5 | 219.4 | 60 | 245 |
| 25 | 1998 | 83.76 | 33.4 | 277.1 | 231.61 | 89 | 242.7 |
| 26 | 1999 | 88.98 | 39.5 | 287.8 | 239.68 | 60 | 241.4 |
| 27 | 2000 | 90.08 | 43 | 306.4 | 254.69 | 62 | 258.2 |
| 28 | 2001 | 89.71 | 43.4 | 337.7 | 262.24 | 74 | 269.4 |
| 29 | 2002 | 94.37 | 43.9 | 331.5 | 271.45 | 85 | 265.8 |
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