Question 1. The file ***wineweather*** (please see the table below) contains the

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Question 1. The file ***wineweather*** (please see the table below) contains the natural log of the average 1989 prices (logprice) of Bordeaux wines for the vintages 1952 to 1989. (vintage) . the age of each vintage is given by the variable age. These data were part of an analysis of Bordeaux wine by economists Orley Ashenfelter and David Ashmore of princeton university. On the computing of the data, some observations became Not available, NA. Clear those up by deleting them, but the couple that are in between observations, just do the average between the two adjacent observations.

a. create a scatterplot with age on the horizontal axis and the log of price on the vertical axis. Do older wines tend to sell for more than younger wines? Does an older wine always sell for more than a younger wine?

b estimate the relationship logprice = a +b(age)
c. estimate the relationship logprice = a + b(vintage) how does the estimated coefficient on vintage compare with the estimated coefficient of age. Why do the intercepts differ as they do?

d. tell me if the intercepts of each equation and estimates of the slopes are statistically significant. (you can use the rule of 2)

Question 2. The file Ch 5 data set on your documents section contain the natural log of the quantity of white zinfandale (LNQWZ) and the natural log of the own price of white zinfandale (LNPWZ) as well as the natural log of the price of other varieties of wines (LNPCH, LNPM, and LNPCS) It additionally includes a time variable called time, and the level data on quantity and prices which you wont be using this time.

a. copy the data from ch 5 data set into a new excel file that you create and run a regression of the data that has already been converted to logs for you in th form lnqwz = a + b*lnpwz + c*lnpch + d*lnpm +e*lnpcs+f*time+g*peak. (important. remember, the interpretation of the coefficients changes for variables transformed to natural logarithms as per the discussion in the appendix of chapter 5).

b. from the regression output, identify which of the explanatory variables are statistically significant. what does it mean for a variable to be statistically significant. (you can use the rule of 2)

c. report the R-squared and provide me with the correct interpretation of this number. what information does it tell me?also report the adjusted R-squared of the regression. why is it necessary to report an adjusted R-quared?

d. from the regression output, tell me what the price elasticity of demand and cross price elasticities of demand are and interpret those numbers. that is, are the goods substitutes compliments and so on using what you learned in your previous lessons and keeping in mind the interpretation of the coefficients as per the discussion in the appendix. Don't forget to use the information about statistical significance from part b to conclude whether goods are related or not related.

f. Identify whether there is a trend that we can identify from the data regarding wine sales. hint, use the coefficient for the time variable to make your assessment. is this trend statistically significant?

The WineWeather
DEGREES HRAIN LOGPRICE TIME_SV VINTAGE WRAIN AGE
17.1167 160 -0.99868 31 1952 600 37
16.7333 80 -0.4544 30 1953 690 36
15.3833 180 NA 29 1954 430 35
17.15 130 -0.80796 28 1955 502 34
15.65 140 NA 27 1956 440 33
16.1333 110 -1.50926 26 1957 420 32
16.4167 187 -1.71655 25 1958 582 31
17.4833 187 -0.418 24 1959 485 30
16.4167 290 -1.97491 23 1960 763 29
17.3333 38 0 22 1961 830 28
16.3 52 -1.10572 21 1962 697 27
15.7167 155 -1.78098 20 1963 608 26
17.2667 96 -1.18435 19 1964 402 25
15.3667 267 -2.24194 18 1965 602 24
16.5333 86 -0.74943 17 1966 819 23
16.2333 118 -1.65388 16 1967 714 22
16.2 292 -2.25018 15 1968 610 21
16.55 244 -2.14784 14 1969 575 20
16.6667 89 -0.90544 13 1970 622 19
16.7667 112 -1.30031 12 1971 551 18
14.9833 158 -2.28879 11 1972 536 17
17.0667 123 -1.857 10 1973 376 16
16.3 184 -2.19958 9 1974 574 15
16.95 171 -1.20168 8 1975 572 14
17.65 247 -1.37264 7 1976 418 13
15.5833 87 -2.23503 6 1977 821 12
15.8167 51 -1.30769 5 1978 763 11
16.1667 122 -1.5396 4 1979 717 10
16 74 -1.99582 3 1980 578 9
16.9667 111 NA 2 1981 535 8
17.4 162 NA 1 1982 712 7
17.3833 119 NA 0 1983 845 6
16.5 119 NA -1 1984 591 5
16.8 38 NA -2 1985 744 4
16.2833 171 NA -3 1986 563 3
16.9833 115 NA -4 1987 452 2
17.1 59 NA -5 1988 808 1
NA 82 NA -6 1989 443 0

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