Production technologies, terroir, and quality of Bordeaux wine. In addition to state-of-the-art technologies, the production of quality wine is strongly influenced by the natural endowments of the grape-growing region—called the “terroir.” The Economic Journal (May 2008) published an empirical study of the factors that yield a quality Bordeaux wine. A quantitative measure of wine quality (y) was modeled as a function of several qualitative independent variables, including grape-picking method (manual or automated), soil type (clay, gravel, or sand), and slope orientation (east, south, west, southeast, or southwest).

  1. Create the appropriate dummy variables for each of the qualitative independent variables.
  2. Write a model for wine quality (y) as a function of grape-picking method. Interpret theβ’s in the model.
  3. Write a model for wine quality (y) as a function of soil type. Interpret theβ’s in the model.
  4. Write a model for wine quality (y) as a function of slope orientation. Interpret theβ’s in the model.

Short Answer

Expert verified
  1. To represent the 3 qualitative independent, 7 dummy variables will be created.
  2. A model for wine quality (y) as a function of the grape-picking method can be written as x6=0where x1represents the grape-picking method.
  3. A model for wine quality (y) as a function of soil type can be written as y=β0+β1x6+β2x3where localid="1649839735830" x2and localid="1649839743019" x3both represent soil type.
  4. A model for wine quality (y) as a function of the grape-picking method can be written as y=β0+β1x4+β2x5+β3x6where x4,x5and localid="1662363239978" x6 represents slope orientation.

Step by step solution

01

Creating dummy variables

The qualitative independent variables here are the grape-picking method, soil type, and slope orientation.

Let x1be a grape-picking method, where x1=1when it is manual and x1=0when it is automated.

Since the soil type is categorized into three types, (k-1) = 2 no of dummy variables will be used

x2= soil type where value of x2= 1 if soil type is clay; role="math" localid="1649840223066" x2= 0 if soil type is gravel

role="math" localid="1649840195626" x3= soil where value of role="math" localid="1649840208029" x3= 1 if soil type is sand; role="math" localid="1649840215484" x3= 0 if soil type is gravel

Similarly, slope orientation has 4 types hence (k-1) = 3dummy variables will be introduced in the model

x4= slope orientation where x4= 1 if slope orientation is east; 0 otherwise

x5= slope orientation where x5= 1 if slope orientation is west; 0 otherwise

x6= slope orientation where x6= 1 if slope orientation is southeast; 0 otherwise

Therefore, to represent the 3 qualitative independent, 7 dummy variables will be created.

02

Dummy variable model

A model for wine quality (y) as a function of the grape-picking method can be written as y=β0+β1x1where x1represents the grape-picking method.

β0represents the wine quality (y) at a base level (here base level means the level when x1= 0, meaning the wine quality when the grapes are picked automatically)

β1 represents the changes in wine quality (y) when the grape-picking is manual.

03

Dolt variable imitation

A model for wine quality (y) as a function of soil type can be written as y=β0+β1x2+β2x3where x2and x3both represent soil type.

β0represents the wine quality (y) at a base level (here base level means the level when x2= 0 and x3= 0, meaning the wine quality when the soil type is gravel)

β1represents the changes in wine quality (y) when the soil type is clay.

β2represents the changes in wine quality (y) when the soil type is sand.

04

Dunce variable representation

A model for wine quality (y) as a function of the grape-picking method can be written as y=β0+β1x4+β2x5+β3x6where x4,x5and x6represents slope orientation.

β0represents the wine quality (y) at a base level (here base level means the level when x4= 0, x5= 0, and x6= 0 meaning the wine quality when the slope orientation is southwest)

β1represents the changes in wine quality (y) when the slope orientation is east.

β2represents the changes in wine quality (y) when the slope orientation is west.

β3represents the changes in wine quality (y) when the slope orientation is southeast.

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Most popular questions from this chapter

Question: Predicting elements in aluminum alloys. Aluminum scraps that are recycled into alloys are classified into three categories: soft-drink cans, pots and pans, and automobile crank chambers. A study of how these three materials affect the metal elements present in aluminum alloys was published in Advances in Applied Physics (Vol. 1, 2013). Data on 126 production runs at an aluminum plant were used to model the percentage (y) of various elements (e.g., silver, boron, iron) that make up the aluminum alloy. Three independent variables were used in the model: x1 = proportion of aluminum scraps from cans, x2 = proportion of aluminum scraps from pots/pans, and x3 = proportion of aluminum scraps from crank chambers. The first-order model, , was fit to the data for several elements. The estimates of the model parameters (p-values in parentheses) for silver and iron are shown in the accompanying table.

(A) Is the overall model statistically useful (at α = .05) for predicting the percentage of silver in the alloy? If so, give a practical interpretation of R2.

(b)Is the overall model statistically useful (at a = .05) for predicting the percentage of iron in the alloy? If so, give a practical interpretation of R2.

(c)Based on the parameter estimates, sketch the relationship between percentage of silver (y) and proportion of aluminum scraps from cans (x1). Conduct a test to determine if this relationship is statistically significant at α = .05.

(d)Based on the parameter estimates, sketch the relationship between percentage of iron (y) and proportion of aluminum scraps from cans (x1). Conduct a test to determine if this relationship is statistically significant at α = .05.

When a multiple regression model is used for estimating the mean of the dependent variable and for predicting a new value of y, which will be narrower—the confidence interval for the mean or the prediction interval for the new y-value? Why?

It is desired to relate E(y) to a quantitative variable x1and a qualitative variable at three levels.

  1. Write a first-order model.

  2. Write a model that will graph as three different second- order curves—one for each level of the qualitative variable.

Question: Estimating repair and replacement costs of water pipes. Refer to the IHS Journal of Hydraulic Engineering (September, 2012) study of the repair and replacement of water pipes, Exercise 11.21 (p. 655). Recall that a team of civil engineers used regression analysis to model y = the ratio of repair to replacement cost of commercial pipe as a function of x = the diameter (in millimeters) of the pipe. Data for a sample of 13 different pipe sizes are reproduced in the accompanying table. In Exercise 11.21, you fit a straight-line model to the data. Now consider the quadratic model,E(y)=β0+β1x+β2x2. A Minitab printout of the analysis follows (next column).

  1. Give the least squares prediction equation relating ratio of repair to replacement cost (y) to pipe diameter (x).
  2. Conduct a global F-test for the model usingα=0.01. What do you conclude about overall model adequacy?
  3. Evaluate the adjusted coefficient of determination,Ra2, for the model.
  4. Give the null and alternative hypotheses for testing if the rate of increase of ratio (y) with diameter (x) is slower for larger pipe sizes.
  5. Carry out the test, part d, using α=0.01.
  6. Locate, on the printout, a 95% prediction interval for the ratio of repair to replacement cost for a pipe with a diameter of 240 millimeters. Interpret the result.

To model the relationship between y, a dependent variable, and x, an independent variable, a researcher has taken one measurement on y at each of three different x-values. Drawing on his mathematical expertise, the researcher realizes that he can fit the second-order model Ey=β0+β1x+β2x2 and it will pass exactly through all three points, yielding SSE = 0. The researcher, delighted with the excellent fit of the model, eagerly sets out to use it to make inferences. What problems will he encounter in attempting to make inferences?

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