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Horticultural Entomology The brown citrus aphid and the melon aphid both spread the citrus tristeza virus to fruit and thus have become important pests. Yokomi and coworkers \({ }^{19}\) collected the data found in the following table. \begin{tabular}{|c|cccc|} \hline\(x\) & 1 & 5 & 10 & 20 \\ \hline\(y\) & 25 & 22 & 50 & 85 \\ \hline\(z\) & 10 & 5 & 18 & 45 \\ \hline \end{tabular} Here \(x\) is the number of aphids per plant, and \(y\) and \(z\) are the percentage of times the virus is transmitted to the fruit for the brown and melon aphid, respectively. a. Use linear regression for each aphid to find the bestfitting line that relates the number of aphids per plant to the percentage of times the virus is transmitted to the fruit. b. Find the correlation coefficients. c. Interpret what the slope of the line means in each case. d. Which aphid is more destructive? Why?

Short Answer

Expert verified
The brown citrus aphid is more destructive because it has a higher slope, showing greater virus transmission per additional aphid.

Step by step solution

01

Organize the Data

We have two sets of data: \( (x, y) \) for the brown citrus aphid and \( (x, z) \) for the melon aphid, where \( x \) is the number of aphids per plant. Rewrite the data points as pairs: \( (1, 25), (5, 22), (10, 50), (20, 85) \) for \( (x, y) \) and \( (1, 10), (5, 5), (10, 18), (20, 45) \) for \( (x, z) \).
02

Calculate the Line of Best Fit for Brown Citrus Aphid

Apply the linear regression formula to the data set \( (x, y) \). The formula for the slope \( m \) is \( m = \frac{N(\sum{xy}) - (\sum{x})(\sum{y})}{N(\sum{x^2}) - (\sum{x})^2} \) and for the y-intercept \( b \) is \( b = \frac{(\sum{y}) - m(\sum{x})}{N} \). Calculate these values to determine the line \( y = mx + b \).
03

Calculate the Line of Best Fit for Melon Aphid

Apply the same linear regression process to the \((x, z)\) dataset to find the equation of the line \( z = mx + b \). Follow the same steps as in Step 2.
04

Find the Correlation Coefficients

Calculate the Pearson correlation coefficient \( r \) for both datasets. Use the formula \( r = \frac{N(\sum{xy}) - (\sum{x})(\sum{y})}{\sqrt{[N\sum{x^2} - (\sum{x})^2][N\sum{y^2} - (\sum{y})^2]}} \) for the brown citrus aphid, and a similar formula for the melon aphid using \( z \) values.
05

Interpret the Slopes

The slope in each linear equation represents the change in the percentage of virus transmission per additional aphid. Calculate the slope from the equations found in Steps 2 and 3 and interpret them: a higher slope implies a greater increase in virus transmission for each additional aphid.
06

Determine the More Destructive Aphid

Compare both the slopes and correlation coefficients obtained in Steps 3 and 4. A higher slope and/or a stronger correlation suggest higher destructiveness. Thus, interpret which aphid is more destructive.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Correlation Coefficient
The correlation coefficient is a statistical measure that helps us understand the strength and direction of the relationship between two variables. In our context, it explains how strongly the number of aphids per plant (\( x \)) is related to the virus transmission percentage (\( y \) or \( z \)). A correlation coefficient, denoted as \( r \), ranges from -1 to 1.
  • If \( r = 1 \), there is a perfect positive linear relationship.
  • If \( r = -1 \), there is a perfect negative linear relationship.
  • If \( r = 0 \), there's no linear relationship at all.
In research like this, higher absolute values indicate stronger relationships between the two variables. In our exercise, calculating the correlation coefficient for both aphids provides insights into how consistently the number of aphids correlates with the increased percentage of virus transmission.
Horticultural Entomology
Horticultural entomology is a branch of science focusing on the study of insects that affect garden crops or fruit plants. In this context, we're looking at two particular pests: the brown citrus aphid and the melon aphid. Both are known to spread the citrus tristeza virus, causing considerable damage to fruits like oranges and grapefruits.

These insects are threats because they directly influence the health and yield of the plants they infest. In managing horticultural pests, scientists like Yokomi and coworkers use data to monitor and predict the severity of pest-related issues in crops. Understanding how these pests spread the virus can lead to better prevention and control strategies. By analyzing patterns, researchers aim to mitigate the destructive impact of these aphids on fruit production, ensuring healthier crops and plant sustainability.
Slope Interpretation
In linear regression, the slope of the line represents the relationship between the two variables plotted on the graph. Specifically, it describes how much the dependent variable (\( y \) or \( z \)) changes for each unit increase in the independent variable (\( x \)). In this exercise, the slope indicates the change in virus transmission percentage for each additional aphid per plant.
  • A positive slope indicates that as the number of aphids increases, the percentage of virus transmission also increases.
  • A steeper slope means that the virus transmission rate increases more significantly with each added aphid.
By comparing the slopes for the two datasets, we gain insights into which aphid has a more significant impact per unit increase. The interpretation helps in determining the more potent transmitter of the virus, which poses a greater threat to plant health.

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Most popular questions from this chapter

Cellular Telephones The following table gives the number of people with cellular telephone service for recent years and can be found in Glassman. \({ }^{59}\) Number is in millions. $$ \begin{array}{|l|ccccc|} \hline \text { Year } & 1984 & 1985 & 1986 & 1987 & 1988 \\ \hline \text { Number } & 0.2 & 0.5 & 0.8 & 1.4 & 2.0 \\ \hline \text { Year } & 1989 & 1990 & 1991 & 1992 & 1993 \\ \hline \text { Number } & 3.8 & 5.7 & 8 & 11 & 13.8 \\ \hline \end{array} $$a. On the basis of this data, find the best-fitting exponential function using exponential regression. Let \(x=0\) correspond to 1984 . Graph. Use this model to estimate the numbers in 1997 . b. Using the model in part (a), estimate when the number of people with cellular telephone service will reach 50 million

Cost Curve Johnston \(^{12}\) made a statistical estimation of the cost-output relationship for 40 firms. The data for the fifth firm is given in the following table. \begin{tabular}{|c|ccccc|} \hline\(x\) & 180 & 210 & 215 & 230 & 260 \\ \hline\(y\) & 130 & 180 & 205 & 190 & 215 \\ \hline\(x\) & 290 & 340 & 400 & 405 & 430 \\ \hline\(y\) & 220 & 250 & 300 & 285 & 305 \\ \hline\(x\) & 430 & 450 & 470 & 490 & 510 \\ \hline\(y\) & 325 & 330 & 330 & 340 & 375 \\ \hline \end{tabular} Here \(x\) is the output in millions of units, and \(y\) is the total cost in thousands of pounds sterling. a. Determine the best-fitting line using least squares and the correlation coefficient. Graph the line. b. What does this model predict the total cost will be when the output is 300 million units? c. What does this model predict the output will be if the total cost is 200,000 of pounds sterling?

Productivity Petralia \({ }^{15}\) studied the correlation between investment as a percent of GNP and productivity growth of nine countries: Belgium (B), Canada (C), France (F), Germany (G), Italy (I), Japan (J), the Netherlands (N), the United Kingdom (UK), and the United States (US). Productivity is given as output per empoyee-hour in manufacturing. The data they collected for the years \(1960-1976\) is given in the following table. \begin{tabular}{|c|ccccc|} \hline Country & US & UK & B & I & C \\ \hline\(x\) & 14 & 17 & 20.5 & 21 & 21.5 \\ \hline\(y\) & 2.2 & 3.3 & 6.4 & 5.8 & 3.8 \\ \hline Country & \(\mathrm{F}\) & \(\mathrm{N}\) & \(\mathrm{G}\) & \(\mathrm{J}\) & \\\ \hline\(x\) & 22 & 24 & 25 & 32 & \\ \hline\(y\) & 5.7 & 6.3 & 5.8 & 9.0 & \\ \hline \end{tabular} Here \(x\) is investment as percent of GNP, and \(y\) is the productivity growth (\%). a. Determine the best-fitting line using least squares and the correlation coefficient. b. What does this model predict the productivity growth will be when investment is \(30 \%\) of GNP? c. What does this model predict investment as a percent of GNP will be if productivity growth is \(7 \% ?\)

Economic Entomology Karr and Coats \(^{67}\) studied the effects of several chemicals on the growth rate of the German cockroach. The following table gives their data for the percent of the chemical \(\alpha\) -terpineol in the diet of the cockroach. $$ \begin{array}{|l|ccc|} \hline \text { Percent } \alpha \text { -terpineol } & 1 & 10 & 25 \\ \hline \text { Days to Adult Stage } & 129 & 113 & 115 \\ \hline \end{array} $$ a. On the basis of the data given in the table, find the bestfitting logarithmic function using least squares. Graph. b. Use this model to estimate the days to adult stage with a diet of \(20 \% \alpha\) -terpineol.

Productivity Recall from Example 3 that Cohen \(^{16}\) studied the correlation between corporate spending on communications and computers (as a percent of all spending on equipment) and annual productivity growth. In Example 3 we looked at his data on 11 companies for the period from 1985 to \(1989 .\) The data found in the following table is for the years \(1977-1984\) \begin{tabular}{|l|llllll|} \hline\(x\) & 0.03 & 0.07 & 0.10 & 0.13 & 0.14 & 0.17 \\ \hline\(y\) & -2.0 & -1.5 & 1.7 & -0.6 & 2.2 & 0.3 \\ \hline\(x\) & 0.24 & 0.29 & 0.39 & 0.62 & 0.83 & \\ \hline\(y\) & 1.3 & 4.2 & 3.4 & 4.0 & -0.5 & \\ \hline \end{tabular} Here \(x\) is the spending on communications and computers as a percent of all spending on equipment, and \(y\) is the annual productivity growth. Determine the best-fitting line using least squares and the correlation coefficient.

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