Chapter 5: Problem 2
Is the following statement correct? Explain why or why not. A correlation coefficient of 0 implies that no relationship exists between the two variables under study.
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Chapter 5: Problem 2
Is the following statement correct? Explain why or why not. A correlation coefficient of 0 implies that no relationship exists between the two variables under study.
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The paper "Postmortem Changes in Strength of Gastropod Shells" (Paleobiology [1992]: 367-377) included scatterplots of data on \(x=\) shell height (in centimeters) and \(y=\) breaking strength (in newtons) for a sample of \(n=38\) hermit crab shells. The least-squares line was \(\hat{y}=-275.1+244.9 x .\) a. What are the slope and the intercept of this line? b. When shell height increases by \(1 \mathrm{~cm}\), by how much does breaking strength tend to change? c. What breaking strength would you predict when shell height is \(2 \mathrm{~cm}\) ? d. Does this approximate linear relationship appear to hold for shell heights as small as \(1 \mathrm{~cm} ?\) Explain.
The article "The Epiphytic Lichen Hypogymnia physodes as a Bioindicator of Atmospheric Nitrogen and Sulphur Deposition in Norway" (Environmental Monitoring and Assessment \([1993]: 27-47\) ) gives the following data (read from a graph in the paper) on \(x=\mathrm{NO}_{3}\) wet deposition (in grams per cubic meter) and \(y=\) lichen (\% dry weight): $$ \begin{array}{llllll} x & 0.05 & 0.10 & 0.11 & 0.12 & 0.31 \\ y & 0.48 & 0.55 & 0.48 & 0.50 & 0.58 \\ x & 0.37 & 0.42 & 0.58 & 0.68 & 0.68 \\ y & 0.52 & 1.02 & 0.86 & 0.86 & 1.00 \\ x & 0.73 & 0.85 & 0.92 & & \\ y & 0.88 & 1.04 & 1.70 & & \end{array} $$ a. What is the equation of the least-squares regression line? b. Predict lichen dry weight percentage for an \(\mathrm{NO}_{3}\) deposition of \(0.5 \mathrm{~g} / \mathrm{m}^{3}\).
A study was carried out to investigate the relationship between the hardness of molded plastic (y, in Brinell units) and the amount of time elapsed since termination of the molding process (x, in hours). Summary quantities include n 5 15, SSResid 5 1235.470, and SSTo 5 25,321.368. Calculate and interpret the coefficient of determination.
As part of a study of the effects of timber management strategies (Ecological Applications [2003]: \(1110-1123\) ) investigators used satellite imagery to study abundance of the lichen Lobaria oregano at different elevations. Abundance of a species was classified as "common" if there were more than 10 individuals in a plot of land. In the table below, approximate proportions of plots in which Lobaria oregano were common are given. $$ \begin{array}{llllllll} \hline \text { Elevation }(\mathrm{m}) & 400 & 600 & 800 & 1000 & 1200 & 1400 & 1600 \\ \hline \text { Prop. of plots } \\ \text { with Lichen } & & & & & & & \\ (>10 / \text { plot }) & 0.99 & 0.96 & 0.75 & 0.29 & 0.077 & 0.035 & 0.01 \\ & & & & & & \\ \hline \end{array} $$ a. As elevation increases, does Lobaria oregano become more common or less common? What aspect(s) of the table support your answer? b. Using the techniques introduced in this section, calculate \(y^{\prime}=\ln \left(\frac{p}{1-p}\right)\) for each of the elevations and fit the line \(y^{\prime}=a+b(\) Elevation \() .\) What is the equation of the best-fit line? c. Using the best-fit line from Part (b), estimate the proportion of plots of land on which Lobaria oregano are classified as "common" at an elevation of \(900 \mathrm{~m}\).
The following data on sale price, size, and land-tobuilding ratio for 10 large industrial properties appeared in the paper "Using Multiple Regression Analysis in Real Estate Appraisal" (Appraisal Journal [2002]: 424-430): $$ \begin{array}{crrr} & \begin{array}{l} \text { Sale Price } \\ \text { (millions } \\ \text { of dollars) } \end{array} & \begin{array}{l} \text { Size } \\ \text { (thousands } \\ \text { of sq. ft.) } \end{array} & \begin{array}{l} \text { Land-to- } \\ \text { Building } \end{array} \\ \text { Property } & 10.6 & 2166 & 2.0 \\ 1 & 2.6 & 751 & 3.5 \\ 2 & 30.5 & 2422 & 3.6 \\ 3 & 1.8 & 224 & 4.7 \\ 4 & 20.0 & 3917 & 1.7 \\ 5 & 8.0 & 2866 & 2.3 \\ 6 & 10.0 & 1698 & 3.1 \\ 7 & 6.7 & 1046 & 4.8 \\ 8 & 5.8 & 1108 & 7.6 \\ 9 & 4.5 & 405 & 17.2 \\ 10 & & & \\ \hline \end{array} $$ a. Calculate and interpret the value of the correlation coefficient between sale price and size. b. Calculate and interpret the value of the correlation coefficient between sale price and land-to-building ratio. c. If you wanted to predict sale price and you could use either size or land- to-building ratio as the basis for making predictions, which would you use? Explain. d. Based on your choice in Part (c), find the equation of the least-squares regression line you would use for predicting \(y=\) sale price.
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