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Baldi and Moore-Practice of Stat. in the Life Sci. (Homework)

James Finch

Statistics, section 2, Fall 2019

Instructor: Dr. Friendly

Current Score : 25 / 29

Due : Sunday, January 27, 2030 23:30 EST

Last Saved : n/a Saving...  ()

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25/29 (86.2%)

  • Instructions

    W. H. Freeman and WebAssign



    W. H. Freeman and WebAssign have partnered to deliver WebAssign Premium - a comprehensive and flexible suite of resources for your Statistics course. Combining the most widely used online homework platform with content directly from The Practice of Statistics in the Life Sciences by Brigette Baldi and David S. Moore, WebAssign Premium extends and enhances the classroom experience for instructors and students. It includes the entire eBook in a user-friendly online format, including all tables, figures, and study aids.

    This remarkably engaging textbook gives biology students an introduction to statistical practice all their own. It covers essential statistical topics with examples and exercises drawn from across the life sciences, including the fields of nursing, public health, and allied health. Based on David Moore’s The Basic Practice of Statistics, PSLS mirrors that #1 bestseller’s signature emphasis on statistical thinking, real data, and what statisticians actually do.

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1. –/1 points BaldiStat3 4.E.019. My Notes
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For a biology project, you measure the weight in grams and the tail length in millimeters (mm) of a group of mice. The equation of the least-squares line for predicting tail length from weight is given below.
predicted tail length = 30 + 4 × weight
How much (on the average) does tail length increase for each additional gram of weight?
    
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2. –/1 points BaldiStat3 4.E.022. My Notes
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For a biology project, you measure the weight in grams and the tail length in millimeters (mm) of a group of mice. The equation of the least-squares line for predicting tail length from weight is given below.
predicted tail length = 31 + 7 × weight
If you had measured the tail length in centimeters instead of millimeters, what would be the slope of the regression line? (There are 10 millimeters in a centimeter.)
    
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3. –/1 points BaldiStat3 4.E.023. My Notes
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Because elderly people may have difficulty standing to have their heights measured, a study looked at predicting overall height from height to the knee. Below are data (in centimeters) for five elderly men.
Knee height x 58.9 47.4 42.3 47.5 55.9
Height y 192.4 151.2 141.8 165.2 165.3
Use your calculator or software: what is the equation of the least-squares regression line for predicting height from knee height?
    
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4. –/17 points BaldiStat3 4.E.035. My Notes
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The table below presents four sets of data prepared by the statistician Frank Anscombe to illustrate the dangers of calculating without first plotting the data.
(a) Without making scatterplots, find the correlation and the least-squares regression line for all four data sets. What do you notice? Use the regression line to predict y for x = 10. (Use 3 decimal places.)
Data Set A
r =
y = x +
With x = 10, prediction for y is
Data Set B
r =
y = x +
With x = 10, prediction for y is
Data Set C
r =
y = x +
With x = 10, prediction for y is
Data Set D
r =
y = x +
With x = 10, prediction for y is

(b) Make a scatterplot for each of the data sets and add the regression line to each plot. (Do this on paper. Your instructor may ask you to turn in this work.)
(c) In which of the four cases would you be willing to use the regression line to describe the dependence of y on x? Explain your answer in each case. (Select all that apply.)

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5. –/5 points BaldiStat3 4.E.038. My Notes
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People with diabetes must manage their blood sugar levels carefully. They measure their fasting plasma glucose (FPG) several times a day with a glucose meter. Another measurement, made at regular medical checkups, is called HbA. This is roughly the percent of red blood cells that have a glucose molecule attached. It measures average exposure to glucose over a period of several months. The table below gives data on both HbA and FPG for 18 diabetics five months after they had completed a diabetes education class.
Subject HbA
(%) 		FPG
(mg/ml) 		Subject HbA
(%) 		FPG
(mg/ml) 		Subject HbA
(%) 		FPG
(mg/ml)
1 6.5 138 7 7.5 97 13 11.2 102
2 6.5 161 8 8.1 81 14 11.3 170
3 6.4 113 9 8.1 145 15 10.9 356
4 7.2 151 10 9.5 175 16 11.2 143
5 7.6 134 11 9.8 197 17 14.5 149
6 7.9 94 12 10.6 268 18 19.7 253
(a) Make a scatterplot with HbA as the explanatory variable. There is a positive linear relationship, but it is surprisingly weak.



(b) Subject 15 is an outlier in the y direction. Subject 18 is an outlier in the x direction. Find the correlation for all 18 subjects, for all except Subject 15, and for all except Subject 18. (Round your answers to four decimal places.)
all subjects r =
all except 15 r =
all except 18     r =

Are either or both of these subjects influential for the correlation?
    
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6. –/1 points BaldiStat3 4.E.501.XP. My Notes
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Researchers studied a group of 10,892 middle-aged adults over a period of nine years. They found that smokers who quit had a higher risk for diabetes within three years of quitting than either nonsmokers or continuing smokers. Does this show that stopping smoking causes the short-term risk for diabetes to increase? (Weight gain has been shown to be a major risk factor for developing type 2 diabetes and is often a side effect of quitting smoking.) Based on this research, should you tell a middle-aged adult who smokes that stopping smoking can cause diabetes and advise him or her to continue smoking?
    
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7. –/3 points BaldiStat3 4.E.507.XP. My Notes
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Ability to grow in shade may help pines found in the dry forests of Arizona resist drought. How well do these pines grow in shade? Investigators planted pine seedlings in a greenhouse in either full light, light reduced to 25% of normal by shade cloth, or light reduced to 5% of normal. At the end of the study, they dried the young trees and weighed them. What are the individuals, the treatments, and the response variable in this experiment?

Individuals
    

Treatments (Select all that apply.)

Response variable
    
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