INSTRUCTOR’S
SOLUTIONS MANUAL
M
Biostatistics for the Biological and Health Sciences, 3rd edition
PR
by Marc Triola, Mario Triola
ES
SI
VE
G
R
AD
ES
, Table of Content
1. Introduction to Statistics
1-1 Statistical and Critical Thinking
M
1-2 Types of Data
1-3 Collecting Sample Data
1-4 Ethics in Statistics (download only)
2. Exploring Data with Tables and Graphs
PR
2-1 Frequency Distributions for Organizing and Summarizing Data
2-2 Histograms
2-3 Graphs That Enlighten and Graphs That Deceive
2-4 Scatterplots, Correlation, and Regression
3. Describing, Exploring, and Comparing Data
ES
3-1 Measures of Center
3-2 Measures of Variation
3-3 Measures of Relative Standing and Boxplots
4. Probability
4-1 Basic Concepts of Probability
SI
4-2 Addition Rule and Multiplication Rule
4-3 Complements, Conditional Probability, and Bayes' Theorem
4-4 Risks and Odds
4-5 Rates of Mortality, Fertility, and Morbidity
VE
4-6 Counting
5. Discrete Probability Distributions
5-1 Probability Distributions
5-2 Binomial Probability Distributions
5-3 Poisson Probability Distributions
G
6. Normal Probability Distributions
6-1 The Standard Normal Distribution
6-2 Real Applications of Normal Distributions
R
6-3 Sampling Distributions and Estimators
6-4 The Central Limit Theorem
6-5 Assessing Normality
AD
6-6 Normal as Approximation to Binomial (download only)
7. Estimating Parameters and Determining Sample Sizes
7-1 Estimating a Population Proportion
7-2 Estimating a Population Mean
7-3 Estimating a Population Standard Deviation or Variance
7-4 Bootstrapping: Using Technology for Estimates
ES
8. Hypothesis Testing
8-1 Basics of Hypothesis Testing
8-2 Testing a Claim About a Proportion
8-3 Testing a Claim About a Mean
, 8-4 Testing a Claim About a Standard Deviation or Variance
8-5 Resampling: Using Technology for Hypothesis Testing<
9. Inferences from Two Samples
9-1 Two Proportions
M
9-2 Two Means: Independent Samples
9-3 Matched Pairs
9-4 Two Variances or Standard Deviations
PR
9-5 Resampling: Using Technology for Inferences
10. Correlation and Regression
10-1 Correlation
10-2 Regression
10-3 Prediction Intervals and Variation
10-4 Multiple Regression
ES
10-5 Dummy Variables and Logistic Regression
11. Goodness-of-Fit and Contingency Tables
11-1 Goodness-of-Fit
11-2 Contingency Tables
12. Analysis of Variance
SI
12-1 One-Way ANOVA
12-2 Two-Way ANOVA
13. Nonparametric Tests
13-1 Basics of Nonparametric Tests
VE
13-2 Sign Test
13-3 Wilcoxon Signed-Ranks Test for Matched Pairs
13-4 Wilcoxon Rank-Sum Test for Two Independent Samples
13-5 Kruskal-Wallis Test for Three or More Samples
13-6 Rank Correlation
G
14. Survival Analysis
14-1 Life Tables
14-2 Kaplan-Meier Survival Analysis
R
AD
ES
, Section 1-1: Statistical and Critical Thinking 1
Chapter 1: Introduction to Statistics
M
Section 1-1: Statistical and Critical Thinking
1. The respondents are a voluntary response sample or a self-selected sample. Because those with strong interests
in the topic are more likely to respond, it is very possible that their responses do not reflect the opinions or
behavior of the general population.
PR
2. a. The sample consists of the 1046 adults who were surveyed. The population consists of all adults.
b. When asked, respondents might be inclined to avoid the shame of the unhealthy habit of not washing their
hands, so the reported rate of 70% might well be much higher than it is in reality. It is generally better to
observe or measure human behavior than to ask subjects about it.
3. Statistical significance is indicated when methods of statistics are used to reach a conclusion that a treatment is
effective, but common sense might suggest that the treatment does not make enough of a difference to justify its
use or to be practical. Yes, it is possible for a study to have statistical significance, but not practical significance.
ES
4. No. Correlation does not imply causation. The example illustrates a correlation that is clearly not the result of
any interaction or cause effect relationship between deaths in swimming pools and power generated from nuclear
power plants.
5. Yes, there does appear to be a potential to create a bias.
6. No, there does not appear to be a potential to create a bias.
7. No, there does not appear to be a potential to create a bias.
SI
8. Yes, there does appear to be a potential to create a bias.
9. The sample is a voluntary response sample and has strong potential to be flawed.
10. The samples are voluntary response samples and have potential for being flawed, but this approach might be
necessary due to ethical considerations involved in randomly selecting subjects and somehow imposing treatments
VE
on them.
11. The sampling method appears to be sound.
12. The sampling method appears to be sound.
13. With only a 1% chance of getting such results with a program that has no effect, the program appears to have
statistical significance. Also, because the average loss of 22 pounds does seem substantial, the program appears to
also have practical significance.
G
14. Because there is a 0.3% chance of getting such results by chance, the increase in scores does appear to have
statistical significance. The typical increase of 5 points suggests that the course does have practical significance.
The course does appear to be successful.
R
15. Because there is a 19% chance of getting that many girls by chance, the method appears to lack statistical
significance. The result of 1020 girls in 2000 births (51% girls) is above the approximately 50% rate expected by
chance, but it does not appear to be high enough to have practical significance. Not many couples would bother
with a procedure that raises the likelihood of a girl from 50% to 51%.
AD
16. Because there is a 15% chance of getting such results with a medication that has no effect, the medication does
not appear to have statistical significance. Because the average decrease is only 2 mm Hg, the medication does not
appear to have practical significance.
17. Yes. Each column of 8 AM and 12 AM temperatures is recorded from the same subject, so each pair is
matched.
18. No. The source is from university researchers who do not appear to gain from distorting the data.
19. The data can be used to address the issue of whether there is a correlation between body temperatures at
ES
8 AM and at 12 AM. Also, the data can be used to determine whether there are differences between body
temperatures at 8 AM and at 12 AM.
TREWQ
SOLUTIONS MANUAL
M
Biostatistics for the Biological and Health Sciences, 3rd edition
PR
by Marc Triola, Mario Triola
ES
SI
VE
G
R
AD
ES
, Table of Content
1. Introduction to Statistics
1-1 Statistical and Critical Thinking
M
1-2 Types of Data
1-3 Collecting Sample Data
1-4 Ethics in Statistics (download only)
2. Exploring Data with Tables and Graphs
PR
2-1 Frequency Distributions for Organizing and Summarizing Data
2-2 Histograms
2-3 Graphs That Enlighten and Graphs That Deceive
2-4 Scatterplots, Correlation, and Regression
3. Describing, Exploring, and Comparing Data
ES
3-1 Measures of Center
3-2 Measures of Variation
3-3 Measures of Relative Standing and Boxplots
4. Probability
4-1 Basic Concepts of Probability
SI
4-2 Addition Rule and Multiplication Rule
4-3 Complements, Conditional Probability, and Bayes' Theorem
4-4 Risks and Odds
4-5 Rates of Mortality, Fertility, and Morbidity
VE
4-6 Counting
5. Discrete Probability Distributions
5-1 Probability Distributions
5-2 Binomial Probability Distributions
5-3 Poisson Probability Distributions
G
6. Normal Probability Distributions
6-1 The Standard Normal Distribution
6-2 Real Applications of Normal Distributions
R
6-3 Sampling Distributions and Estimators
6-4 The Central Limit Theorem
6-5 Assessing Normality
AD
6-6 Normal as Approximation to Binomial (download only)
7. Estimating Parameters and Determining Sample Sizes
7-1 Estimating a Population Proportion
7-2 Estimating a Population Mean
7-3 Estimating a Population Standard Deviation or Variance
7-4 Bootstrapping: Using Technology for Estimates
ES
8. Hypothesis Testing
8-1 Basics of Hypothesis Testing
8-2 Testing a Claim About a Proportion
8-3 Testing a Claim About a Mean
, 8-4 Testing a Claim About a Standard Deviation or Variance
8-5 Resampling: Using Technology for Hypothesis Testing<
9. Inferences from Two Samples
9-1 Two Proportions
M
9-2 Two Means: Independent Samples
9-3 Matched Pairs
9-4 Two Variances or Standard Deviations
PR
9-5 Resampling: Using Technology for Inferences
10. Correlation and Regression
10-1 Correlation
10-2 Regression
10-3 Prediction Intervals and Variation
10-4 Multiple Regression
ES
10-5 Dummy Variables and Logistic Regression
11. Goodness-of-Fit and Contingency Tables
11-1 Goodness-of-Fit
11-2 Contingency Tables
12. Analysis of Variance
SI
12-1 One-Way ANOVA
12-2 Two-Way ANOVA
13. Nonparametric Tests
13-1 Basics of Nonparametric Tests
VE
13-2 Sign Test
13-3 Wilcoxon Signed-Ranks Test for Matched Pairs
13-4 Wilcoxon Rank-Sum Test for Two Independent Samples
13-5 Kruskal-Wallis Test for Three or More Samples
13-6 Rank Correlation
G
14. Survival Analysis
14-1 Life Tables
14-2 Kaplan-Meier Survival Analysis
R
AD
ES
, Section 1-1: Statistical and Critical Thinking 1
Chapter 1: Introduction to Statistics
M
Section 1-1: Statistical and Critical Thinking
1. The respondents are a voluntary response sample or a self-selected sample. Because those with strong interests
in the topic are more likely to respond, it is very possible that their responses do not reflect the opinions or
behavior of the general population.
PR
2. a. The sample consists of the 1046 adults who were surveyed. The population consists of all adults.
b. When asked, respondents might be inclined to avoid the shame of the unhealthy habit of not washing their
hands, so the reported rate of 70% might well be much higher than it is in reality. It is generally better to
observe or measure human behavior than to ask subjects about it.
3. Statistical significance is indicated when methods of statistics are used to reach a conclusion that a treatment is
effective, but common sense might suggest that the treatment does not make enough of a difference to justify its
use or to be practical. Yes, it is possible for a study to have statistical significance, but not practical significance.
ES
4. No. Correlation does not imply causation. The example illustrates a correlation that is clearly not the result of
any interaction or cause effect relationship between deaths in swimming pools and power generated from nuclear
power plants.
5. Yes, there does appear to be a potential to create a bias.
6. No, there does not appear to be a potential to create a bias.
7. No, there does not appear to be a potential to create a bias.
SI
8. Yes, there does appear to be a potential to create a bias.
9. The sample is a voluntary response sample and has strong potential to be flawed.
10. The samples are voluntary response samples and have potential for being flawed, but this approach might be
necessary due to ethical considerations involved in randomly selecting subjects and somehow imposing treatments
VE
on them.
11. The sampling method appears to be sound.
12. The sampling method appears to be sound.
13. With only a 1% chance of getting such results with a program that has no effect, the program appears to have
statistical significance. Also, because the average loss of 22 pounds does seem substantial, the program appears to
also have practical significance.
G
14. Because there is a 0.3% chance of getting such results by chance, the increase in scores does appear to have
statistical significance. The typical increase of 5 points suggests that the course does have practical significance.
The course does appear to be successful.
R
15. Because there is a 19% chance of getting that many girls by chance, the method appears to lack statistical
significance. The result of 1020 girls in 2000 births (51% girls) is above the approximately 50% rate expected by
chance, but it does not appear to be high enough to have practical significance. Not many couples would bother
with a procedure that raises the likelihood of a girl from 50% to 51%.
AD
16. Because there is a 15% chance of getting such results with a medication that has no effect, the medication does
not appear to have statistical significance. Because the average decrease is only 2 mm Hg, the medication does not
appear to have practical significance.
17. Yes. Each column of 8 AM and 12 AM temperatures is recorded from the same subject, so each pair is
matched.
18. No. The source is from university researchers who do not appear to gain from distorting the data.
19. The data can be used to address the issue of whether there is a correlation between body temperatures at
ES
8 AM and at 12 AM. Also, the data can be used to determine whether there are differences between body
temperatures at 8 AM and at 12 AM.
TREWQ
