INSTRUCTOR’S SOLUTIONS
MANUAL
Elementary Statistics Using Excel, 7th edition
By Mario F. Triola
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ST
SO
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, TABLE OF CONTENT
1. Introduction to Statistics
1.1 Statistical and Critical Thinking
1.2 Types of Data
1.3 Collecting Sample Data
1.4 Ethics in Statistics (download only)
2. Exploring Data with Tables and Graphs
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
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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
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4.2 Addition Rule and Multiplication Rule
4.3 Complements, Conditional Probability, and Bayes' Theorem
4.4 Counting
4.5 Simulations for Hypothesis Tests
5. Discrete Probability Distributions
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5.1 Probability Distributions
5.2 Binomial Probability Distributions
5.3 Poisson Probability Distributions
6. Normal Probability Distributions
6.1 The Standard Normal Distribution
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6.2 Real Applications of Normal Distributions
6.3 Sampling Distributions and Estimators
6.4 The Central Limit Theorem
6.5 Assessing Normality
6.6 Normal as Approximation to Binomial (download only)
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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
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7.4 Bootstrapping: Using Technology for Estimates
8. Hypothesis Testing
8.1 Basics of Hypothesis Testing
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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
9.2 Two Means: Independent Samples
9.3 Matched Pairs
9.4 Two Variances or Standard Deviations
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
10.5 Nonlinear Regression
11. Goodness-of-Fit and Contingency Tables
11.1 Goodness-of-Fit
11.2 Contingency Tables
12. Analysis of Variance
12.1 One-Way ANOVA
12.2 Two-Way ANOVA
13. Nonparametric Tests
13.1 Basics of Nonparametric Tests
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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
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13.7 Runs Test for Randomness
14. Statistics Process Control
14.1 Control Charts for Variation and Mean
14.2 Control Charts for Attributes
15. Holistic Statistics
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, Section 1-1: Statistical and Critical Thinking 1
Chapter 1: Introduction to Statistics
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.
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
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use or to be practical. Yes, it is possible for a study to have statistical significance, but not practical
significance.
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 per capita consumption of margarine and the divorce rate in
Maine.
ST
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.
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.
SO
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 on them.
11. The sampling method appears to be sound.
12. The sampling method appears to be sound.
13. The Ornish weight loss program has statistical significance, because the results are so unlikely (3 chances in
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1000) to occur by chance. It does not have practical significance because the amount of lost weight (3.3 lb) is so
small.
14. Because there is only one chance in a thousand of getting such success rates by chance, the difference does
appear to have statistical significance. The 92% success rate for surgery appears to be substantially better than
the 72% success rate for splints, so the difference does appear to have practical significance.
15. The difference between Mendel’s 25% rate and the result of 26% is not statistically significant. According to
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Mendel’s theory, 145 of the 580 peas would have yellow pods, but the results consisted of 152 peas with yellow
pods. The difference of 7 peas with yellow pods among the 580 offspring does not appear to be statistically
significant. The difference does not appear to have practical significance.
16. Because there is a 25% chance of getting such results with a program that has no effect, the program does not
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appear to have statistical significance. Because the average increase is only 3 IQ points, the program does not
appear to have practical significance.
17. With 40 out of 41 ballots having the Democrat first, it appears that the result is statistically significant. Because
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of the great advantage enjoyed by Democrats, the results also have practical significance.
18. Because it is so unlikely (0.3%) to get these results by chance, the results have statistical significance. With
about 57% (from 235/414) of the coin toss winners going on to win the game, the result appears to have
practical significance.
19. There appears to be statistical significance given the large discrepancy between 79.1% and 39%. Because the
results are so far from yielding a jury of peers, it appears that the results have practical significance.
20. With only a 0.0000006% chance of getting such results, it appears that the results are statistically significant.
The discrepancy between the 61% rate for voters who actually did vote and the 70% rate of those who said that
they voted is a fairly large discrepancy, and the results appear to have practical significance.
Copyright © 2022 Pearson Education, Inc.
MANUAL
Elementary Statistics Using Excel, 7th edition
By Mario F. Triola
TE
ST
SO
LU
TI
O
N
, TABLE OF CONTENT
1. Introduction to Statistics
1.1 Statistical and Critical Thinking
1.2 Types of Data
1.3 Collecting Sample Data
1.4 Ethics in Statistics (download only)
2. Exploring Data with Tables and Graphs
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
TE
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
ST
4.2 Addition Rule and Multiplication Rule
4.3 Complements, Conditional Probability, and Bayes' Theorem
4.4 Counting
4.5 Simulations for Hypothesis Tests
5. Discrete Probability Distributions
SO
5.1 Probability Distributions
5.2 Binomial Probability Distributions
5.3 Poisson Probability Distributions
6. Normal Probability Distributions
6.1 The Standard Normal Distribution
LU
6.2 Real Applications of Normal Distributions
6.3 Sampling Distributions and Estimators
6.4 The Central Limit Theorem
6.5 Assessing Normality
6.6 Normal as Approximation to Binomial (download only)
TI
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
O
7.4 Bootstrapping: Using Technology for Estimates
8. Hypothesis Testing
8.1 Basics of Hypothesis Testing
N
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
9.2 Two Means: Independent Samples
9.3 Matched Pairs
9.4 Two Variances or Standard Deviations
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
10.5 Nonlinear Regression
11. Goodness-of-Fit and Contingency Tables
11.1 Goodness-of-Fit
11.2 Contingency Tables
12. Analysis of Variance
12.1 One-Way ANOVA
12.2 Two-Way ANOVA
13. Nonparametric Tests
13.1 Basics of Nonparametric Tests
TE
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
ST
13.7 Runs Test for Randomness
14. Statistics Process Control
14.1 Control Charts for Variation and Mean
14.2 Control Charts for Attributes
15. Holistic Statistics
SO
LU
TI
O
N
, Section 1-1: Statistical and Critical Thinking 1
Chapter 1: Introduction to Statistics
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.
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
TE
use or to be practical. Yes, it is possible for a study to have statistical significance, but not practical
significance.
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 per capita consumption of margarine and the divorce rate in
Maine.
ST
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.
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.
SO
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 on them.
11. The sampling method appears to be sound.
12. The sampling method appears to be sound.
13. The Ornish weight loss program has statistical significance, because the results are so unlikely (3 chances in
LU
1000) to occur by chance. It does not have practical significance because the amount of lost weight (3.3 lb) is so
small.
14. Because there is only one chance in a thousand of getting such success rates by chance, the difference does
appear to have statistical significance. The 92% success rate for surgery appears to be substantially better than
the 72% success rate for splints, so the difference does appear to have practical significance.
15. The difference between Mendel’s 25% rate and the result of 26% is not statistically significant. According to
TI
Mendel’s theory, 145 of the 580 peas would have yellow pods, but the results consisted of 152 peas with yellow
pods. The difference of 7 peas with yellow pods among the 580 offspring does not appear to be statistically
significant. The difference does not appear to have practical significance.
16. Because there is a 25% chance of getting such results with a program that has no effect, the program does not
O
appear to have statistical significance. Because the average increase is only 3 IQ points, the program does not
appear to have practical significance.
17. With 40 out of 41 ballots having the Democrat first, it appears that the result is statistically significant. Because
N
of the great advantage enjoyed by Democrats, the results also have practical significance.
18. Because it is so unlikely (0.3%) to get these results by chance, the results have statistical significance. With
about 57% (from 235/414) of the coin toss winners going on to win the game, the result appears to have
practical significance.
19. There appears to be statistical significance given the large discrepancy between 79.1% and 39%. Because the
results are so far from yielding a jury of peers, it appears that the results have practical significance.
20. With only a 0.0000006% chance of getting such results, it appears that the results are statistically significant.
The discrepancy between the 61% rate for voters who actually did vote and the 70% rate of those who said that
they voted is a fairly large discrepancy, and the results appear to have practical significance.
Copyright © 2022 Pearson Education, Inc.
