Problem 1BSC Simulating Dice When two dice are rolled, the total is between 2 and 12 inclusive. A student simulates the rolling of two dice by randomly generating numbers between 2 and 12. Does this simulation behave in a way that is similar to actual dice? Why or why not?
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1
Introduction to Statistics
1-2
Statistical and Critical Thinking
1-3
Types of Data
1-4
Collecting Sample Data
1.2
Statistical and Critical Thinking
1.3
Types of Data
1.4
Collecting Sample Data
2
Summarizing and Graphing
2-2
Frequency Distributions
2-3
Histograms
2-4
Graphs That Enlighten and Graphs That Deceive
2.2
Frequency Distributions
2.3
Histograms
2.4
Graphs That Enlighten and Graphs That Deceive
3
Statistics for Describing, Exploring, and Comparing Data
3-2
Measures of Center
3-3
Measures of Variation
3-4
Measures of Relative Standing and Boxplots
3.2
Measures of Center
3.3
Measures of Variation
3.4
Measures of Relative Standing and Boxplots
4
Probability
4-2
Basic Concepts of Probability
4-3
Addition Rule
4-4
Multiplication Rule: Basics
4-5
Multiplication Rule: Complements and Conditional Probability
4-6
Counting
4.2
Basic Concepts of Probability
4.3
Addition Rule
4.4
Multiplication Rule: Basics
4.5
Multiplication Rule: Complements and Conditional Probability
4.6
Counting
4.7
Probabilities Through Simulations (on CD-ROM)
4.8
Bayes' Theorem (on CD-ROM)
5
Discrete Probability Distributions
5-2
Probability Distributions
5-3
Binomial Probability Distributions
5-4
Parameters for Binomial Distributions
5-5
Poisson Probability Distributions
5.2
Probability Distributions
5.3
Binomial Probability Distributions
5.4
Parameters for Binomial Distributions
5.5
Poisson Probability Distributions
6
Normal Probability Distributions
6-2
The Standard Normal Distribution
6-3
Applications of Normal Distributions
6-4
Sampling Distributions and Estimators
6-5
The Central Limit Theorem
6-6
Assessing Normality
6-7
Normal as Approximation to Binomial
6.2
The Standard Normal Distribution
6.3
Applications of Normal Distributions
6.4
Sampling Distributions and Estimators
6.5
The Central Limit Theorem
6.6
Assessing Normality
6.7
Normal as Approximation to Binomial
7
Estimates and Sample Sizes
7-2
Estimating a Population Proportion
7-3
Estimating a Population Mean
7-4
Estimating a Population Standard Deviation or Variance
7.2
Estimating a Population Proportion
7.3
Estimating a Population Mean
7.4
Estimating a Population Standard Deviation or Variance
8
Hypothesis Testing
8-2
Basics of Hypothesis Testing
8-3
Testing a Claim About a Proportion
8-4
Testing a Claim About a Mean
8-5
Testing a Claim About a Standard Deviation or Variance
8.2
Basics of Hypothesis Testing
8.3
Testing a Claim About a Proportion
8.4
Testing a Claim About a Mean
8.5
Testing a Claim About a Standard Deviation or Variance
9
Inferences from Two Samples
9-2
Two Proportions
9-3
Two Means: Independent Samples
9-4
Two Dependent Samples (Matched Pairs)
9-5
Two Variances or Standard Deviations
9.2
Two Proportions
9.3
Two Means: Independent Samples
9.4
Two Dependent Samples (Matched Pairs)
9.5
Two Variances or Standard Deviations
10
Correlation and Regression
10-2
Correlation
10-3
Regression
10-4
Prediction Intervals and Variation
10-5
Multiple Regression
10-6
Nonlinear Regression
10.2
Correlation
10.3
Regression
10.4
Prediction Intervals and Variation
10.5
Multiple Regression
10.6
Nonlinear Regression
11
Goodness-of-Fit and Contingency Tables
11-2
Goodness-of-Fit
11-3
Contingency Tables
11.2
Goodness-of-Fit
11.3
Contingency Tables
12
Analysis of Variance
12-2
One-Way ANOVA
12-3
Two-Way ANOVA
12.2
One-Way ANOVA
12.3
Two-Way ANOVA
13
Nonparametric Tests
13-3
Wilcoxon Signed-Ranks Test for Matched Pairs
13-4
Wilcoxon Rank-Sum Test for Two Independent Samples
13-5
Kruskal-Wallis Test
13-6
Rank Correlation
13-7
Runs Test for Randomness
13.2
Sign Test
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
13.6
Rank Correlation
13.7
Runs Test for Randomness
14
Statistical Process Control
14-2
Control Charts for Variation and Mean
14-3
Control Charts for Attributes
14.2
Control Charts for Variation and Mean
14.3
Control Charts for Attributes
Textbook Solutions for Elementary Statistics
Chapter 4.7 Problem 2BSC
Question
Problem 2BSC
Simulating dice Assume that you have access to a computer that can randomly generate whole numbers between any two values. Describe how this computer can be used to simulate the rolling of a pair of dice.
Solution
Solution 2BSC
When we roll a dice outcome is between 1 and 6. Using computer generate a random integer from 1 to 6 and take it as the outcome of first dice. And then generate a second random number from 1 to 6 and take it as the outcome of second dice. Now add the two numbers. Following is an example of above simulation:
Minitab code and output are:
MTB > Random 6 ‘Dice 1’ ‘Dice 2’ ;
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full solution
full solution
Title
Elementary Statistics 12
Author
Mario F. Triola
ISBN
9780321836960