Solution Manual for Business Statistics: A First Course, 2/E, Norean R. Sharpe, Richard D. De Veaux, Paul Velleman, ISBN-10: 032194657X, ISBN-13: 9780321946577
Solution Manual for Business Statistics: A First Course, 2/E, Norean R. Sharpe, Richard D. De Veaux, Paul Velleman, ISBN-10: 032194657X, ISBN-13: 9780321946577
What is Solution Manual (SM)/ Instructor Manual(IM)/ Instructor Solution Manual (ISM)?
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Step-Step Solutions of End of Chapter Questions/Problems in the text book
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Table of Contents
Part I Exploring and Collecting Data
Chapter 1 Statistics and Variation
1.1 So, What is Statistics?
1.2 How Will This Book Help?
Chapter 2 Data
2.1 What Are Data?
2.2 Variable Types
2.3 Data Sources: Where, How and When
Chapter 3 Surveys and Sampling
3.1 Three Ideas of Sampling
3.2 Population Parameters
3.3 Common Sampling Designs
3.4 The Valid Survey
3.5 How to Sample Badly
Chapter 4 Displaying and Describing Categorical Data
4.1 Summarizing a Categorical Variable
4.2 Displaying a Categorical Variable
4.3 Exploring Two Categorical Variables: Contingency Tables
Chapter 5 Displaying and Describing Quantitative Data
5.1 Displaying Quantative Variables
5.2 Shape
5.3 Center
5.4 Spread of the Distribution
5.5 Shape, Center, and Spread – A Summary
5.6 Five-Number Summary and Boxplots
5.7 Comparing Groups
5.8 Identifying Outliers
5.9 Standardizing
5.10 Time Series Plots
Transforming Skewed Data – on CD
Chapter 6 Correlation and Linear Regression
6.1 Looking at Scatterplots
6.2 Assigning Roles to Variables in Scatterplots
6.3 Understanding Correlation
6.4 Lurking Variables and Caustation
6.5 The Linear Model
6.6 Correlation and the Line
6.7 Regression to the Mean
6.8 Checking the Model
6.9 Variation in the Model and R2
6.10 Reality Check: Is the Regression Reasonable?
Part II Understanding Data and Distributions
Chapter 7 Randomness and Probability
7.1 Random Phenomena and Probability
7.2 The Nonexistent Law of Averages
7.3 Different Types of Probability
7.4 Probability Rules
7.5 Joint Probability and Contingency Tables
7.6 Conditional Probability
7.7 Constructing Contingency Tables
7.8 Probability Trees
*7.9 Reversing the Conditioning: Bayes’ Rule
Chapter 8 Random Variables and Probability Models
8.1 Expected Value of a Random Variable
8.2 Standard Deviation of a Random Variable
8.3 Properties of Expected Values and Variances
8.4 Discrete Probability Models
8.5 Continuous Random Variables
Chapter 9 Sampling Distributions and Confidence Intervals for Proportions
9.1 The Distribution of Sample Proportions
9.2 Sampling Distributions for Proportions
9.3 The Central Limit Theorem
9.4 A Confidence Interval
9.5 Margin of Error: Certainty vs. Precision
9.6 Assumptions and Conditions
9.7 Choosing the Sample Size
A Confidence Interval for Small Samples – on CD
Chapter 10 Testing Hypotheses about Proportions
10.1 Hypotheses
10.2 A Trial as a Hypothesis Test
10.3 P-Values
10.4 The Reasoning of Hypothesis Testing
10.5 Alternative Hypotheses
10.6 Alpha Levels and Significance
10.7 Critical Values
10.8 Confidence Intervals and Hypothesis Testing
10.9 Two Types of Errors
*10.10 Power
Chapter 11 Confidence Intervals and Hypothesis Tests for Means
11.1 The Sampling Distribution of the Mean
11.2 How Sampling Distribution Models Work
11.3 Gossett and the t-Distribution
11.4 Confidence Interval for Means
11.5 Assumptions and Conditions
11.6 Testing Hypothesis about Means – the One-Sample t-Test
Chapter 12 Comparing Two Groups
12.1 Comparing Two Means
12.2 The Two-Sample t-Test
12.3 Assumptions and Conditions
12.4 A Confidence Interval for the Difference Between Two Means
12.5 The Pooled t-Test
*12.6 Tukey’s Quick Test
12.7 Paired Data
12.8 The Paired t-Test
Chapter 13 Inference for Counts: Chi-Square Tests
13.1 Goodness-of-Fit-Tests
13.2 Interpreting Chi-Square Values
13.3 Examining the Residuals
13.4 The Chi-Square Tests of Homogeneity
13.5 Comparing Two Proportions
13.6 Chi-Square Test of Independence
Part III Building Models for Decision Making
Chapter 14 Inference for Regression
14.1 The Population and the Sample
14.2 Assumptions and Conditions
14.3 Regression Inference
14.4 Standard Errors for Predicted Values
14.5 Using Confidence and Prediction Intervals
14.6 Extrapolation and Prediction
14.7 Unusual and Extraordinary Observations
*14.8 Working with Summary Values
*14.9 Linearity
Transforming (Re-Expressing) Data – on CD
The Ladder of Powers – on CD
Chapter 15 Multiple Regression
15.1 The Multiple Regression Model
15.2 Interpreting Multiple Regression Coefficients
15.3 Assumptions and Conditions for the Multiple Regression Model
15.4 Testing the Multiple Regression Model
15.5 Adjusted R2 and the F-Statistic
The Logistic Regression Model – on CD
Indicator (or Dummy) Variables – on CD
Adjusting for Different Slopes
Interaction Terms – on CD
Collinearity – on CD
Chapter 16 Introduction to Data Mining
16.1 Direct Marketing
16.2 The Data
16.3 The Goals of Data Mining
16.4 Data Mining Myths
16.5 Successful Data Mining
16.6 Data Mining
16.7 Data Mining Algorithms
16.8 The Data Mining Process
16.9 Summary
*Indicates an optional topic
Appendixes
A Answers A
B Photo Acknowledgments
C Tables and Selected Formulas
D Index
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