Complete Business Statistics (7th Edition)

Download Complete Business Statistics (7th Edition) written by Amir D. Aczel in PDF format. This book is under the category Business and bearing the isbn/isbn13 number 0073373605 / 0071284931/9780073373607 / 9780071284936. You may reffer the table below for additional details of the book.


SKU: 1f72e258ff73 Category: Tags: ,



Amir D. Aczel


Irwin Professional Pub; 7th edition




888 pages




0073373605 / 0071284931


9780073373607 / 9780071284936

Book Description

This Complete Business Statistics 7th edition (PDF) seeks to give college students a basic understanding of the concepts underlying statistics. Students of business are also introduced to the more modern applications of technology in business statistics through this course. This ebook includes the following features: Excel output in textbook examples; to give students a foundation for learning and applying statistics with commercial software tools; a visual statistics software bundle that teaches statistics using multimedia enhanced visuals to help students understand business statistics concepts; and examples, exercises, problems, and cases that offer students examples drawn from real business situations. All of these features can be found in the ebook. This e-primary textbook’s objective is to provide students of business with a practical understanding of statistical concepts and how such concepts are utilized in the business world.

Table of contents

Table of contents :
1 Introduction and Descriptive Statistics
1–1 Using Statistics
Samples and Populations
Data and Data Collection
1–2 Percentiles and Quartiles
1–3 Measures of Central Tendency
1–4 Measures of Variability
1–5 Grouped Data and the Histogram
1–6 Skewness and Kurtosis
1–7 Relations between the Mean and the Standard Deviation
Chebyshev’s Theorem
The Empirical Rule
1–8 Methods of Displaying Data
Pie Charts
Bar Charts
Frequency Polygons and Ogives
A Caution about Graphs
Time Plots
1–9 Exploratory Data Analysis
Stem-and-Leaf Displays
Box Plots
1–10 Using the Computer
Using Excel for Descriptive Statistics and Plots
Using MINITAB for Descriptive Statistics and Plots
1–11 Summary and Review of Terms
Case 1: NASDAQ Volatility
2 Probability
2–1 Using Statistics
2–2 Basic Definitions: Events, Sample Space, and Probabilities
2–3 Basic Rules for Probability
The Range of Values
The Rule of Complements
Mutually Exclusive Events
2–4 Conditional Probability
2–5 Independence of Events
Product Rules for Independent Events
2–6 Combinatorial Concepts
2–7 The Law of Total Probability and Bayes’ Theorem
The Law of Total Probability
Bayes’ Theorem
Case 2: Job Applications
2–8 The Joint Probability Table
2–9 Using the Computer
Excel Templates and Formulas
2–10 Summary and Review of Terms
3 Random Variables
3–1 Using Statistics
Discrete and Continuous Random Variables
Cumulative Distribution Function
3–2 Expected Values of Discrete Random Variables
The Expected Value of a Function of a Random Variable
Variance and Standard Deviation of a Random Variable
Variance of a Linear Function of a Random Variable
3–3 Sum and Linear Composites of Random Variables
Chebyshev’s Theorem
The Templates for Random Variables
3–4 Bernoulli Random Variable
3–5 The Binomial Random Variable
Conditions for a Binomial Random Variable
Binomial Distribution Formulas
The Template
Problem Solving with the Template
3–6 Negative Binomial Distribution
Negative Binomial Distribution Formulas
Problem Solving with the Template
3–7 The Geometric Distribution
Geometric Distribution Formulas
Problem Solving with the Template
3–8 The Hypergeometric Distribution
Hypergeometric Distribution Formulas
Problem Solving with the Template
3–9 The Poisson Distribution
Problem Solving with the Template
3–10 Continuous Random Variables
3–11 The Uniform Distribution
Problem Solving with the Template
3–12 The Exponential Distribution
A Remarkable Property
3–14 Summary and Review of Terms
The Template
Value at Risk
3–13 Using the Computer
Using Excel Formulas for Some Standard Distributions
Using MINITAB for Some Standard Distributions
Case 3: Concepts Testing
4 The Normal Distribution
4–1 Using Statistics
4–2 Properties of the Normal Distribution
4–3 The Standard Normal Distribution
Finding Probabilities of the Standard Normal Distribution
Finding Values of Z Given a Probability
4–4 The Transformation of Normal Random Variables
Using the Normal Transformation
4–5 The Inverse Transformation
4–6 The Template
Problem Solving with the Template
4–7 Normal Approximation of Binomial Distributions
4–8 Using the Computer
Using Excel Functions for a Normal Distribution
Using MINITAB for a Normal Distribution
4–9 Summary and Review of Terms
Case 4: Acceptable Pins
Case 5: Multicurrency Decision
5 Sampling and Sampling Distributions
5–1 Using Statistics
5–2 Sample Statistics as Estimators of Population Parameters
Obtaining a Random Sample
Other Sampling Methods
5–3 Sampling Distributions
The Central Limit Theorem
The History of the Central Limit Theorem
The Standardized Sampling Distribution of the Sample Mean When � Is Not Known
The Sampling Distribution of the Sample Proportion ˆ P
5–4 Estimators and Their Properties
Applying the Concepts of Unbiasedness, Efficiency, Consistency, and Sufficiency
5–5 Degrees of Freedom
5–6 Using the Computer
Using Excel for Generating Sampling Distributions
Using MINITAB for Generating Sampling Distributions
5–7 Summary and Review of Terms
Case 6: Acceptance Sampling of Pins
Case 9: Tiresome Tires I
6 Confidence Intervals
6–1 Using Statistics
6–2 Confidence Interval for the Population Mean When the Population Standard Deviation Is Known
The Template
6–3 Confidence Intervals for � When � Is Unknown— The t Distribution
The t Distribution
6–4 Large-Sample Confidence Intervals for the Population Proportion p
The Template
6–5 Confidence Intervals for the Population Variance
The Template
6–6 Sample-Size Determination
6–7 The Templates
Optimizing Population Mean Estimates
Determining the Optimal Half-Width
Using the Solver
Optimizing Population Proportion Estimates
6–8 Using the Computer
Using Excel Built-In Functions for Confidence Interval Estimation
Using MINITAB for Confidence Interval Estimation
6–9 Summary and Review of Terms
Case 7: Presidential Polling
Case 8: Privacy Problem
7 Hypothesis Testing
7–1 Using Statistics
The Null Hypothesis
7–2 The Concepts of Hypothesis Testing
Evidence Gathering
Type I and Type II Errors
The p-Value
The Significance Level
Optimal � and the Compromise between Type I and Type II Errors
� and Power
Sample Size
7–3 Computing the p-Value
The Test Statistic
p-Value Calculations
One-Tailed and Two-Tailed Tests
Computing �
7–4 The Hypothesis Test
Testing Population Means
A Note on t Tables and p-Values
The Templates
Testing Population Proportions
Testing Population Variances
7–5 Pretest Decisions
Testing Population Means
Manual Calculation of Required Sample Size
Testing Population Proportions
Manual Calculation of Sample Size
7–6 Using the Computer
Using Excel for One-Sample Hypothesis Testing
Using MINITAB for One-Sample Hypothesis Testing
7–7 Summary and Review of Terms
8 The Comparison of Two Populations
8–1 Using Statistics
8–2 Paired-Observation Comparisons
The Template
Confidence Intervals
The Template
8–3 A Test for the Difference between Two Population Means Using Independent Random Samples
The Templates
Confidence Intervals
The Templates
Confidence Intervals
8–4 A Large-Sample Test for the Difference between Two Population Proportions
Confidence Intervals
The Template
8–5 The F Distribution and a Test for Equality of Two Population Variances
A Statistical Test for Equality of Two Population Variances
The Templates
8–6 Using the Computer
Using Excel for Comparison of Two Populations
Using MINITAB for Comparison of Two Samples
8–7 Summary and Review of Terms
Case 10: Tiresome Tires II
9 Analysis of Variance
9–1 Using Statistics
9–2 The Hypothesis Test of Analysis of Variance
The Test Statistic
9–3 The Theory and the Computations of ANOVA
The Sum-of-Squares Principle
The Degrees of Freedom
The Mean Squares
The Expected Values of the Statistics MSTR and MSE under the Null Hypothesis
The F Statistic
9–4 The ANOVA Table and Examples
9–5 Further Analysis
The Tukey Pairwise-Comparisons Test
Conducting the Tests
The Case of Unequal Sample Sizes, and Alternative Procedures
The Template
9–6 Models, Factors, and Designs
One-Factor versus Multifactor Models
Fixed-Effects versus Random-Effects Models
Experimental Design
9–7 Two-Way Analysis of Variance
The Two-Way ANOVA Model
The Hypothesis Tests in Two-Way ANOVA
Sums of Squares, Degrees of Freedom, and Mean Squares
The F Ratios and the Two-Way ANOVA Table
The Template
The Overall Significance Level
The Tukey Method for Two-Way Analysis
Extension of ANOVA to Three Factors
Two-Way ANOVA with One Observation per Cell
9–8 Blocking Designs
Randomized Complete Block Design
The Template
9–9 Using the Computer
Using Excel for Analysis of Variance
Using MINITAB for Analysis of Variance
9–10 Summary and Review of Terms
Case 11: Rating Wines
Case 12: Checking Out Checkout
10 Simple Linear Regression and Correlation
10–1 Using Statistics
Model Building
10–2 The Simple Linear Regression Model
10–3 Estimation: The Method of Least Squares
The Template
10–4 Error Variance and the Standard Errors of Regression Estimators
Confidence Intervals for the Regression Parameters
10–5 Correlation
10–6 Hypothesis Tests about the Regression Relationship
Other Tests
10–7 How Good Is the Regression?
10–8 Analysis-of-Variance Table and an F Test of the Regression Model
10–9 Residual Analysis and Checking for Model Inadequacies
A Check for the Equality of Variance of the Errors
Testing for Missing Variables
Detecting a Curvilinear Relationship between Y and X
The Normal Probability Plot
10–10 Use of the Regression Model for Prediction
Point Predictions
Prediction Intervals
A Confidence Interval for the Average Y, Given a Particular Value of X
10–11 Using the Computer
The Excel Solver Method for Regression
The Excel LINEST Function
Using MINITAB for Simple Linear Regression Analysis
10–12 Summary and Review of Terms
Case 13: Firm Leverage and Shareholder Rights
Case 14: Risk and Return
11 Multiple Regression
11–1 Using Statistics
11–2 The k-Variable Multiple Regression Model
The Estimated Regression Relationship
11–3 The F Test of a Multiple Regression Model
11–4 How Good Is the Regression?
11–5 Tests of the Significance of Individual Regression Parameters
11–6 Testing the Validity of the Regression Model
Residual Plots
Standardized Residuals
The Normal Probability Plot
Outliers and Influential Observations
Lack of Fit and Other Problems
11–7 Using the Multiple Regression Model for Prediction
The Template
Setting Recalculation to “Manual” on the Template
11–8 Qualitative Independent Variables
Interactions between Qualitative and Quantitative Variables
11–9 Polynomial Regression
Other Variables and Cross-Product Terms
11–10 Nonlinear Models and Transformations
Variance-Stabilizing Transformations
Regression with Dependent Indicator Variable
11–11 Multicollinearity
Causes of Multicollinearity
Detecting the Existence of Multicollinearity
Solutions to the Multicollinearity Problem
11–12 Residual Autocorrelation and the Durbin-Watson Test
11–13 Partial F Tests and Variable Selection Methods
Partial F Tests
Variable Selection Methods
11–14 Using the Computer
Multiple Regression Using the Solver
LINEST Function for Multiple Regression
Using MINITAB for Multiple Regression
11–15 Summary and Review of Terms
Case 15: Return on Capital for Four Different Sectors
12 Time Series, Forecasting, and Index Numbers
12–1 Using Statistics
12–2 Trend Analysis
12–3 Seasonality and Cyclical Behavior
12–4 The Ratio-to-Moving-Average Method
The Template
The Cyclical Component of the Series
Forecasting a Multiplicative Series
12–5 Exponential Smoothing Methods
The Template
12–6 Index Numbers
The Consumer Price Index
The Template
12–7 Using the Computer
Using Microsoft Excel in Forecasting and Time Series
Using MINITAB in Forecasting and Time Series
12–8 Summary and Review of Terms
Case 16: Auto Parts Sales Forecast
13 Quality Control and Improvement
13–1 Using Statistics
13–2 W. Edwards Deming Instructs
13–3 Statistics and Quality
Deming’s 14 Points
Process Capability
Control Charts
Pareto Diagrams
Six Sigma
Acceptance Sampling
Analysis of Variance and Experimental Design
Taguchi Methods
The Template
13–4 The x Chart
The Template
13–5 The R Chart and the s Chart
The R Chart
The s Chart
13–6 The p Chart
The Template
13–7 The c Chart
The Template
13–8 The x Chart
13–9 Using the Computer
Using MINITAB for Quality Control
13–10 Summary and Review of Terms
Case 17: Quality Control and Improvement at Nashua Corporation
14 Nonparametric Methods and Chi-Square Tests
14–1 Using Statistics
14–2 The Sign Test
14–3 The Runs Test—A Test for Randomness
Large-Sample Properties
The Template
The Wald-Wolfowitz Test
14–4 The Mann-Whitney U Test
The Computational Procedure
14–5 The Wilcoxon Signed-Rank Test
The Paired-Observations Two-Sample Test
Large-Sample Version of the Test
A Test for the Mean or Median of a Single Population
The Template
14–6 The Kruskal-Wallis Test—A Nonparametric Alternative to One-Way ANOVA
The Template
Further Analysis
14–7 The Friedman Test for a Randomized Block Design
The Template
14–8 The Spearman Rank Correlation Coefficient
The Template
14–9 A Chi-Square Test for Goodness of Fit
A Goodness-of-Fit Test for the Multinomial Distribution
The Template
Unequal Probabilities
The Template
14–10 Contingency Table Analysis—A Chi-Square Test for Independence
The Template
14–11 A Chi-Square Test for Equality of Proportions
The Median Test
14–12 Using the Computer
Using MINITAB for Nonparametric Tests
14–13 Summary and Review of Terms
Case 18: The Nine Nations of North America
15 Bayesian Statistics and Decision Analysis
15–1 Using Statistics
15–2 Bayes’ Theorem and Discrete Probability Models
The Template
15–3 Bayes’ Theorem and Continuous Probability Distributions
The Normal Probability Model
Credible Sets
The Template
15–4 The Evaluation of Subjective Probabilities
Assessing a Normal Prior Distribution
15–5 Decision Analysis: An Overview
Chance Occurrences
Final Outcomes
Additional Information
15–6 Decision Trees
The Payoff Table
15–7 Handling Additional Information Using Bayes’ Theorem
Determining the Payoffs
Determining the Probabilities
15–8 Utility
A Method of Assessing Utility
15–9 The Value of Information
15–10 Using the Computer
The Template
15–11 Summary and Review of Terms
Case 19: Pizzas ‘R’ Us
Case 20: New Drug Development
A References
B Answers to Most Odd-Numbered Problems
C Statistical Tables

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