Schaum’s Outline of Business Statistics 4th Edition by Leonard Kazmier 0071430997 9780071430999

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ISBN 10: 0071430997 

ISBN 13: 9780071430999

Author: Leonard J. Kazmier

Confusing Textbooks? Missed Lectures? Not Enough Time? Fortunately for you, there’s Schaum’s Outlines. More than 40 million students have trusted Schaum’s to help them succeed in the classroom and on exams. Schaum’s is the key to faster learning and higher grades in every subject. Each Outline presents all the essential course information in an easy-to-follow, topic-by-topic format. You also get hundreds of examples, solved problems, and practice exercises to test your skills. This Schaum’s Outline gives you Practice problems with full explanations that reinforce knowledge Coverage of the most up-to-date developments in your course field In-depth review of practices and applications Fully compatible with your classroom text, Schaum’s highlights all the important facts you need to know. Use Schaum’s to shorten your study time-and get your best test scores! Schaum’s Outlines-Problem Solved.

Table of contents:

Chapter 1 Analyzing Business Data
1.1 Definition of Business Statistics
1.2 Descriptive and Inferential Statistics
1.3 Types of Applications in Business
1.4 Discrete and Continuous Variables
1.5 Obtaining Data Through Direct Observation vs. Surveys
1.6 Methods of Random Sampling
1.7 Other Sampling Methods
1.8 Using Excel and Minitab to Generate Random Numbers

Chapter 2 Statistical Presentations and Graphical Displays
2.1 Frequency Distributions
2.2 Class Intervals
2.3 Histograms and Frequency Polygons
2.4 Frequency Curves
2.5 Cumulative Frequency Distributions
2.6 Relative Frequency Distributions
2.7 The “And-Under” Type of Frequency Distribution
2.8 Stem-and-Leaf Diagrams
2.9 Dotplots
2.10 Pareto Charts
2.11 Bar Charts and Line Graphs
2.12 Run Charts
2.13 Pie Charts
2.14 Using Excel and Minitab

Chapter 3 Describing Business Data: Measures of Location
3.1 Measures of Location in Data Sets
3.2 The Arithmetic Mean
3.3 The Weighted Mean
3.4 The Median
3.5 The Mode
3.6 Relationship Between the Mean and the Median
3.7 Mathematical Criteria Satisfied by the Median and the Mean
3.8 Use of the Mean, Median, and Mode
3.9 Use of the Mean in Statistical Process Control
3.10 Quartiles, Deciles, and Percentiles
3.11 Using Excel and Minitab

Chapter 4 Describing Business Data: Measures of Dispersion
4.1 Measures of Variability in Data Sets
4.2 The Range
4.3 Modified Ranges
4.4 Box Plots
4.5 The Mean Absolute Deviation
4.6 The Variance and Standard Deviation
4.7 Simplified Calculations for the Variance and Standard Deviation
4.8 The Mathematical Criterion Associated with the Variance and Standard Deviation
4.9 Use of the Standard Deviation in Data Description
4.10 Use of the Range and Standard Deviation in Statistical Process Control
4.11 The Coefficient of Variation
4.12 Pearson’s Coefficient of Skewness
4.13 Using Excel and Minitab

Chapter 5 Probability
5.1 Basic Definitions of Probability
5.2 Expressing Probability
5.3 Mutually Exclusive and Nonexclusive Events
5.4 The Rules of Addition
5.5 Independent Events, Dependent Events, and Conditional Probability
5.6 The Rules of Multiplication
5.7 Bayes’ Theorem
5.8 Joint Probability Tables
5.9 Permutations
5.10 Combinations

Chapter 6 Probability Distributions for Discrete Random Variables: Binomial, Hypergeometric, and Poisson
6.1 What Is a Random Variable?
6.2 Describing a Discrete Random Variable
6.3 The Binomial Distribution
6.4 The Binomial Variable Expressed by Proportions
6.5 The Hypergeometric Distribution
6.6 The Poisson Distribution
6.7 Poisson Approximation of Binomial Probabilities
6.8 Using Excel and Minitab

Chapter 7 Probability Distributions for Continuous Random Variables: Normal and Exponential
7.1 Continuous Random Variables
7.2 The Normal Probability Distribution
7.3 Percentile Points for Normally Distributed Variables
7.4 Normal Approximation of Binomial Probabilities
7.5 Normal Approximation of Poisson Probabilities
7.6 The Exponential Probability Distribution
7.7 Using Excel and Minitab

Chapter 8 Sampling Distributions and Confidence Intervals for the Mean
8.1 Point Estimation of a Population or Process Parameter
8.2 The Concept of a Sampling Distribution
8.3 Sampling Distribution of the Mean
8.4 The Central Limit Theorem
8.5 Determining Probability Values for the Sample Mean
8.6 Confidence Intervals for the Mean Using the Normal Distribution
8.7 Determining the Required Sample Size for Estimating the Mean
8.8 The t Distribution and Confidence Intervals for the Mean
8.9 Summary Table for Interval Estimation of the Population Mean
8.10 Using Excel and Minitab

Chapter 9 Other Confidence Intervals
9.1 Confidence Intervals for the Difference Between Two Means Using the Normal Distribution
9.2 The t Distribution and Confidence Intervals for the Difference Between Two Means
9.3 Confidence Intervals for the Population Proportion
9.4 Determining the Required Sample Size for Estimating the Proportion
9.5 Confidence Intervals for the Difference Between Two Proportions
9.6 The Chi-Square Distribution and Confidence Intervals for the Variance and Standard Deviation
9.7 Using Excel and Minitab

Chapter 10 Testing Hypotheses Concerning the Value of the Population Mean
10.1 Introduction
10.2 Basic Steps in Hypothesis Testing by the Critical Value Approach
10.3 Testing a Hypothesis Concerning the Mean by Use of the Normal Distribution
10.4 Type I and Type II Errors in Hypothesis Testing
10.5 Determining the Required Sample Size for Testing the Mean
10.6 Testing a Hypothesis Concerning the Mean by Use of the t Distribution
10.7 The P-Value Approach to Testing Hypotheses Concerning the Population Mean
10.8 The Confidence Interval Approach to Testing Hypotheses Concerning the Mean
10.9 Testing with Respect to the Process Mean in Statistical Process Control
10.10 Summary Table for Testing a Hypothesized Value of the Mean
10.11 Using Excel and Minitab

Chapter 11 Testing Other Hypotheses
11.1 Testing the Difference Between Two Means Using the Normal Distribution
11.2 Testing the Difference Between Means Using the t Distribution
11.3 Testing the Difference Between Means Based on Paired Observations
11.4 Testing a Hypothesis Concerning the Value of the Population Proportion
11.5 Determining the Required Sample Size for Testing the Proportion
11.6 Testing with Respect to the Process Proportion in Statistical Process Control
11.7 Testing the Difference Between Two Population Proportions
11.8 Testing a Hypothesized Value of the Variance Using the Chi-Square Distribution
11.9 Testing with Respect to Process Variability in Statistical Process Control
11.10 The F Distribution and Testing the Equality of Two Population Variances
11.11 Alternative Approaches to Testing Null Hypotheses
11.12 Using Excel and Minitab

Chapter 12 The Chi-Square Test for the Analysis of Qualitative Data
12.1 General Purpose of the Chi-Square Test
12.2 Goodness of Fit Tests
12.3 Tests for the Independence of Two Categorical Variables (Contingency Table Tests)
12.4 Testing Hypotheses Concerning Proportions
12.5 Using Computer Software

Chapter 13 Analysis of Variance
13.1 Basic Rationale Associated with Testing the Differences Among Several Population Means
13.2 One-Factor Completely Randomized Design (One-Way ANOVA)
13.3 Two-Way Analysis of Variance (Two-Way ANOVA)
13.4 The Randomized Block Design (Two-Way ANOVA, One Observation per Cell)
13.5 Two-Factor Completely Randomized Design (Two-Way ANOVA, n Observations per Cell)
13.6 Additional Considerations
13.7 Using Excel and Minitab

Chapter 14 Linear Regression and Correlation Analysis
14.1 Objectives and Assumptions of Regression Analysis
14.2 The Scatter Plot
14.3 The Method of Least Squares for Fitting a Regression Line
14.4 Residuals and Residual Plots
14.5 The Standard Error of Estimate
14.6 Inferences Concerning the Slope
14.7 Confidence Intervals for the Conditional Mean
14.8 Prediction Intervals for Individual Values of the Dependent Variable
14.9 Objectives and Assumptions of Correlation Analysis
14.10 The Coefficient of Determination
14.11 The Coefficient of Correlation
14.12 The Covariance Approach to Understanding the Correlation Coefficient
14.13 Significance Testing with Respect to the Correlation Coefficient
14.14 Pitfalls and Limitations Associated with Regression and Correlation Analysis
14.15 Using Excel and Minitab

Chapter 15 Multiple Regression and Correlation
15.1 Objectives and Assumptions of Multiple Regression Analysis
15.2 Additional Concepts in Multiple Regression Analysis
15.3 The Use of Indicator (Dummy) Variables
15.4 Residuals and Residual Plots
15.5 Analysis of Variance in Linear Regression Analysis
15.6 Objectives and Assumptions of Multiple Correlation Analysis
15.7 Additional Concepts in Multiple Correlation Analysis
15.8 Pitfalls and Limitations Associated with Multiple Regression and Multiple Correlation Analysis
15.9 Using Excel and Minitab

Chapter 16 Time Series Analysis and Business Forecasting
16.1 The Classical Time Series Model
16.2 Trend Analysis
16.3 Analysis of Cyclical Variations
16.4 Measurement of Seasonal Variations
16.5 Applying Seasonal Adjustments
16.6 Forecasting Based on Trend and Seasonal Factors
16.7 Cyclical Forecasting and Business Indicators
16.8 Forecasting Based on Moving Averages
16.9 Exponential Smoothing as a Forecasting Method
16.10 Other Forecasting Methods That Incorporate Smoothing
16.11 Using Computer Software

Chapter 17 Nonparametric Statistics
17.1 Scales of Measurement
17.2 Parametric vs. Nonparametric Statistical Methods
17.3 The Runs Test for Randomness
17.4 One Sample: The Sign Test
17.5 One Sample: The Wilcoxon Test
17.6 Two Independent Samples: The Mann–Whitney Test
17.7 Paired Observations: The Sign Test
17.8 Paired Observations: The Wilcoxon Test
17.9 Several Independent Samples: The Kruskal–Wallis Test
17.10 Using Minitab

Chapter 18 Decision Analysis: Payoff Tables and Decision Trees
18.1 The Structure of Payoff Tables
18.2 Decision Making Based upon Probabilities Alone
18.3 Decision Making Based upon Economic Consequences Alone
18.4 Decision Making Based upon Both Probabilities and Economic Consequences: The Expected Payoff Criterion
18.5 Decision Tree Analysis
18.6 Expected Utility as the Decision Criterion

Chapter 19 Statistical Process Control
19.1 Total Quality Management
19.2 Statistical Quality Control
19.3 Types of Variation in Processes
19.4 Control Charts
19.5 Control Charts for the Process Mean: X Charts
19.6 Standard Tests Used for Interpreting X Charts
19.7 Control Charts for the Process Standard Deviation: s Charts
19.8 Control Charts for the Process Range: R Charts
19.9 Control Charts for the Process Proportion: p Charts
19.10 Using Minitab

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