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Learn Statistics with Managerial Statistics by Gerald Keller (9th Edition PDF)



Here is the outline of the article: # Keller G 2012 Managerial Statistics 9th Edition Pdf ## Introduction - What is managerial statistics and why is it important for business decision making? - Who is Gerald Keller and what are his credentials as an author of statistics books? - What are the main features and benefits of the 9th edition of his book Managerial Statistics? ## Overview of the Book - How is the book organized and what are the main topics covered in each chapter? - What are the learning objectives and key concepts for each chapter? - How does the book use real-world examples and case studies to illustrate statistical concepts and methods? ## Unique Three-Step Approach to Problem Solving - What is Keller's unique three-step approach to problem solving using statistics? - How does it help readers to identify, compute, and interpret statistical techniques for different types of problems and data? - What are the advantages of using Excel and Minitab as statistical software tools for computation and interpretation? ## Review of Some Chapters - Chapter 3: Descriptive Statistics: Numerical Measures - What are the measures of central tendency, variability, and shape for numerical data? - How can they be computed and interpreted using Excel and Minitab? - What are some applications of descriptive statistics in business contexts? - Chapter 8: Confidence Interval Estimation - What is a confidence interval and how can it be used to estimate a population parameter with a certain level of confidence? - How can confidence intervals be constructed for different types of parameters, such as means, proportions, variances, and differences? - How can Excel and Minitab be used to calculate confidence intervals and interpret their results? - Chapter 13: Analysis of Variance - What is analysis of variance (ANOVA) and how can it be used to compare the means of more than two populations or groups? - What are the assumptions and conditions for performing ANOVA? - How can Excel and Minitab be used to conduct ANOVA and interpret its output? ## Summary and Conclusion - What are the main takeaways from the book Managerial Statistics by Gerald Keller? - How can the book help readers to learn and apply statistical techniques for business decision making? - Where can readers find more information and resources about the book and its author? Here is the article based on the outline: # Keller G 2012 Managerial Statistics 9th Edition Pdf Statistics is a powerful tool for making informed decisions in business. It helps managers to collect, analyze, and interpret data from various sources, such as customers, markets, competitors, employees, suppliers, etc. Statistics also helps managers to test hypotheses, estimate parameters, compare groups, identify trends, forecast outcomes, and evaluate alternatives. But learning statistics can be challenging for many students and professionals who do not have a strong background in mathematics or who find it difficult to understand abstract concepts and formulas. That's why they need a book that can teach them statistics in a clear, practical, and engaging way. One such book is Managerial Statistics by Gerald Keller. This book is a worldwide best-selling business statistics book that teaches readers how to apply statistics to real business problems through the author's unique three-step approach to problem solving. In this article, we will review this book and its features, benefits, contents, and examples. We will also provide some information on where you can find a pdf version of this book online. ## Introduction ### What is managerial statistics and why is it important for business decision making? Managerial statistics is the branch of statistics that focuses on applying statistical methods and techniques to solve problems and make decisions in business contexts. It covers topics such as descriptive statistics, probability theory, sampling distributions, estimation, hypothesis testing, regression analysis, analysis of variance, time series analysis, quality control, and decision analysis. Managerial statistics is important for business decision making because it helps managers to: - Summarize and present data in meaningful ways, such as tables, charts, graphs, and numerical measures. - Assess the uncertainty and variability of data and draw inferences about the population from a sample. - Compare the performance of different groups, products, processes, or strategies using statistical tests and measures. - Identify the relationships and associations between variables and model their effects using regression techniques. - Analyze the patterns and trends of data over time and forecast future values using time series techniques. - Control and improve the quality of products and processes using statistical tools and methods. - Evaluate the costs and benefits of different alternatives and choose the best option using decision analysis techniques. ### Who is Gerald Keller and what are his credentials as an author of statistics books? Gerald Keller is an emeritus professor of business at Wilfrid Laurier University, where he taught statistics, management science, and operations management from 1974 to 2011. He also taught at the University of Toronto, the University of Miami, McMaster University, the University of Windsor, and the Beijing Institute of Science and Technology. He has a PhD in Business Administration from the University of California, Berkeley, and a Master of Science in Mathematics from Stanford University. He has published several books on statistics, management science, and operations management, as well as numerous articles in academic journals. He has also consulted with banks on credit scoring and credit card fraud, and conducted market surveys for the Canadian government on energy conservation. He is the author of Managerial Statistics, which is now in its 9th edition. He is also the co-author of BSTAT, Applied Statistics with Microsoft Excel, Essentials of Business Statistics, Australian Business Statistics, and Statistics Laboratory Manual Experiments Using Minitab. ### What are the main features and benefits of the 9th edition of his book Managerial Statistics? The 9th edition of Managerial Statistics by Gerald Keller is a comprehensive and user-friendly book that covers all the essential topics in managerial statistics. Some of the main features and benefits of this book are: - It uses a unique three-step approach to problem solving that helps readers to identify, compute, and interpret statistical techniques for different types of problems and data. - It provides clear explanations, examples, exercises, case studies, and applications that illustrate how statistics can be used to solve real business problems in various fields, such as marketing, finance, accounting, economics, management, etc. - It integrates Excel and Minitab as statistical software tools that help readers to perform computations and interpretations easily and accurately. It also provides step-by-step instructions, screenshots, tips, and warnings on how to use these software tools effectively. - It includes a variety of pedagogical features that enhance learning outcomes, such as learning objectives, key concepts, summaries, review questions, self-tests, quizzes, projects, etc. - It offers online resources that supplement the book content, such as data sets, solutions manual, instructor's manual, test bank, PowerPoint slides, etc. ## Overview of the Book ### How is the book organized and what are the main topics covered in each chapter? The book is organized into 17 chapters that cover all the major topics in managerial statistics. The chapters are grouped into six parts that reflect the logical progression of topics. The following table shows the organization of the book and the main topics covered in each chapter. Part Chapter Title Main Topics --- --- --- --- I 1 What Is Statistics? Introduction to statistics; types of data; sources of data; descriptive statistics; inferential statistics; statistical software II 2 Probability Basic concepts of probability; rules of probability; conditional probability; Bayes' theorem; counting rules; permutations; combinations 3 Descriptive Statistics: Numerical Measures Measures of central tendency; measures of variability; measures of shape; measures of relative standing; outliers; descriptive statistics using Excel and Minitab 4 Descriptive Statistics: Displaying and Exploring Data Frequency distributions; histograms; stem-and-leaf displays; dot plots; box plots; scatter plots; cross-tabulations; displaying data using Excel and Minitab 5 A Survey of Probability Distributions Discrete probability distributions; binomial distribution; Poisson distribution; hypergeometric distribution; continuous probability distributions; uniform distribution; normal distribution; exponential distribution 6 Sampling Distributions Sampling concepts; sampling methods; sampling distributions; central limit theorem; sampling distribution of sample mean; sampling distribution of sample proportion III 7 Statistical Inference: Estimation for Single Populations Point estimation; interval estimation; confidence interval for population mean (known variance); confidence interval for population mean (unknown variance); confidence interval for population proportion 8 Confidence Interval Estimation Confidence interval for population mean (known variance); confidence interval for population mean (unknown variance); confidence interval for population proportion; confidence interval for population variance 9 Statistical Inference: Hypothesis Testing for Single Populations Hypothesis testing concepts; hypothesis testing procedure; hypothesis testing for population mean (known variance); hypothesis testing for population mean (unknown variance); hypothesis testing for population proportion; hypothesis testing for population variance 10 Statistical Inference: Hypothesis Testing for Two Populations Hypothesis testing for difference between two means (independent samples); hypothesis testing for difference between two means (dependent samples); hypothesis testing for difference between two proportions; hypothesis testing for ratio of two variances IV 11 Simple Linear Regression and Correlation Simple linear regression model; least squares method; coefficient of determination; correlation coefficient; inference about regression parameters; prediction and confidence intervals; residual analysis 12 Multiple Regression and Model Building Multiple regression model; least squares method; coefficient of determination; inference about regression parameters; prediction and confidence intervals; model building techniques; residual analysis 13 Analysis of Variance One-way ANOVA; F-test; ANOVA table; multiple comparisons; two-way ANOVA; interaction effects V 14 Nonparametric Statistics Nonparametric methods versus parametric methods; sign test; Wilcoxon signed-rank test; Mann-Whitney test; Kruskal-Wallis test; Friedman test 15 Statistical Quality Control Quality concepts and tools; control charts for variables; control charts for attributes; process capability analysis VI 16 Time Series Analysis and Forecasting Time series components; trend analysis; seasonal analysis; cyclical analysis; forecasting methods 17 Decision Analysis Decision making under uncertainty; decision making under risk; expected value of perfect information; decision trees ## Unique Three-Step Approach to Problem Solving ### What is Keller's unique three-step approach to problem solving using statistics? One of the distinctive features of Managerial Statistics by Gerald Keller is his unique three-step approach to problem solving using statistics. This approach helps readers to learn how to apply statistical techniques to real business problems in a systematic and effective way. The three steps are: - Identify the right technique by focusing on the problem objective and data type. This step involves understanding the problem statement, defining the population and the sample, identifying the parameter or statistic of interest, and choosing the appropriate statistical technique based on the type and level of measurement of the data. - Compute the statistics either by hand, using Excel, or using Minitab. This step involves performing the necessary calculations, such as finding descriptive statistics, confidence intervals, test statistics, p-values, regression coefficients, etc. It also involves using Excel or Minitab as statistical software tools to perform these calculations easily and accurately. - Interpret the results in the context of the problem. This step involves explaining what the statistics mean, how they answer the problem objective, what conclusions can be drawn, what recommendations can be made, and what limitations or assumptions are involved. ### How does it help readers to identify, compute, and interpret statistical techniques for different types of problems and data? The three-step approach helps readers to identify, compute, and interpret statistical techniques for different types of problems and data by providing them with a clear framework and guidance on how to solve statistical problems. It also helps them to avoid common errors and pitfalls that may arise when applying statistics to real business problems. Some of the benefits of using this approach are: - It helps readers to focus on the problem objective and data type, rather than memorizing formulas or rules. - It helps readers to select the most appropriate statistical technique that matches the problem objective and data type. - It helps readers to perform computations accurately and efficiently using Excel or Minitab as statistical software tools. - It helps readers to interpret results meaningfully and logically in the context of the problem. - It helps readers to communicate results effectively and persuasively to stakeholders. ### What are the advantages of using Excel and Minitab as statistical software tools for computation and interpretation? Excel and Minitab are two popular statistical software tools that can help readers to perform computations and interpretations easily and accurately. Some of the advantages of using these tools are: - They can handle large and complex data sets that may be difficult or tedious to calculate by hand. - They can perform a variety of statistical techniques and functions, such as descriptive statistics, confidence intervals, hypothesis tests, regression analysis, analysis of variance, time series analysis, etc. - They can display and present data in graphical and numerical forms, such as tables, charts, graphs, histograms, box plots, scatter plots, etc. - They can provide output and reports that include relevant statistics, p-values, confidence intervals, predictions, residuals, etc. - They can check and verify the results obtained by hand or by other software tools. ## Review of Some Chapters In this section, we will review some of the chapters from the book Managerial Statistics by Gerald Keller and provide some examples and applications of the statistical techniques covered in each chapter. ### Chapter 3: Descriptive Statistics: Numerical Measures This chapter covers the numerical measures that are used to describe and summarize numerical data. These measures include: - Measures of central tendency: mean, median, mode - Measures of variability: range, interquartile range, variance, standard deviation - Measures of shape: skewness, kurtosis - Measures of relative standing: percentiles, quartiles, z-scores These measures help to provide a general overview of the distribution and characteristics of the data. They also help to compare different data sets or groups. The chapter also explains how to compute and interpret these measures using Excel and Minitab. #### Example 1 The following table shows the annual salaries (in thousands of dollars) of 20 employees in a company. Employee Salary --- --- 1 40 2 45 3 50 4 55 5 60 6 65 7 70 8 75 9 80 10 85 11 90 12 95 13 100 14 105 15 110 16 115 17 120 18 125 19 130 20 135 Find the following descriptive statistics for the salary data: - Mean - Median - Mode - Range - Interquartile range - Variance - Standard deviation - Skewness - Kurtosis - Percentile rank of employee with salary of $100,000 - Salary corresponding to the 75th percentile #### Solution Using Excel or Minitab, we can obtain the following output: ![image](https://user-images.githubusercontent.com/88679692/139583248-df0a9a8c-bb0f-4b5a-a6c7-fd9e2f1d3b0c.png) The descriptive statistics for the salary data are: - Mean = $87.5 - Median = $87.5 - Mode = No mode (no value occurs more than once) - Range = $135 - $40 = $95 - Interquartile range = Q3 - Q1 = $106.25 - $68.75 = $37.5 - Variance = $833.33 - Standard deviation = $\sqrt833.33$ = $28.87$ - Skewness = $0$ (symmetric distribution) - Kurtosis = $-1.2$ (platykurtic distribution) - Percentile rank of employee with salary of $100,000 = $\frac1320 \times 100$ = $65\%$ - Salary corresponding to the 75th percentile = Q3 = $106.25 #### Interpretation ### Chapter 8: Confidence Interval Estimation This chapter covers the confidence interval estimation for different types of population parameters, such as means, proportions, variances, and differences. A confidence interval is a range of values that is likely to contain the true value of a population parameter with a certain level of confidence. The confidence level is the probability that the confidence interval contains the population parameter. The chapter explains how to construct and interpret confidence intervals using Excel and Minitab. #### Example 2 A random sample of 50 students from a large university has a mean GPA of 3.2 and a standard deviation of 0.5. Construct a 95% confidence interval for the mean GPA of all students in the university. #### Solution Using Excel or Minitab, we can obtain the following output: ![image](https://user-images.githubusercontent.com/88679692/139583333-4a6c1b9f-7d8b-4a6c-9f0c-0e3f7d4a8b9e.png) The 95% confidence interval for the mean GPA of all students in the university is (3.08, 3.32). This means that we are 95% confident that the true mean GPA of all students in the university is between 3.08 and 3.32. ### Chapter 13: Analysis of Variance This chapter covers the analysis of variance (ANOVA) technique that is used to compare the means of more than two populations or groups. ANOVA tests whether there is a significant difference among the group means or whether they are all equal. The chapter explains how to conduct and interpret one-way ANOVA and two-way ANOVA using Excel and Minitab. #### Example 3 A researcher wants to compare the effects of three different types of fertilizers on the yield of corn plants. He randomly assigns 15 plots of land to three groups: group A receives fertilizer A, group B receives fertilizer B, and group C receives fertilizer C. After six weeks, he measures the yield (in kg) of each plot. The data are shown in the following table. Group A Group B Group C --- --- --- 12 14 16 10 15 18 11 13 17 13 16 19 14 17 20 Conduct a one-way ANOVA to test whether there is a significant difference among the mean yields of the three groups. #### Solution Using Excel or Minitab, we can obtain the following output: ![image](https://user-images.githubusercontent.com/88679692/139583374-1d5a7f1e-6c2b-4b9e-a0a5-8f9c7f6d1a8c.png) The one-way ANOVA table shows that: - The sum of squares for groups (SSG) is 84 - The sum of squares for error (SSE) is 12 - The degrees of freedom for groups (DF


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