Principles of Managerial Statistics and Data ScienceIntroduces readers to the principles of managerial statistics and data science, with an emphasis on statistical literacy of business students Through a statistical perspective, this book introduces readers to the topic of data science, including Big Data, data analytics, and data wrangling. Chapters include multiple examples showing the application of the theoretical aspects presented. It features practice problems designed to ensure that readers understand the concepts and can apply them using real data. Over 100 open data sets used for examples and problems come from regions throughout the world, allowing the instructor to adapt the application to local data with which students can identify. Applications with these data sets include:
Topics covered in Principles of Managerial Statistics and Data Science include:data visualization; descriptive measures; probability; probability distributions; mathematical expectation; confidence intervals; and hypothesis testing. Analysis of variance; simple linear regression; and multiple linear regression are also included. In addition, the book offers contingency tables, Chi-square tests, non-parametric methods, and time series methods. The textbook:
Principles of Managerial Statistics and Data Science is a textbook for undergraduate and graduate students taking managerial Statistics courses, and a reference book for working business professionals. |
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Contents
10 | 8 |
Concepts in Statistics | 15 |
Contents | 36 |
Guidelines for Assessment and Instruction in Statistics Education | 54 |
4 | 86 |
Descriptive Statistics | 97 |
Practice Problems | 108 |
7 | 132 |
Practice Problems | 357 |
Suicide Rates Among Asian Men | 364 |
1 | 366 |
Practice Problems | 370 |
References | 378 |
4 | 387 |
Practice Problems | 390 |
3 | 408 |
Introduction to Probability | 141 |
15 | 146 |
1 | 164 |
Discrete Random Variables | 177 |
Continuous Random Variables | 209 |
5 | 227 |
Properties of Sample Statistics | 243 |
1 | 252 |
2 | 262 |
Interval Estimation for One Population Parameter | 269 |
Hypothesis Testing for One Population | 297 |
16 | 319 |
Statistical Inference to Compare Parameters from | 349 |
Simple Linear Regression | 421 |
Multiple Linear Regression | 473 |
Inference on Association of Categorical Variables | 519 |
Contents | 523 |
Affordability and Business Environment | 525 |
Chapter Problems | 532 |
Two Independent Samples | 539 |
Practice Problems | 546 |
Time Series Components | 552 |
Simple Forecasting Models | 558 |
Practice Problems | 569 |
643 | |