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230201 ||| eng |
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|a 9783031118333
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| 100 |
1 |
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|a Heumann, Christian
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| 245 |
0 |
0 |
|a Introduction to Statistics and Data Analysis
|h Elektronische Ressource
|b With Exercises, Solutions and Applications in R
|c by Christian Heumann, Michael Schomaker, Shalabh
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| 250 |
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|a 2nd ed. 2022
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2022, 2022
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| 300 |
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|a XVII, 584 p. 118 illus., 6 illus. in color
|b online resource
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| 505 |
0 |
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|a Part I Descriptive Statistics: Introduction and Framework -- Frequency Measures and Graphical Representation of Data -- Measures of Central Tendency and Dispersion -- Association of Two Variables -- Part I Probability Calculus: Combinatorics -- Elements of Probability Theory -- Random Variables -- Probability Distributions -- Part III Inductive Statistics: Inference -- Hypothesis Testing -- Linear Regression -- Logistic Regression -- Part IV Additional Topics Simple Random Sampling and Bootstrapping -- Causality -- Part V Appendices: Introduction to R -- Solutions to Exercises -- Technical Appendix -- Visual Summaries
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| 653 |
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|a Data Analysis and Big Data
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| 653 |
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|a Statistical Theory and Methods
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| 653 |
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|a Statistics
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| 653 |
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|a Quantitative research
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| 653 |
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|a Applied Statistics
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| 653 |
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|a Statistical Software
|
| 653 |
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|a Statistics—Computer programs
|
| 700 |
1 |
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|a Schomaker, Michael
|e [author]
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| 700 |
1 |
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|a Shalabh
|e [author]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
|
| 028 |
5 |
0 |
|a 10.1007/978-3-031-11833-3
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-031-11833-3?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 519.5
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| 520 |
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|a Now in its second edition, this introductory statistics textbook conveys the essential concepts and tools needed to develop and nurture statistical thinking. It presents descriptive, inductive and explorative statistical methods and guides the reader through the process of quantitative data analysis. This revised and extended edition features new chapters on logistic regression, simple random sampling, including bootstrapping, and causal inference. The text is primarily intended for undergraduate students in disciplines such as business administration, the social sciences, medicine, politics, and macroeconomics. It features a wealth of examples, exercises and solutions with computer code in the statistical programming language R, as well as supplementary material that will enable the reader to quickly adapt the methods to their own applications
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