For algebra-based Introductory Statistics Courses. The overarching goal of this text is to empower students to be statistical thinkers Alan Agresti and Chris Franklin have merged their research expertise, as well as their extensive real-world and teaching experience, to develop a new introductory statistics text that makes students statistically literate, while encouraging them to ask and answer interesting statistical questions. The authors have successfully crafted a text that takes the ideas that have turned statistics into a central science in modern life and makes them accessible and engaging to students without compromising necessary rigor. The varied and data-rich examples and exercises place heavy emphasis on thinking about and understanding statistical concepts. The applications are topical and current and successfully illustrate the relevance of statistics. The authors understand that most of the real-world data that students encounter outside the class room are categorical data. Unlike many texts at this level, Agresti/Franklin incorporates categorical data where appropriate and draws distinctions between theory and practical application of statistical ideas and methods. The text was written, from the ground up, to embrace and support the 6 recommendations of the ASA endorsed GAISE (Guidelines for Assessment for Instruction in Statistical Education) Report - http://www.amstat.org/education/gaise... *Emphasize statistical literacy and develop statistical thinking.*Use real data.*Stress conceptual understanding rather than mere knowledge of procedures.*Foster active learning in the classroom.*Use technology for developing concepts and analyzing data.*Use assessment to evaluate and improve student learning. Comes with a CD containing data sets, additional activities, and applets. The CD with the IE, furthermore, includes Instructor-to-Instructor videos which further detail, by chapter, the authors approach and provides suggestions on how to present concepts. These Instructor-to-Instructor videos are very complimentary to the IE chapter introductions.
آلن اگرستی تجربه 38 سال معلمی خودش رو تو این کتاب نشون داده و چه نشون دادنی. بهترین کتاب تدریس آمار و مفاهیم پایه در قرن بیست و یک. کتاب سایت محشری هم واسه درک بهتر و عملی مفاهیم آماری داره.
This was the required textbook for STAT 200 at Penn State. It was a good companion to the online lessons and I turned to it frequently for examples and alternate or better explanations of the concepts that were introduced in the instructor's online lessons. I felt it explained things clearly and in a way that helped see how all of the different tests fit together. I liked that it used examples extensively to illustrate problems and help you think them through. I'm going to keep this book for future reference and refreshing knowledge.
I actually used the looseleaf version, which I recommend for convenience. It's a heavy book and the looseleaf lets you pull out just the material that you need to read instead of having to carry the whole book.
This textbook provides a comprehensive introduction to statistics, focusing on concepts and applications rather than complex formulas. It emphasizes the practical side of statistics while maintaining a balance with theory, making it suitable for students across various disciplines. It is definitely, so to say, a school book which to me wasn't the best fit.
The main topics Descriptive Statistics: Organizing and summarizing data using measures like mean, median, standard deviation, and graphical tools such as histograms and boxplots. Probability: Basics of probability, probability distributions (like the normal and binomial), and how these underpin statistical inference. Inferential Statistics: Estimation, confidence intervals, and hypothesis testing to draw conclusions about populations from samples. Regression Analysis: Simple and multiple regression techniques for modeling relationships between variables. However, more advanced topics are not explored.
Statistics is About Learning from Data: Statistics provides tools to understand patterns, summarize data, and draw meaningful conclusions. It's not just about numbers but interpreting what the data reveals about real-world situations. Variability is Key: Understanding and managing variability in data is central to statistics. Measures like standard deviation and variance help quantify how much data points differ, enabling better insights. Descriptive Statistics Summarize Data: Techniques like mean, median, mode, and graphical tools (e.g., histograms and boxplots) provide a snapshot of the data, making complex datasets easier to interpret. Probability Links Data to Inference: Probability is the foundation for making predictions and drawing conclusions about populations from sample data. It bridges descriptive and inferential statistics. Statistical Inference is Powerful: Through confidence intervals and hypothesis testing, statisticians can make predictions, estimate population parameters, and assess relationships between variables with a degree of certainty.
Fantastic read that starts with a very basic but comprehensive introduction to statistics and then gradually progresses in complexity. I wish this was the first book I picked up on the topic of statistics. I will definitely recommend it for any professional analyst because it improves your understanding of data and how you gather meaningful insights from your data.
It's certainly better than the statistics texts that were available when I did my undergrad. The writing is straightforward, the examples are clear, and there are a lot of practice questions.