I am going to teach a Statistics course next year and I should cover the basics of probabilities and statistics to undergrad students in business. They don't have any background in probability, so, at first, I should start with its basics, then cover some topics like descriptive statistics, confidence intervals, hypothesis testing, and regression.
I would be thankful if you can recommend some books which provide many real-world and tangible examples and provide intuition behind each topic. Although I personally like a course with rigorous math, I should discuss the motivation and intuition behind them.
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$\begingroup$ Could you give us some context on what those students are going to use statistics for? What concepts you want to teach them? $\endgroup$– Tim ♦Jul 22, 2021 at 21:59
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$\begingroup$ Descriptive statistics, histogram, box plot, and other plots. Probability: Random variable, discrete R.V. Bernoulli, Binomial, Poisson, Continuous Random Variable: Normal, Uniform, Exponential, Conditional probabilities, Bayes rule, The law of total probability, Central limit theorem (to use for confidence interval), Correlation and covariance, Confidence interval for the mean, the proportion for a single or two populations (normal and t-student), paired t-test, Hypothesis testing for all topics discussed for confidence interval. Single and multiple regression. $\endgroup$– AminJul 22, 2021 at 22:23
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1$\begingroup$ Getting into regression opens up a big can of worms, and I say that as someone who likes the topic. $\endgroup$– DaveJul 22, 2021 at 22:26
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1$\begingroup$ I am not sure if time allows me or not. But, I think A/B testing can be beneficial. I prefer to discuss topics related to Business topics like sales, price prediction, marketing, economy. Also, some healthcare problems are interesting to be included but not technical issues. For example, the time that a customer spends in a hospital, expenses in hospital, demand for a medicine, etc. $\endgroup$– AminJul 22, 2021 at 22:26
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$\begingroup$ I am so willing to show a phenomenon in the real world and then define it officially. For example, I think it is not tangible for them to define Normal distribution without any observation in real cases for them. $\endgroup$– AminJul 22, 2021 at 22:31
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4 Answers
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I would go for
Statistics, 4th edition from Freedman et al. https://www.amazon.com/-/de/dp/8130915871/ref=sr_1_1?__mk_de_DE=%C3%85M%C3%85%C5%BD%C3%95%C3%91&dchild=1&keywords=statistics+david+freedman&qid=1629053359&sr=8-1
Why? Because they start with: What went wrong in the world before statistics were invented. And they show how a simple example of not working with control/treatment group and double blind random selection of study participants can mess all up, and what the consequences were.
And all the previous references calling upon Bayesian...no one starts with that when you start with statistics basically, well we all probably use some of it, but don't name it. I made you a few scans from my book:
They start the preface or first chapter with (I hope you can read something, my scanner sucks and the book is over 500 pages):
And then they make it really simple with mini chapters e.g. on the average :
and slowly increase the pace with a lot of stuff, and there is also hypotheses testing as you wanted it:
You have to keep in mind that this book shows everything practical, it weighs not heavily on math, but YOU WILL FIND a lot about the motivation behind statistics; I can guarantee that. There are even a lot of questions for students/graduates etc. in this book.
I would go for it.
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Business Statistics: For Contemporary Decision Making by Ken Black was one of my course book.
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Not about business, even about Bayesian, but this book provides funny examples on essential concepts.
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Multiple choices here, since I assume the course's topics will be quite different.
One of the best books, especially if you think you'll need to teach some Bayesian theory, is this:Doing Bayesian Data Analysis. Even if is "Bayesian-oriented", it aims to teach to a wide range of students; thus it starts from basics probability concepts, then go through inference and regression and provide a wide range of examples and the main differences between Frequentist an Bayesian approach. Give it a look.
Other relly helpful books (but probably too advanced) are: Multilevel-Hierarchical models Regressions
They both start from simple probability concepts and go on with regressions and inference. Also, they both cover the Frequentist and Bayesian approach and give different examples on social sciences (mostly political elections-referred).
Hope I've been helpful somehow.
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1$\begingroup$ Neither of these sounds like a first book with any orientation to business. $\endgroup$– Nick CoxAug 15, 2021 at 20:30