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Statistics
Provides an introductory text to statistics, emphasizing inference, with extensive coverage of data collection and analysis as needed to evaluate the reported results of statistical studies and act accordingly. 3 1/2 inch disk included. Statistics.
- GenresTextbooksMathematics
Hardcover
First published January 28, 1988
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Displaying 1 - 5 of 5 reviews
June 19, 2016
nice
This entire review has been hidden because of spoilers.
May 8, 2020
Time homicide, inexplicable rules, and excessive peanut butter consumption. Is it possible to regress to challenges from our early twenties? Anyone remotely close to me during the last few months would respond with an emphatic yes.
This textbook was purchased to accompany an applied statistics course. McClave and Sincich provide an almost perfect textbook during the descriptive and probability chapters. Their explanations are accessible, concise, and clear.
The problem sets are pragmatic and thought provoking because these academics explore the varied academic opinions regarding key specific concepts. I fear that this review may be too esoteric. Let me share a concrete example.
May mathematicians disagree about the threshold for a small sample size especially when it comes to the t versus z score debate. I love that an upwards increasing sample size makes the two indistinguishable. It's as beautiful as the color gradient/color theories explored in my childhood painting classes.
My undergraduate professors used to disparage their peers for holding an opposing stance. It's not hyperbolic to say that statisticians fight about these rules with the same passionate judgment that my female alumnae group directs towards Bernie Sanders voters. I find them both unpleasant.
McClave and Sincich realize the pettiness of including this kind of dialogue in their 13th edition. Of course, the problem sets have to make a determination regarding alpha, small sample size threshold, etc. Their explanations however demonstrate why other academics may take a different stance. This is invaluable to the layperson.
It's fair to say that their commendable, comprehensive approach is compromised when you get to the statistical modeling chapters. Where are all of the clear explanations? There are concepts and exceptions that they don't even mention. I had to resort to YouTube often.
Our diligent, amazing professor had to augment this textbook with an extra lecture on model adequacy. Understanding how the residual versus predicted plots show non-constant variance and non-linear data trends was a huge omission.
Although the later chapters need added detail, Statistics is still by far the best statistics textbook that I have encountered in an academic setting. This will be retained as a reference guide. If you are confused in statistics, I'd highly recommend that you purchase this book.
This last paragraph is an addendum for my friends. I'm sorry that my inexplicably rigid social rules prevented me from accompanying you on snowshoe adventures, Blue Hill trips, Cape Cod weekends and theater performances. We can do these things after the pandemic.
This textbook was purchased to accompany an applied statistics course. McClave and Sincich provide an almost perfect textbook during the descriptive and probability chapters. Their explanations are accessible, concise, and clear.
The problem sets are pragmatic and thought provoking because these academics explore the varied academic opinions regarding key specific concepts. I fear that this review may be too esoteric. Let me share a concrete example.
May mathematicians disagree about the threshold for a small sample size especially when it comes to the t versus z score debate. I love that an upwards increasing sample size makes the two indistinguishable. It's as beautiful as the color gradient/color theories explored in my childhood painting classes.
My undergraduate professors used to disparage their peers for holding an opposing stance. It's not hyperbolic to say that statisticians fight about these rules with the same passionate judgment that my female alumnae group directs towards Bernie Sanders voters. I find them both unpleasant.
McClave and Sincich realize the pettiness of including this kind of dialogue in their 13th edition. Of course, the problem sets have to make a determination regarding alpha, small sample size threshold, etc. Their explanations however demonstrate why other academics may take a different stance. This is invaluable to the layperson.
It's fair to say that their commendable, comprehensive approach is compromised when you get to the statistical modeling chapters. Where are all of the clear explanations? There are concepts and exceptions that they don't even mention. I had to resort to YouTube often.
Our diligent, amazing professor had to augment this textbook with an extra lecture on model adequacy. Understanding how the residual versus predicted plots show non-constant variance and non-linear data trends was a huge omission.
Although the later chapters need added detail, Statistics is still by far the best statistics textbook that I have encountered in an academic setting. This will be retained as a reference guide. If you are confused in statistics, I'd highly recommend that you purchase this book.
This last paragraph is an addendum for my friends. I'm sorry that my inexplicably rigid social rules prevented me from accompanying you on snowshoe adventures, Blue Hill trips, Cape Cod weekends and theater performances. We can do these things after the pandemic.
May 17, 2010
I lost access to the e-book after shelling out $75. Big snafu with the prof and the publisher with coursecompass. I rented a print text from Chegg, but the copy was nasty. So all around I haven't been happy with the book, mostly due to TRYING to use it. I liked the MTH 241 text (Elementary Statistics)better (e.g.,formatting of tables, the callout boxes, the TI-84 instructions) so I read those chapters for the last half of this 6-wk course too.
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November 8, 2013relate to my course
Displaying 1 - 5 of 5 reviews