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Feedback on Resources: is OpenIntro Stats textbook good preparation for my class? (info in post)

newent718
Mar 13
ReplyFlag

This is the intro stats course for my PhD curriculum. My goal is to be as prepared as possible, as I have not taken a stats course in over 10 years.

Thanks everyone!

Required readings:

Wooldridge, Jeffrey M. 2012. Introductory Econometrics: A Modern Approach

Topics in course:

Introduction, data & measurement / Frequency Distributions, Intro to Stata / Graphing Techniques (+ w/Stata) / Indices / Central tendency statistics / Dispersion statistics / Contingency Tables / Association and causality / Bivariate correlation and regression / Intro to multivariate regression / Probability and the normal distribution / Sampling distributions and Confidence Intervals / Intro to Hypothesis Testing; Differences in means and proportions / Inference for contingency tables / ANOVA / Inference for regression analysis /

==

Optional reading:

Hamilton, Lawrence. 2009. Statistics with Stata 10. [more detail on the use of Stata; most applies to version 11 as well]

Hagle, T.M. 1995. Basic Math for Social Scientists: Concepts. [good math brush-up if you are feeling behind]

Hagle, T.M. 1996. Basic Math for Social Scientists: Problems and Solutions.

Rudas. T. 2004. Probability Theory: A Primer.

==

Course Overview:

This course introduces basic statistical techniques used in the social sciences. The course is divided into three sections. In the first few weeks, we will review statistical and graphical techniques used to describe the distribution of variables. In the second segment of the course, we will delve into methods used to characterize the relationships between variables, as well as how to elaborate on these relationships. The final section of the course will be dedicated to inferential techniques—those that rely on sample data to draw conclusions about a broader population. Throughout the course, we will continually emphasize the link between theoretical/conceptual arguments and the data analysis.

==

Course Objectives:

introduce the basic concepts, terminology and procedures of data analysis

build statistical literacy and the foster the ability to select, apply, and interpret appropriate statistical tools

introduce methods to calculate and interpret basic descriptive and inferential statistics

learn Stata, a statistical software package used to access, manage, and analyze data

develop analytic strategies that rely on statistical methods to test theoretical arguments

Thanks everyone!

Required readings:

Wooldridge, Jeffrey M. 2012. Introductory Econometrics: A Modern Approach

Topics in course:

Introduction, data & measurement / Frequency Distributions, Intro to Stata / Graphing Techniques (+ w/Stata) / Indices / Central tendency statistics / Dispersion statistics / Contingency Tables / Association and causality / Bivariate correlation and regression / Intro to multivariate regression / Probability and the normal distribution / Sampling distributions and Confidence Intervals / Intro to Hypothesis Testing; Differences in means and proportions / Inference for contingency tables / ANOVA / Inference for regression analysis /

==

Optional reading:

Hamilton, Lawrence. 2009. Statistics with Stata 10. [more detail on the use of Stata; most applies to version 11 as well]

Hagle, T.M. 1995. Basic Math for Social Scientists: Concepts. [good math brush-up if you are feeling behind]

Hagle, T.M. 1996. Basic Math for Social Scientists: Problems and Solutions.

Rudas. T. 2004. Probability Theory: A Primer.

==

Course Overview:

This course introduces basic statistical techniques used in the social sciences. The course is divided into three sections. In the first few weeks, we will review statistical and graphical techniques used to describe the distribution of variables. In the second segment of the course, we will delve into methods used to characterize the relationships between variables, as well as how to elaborate on these relationships. The final section of the course will be dedicated to inferential techniques—those that rely on sample data to draw conclusions about a broader population. Throughout the course, we will continually emphasize the link between theoretical/conceptual arguments and the data analysis.

==

Course Objectives:

introduce the basic concepts, terminology and procedures of data analysis

build statistical literacy and the foster the ability to select, apply, and interpret appropriate statistical tools

introduce methods to calculate and interpret basic descriptive and inferential statistics

learn Stata, a statistical software package used to access, manage, and analyze data

develop analytic strategies that rely on statistical methods to test theoretical arguments

David
Mar 15
ReplyFlag

It sounds like it is the same material. OpenIntro Statistics (OS) doesn't include usage of Stata, but the statistical concepts all seem the same.

Even if OS is a bit easier than this upcoming course, that probably wouldn't be a bad thing. I think it's common for PhD courses to assume too much knowledge and get too abstract too quickly, and that can make the actual application of the methods seem more difficult. So even if the course you are preparing for is more abstract in nature, I'd expect OS' focus on more concrete applications and details would make understanding and learning the more abstract components easier.

Best,

David

Even if OS is a bit easier than this upcoming course, that probably wouldn't be a bad thing. I think it's common for PhD courses to assume too much knowledge and get too abstract too quickly, and that can make the actual application of the methods seem more difficult. So even if the course you are preparing for is more abstract in nature, I'd expect OS' focus on more concrete applications and details would make understanding and learning the more abstract components easier.

Best,

David

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