Applications of statistical techniques and methods will be explored, including fundamental statistical tests for central values, variances, and categorical variables, as well as regression analysis and the general linear model. The emphasis will be on selecting and applying the appropriate statistical techniques as well as the interpretation and reporting of results with the use of a major statistical software. The course is also designed to provide numerous opportunities to critique statistical techniques commonly used in empirical research articles.
Hello, doctoral learners! Welcome to MATH 810 – Applied Statistics.
Statistics is the science of designing studies or experiments, collecting data, and modeling/analyzing data for the purpose of decision-making and scientific discovery when the available information is both limited and variable. That is, statistics is the science of learning from data. Almost everyone, including social scientists, medical researchers, superintendents of public schools, corporate executives, market researchers, engineers, government employees, and consumers, deals with data. These data could be in the form of quarterly sales figures, percent increase in juvenile crime, contamination levels in water samples, survival rates for patients undergoing medical therapy, census figures, or information that helps determine which brand of car to purchase. In this course, we approach the study of statistics by considering the four-step process in learning from data: (1) defining the problem, (2) collecting the data, (3) summarizing the data, and (4) analyzing the data, interpreting the analyses, and communicating the results.
You might be asking yourself, “why do I need to study statistics at this level?” Well, as educated professionals with a doctoral degree, you need to know how to evaluate published numerical facts. You can agree that every person can be exposed to manufacturers’ claims for products, to the results of sociological, consumer, and political polls, or to the published results of scientific research. Many of these results are inferences based on sampling. Some inferences are valid; others are invalid. Some are based on samples of adequate size; others are not. Yet all these published results bear the ring of truth. It is thus crucial that you become an informed and critical reader of data-based reports and articles.
Another reason for studying statistics is that your profession or employment may require you to interpret the results of sampling (surveys or experimentation) or to employ statistical methods of analysis to make inferences in your work. For example, practicing physicians receive large amounts of advertising describing the benefits of new drugs. These advertisements frequently display the numerical results of experiments that compare a new drug with an older one. Do such data really imply that the new drug is more effective, or is the observed difference in results due simply to random variation in the experimental measurements? This is an important question, and in order to be able to answer it, one needs to be educated about the statistical methods that have been used. Therefore, statistics plays an important role in almost all areas of science, business, education, and industry; persons employed in these areas need to know its basic concepts, strengths, and limitations.
Since this course is a continuation of MATH 807 – Introduction to Statistics with SAS, topics regarding descriptive statistics and probability distributions will not be covered here. This course will focus on inferential methods for one, two, or more population parameters, categorical data, comparison methods, linear regression and multiple regression methods. Weekly assignments with exercise problems will give you opportunities to practice the procedures. Three mini-projects will help you apply what you learn to real-world case studies. In the final presentation, you will present one of the mini-projects to your classmates as if you were presenting to a doctoral committee or to an executive business team. You will not only practice the most used statistical methods with the help of a software program called SAS but you will also communicate your results to your classmates in written and oral forms.
What you learn in this course will not only be crucial in your future research but also in your professional work. I hope that you feel challenged, enjoy the course, and achieve course goals by the end of the term.
1. Determine adequate statistical analysis techniques for a research problem or a project.
2. Conduct fundamental statistical tests to answer research questions.
3. Apply regression analysis to make decisions.
4. Apply a major statistical software program to conduct appropriate statistical analysis.
5. Interpret the statistical analysis results.
6. Critique statistical analysis techniques commonly reported in empirical research articles.
Course Description
Applications of statistical techniques and methods will be explored, including
fundamental statistical tests for central values, variances, and categorical variables,
as well as regression analysis and the general linear model. The emphasis will be on
sele
cting and applying the appropriate statistical techniques as well as the
interpretation and reporting of results with the use of a major statistical software.
The course is also designed to provide numerous opportunities to critique statistical
techniques
commonly used in empirical research articles.
Course Introduction
Hello, doctoral learners! Welcome to MATH 810
–
Applied Statistics.
Statistics is the science of designing studies or experiments, collecting data, and
modeling/analyzing data for the purpos
e of decision
-
making and scientific discovery
when the available information is both limited and variable. That is, statistics is the
science of
learning from data
. Almost everyone, including social scientists, medical
researchers, superintendents of publi
c schools, corporate executives, market
researchers, engineers, government employees, and consumers, deals with data.
These data could be in the form of quarterly sales figures, percent increase in
juvenile crime, contamination levels in water samples, sur
vival rates for patients
undergoing medical therapy, census figures, or information that helps determine
which brand of car to purchase. In this course, we approach the study of statistics
by considering the four
-
step process in learning from data: (1) def
ining the problem,
(2) collecting the data, (3) summarizing the data, and (4) analyzing the data,
interpreting the analyses, and communicating the results.
You might be asking yourself, “why do I need to study statistics at this level?” Well,
as educated p
rofessionals with a doctoral degree, you need to know how to evaluate
published numerical facts. You can agree that every person can be exposed to
manufacturers’ claims for products, to the results of sociological, consumer, and
political polls, or to the
published results of scientific research. Many of these results
are inferences based on sampling. Some inferences are valid; others are invalid.
Some are based on samples of adequate size; others are not. Yet all these published
results bear the ring of tr
uth. It is thus crucial that you become an informed and
critical reader of data
-
based reports and articles.
Another reason for studying statistics is that your profession or employment may
require you to interpret the results of sampling (surveys or experi
mentation) or to
employ statistical methods of analysis to make inferences in your work. For
example, practicing physicians receive large amounts of advertising describing the
benefits of new drugs. These advertisements frequently display the numerical res
ults
of experiments that compare a new drug with an older one. Do such data really
imply that the new drug is more effective, or is the observed difference in results
due simply to random variation in the experimental measurements? This is an
important que
stion, and in order to be able to answer it, one needs to be educated
about the statistical methods that have been used. Therefore, statistics plays an
important role in almost all areas of science, business, education, and industry;
persons employed in th
ese areas need to know its basic concepts, strengths, and
limitations.
Since this course is a continuation of MATH 807
–
Introduction to Statistics with
SAS, topics regarding descriptive statistics and probability distributions will not be
Course Description
Applications of statistical techniques and methods will be explored, including
fundamental statistical tests for central values, variances, and categorical variables,
as well as regression analysis and the general linear model. The emphasis will be on
selecting and applying the appropriate statistical techniques as well as the
interpretation and reporting of results with the use of a major statistical software.
The course is also designed to provide numerous opportunities to critique statistical
techniques commonly used in empirical research articles.
Course Introduction
Hello, doctoral learners! Welcome to MATH 810 – Applied Statistics.
Statistics is the science of designing studies or experiments, collecting data, and
modeling/analyzing data for the purpose of decision-making and scientific discovery
when the available information is both limited and variable. That is, statistics is the
science of learning from data. Almost everyone, including social scientists, medical
researchers, superintendents of public schools, corporate executives, market
researchers, engineers, government employees, and consumers, deals with data.
These data could be in the form of quarterly sales figures, percent increase in
juvenile crime, contamination levels in water samples, survival rates for patients
undergoing medical therapy, census figures, or information that helps determine
which brand of car to purchase. In this course, we approach the study of statistics
by considering the four-step process in learning from data: (1) defining the problem,
(2) collecting the data, (3) summarizing the data, and (4) analyzing the data,
interpreting the analyses, and communicating the results.
You might be asking yourself, “why do I need to study statistics at this level?” Well,
as educated professionals with a doctoral degree, you need to know how to evaluate
published numerical facts. You can agree that every person can be exposed to
manufacturers’ claims for products, to the results of sociological, consumer, and
political polls, or to the published results of scientific research. Many of these results
are inferences based on sampling. Some inferences are valid; others are invalid.
Some are based on samples of adequate size; others are not. Yet all these published
results bear the ring of truth. It is thus crucial that you become an informed and
critical reader of data-based reports and articles.
Another reason for studying statistics is that your profession or employment may
require you to interpret the results of sampling (surveys or experimentation) or to
employ statistical methods of analysis to make inferences in your work. For
example, practicing physicians receive large amounts of advertising describing the
benefits of new drugs. These advertisements frequently display the numerical results
of experiments that compare a new drug with an older one. Do such data really
imply that the new drug is more effective, or is the observed difference in results
due simply to random variation in the experimental measurements? This is an
important question, and in order to be able to answer it, one needs to be educated
about the statistical methods that have been used. Therefore, statistics plays an
important role in almost all areas of science, business, education, and industry;
persons employed in these areas need to know its basic concepts, strengths, and
limitations.
Since this course is a continuation of MATH 807 – Introduction to Statistics with
SAS, topics regarding descriptive statistics and probability distributions will not be
Barras
CourseDescription.docx
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