MATH 110 FINAL
EXAM STUDY REVIEW
EXAM QUESTIONS
AND CORRECT
ANSWERS LATEST
GUIDE .
, MATH 110 Introduction to Statistics 3 credits
Prerequisites: Algebra proficiency required (high-school algebra 2 or a college equivalent)
Instructor: Matthew Dodd, MS
H. Elaine Frey, MA Laurie
Key, MS
Nick Lagios, MS, MBA
Contact Information: Faculty may be contacted through the Portage messaging system
Additional Information: www.portagelearning.com*
Course meeting times: MATH 110 is offered continuously
Course Description: A general introduction to mathematical statistics as a tool used in the decision-making process. The
course is designed to help students develop an understanding of summarized data in both descriptive and inferential
statistical applications through the use of frequency distributions, measures of central tendency, measures of dispersion,
probability distributions, random sampling, interval estimation, hypothesis testing, comparisons involving means, and
regression analysis.
Course Outcomes: As a result of this course experience a student should be able to explain:
The difference between qualitative and quantitative data, be able to organize the data and present a meaningful
overview of the data through the use of frequency distributions, measures of central tendency (i.e. the mean,
median and mode) and measures of dispersion (i.e. the variance, standard deviation and coefficient of
variation)
The rules involved in developing outcome probabilities and how to apply the appropriate counting methods
in the development of the probabilities of outcomes in an experiment.
The difference between a discrete probability distribution and a continuous probability distribution.
The concepts involving random sampling, the sampling distributions of x-bar (𝑥) and p-bar (𝑝) and other
methods.
The null & alternative hypothesis in classical hypothesis testing along with type I and II errors; one- tailed &
two-tailed testing involving populations and both large & small samples.
Linear regression analysis and lines of best fit.
*Please see the Module Topics section below for expanded course outcomes.
* Portage Learning college courses are offered by Geneva College, which is regionally accredited by the Middle States Commission on Higher
Education. Portage Learning is included in the College’s Department of Professional and Online Graduate Studies; courses are delivered through the
PortageLearning.com platform.
EXAM STUDY REVIEW
EXAM QUESTIONS
AND CORRECT
ANSWERS LATEST
GUIDE .
, MATH 110 Introduction to Statistics 3 credits
Prerequisites: Algebra proficiency required (high-school algebra 2 or a college equivalent)
Instructor: Matthew Dodd, MS
H. Elaine Frey, MA Laurie
Key, MS
Nick Lagios, MS, MBA
Contact Information: Faculty may be contacted through the Portage messaging system
Additional Information: www.portagelearning.com*
Course meeting times: MATH 110 is offered continuously
Course Description: A general introduction to mathematical statistics as a tool used in the decision-making process. The
course is designed to help students develop an understanding of summarized data in both descriptive and inferential
statistical applications through the use of frequency distributions, measures of central tendency, measures of dispersion,
probability distributions, random sampling, interval estimation, hypothesis testing, comparisons involving means, and
regression analysis.
Course Outcomes: As a result of this course experience a student should be able to explain:
The difference between qualitative and quantitative data, be able to organize the data and present a meaningful
overview of the data through the use of frequency distributions, measures of central tendency (i.e. the mean,
median and mode) and measures of dispersion (i.e. the variance, standard deviation and coefficient of
variation)
The rules involved in developing outcome probabilities and how to apply the appropriate counting methods
in the development of the probabilities of outcomes in an experiment.
The difference between a discrete probability distribution and a continuous probability distribution.
The concepts involving random sampling, the sampling distributions of x-bar (𝑥) and p-bar (𝑝) and other
methods.
The null & alternative hypothesis in classical hypothesis testing along with type I and II errors; one- tailed &
two-tailed testing involving populations and both large & small samples.
Linear regression analysis and lines of best fit.
*Please see the Module Topics section below for expanded course outcomes.
* Portage Learning college courses are offered by Geneva College, which is regionally accredited by the Middle States Commission on Higher
Education. Portage Learning is included in the College’s Department of Professional and Online Graduate Studies; courses are delivered through the
PortageLearning.com platform.
