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Reviewing for the Final Exam

STAT 1350

The following outline is meant to highlight the important terms, concepts, and ideas we have
covered during the second half of the course. This is not a comprehensive list of everything that
could be on the Final Exam. Rather, it should serve as a general outline in studying. In addition
to going through review problem from lecture and recitation, and you should review by (a)
reading through notes from lecture, (b) going over homework assignments and looking carefully
at the feedback you received on these assignments, (c) reviewing lab activities and the answer
keys we have posted for these activities on Carmen, (d) reviewing assigned textbook readings
from Chapters 14, 15, 17, 18, 21, 22, and 23, (e) asking questions of your lecturer or recitation
instructor if you need extra help.

Please remember that you can prepare ONE sheet of notes (front and back, typed or handwritten,
8 ½ by 11 inches) for the exam, and you will need to have a calculator with you. You should also
plan to bring a photo ID. Details will be shared prior to the final exam, via email and during
lecture and recitation, about the location where you will take the exam.

• Chapter 17 (Thinking about Chance)

– Understand the meaning of the term “random” within a statistics context

– Understand what probability refers to

– Know that probabilities range from 0 to 1

– Understand the myths about chance behavior (i.e., what is the myth of the Law of
Averages, and what does that law actually tell us? What is the myth of short-run
regularity?)

– Understand what personal probability refers to

• Chapter 18 (Probability Models)

– What is a probability model?

– What are the rules of probability? You should know these rules and how to apply them.

– Understand what a sampling distribution is

– Know the characteristics of a sampling distribution: where it is centered, what the spread
or variability is, and that, under certain conditions, the shape is Normal

– Understand what the 68-95-99.7 rule (or the Empirical rule) can be used to work through
particular problems related to sample statistics (problems like some of those from
Homework 7, and problems worked through in the Chapter 18 lecture)

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– Know how to convert a sample statistics to a z-score and use Table B to find percentiles
(or probabilities)

• Chapter 21 (Confidence Intervals)

– Know how sampling distributions for sample proportions differ from sampling
distributions for sample means. Each type of distribution is centered at the population
parameter, but the spread (or standard deviation) is figured out in different ways. Both
types of sampling distributions are Normal in shape if the sample size is large enough
(based on what is known as the Central Limit Theorem)

– Understand the purpose or goal of constructing a confidence interval: to estimate an

– Know how to construct and interpret confidence intervals for proportions and means

– Know the general form of the confidence interval: sample statistic ± margin of error

– Understand what the margin of error refers to and why we need to take into account
margin of error when estimating an unknown population parameter

– Know that as sample size increases, the margin of error decreases (similarly, as sample
size decreases, the margin of error increases)

– Know that as the confidence level increases, the margin of error increases (similarly, as
the confidence level decreases, the margin of error decreases)

– Understand how you could work backward to determine the center of a confidence
interval (i.e., the sample statistic) and the margin of error simply by knowing what the
interval is

– Know what the following symbols refer to: µ, σ, p, , , s, n, z*

• Chapters 22 and 23 (Hypothesis Testing)

– Understand the terminology involved in hypothesis testing:

– Know the steps involved in conducting a hypothesis test

– Understand how to decide if you should conduct a hypothesis test for a mean or for a
proportion

– Understand how to recognize key words in a problem so you know if you should set up a
one-sided or a two-sided alternative hypothesis

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– Know that the null and alternative hypotheses are claims about population parameters

– Recognize and understand how practical significance is different from statistical

– Know the different formulas for computing test statistics for means and for proportions
and how to use those formulas

– Know how to make decisions (i.e., whether or not to reject the null hypothesis) based on
how the p-value compares to the alpha level; remember how to properly phrase your
decisions as well

– Know how to use Table B to find a p-value (and know how to take a percentage from
Table B and convert it to a probability)

• Chapter 14 (Describing Relationships: Scatterplots and Correlation)

– Know that a scatterplot displays the relationship between two quantitative variables and
understand how to use a scatterplot to describe the form, direction, and strength of a
relationship

– Know that the correlation coefficient, or r, tells us something about the strength and
direction of a relationship; we need a linear relationship to use r

– Know how to decide if a relationship is strong, moderate, or weak (see indices given in
lecture notes)

– Know that a correlation coefficient can range between -1 to +1

– Remember that correlation does not imply causation!

– Remember that correlation has no units, and a correlation between two variables will not
change if the units of analysis change (e.g., a change from pounds to kilograms, or from
dollars to cents, etc.), or if we change which variable is x and which variable is y.

• Chapter 15 (Describing Relationships: Regression, Prediction, and Causation)

– Within the context of regression, the variable we are attempting to predict or explain is
the response variable (or y), and the variable we are using to make the prediction is the
explanatory variable (or x)

– Know the equation for a regression line (y = a + bx, or Predict y = a + bx) and know how
to identify and interpret the components of this equation (i.e., know that a is the y-
intercept and b is the slope, and know how to interpret these values)

– Know how to use the regression equation to make a prediction

– Understand what extrapolation is and why it is bad

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– Understand how to find and interpret r-squared

– Know what is necessary to conclude that one variable causes the other

– Understand the difference between common response and confounding