STAT 210 Review Topics
Basic Definitions/Terminology
 Descriptive Statistics
 Inferential Statistics
 Population
 Sample
 Variable
 Types of Data
o Numeric
o Categorical
 Explanatory Variable
 Response Variable
Methods for a Single Categorical Variable
 Simulation
o Setting up the spinner
o Reading/interpreting the simulation graph
o Estimating a p-value
o Decision making

Binomial Distribution
o Conditions in context
o Reading JMP output
o Finding p-value
o Decision making

Confidence Interval
o Why do we even use them?
o Reading JMP output
o Interpreting the interval
o Decision making
o Margin of error – what changes it, how the interval changes, etc.

Goodness of Fit
o Setting up hypotheses
o Computing expected counts
o Reading JMP output (Pearson Chi-square Test)
 Test statistic
 P-value
o Conclusion in context
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Methods for Two Categorical Variables
 Case 1:
H0: p1 ≤ p2
Ha: p1 > p2
OR
H0: p1 ≥ p2
Ha: p1 < p2
o

Fisher’s Exact Test – uses hypergeometric distribution, no test statistic, p-value from
bottom of JMP output
Case 2:
H0: p1 = p2
Ha: p1 ≠ p2
o




Chi-square Test – if at least 3 expected counts are 5 or more
 expected counts, test statistic, p-value from Pearson part of JMP output.
o Fisher’s Exact Test – if 2 or more expected counts are less than 5
Relative Risk
o Interpreting mosaic plot
o Reading JMP output
o Interpreting the relative risk
o When does an association/difference exist?
Odds Ratio
o Interpreting mosaic plot
o Reading JMP output
o Interpreting the odds ratio
o When does an association/difference exist?
Confidence Interval for p1 – p2
o Need 3 things – estimate ( pˆ 1  pˆ 2 ), quantile, standard deviation (standard error)
o Interpreting the interval
o Conclusions based upon interval
R x C Tables
o Interpreting mosaic plot
o Hypotheses
o Computing expected counts
o Reading JMP output – test statistic, p-value
Methods for a Single Numeric Variable
 Descriptive Statistics
o Numerical – mean, standard deviation, variance, Five Number Summary, IQR, Range
o Graphical – identifying shape, outliers, standard deviation comparisons
o Skewness, Kurtosis
o Percentiles
 Sampling Distribution for the Sample Mean
o Center, shape, variability
o Central Limit Theorem
 Hypothesis Test
o Setting up hypotheses
o Conditions/assumptions – n is sufficiently large (≥30) or normal distribution
o Reading JMP output – p-value, graphs
o Computing test statistic
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o
o
o
o

Finding p-value
Conclusion in context
Wilcoxon Signed Rank Test – when n < 30 and not normal, but SYMMETRIC
Being able to describe in context a Type I Error and Type II Error and
consequences/implications
Confidence Interval
o Reading JMP output
o Interpreting the interval
o Decision making
Methods for a Single Numeric Variable across Two Groups
 Dependent vs. Independent Samples
 Dependent Samples
o Use µd in hypotheses
o Assumptions/conditions – npairs ≥ 30 or differences normal
o Computing test statistic
o Reading JMP output
o Finding p-value
o Conclusion in context
 Independent Samples
o Use µ1 and µ2 in hypotheses
o Assumptions/conditions – both n1 and n2 ≥ 30, or both populations normal, unequal
variance test
 H0: variances are equal
Ha: variances are not equal
 Assume variances are equal (big p-values)  Means/ANOVA/Pooled t in JMP
 Assume Variances are unequal (small p-values)  t Test in JMP
o Reading JMP output
o Computing test statistic
o Finding p-value
o Conclusion in context
 Confidence Intervals
o Reading JMP output
o Interpreting the interval
o Decision making
Methods for Two Numeric Variables
 Linear Regression
o Trends when looking at a scatterplot
o Linear equation from JMP output
o Predicting a value using equation
o Correlation
 What might it be based on a graph?
 Computing from JMP output
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