STAT 210 Review Topics
Basic Definitions/Terminology
 Descriptive Statistics
 Inferential Statistics
 Population
 Sample
 Variable
 Types of Data
o Numeric
o Categorical
Methods for a Single Categorical Variable
 Simulation
o Setting up spinner
o Reading/interpreting the simulation graph
o Estimating a p-value
o Decision making
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Binomial Distribution
o Conditions in context
o Reading JMP output
o Finding p-value
o Decision making
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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.
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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
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OR
H0: p1 ≥ p2
Ha: p1 < p2
o Fisher’s Exact Test – no test statistic, p-value from bottom of JMP output
Case 2:
H0: p1 = p2
Ha: p1 ≠ p2
o
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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
Mosaic Plots
o What they look like if no relationship
o What they look like if there is a relationship
Methods for a Single Numeric Variable
 Descriptive Statistics
o Numerical – mean, standard deviation, variance, Five Number Summary, IQR, Range,
skewness, kurtosis
o Graphical – identifying shape, outliers, standard deviation comparisons
 Data Concentration/Outliers
o Z-scores
o Empirical Rule
o Chebyshev’s Rule
 Sampling Distribution for the Sample Mean
o Center, shape, variability
o Central Limit Theorem
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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
o Finding p-value
o Conclusion in context
o Wilcoxon Signed Rank Test – when n < 30 and not normal, but SYMMETRIC
o Being able to describe in context a Type I Error and Type II Error and
consequences/implications
Type I and Type II Errors
o Type I – Have evidence for RQ (Reject H0) when H0 is true
o Type II – No evidence for RQ (Do not reject H0) when H0 is false
o Consequences/implications of making either error
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, Equal
variances
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
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Methods for a Single Numeric Variable across More than Two Groups
 When is ANOVA used?
 Hypotheses
 Assumptions/conditions – equal variances, normality, independent
 Reading JMP output
 Finding test statistic and p-value
 Conclusion in context
Methods for Two Numeric Variables
 Correlation
o Measures strength and direction of linear relationship
o Between -1 and 1
o Strongest linear relationships at +1 and -1
o No linear relationship around 0
o Square root of R2
 Simple Linear Regression
o Estimated equation from JMP output
o Interpreting slope
o Interpreting intercept
o Predicting
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