What we saw & where it was… aka: What’s fair game for the MAT 190 final?
1.2
Lots of good definitions. Might be good to review them:
Elements/Variable/Observation, Nominal/Ordinal/Interval/Ratio, Categorical/quantitative,
Time series/Cross-sectional
1.4
Descriptive Statistics – not much here
1.5
Population/Sample, census/survey, statistical inference
2.1
Categorical (Qualitative data) -Frequency distribution, relative frequency and percent
frequency distributions, bar charts/pie charts
2.2
Quantitative data – frequency distribution (class width, limits, midpoints…) Relative
frequency & percent frequency distributions, dot plots, histograms, cumulative frequency
distributions
2.3
Stem & leaf plots
2.4
Crosstabulations, scatterplots, trendlines
3.1
3.2
3.3
3.4
3.5
3.6
Mean/median/mode/midrange, percentile/decile/quarties
Range, standard deviation, IQR, variance, coefficient of variation
Skew, z score, Chebyshev’s theorem, empirical rule, outliers
Box plots, 5 number summary
Covariance, correlation coefficient
Weighted mean, grouped data
4.1
Probability 101: sample space, counting rules, combinations, permutations, tree
diagrams, subjective /relative frequency/classical probability
4.2
Events
4.3
Complement, venn diagrams, addition law, union, intersections, mutually exclusive
4.4
Conditional probability, joint/marginal probability, independence, multiplication law
4.5
Bayes’ Theorem
5.1
5.2
5.3
5.4
5.5
5.6
Discrete & continuous random variables 101
More on discrete distributions, Uniform distribution
Expected value, mean & std. dev of a discrete random variable
Binomial distribution
Poisson distribution
Hypergeometric distribution
6.1
6.2
6.3
6.4
Uniform (continuous) distribution
Normal distribution, standard normal distribution
Normal approximation of the binomial distribution
Exponential distribution
7.5
Central limit theorem
8.1
8.2
8.3
8.4
Confidence interval for mu (sigma known)
Confidence interval for mu (sigma unknown)
Finding n for mu
Confidence interval for p, finding n for p
9.1
9.2
9.3
9.4
9.5
Testing claims 101: Null/alternative hypothesis
Type I & II errors
Testing a claim about mu when sigma is known – traditional & p-value rules
Testing a claim about mu when sigma is NOT known – traditional & p-value rules
Testing a claim about p
10.1
10.2
10.3
10.4
10.5
Testing claim about difference of means (independent samples, sigma known)
Testing claim about difference of means (independent samples, sigma not known)
Testing claim about difference of means (matched pairs)
ANOVA part 1
ANOVA part 2
12.1
12.2
Linear regression 101
Finding the least squares equation
How should you study? Start NOW – re-do all quizzes & exams. Review your notes for sample
problems. Do an hour or two every day – don’t try to do all of your studying in a day or two!
As a reminder, you can put formulas on an 8 ½ x 11 sheet of paper for the final. Worked
problems or examples are not allowed and will result in not being able to use a formula sheet at
all.