simple moving average - ANSWER: ➡ forecast is the average of a fixed number of past periods
o o o o oo o o o o o o o o o o o
simple exponential smoothing - ANSWER: ➡ allows you to adjust the weight between recent
o o o o oo o o o o o o o o
and historic data; forecast t + 1
o o o o o o o
autoregressive methods - ANSWER: ➡ good for quarterly trends and seasonal*, uses lag o o o oo o o o o o o o o
variables (start from bottom to top/last to first)
o o o o o o o o
y hat = b0 + b1ylag1 + b2ylag2 + etc.
o o o o o o o o o
(# means time period back)
o o o o
- JMP: fit model, add: lags, run
o o o o o o
random walks - ANSWER: ➡ previous point is our best guess of the next point
o o o oo o o o o o o o o o o
- assumptions: quantitative data, measured on negative intervals
o o o o o o o
multiple regression models (2 types) - ANSWER: ➡ better at predicting more than just the
o o o o o o oo o o o o o o o
next point & seasonal components (uses indicator variables)
o o o o o o o o
- linear (additive)
o o
- exponential (multiplicative, uses log(y), consistent % increase)
o o o o o o o
additive model slope interpretation - ANSWER: ➡ b is the increase in y for a one-unit change in
o o o o o oo o o o o o o o o o o o
x (consistent increase)
o o o
interpreting the slope of log/multiplicative model - ANSWER: ➡ log10(y)--> multiplying y by
o o o o o o o oo o o o o
10^slope
o
=> % of consistent increase/decrease in y, incr/decr shown by if there is a 1.05 or just 0.05
o o o o o o o o o o o o o o o o o
, one way ANOVA - ANSWER: ➡ to compare 2 or more means
o o o o oo o o o o o o
H0: mu1 = mu2 = ... = mu k (no diff in treatments)
o o o o o o o o o o o o
Ha: at least one population mean is diff from the rest
o o o o o o o o o o
assumptions: indepence/random, equal variance, normal populationo o o o o
one way ANOVA assumptions - ANSWER: ➡ independence- randomizaiton
o o o o o oo o o
equal variance- resid vs. predicted plot
o o o o o
normal population- outliers of boxplots, histogram bell-shaped, normal quantile plot
o o o o o o o o o
Bonferroni- finding # of comparisons - ANSWER: ➡ a (a-1)/2 o o o o o o oo o o
--> a = #levels
o o o
Bonferroni- individual error rate - ANSWER: ➡ family wise error rate / # comparisons
o o o o o oo o o o o o o o
Bonferroni- Family wise confidence level - ANSWER: ➡ 1 - (alpha / individual error rate)
o o o o o o oo o o o o o o o
Tukey's method - ANSWER: ➡ done after ANOVA
o o o oo o o o
estimates the difference in population means o o o o o
two way ANOVA - ANSWER: ➡ 2 factors with multiple levels
o o o o oo o o o o o
H0: pop mean error is same at all levels for the factor- interaction is NOT signif- look at main
o o o o o o o o o o o o o o o o o o
effects
o
Ha: at least one pop mean errors is diff- interaction is signif- multiple comparisonsTukey
o o o o o o o o o o o o o
Wilcoxon Rank Sum test - ANSWER: ➡ nonparametric test that compares sample medians of
o o o o o oo o o o o o o o
two independent groups of scores
o o o o o
H0: two samples come from the same distribution
o o o o o o o
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