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QMB3250 EXAM 3 With Questions And 100% ALL SURE ANSWERS
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QMB3250 EXAM 3 With Questions And 100% ALL SURE ANSWERS
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QMB3250 EXAM 3 With Questions And 100% ALL SURE ANSWERS
Practice questions for this set
, Terms in this set (40)
simple moving average forecast is the average of a fixed number of past periods
simple exponential smoothing allows you to adjust the weight between recent and historic data; forecast t + 1
good for quarterly trends and seasonal*, uses lag variables (start from bottom to top/last to first)
y hat = b0 + b1ylag1 + b2ylag2 + etc.
autoregressive methods
(# means time period back)
- JMP: fit model, add: lags, run
previous point is our best guess of the next point
random walks
- assumptions: quantitative data, measured on negative intervals
better at predicting more than just the next point & seasonal components (uses indicator variables)
multiple regression models (2 types) - linear (additive)
- exponential (multiplicative, uses log(y), consistent % increase)
additive model slope interpretation b is the increase in y for a one-unit change in x (consistent increase)
log10(y)--> multiplying y by 10^slope
interpreting the slope of log/multiplicative model
=> % of consistent increase/decrease in y, incr/decr shown by if there is a 1.05 or just 0.05
to compare 2 or more means
H0: mu1 = mu2 = ... = mu k (no diff in treatments)
one way ANOVA
Ha: at least one population mean is diff from the rest
assumptions: indepence/random, equal variance, normal population
independence- randomizaiton
one way ANOVA assumptions equal variance- resid vs. predicted plot
normal population- outliers of boxplots, histogram bell-shaped, normal quantile plot
a (a-1)/2
Bonferroni- finding # of comparisons
--> a = #levels
Bonferroni- individual error rate family wise error rate / # comparisons
Bonferroni- Family wise confidence level 1 - (alpha / individual error rate)
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