QMB 3250 HW-QUESTIONS WITH 100%
CORRECT SOLUTIONS!!
For simple exponential smoothing, for which alpha would trend be the smoothest?
alpha = 0.1
alpha = 1.0
alpha = 0.9
alpha = 0.5
alpha = 0.1
What are the key differences between trends and seasonal components in time series data?
Trends are a consistent pattern (either linear or curves) that approach either a negative or positive
direction; Seasonal Components are fluctuation in data that occur around the same time every
period.
For a time series for simple moving average, a shorter period will be smoother than a
larger period because shorter periods do not react as quickly.
True
False
False
For a time series for exponential smoothing, which of the following is the smoothest?
alpha = 0.3
,alpha = 0.9
alpha = 0.7
alpha = 0.5
alpha = 0.3
If you were dealing with a data set that fluctuates quarterly, what type of method would be
best?
Random walk
Exponential smoothing
Simple moving averages
Autoregressive models
Autoregressive models
If you are dealing with a data set that consists of sales with a consistent percentage
increase, what type of method would be best?
Multiplicative regression model
Random walk
Simple moving average
additive regression model
Multiplicative regression model
Suppose you ran a additive regression-based model of price vs time (years) and the point
estimate for change in price per year was 1140. Interpret the slope.
, The price tended to decrease by 11.40% dollars per year.
The price tended to decrease by 1140 dollars per year.
The price tended to increase by 11.40% dollars per year.
The price tended to increase by 1140 dollars per year.
The price tended to increase by 1140 dollars per year.
Suppose you fit a multiplicative regression model for log10(sales in $1000s) vs time (years)
and the point estimate for change in log10(sales in $1000s) per year is .06. Interpret the
slope.
(Hint: You will need to raise the value of the slope to the power of 10.)
The sales increase by $60 per year typically.
The sales decreased by 14.8% per year typically.
The sales increased by 14.8% per year typically.
The sales decrease by $60 per year typically.
An autoregressive model uses [ Select ] ["interaction", "exponential", "lagged"] variables,
and is also better at modeling [ Select ] ["weaker", "quarterly"] trends.
lagged; quarterly
What is the correct alternative hypothesis for a One way ANOVA test?
CORRECT SOLUTIONS!!
For simple exponential smoothing, for which alpha would trend be the smoothest?
alpha = 0.1
alpha = 1.0
alpha = 0.9
alpha = 0.5
alpha = 0.1
What are the key differences between trends and seasonal components in time series data?
Trends are a consistent pattern (either linear or curves) that approach either a negative or positive
direction; Seasonal Components are fluctuation in data that occur around the same time every
period.
For a time series for simple moving average, a shorter period will be smoother than a
larger period because shorter periods do not react as quickly.
True
False
False
For a time series for exponential smoothing, which of the following is the smoothest?
alpha = 0.3
,alpha = 0.9
alpha = 0.7
alpha = 0.5
alpha = 0.3
If you were dealing with a data set that fluctuates quarterly, what type of method would be
best?
Random walk
Exponential smoothing
Simple moving averages
Autoregressive models
Autoregressive models
If you are dealing with a data set that consists of sales with a consistent percentage
increase, what type of method would be best?
Multiplicative regression model
Random walk
Simple moving average
additive regression model
Multiplicative regression model
Suppose you ran a additive regression-based model of price vs time (years) and the point
estimate for change in price per year was 1140. Interpret the slope.
, The price tended to decrease by 11.40% dollars per year.
The price tended to decrease by 1140 dollars per year.
The price tended to increase by 11.40% dollars per year.
The price tended to increase by 1140 dollars per year.
The price tended to increase by 1140 dollars per year.
Suppose you fit a multiplicative regression model for log10(sales in $1000s) vs time (years)
and the point estimate for change in log10(sales in $1000s) per year is .06. Interpret the
slope.
(Hint: You will need to raise the value of the slope to the power of 10.)
The sales increase by $60 per year typically.
The sales decreased by 14.8% per year typically.
The sales increased by 14.8% per year typically.
The sales decrease by $60 per year typically.
An autoregressive model uses [ Select ] ["interaction", "exponential", "lagged"] variables,
and is also better at modeling [ Select ] ["weaker", "quarterly"] trends.
lagged; quarterly
What is the correct alternative hypothesis for a One way ANOVA test?
