Cyclical Variations are due to weather, customs etc

Group of answer choices

True

False

Flag question: Question 9Question 95 pts

Possible causes of random or irregular Time Series component: (Choose all that apply)

Group of answer choices

Unseasonable weather/natural disasters

Strikes

Accidents or unusual events

Flag question: Question 10Question 105 pts

Qualitative Methods forecasting approach, you need Data ?

Group of answer choices

True

False

Flag question: Question 11Question 115 pts

Which of the following methods do we use to find the best fit line for data in Linear Regression?

Group of answer choices

C) Logarithmic Loss

C) Logarithmic Loss

B) Maximum Likelihood

A) Least Square Error

Flag question: Question 12Question 125 pts

Which of the following statement is true about outliers in Linear regression?

Group of answer choices

Linear regression is sensitive to outliers

Linear regression is not sensitive to outliers

Depends on a situation

None of these

Flag question: Question 13Question 135 pts

An autoregression model makes an assumption that the observations at previous time steps are useful to predict the value at the next time step

Group of answer choices

True

False

Flag question: Question 14Question 145 pts

Simple linear regression and AR models differ is that in AR Models Y is dependent on X and previous values for Y.

Group of answer choices

True

False

Flag question: Question 15Question 155 pts

For an autoregressive process to be considered stationary

Group of answer choices

The roots of the characteristic equation must all lie outside the unit circle

The roots of the characteristic equation must all be less than one in absolute value

The roots of the characteristic equation must all lie inside the unit circle

The roots of the characteristic equation must all lie on the unit circle

Flag question: Question 16Question 165 pts

Which of the following statements are true concerning the autocorrelation function (acf) and partial autocorrelation function (pacf)?

Group of answer choices

The acf and pacf will be the same at lag two for an MA(1) model

The pacf for an AR(p) model will be zero beyond lag p

No answer text provided.

No answer text provided.

The acf and pacf will always be identical at lag one whatever the model

The pacf for an MA(q) model will in general be non-zero beyond lag q