Time Series Quiz 5

The post is about the Time Series Quiz. There are 20 multiple-choice questions related to components of time series, Time series analysis, Arima model, Sarima Model, Naive Model, and Autoregression. Let us start the Time Series Quiz now.

Online time Series Quiz with Analysis

Online Time Series Quiz with Answers

1. In SARIMA modeling, what is the primary purpose of the “MA” component?

 
 
 
 

2. What is the primary purpose of using a power transformation in data analysis?

 
 
 
 

3. Analysis based on study of price fluctuations, production of commodities and deposits in banks is classified as

 
 
 
 

4. Which of the following best describes a time series in Python?

 
 
 
 

5. In SARIMA modeling, what does the “AR” component focus on?

 
 
 
 

6. In the Naïve (Persistence) Model for time series forecasting, what is the forecast value for a future time step?

 
 
 
 

7. Which of the following is an example of time series data?

 
 
 
 

8. When dealing with time-series data, what is the typical format of a timestamp column in a dataset?

 
 
 
 

9. In the Naïve (Persistence) Model, how does the forecast change when applied to a different time series with the same historical values?

 
 
 
 

10. In ARIMA modeling, what is the primary purpose of the “AR” component of the model?

 
 
 
 

11. A time series changes at an exact constant percentage and then

 
 
 
 

12. Which statistical technique is used in AutoRegression (AR) models to estimate the coefficients that relate a variable to its past values?

 
 
 
 

13. In an AutoRegression (AR) model, what does the “order p” represent?

 
 
 
 

14. What does the “S” represent in the acronym SARIMA?

 
 
 
 

15. In the ARIMA modeling framework, what does the “I” represent in the acronym ARIMA?

 
 
 
 

16. Aslam is interested in examining the occupational mobility of women in Pakistan.  Aslam believes that people’s first job affects their second job and that job in turn affects their third job, and so on. To analyze the data Aslam should use

 
 
 
 

17. What is the role of the “I” component in SARIMA modeling?

 
 
 
 

18. What is a crucial step in the forecasting process for improving accuracy?

 
 
 
 

19. What is the primary limitation of the Naïve (Persistence) Model for time series forecasting?

 
 
 
 

20. When might the Naïve (Persistence) Model be a reasonable choice for time series forecasting?

 
 
 
 

Online Time Series Quiz

  • Which of the following best describes a time series in Python?
  • What is a crucial step in the forecasting process for improving accuracy?
  • What is the primary purpose of using a power transformation in data analysis?
  • In the Naïve (Persistence) Model for time series forecasting, what is the forecast value for a future time step?
  • When might the Naïve (Persistence) Model be a reasonable choice for time series forecasting?
  • In ARIMA modeling, what is the primary purpose of the “AR” component of the model?
  • What does the “S” represent in the acronym SARIMA?
  • In the ARIMA modeling framework, what does the “I” represent in the acronym ARIMA?
  • When dealing with time-series data, what is the typical format of a timestamp column in a dataset?
  • In SARIMA modeling, what does the “AR” component focus on?
  • What is the primary limitation of the Naïve (Persistence) Model for time series forecasting?
  • In the Naïve (Persistence) Model, how does the forecast change when applied to a different time series with the same historical values?
  • In an AutoRegression (AR) model, what does the “order p” represent?
  • Which statistical technique is used in AutoRegression (AR) models to estimate the coefficients that relate a variable to its past values?
  • In SARIMA modeling, what is the primary purpose of the “MA” component?
  • What is the role of the “I” component in SARIMA modeling?
  • Which of the following is an example of time series data?
  • Aslam is interested in examining the occupational mobility of women in Pakistan.  Aslam believes that people’s first job affects their second job and that job in turn affects their third job, and so on. To analyze the data Aslam should use
  • Analysis based on study of price fluctuations, production of commodities and deposits in banks is classified as
  • A time series changes at an exact constant percentage and then

R Programming Frequently Asked Questions

Basics of Probability

In this post, I will discuss the Basics of Probability theory. First I will start with the concept of Set and Event.

Set

In statistical theory, a set is a well-defined collection of distinct events. For example, whenever a coin is tossed or die rolled, something (event) will happen. Distinct events comprise the set, that is when a coin is tossed, either Hear or Tail. It can be denoted with a Set.

$$A=\{Head, \, \Tail\}$$

Similarly, for a fair die, the distinct events can be represented as set $B$, that is,

$$B = \{1, 2, 3, 4, 5, 6\}$$

When two fair dice are rolled, there will be 36 events that can be represented in a set say $C$.

$$ \begin{split}
\left\{ (1, 1), (1, 2), (1, 3), (1, 4), (1, 5), (1, 6), \\
\,\, (2, 1), (2, 2), (2, 3), (2, 4), (2, 5), (2, 6), \\
\,\, (3, 1), (3, 2), (3, 3), (3, 4), (3, 5), (3, 6), \\
\,\, (4, 1), (4, 2), (4, 3), (4, 4), (4, 5), (4, 6), \\
\,\, (5, 1), (5, 2), (5, 3), (5, 4), (5, 5), (5, 6), \\
\,\, (6, 1), (6, 2), (6, 3), (6, 4), (6, 5), (6, 6)\right\}
\end{split}$$

Basics of Probability

Probability is the chance of occurrence of an event described in a set (or sample space). For example, what is the chance of rain today? what is the chance that Pakistan will win the T20 World Cup? Probability is the estimation of chance and it deals with the occurrence of an event in the future. The estimates are presented numerically. For example, (i) There is a 75% chance of rain today, (ii) The insurance industry requires precise knowledge about the risk of loss to calculate premiums, and (iii) The chances of winning the lottery game are 1 in 2.3 million.

Random Experiment

Regarding probability, it is important to understand the concept of random experiments. It is a planned process/activity that gives different results known as outcomes. For example, as discussed earlier, when a coin is tossed, there may be two possible outcomes, Head or Tail. Any experiment or planned process which has only one outcome cannot be regarded as a random experiment. A random experiment has at least a minimum of two outcomes. Outcomes are the results of the experiment. The random experiment has the following properties:

  1. It can be repeated any number of times practically or theoretically.
  2. Each experiment has a minimum of two possible outcomes.
  3. All the outcomes are known in advance but each outcome is unpredictable.

So, we can say that probability is the measure of the degree of uncertainty or quantification of uncertainty.

Sample Space

When we collect all possible outcomes, it is known as sample space, and represented by $S$. For example,

$S=\{H, T\}$ sample space for tossing a single coin

$S=\{HH, HT, TH, TT\}$ sample space when tossing two coins simultaneously

Each outcome of a sample space is called a sample point.

Event

The individual outcome from a sample space in which one is interested is called an event. Events may be based on a single sample point or more than one sample point. For example, Let the even be even numbers when a single dice is thrown, that is, $A=\{2, 4, 6\}$, or even maybe $T$ (Tail) when tossing a coin. $B=\{T\}$. When we throw two dice the event may be the same number on the upper face of the dice, $C=\{(1, 1), (2, 2), (3, 3), (4, 4), (5, 5), (6, 6)\}$

Similarly, the sum of dots on the top face of two dice is 4 is another event, that is, $D=\{(2, 2), (1, 3), (3, 1)\}$

Types of Events

The probability of an event lies between 0 and 1 inclusive. If the probability of an event is 1, it is known as a sure event. If the probability of an event is zero it is an impossible event. When two or more events cannot occur at the same time it is called a mutually exclusive event. For example, in the coin tossing example, either $H$ will occur or Tail, both head and tail cannot occur at the same time.

Events are equally likely when events have the same chance of occurrence. For example, either a student will pass or fail, there is a 50% chance for both events. Collectively Exhaustive Events are events whose union is equal to the sample space.

Random Variable

A random variable is that which takes values randomly. A random variable may be represented by $X$, $Y$, and $Z$, etc. Random variables can be classified as discrete random variables or continuous random variables. A discrete random variable is based on a counting procedure, while a continuous random variable is based on measurements.

A random variable is a variable that takes values randomly. These values may be integers for discrete variables and real for continuous variables. When we toss a coin there may be $H$ or $T$. Suppose, you are interested in Head then the random variable may be denoted by $X$ for various numbers of heads (for example, 0 head and 1 head)

x(Heads)$P(X)$
0 head$\frac{1}{2}$
1 head$\frac{1}{2}$
Total$1.0$

The sample space is $S=\{H, T\}$.

For two coins

x(Heads)$P(X)$
0 head$\frac{1}{4}$
1 head$\frac{2}{4}$
2 heads$\frac{1}{4}$
Total$1.0$

The sample space is $S=\{HH, Ht, TH, TT\}$.

MS Excel Quiz

Basics of Probability Theory

Online Big Data MCQs 5

The post is about Online Big Data MCQs with Answers. There are 20 multiple-choice questions about Big Data 5’s, IaaS, Paas, NameNode, HDFS, Map Reduce, Hadoop, Apache Spark, and YARN. Let us start with the Online Big Data MCQs with Answers now.

Online Big Data MCQs with Answers
Please go to Online Big Data MCQs 5 to view the test

Online Big Data MCQs with Answers

  • What does IaaS provide?
  • What does PaaS provide?
  • What does SaaS provide?
  • What are the two key components of HDFS and what are they used for?
  • What is the job of the NameNode?
  • What is the order of the three steps to Map Reduce?
  • What is the benefit of using pre-built Hadoop images?
  • What are some examples of open-source tools built for Hadoop and what does it do?
  • What is the difference between low-level interfaces and high-level interfaces?
  • Which of the following are problems to look out for when integrating your project with Hadoop?
  • Which of the following are Hadoop’s major goals?
  • What is the purpose of YARN?
  • What are the two main components of a data computation framework that were described in the slides?
  • What is the primary characteristic of Big Data that refers to the scale of data?
  • Which of the following is NOT one of the 5 Vs of Big Data?
  • What does the term “Velocity” in Big Data refer to?
  • Which of the following is a distributed file storage system used in Big Data?
  • What is Apache Spark primarily used for in Big Data?
  • Which tool is used for real-time data streaming in Big Data?
  • What is the purpose of data preprocessing in Big Data analytics?

MS Excel Quiz Questions