Factorial Experiment Design MCQs 17

Test your knowledge of Factorial Experiment Design MCQs with this 20-question MCQ quiz! Perfect for students, statisticians, data analysts, and data scientists, this quiz covers key concepts like full factorial designs, interactions, orthogonality, contrasts, and fractional factorial experiments. Whether you’re preparing for exams, job interviews, or research, this quiz helps you master essential DOE (Design of Experiments) principles. Check your understanding of factors, levels, efficiency, and experimental regions with detailed answers provided. Sharpen your skills and boost your confidence in statistical experimental design today! Let us start with the Online Factorial Experiment Design MCQs now.

Online Factorial Experiment Quiz, Design of Experiment MCQs with Answers

1. A contrast may be used to know the magnitude or direction of —————.

 
 
 
 

2. Contrast can be used to compute

 
 
 
 

3. Factorial designs provide a chance to estimate the effect of a factor at ———— levels of the other factor

 
 
 
 

4. A factorial experiment is an experiment whose design consists of two or more factors, each with

 
 
 
 

5. When Interaction is present, we should prefer

 
 
 
 

6. A full factorial design is also called a fully

 
 
 
 

7. In the case of two factors, the relative efficiency of factorial design to one-factor-at-a-time experimental design is:

 
 
 
 

8. Factorial experiments can involve factors with ————— levels

 
 
 
 

9. Factorial experiments can involve factors with —————– numbers of levels

 
 
 
 

10. Average effect of $B$ for 3 replicates of experiment with factors $A$ and $B$ is computed by diving contrast to

 
 
 
 

11. In the first phase of the experiment, the stage that is completed is called

 
 
 
 

12. Orthogonality of a design can be checked by putting the levels of factors in

 
 
 
 

13. A factorial experiment requires that factors

 
 
 
 

14. The range of factor levels in which an experiment can be performed is commonly known as

 
 
 
 

15. The factorial analysis requires that dependent variables be measured as

 
 
 
 

16. Typically region of experimentation is a cuboidal or a

 
 
 
 

17. ————— factorial designs fill the gaps of the run size of the common factorial design.

 
 
 
 

18. In a $2^2$ design, the number of trials is equal to

 
 
 
 

19. The runs of two or more fractional factorial designs may be —————– to estimate the effects of vital interest

 
 
 
 

20. Ronald Fisher and —————– are the pioneers of factorial design

 
 
 
 


Online Factorial Experiment Design MCQs with Answers

Online Factorial Experiment Design MCQs with Answers

  • A factorial experiment is an experiment whose design consists of two or more factors, each with
  • Ronald Fisher and —————– are the pioneers of factorial design
  • A full factorial design is also called a fully
  • When Interaction is present, we should prefer
  • Factorial designs provide a chance to estimate the effect of a factor at ———— levels of the other factor
  • In the case of two factors, the relative efficiency of factorial design to one-factor-at-a-time experimental design is:
  • The factorial analysis requires that dependent variables be measured as
  • A factorial experiment requires that factors
  • In a $2^2$ design, the number of trials is equal to
  • Factorial experiments can involve factors with ————— levels
  • Orthogonality of a design can be checked by putting the levels of factors in
  • Factorial experiments can involve factors with —————– numbers of levels
  • The range of factor levels in which an experiment can be performed is commonly known as
  • In the first phase of the experiment, the stage that is completed is called
  • Typically region of experimentation is a cuboidal or a
  • A contrast may be used to know the magnitude or direction of —————.
  • Contrast can be used to compute
  • Average effect of $B$ for 3 replicates of experiment with factors $A$ and $B$ is computed by diving contrast to
  • The runs of two or more fractional factorial designs may be —————– to estimate the effects of vital interest
  • ————— factorial designs fill the gaps of the run size of the common factorial design.

Exploratory Data Analysis in R Language

Combining Events Using OR

In probability and logic theory, combining events using OR (denoted as $\cup$) means considering situations where either one event occurs, or the other occurs, or both occur. This is known as the “inclusive OR.”

Given two events $A$ and $B$, one can define the event $A$ or $B$ to be the event that at least one of the events $A$ or $B$ occurs. The probability of the events $A$ or $B$ using the Addition Rule of probability can be computed easily. Learn the Basics of Probability.

Addition Rule of Probability (for Non-Mutually Exclusive Events)

If $A$ and $B$ are two events for an experiment, then
$$P(A\,\, or \,\,B) = P(A\cup B) = P(A) + P(B) – P(A\,\,and \,\, B)$$
This accounts for the overlap between events to avoid double-counting

Addition Rule Probability (for Mutually Exclusive Events)

Two events are called mutually exclusive events if both events cannot occur at the same time (cannot occur together). In this case, when the mutually exclusive events, $P(A\,\,\cap\,\,B)=0$, so the addition rule simplies to:
$$P(A\,\,or\,\,B) = P(A) + P(B)$$
This does not account for the overlap between events to avoid double-counting.

Combining Events using OR

Real Life Examples of Combining Events using OR

The following are a few real-life examples of Combining Events Using OR.

Weather Forecast Example

Suppose Event $A$ represents that it will rain tomorrow and Event $B$ that it will snow tomorrow. One can compute the probability that it will rain OR snow tomorrow. This means that at least one of them happens (it could be rain, snow, or both).
Suppose that the chance of rain tomorrow = $P(A)$ = 30% = 0.3. Supose that the probability of snow tomorrow = $P(B)$ = 20% = 0.2. Suppose the chances of both rain and snow are $P(A \cap B)$ = 5% = 0.5.
Therefore,
\begin{align*}
P(A \cup B) &= P(A) + P(B) – P(A \cup B) \\
& = 0.3 + 0.2 – 0.05 = 0.45
\end{align*}
There is a 45% chance that it will rain or snow tomorrow.

Job Requirements

Suppose Event $A$ represents that applicants must have a Bachelor’s degree, and Event $B$ represents that applicants must have 3 years of experience. One can compute the probability (or count) that the applicant must have a bachelor’s degree OR 3 years of experience to apply. The applicant will qualify if he/she have either one or both experiences.
Suppose there are 100 applicants for a certain job. For Event $A$, there are 40 applicants who have a Bachelor’s degree, and Event $B$ represents that there are 30 applicants who have more than 5 years of experience. Similarly, 10 applicants have both a Bachelor’s degree and have more than 3 years of experience. The number of qualifying applicants will be

\begin{align*}
A \cup B &= A + B – A \cap B \\
& = 40 + 30 – 10 = 60
\end{align*}
Therefore, 60 applicants meet at least one requirement (degree OR experience).

Restaurant Menu Choices

Consider Event $A$ represents the meal comes with fries, and Event $B$ represents the meal comes with a salad. One can compute if a customer can pick one, or sometimes both, if allowed. For illustrative purposes, suppose a Fast-Food Chain tracks 1000 orders. The Event $A$ represents 400 customers who choose fries, and Event $B$ represents 300 customers who choose a salad. Similarly, there are 100 customers who both choose fries and salad. The number of customers’ choices for both fries and salad will be

\begin{align*}
A \cup B &= A + B – A\cap B\\
&= 400 + 300 – 100 = 600
\end{align*}
600 customers ordered fries OR salad (or both).

Discount Offers

Let Event $A$ represent the use of a promo code for 10% off, Event $B$ represents a Student ID for 15% off. One uses a promo code or a Student ID to get a discount. Suppose a store offers two discount options to 200 customers. Event $A$ represents 65% of customers who used a coupon, Event $B$ represents that 13% customers showed their Student ID. 7% customers have used both the coupon and the Student ID. The probability that at least one discount is used will be

\begin{align*}
P(A \cup B) &= P(A) + P(B) – P(A \cap B)\\
& = 0.65 + 0.13 – 0.07 = 0.71
\end{align*}
71% of the customers have used at least one discount.

Security System Access

Suppose a building logs 500 entry attempts. Out of 500, 300 entries used a keycard, 200 used a PIN code, and 50 used both methods. What is the probability that both entry attempts are made?
\begin{align*}
P(A\cap B) &= P(A) + P(B) – P(A \cap B)\\
& = \frac{300}{500} + \frac{200}{500} – \frac{50}{500} = 0.6 + 0.4 – 0.1 = 0.9
\end{align*}
There are 90% ($500\times 0.9=450$) entries that used a keycard OR a PIN.

General Knowledge Quizzes

FAQs about Combining Events

  • What is meant by Combining Events?
  • What symbol is used to combine two or more events?
  • What rule of probability is used to combine events?
  • Give some real-life examples of Combining Events using OR.
  • What are mutually and Non-Mutually Exclusive Events?

Properties of Measure of Central Tendency

Understanding the Properties of Measure of Central Tendency helps in selecting the appropriate measure for accurate data interpretation. This blog post explores the key properties of measures of central tendency: mean, median, and mode, along with their advantages and limitations.

Introduction: Properties of Measure of Central Tendency

In statistics, measures of central tendency are crucial for summarizing and interpreting data. Measures of central tendency provide a single value that represents the center or typical value of a dataset. The three most common measures of central tendency are the mean, median, and mode. Each central tendency has unique properties that make it suitable for different types of data and analytical purposes.

Mean (Arithmetic Average)

The mean (the most widely used measure of central tendency) is the sum of all values in a dataset divided by the number of values $\left(\frac{\sum\limits_{i=1}^n X_i}{n}\right)$.

Properties of Mean

  • Sensitive to All Data Points
    The mean considers every value in the dataset, making it highly responsive to changes. A single extreme value (outlier) can significantly affect the mean.
  • Algebraic Manipulability
    The mean is used in further mathematical operations (measures of dispersion, e.g., calculating variance, standard deviation). The sum of deviations from the mean ($x-\overline{x}$) is always zero:
    $$\sum\limits_{i=1}^n (X_i – \overline{X}) =0$$
  • Applicable to Interval and Ratio Data
    The mean is suitable for continuous numerical data (for example, height, weight, and income). It is not appropriate for nominal or ordinal data.
  • Affected by Skewness
    In skewed distributions, the mean is pulled toward the tail, making it less representative of central tendency.

Advantages of the Mean

  • Mean uses all data points, providing a comprehensive measure.
  • It is useful in statistical inferences and parametric tests.

Limitations of the Mean

  • Distorted by outliers.
  • Mean should not be used for highly skewed data.
properties of measures of central tendency

Median (Middle Value)

The median is the middle value (the most central data value) in an ordered dataset/array. If the dataset has an even number of observations, the median is the average of the two central values.

Properties of Median

  • Resistant to Outliers
    Unlike the mean, the median is not influenced/affected by extreme values (outliers). It is because the median only depends on the middle value(s) in the ordered dataset. It is also applicable to Ordinal, Interval, and Ratio Data. On the other hand, median works well for ranked (ordinal) and continuous numerical data. However, the median is not suitable for nominal data (categories without order).
  • Unaffected by Skewness
    The median remains stable in skewed distributions, making it a better measure than the mean in such cases.
  • Not Algebraically Manipulable
    Unlike the mean, the median cannot be used in further mathematical computations (for example, standard deviation).

Advantages of the Median

  • Median is robust against outliers.
  • Median better represents the central tendency in skewed distributions.

Limitations of the Median

  • Median does not consider all data points.
  • It is less efficient than the mean for normally distributed data.

Mode (Most Frequent Value)

The mode is the value that appears most frequently in a dataset. A dataset can have one mode (unimodal), two modes (bimodal), or multiple modes (multimodal). It is the only measure of central tendency that can have more than one value.

Properties of Mode

Mode applies to All Data Types (that is, it works with nominal, ordinal, interval, and ratio data). However, it is the only measure of central tendency suitable for categorical data (e.g., colors, brands).

  • Unaffected by Outliers
    Since the mode depends on frequency, extreme values do not impact the mode.
  • Not Necessarily Unique
    Some datasets have no mode (if all values are unique or no value repeats in the dataset,) or data may have multiple modes.
  • Not Useful for Small Datasets
    In small samples, the mode may not accurately represent central tendency.

Advantages of the Mode

  • Mode is useful for categorical data.
  • Mode helps identify peaks in frequency distributions.

Limitations of the Mode

  • May not exist in some datasets.
  • Less informative for continuous numerical data with no repeated values.

Comparison of Mean, Median, and Mode

PropertyMeanMedianMode
Sensitive to OutliersYesNoNo
Works with Skewed DataNoYesSometimes
Applicable to Nominal DataNoNoYes
Mathematical UsabilityHighLowLow
Best for Symmetric DataYesYesSometimes

Choosing the Right Measures of Central Tendency

The choice between mean, median, and mode depends on:

  • Data Type
    • Use the mean for normally distributed numerical data, that is, data points are homogeneous.
    • Use the median for ordinal or skewed numerical data, that is, data points are heterogeneous.
    • Use mode for categorical data, or when data points repeat.
  • Presence of Outliers
    • If outliers are present, the median is preferred.
    • If data is clean and normally distributed, the mean is ideal.
  • Purpose of Analysis
    • For statistical computations (e.g., regression), the mean is necessary.
    • For descriptive summaries (e.g., income distribution), the median is better.

Summary: Properties of Measures of Central Tendency

Measures of central tendency: mean, median, and mode, each has unique properties that determine their suitability for different datasets. The mean is precise but affected by outliers, the median is robust against skewness, and the mode is versatile for categorical data. Understanding these properties ensures accurate data interpretation and informed decision-making in statistical analysis.

By selecting the appropriate measure based on data characteristics, analysts can derive meaningful insights and avoid misleading conclusions. Whether summarizing exam scores, income levels, or survey responses, the right measure of central tendency provides clarity in a world of data.

General Knowledge Quiz