The post is about MS Excel Tables Pivot Table Quiz Questions. It contains 20 multiple-choice questions covering the basics of MS Excel Tables, filtering and sorting, and Pivot Tables. Let us start with the MS Excel Tables Pivot Table Quiz Questions now.
Online MS Excel Pivot Table Quiz with Answers
MS Excel Tables Pivot Table Quiz
What must you do first before adding another slicer to a pivot table?
What are slicers?
Which of the following features in Excel provides suggested combinations of data for creating Pivot Tables based on the selected data?
What should you remove before making a Pivot Table?
What is automatically added after formatting data as a table?
After creating a pivot table and selecting it, what pane appears to the right of the pivot table?
What do Timelines provide in pivot tables?
What is one way to remove a slicer or timeline?
Before creating a pivot table, how should you format your data?
How can you add more filters to the pivot table?
If you want to create a Table, you need to click somewhere in the data before creating it.
When naming a Table, the same restrictions apply just as when naming a Named Range.
Suppose that Zara wanted to change the name of a Table, what could she do?
When we add an extra row or column to a Named Range, and the Named Range is not part of a Table, it automatically extends.
When we add an extra row/column to a Table, it automatically extends.
Which of the following does a Table automatically update when creating a new record?
A Slicer is essentially a Filter, but is more intuitive and makes interacting with the data simpler.
What would be the fastest way to observe only the invoices that were over $10,000?
Removing a Table by clicking on Convert to a Range is not recommended because this will impact all the formulas negatively.
The values in the Total Row apply to the whole Table, once the data is filtered, the Total Row will not adjust.
The Latin Square Designs is an effective tool that can simultaneously handle two sources of variation among the treatments, which are treated as two independent blocking criteria. These blocks are known as row-block and column-block, also called double-block. Both sources of variations (blocks) are perpendicular to each other. Latin Square Designs are used to simultaneously eliminate (or control) the two sources of nuisance variability (Rows and Columns).
Table of Contents
Introduction
In a Latin square, treatments are arranged in a square matrix such that each treatment appears exactly once in each row and once in each column. This structure helps mitigate the influence of extraneous variables, allowing researchers to focus on the effects of the treatments themselves.
Latin square designs are widely used in agriculture (field experiments), psychology, and many fields where controlled experiments are necessary. The Latin Square Designs are applied in field trials, where
the experimental area has two fertility gradients running perpendicular to each other
in the greenhouse experiments in which the experimental pots are arranged in straight lines perpendicular to the sheets or walls of the greenhouse such that the difference between rows and the distance from the wall is expected to be two major extraneous sources of variation,
in laboratory experiments where the trials are replicated over time such that the difference between the experimental units conducted at the same time and those conducted over different time period constitute the two known sources of variations
Rows of Tree
Water Channel
A
B
C
B
C
A
C
A
B
Key Features of Latin Square Designs
The Latin square designs have the following key features:
Control for Two Variables: The design simultaneously accounts for variability in two factors (e.g., time and location).
Efficient Use of Resources: These designs allow for the evaluation of multiple treatments without requiring a full factorial design, which can be resource-intensive.
Simple Analysis: The data collected can be analyzed using standard statistical techniques such as ANOVA.
Randomization and Layout Plan for Latin Square Designs
Suppose, there are five treatments (A, B, C, D, E) for this we need $5 \times 5$ LS-Designs, which means we should layout the experiment with five rows and five columns:
A
B
C
D
E
B
C
D
E
A
C
D
E
A
B
D
E
A
B
C
E
A
B
C
D
First of all, randomize the row arrangement by using random numbers then randomize the column arrangement by using random numbers. One can generate five random numbers on your calculator or computer. For example,
Random Numbers
Sequence
Rank
628
1
3
846
2
4
475
3
2
902
4
5
452
5
1
The first rank is 3, treatment c is allocated to cell-1 in column-1, then treatment D is allocated to cell-2 of column-1, and so on.
C
D
A
E
B
D
E
B
A
C
B
A
E
C
D
E
C
D
B
A
A
B
C
D
E
Now, generate random numbers for the columns
Random Numbers
Sequence
Rank
792
1
4
032
2
1
947
3
5
293
4
3
196
5
2
For the layout of LS-Designs, the 4th column from the first random generation is used as the 1st column of LS-Designs, then the 1st column as the 2nd of LS-Design, and so on. The complete Design is:
ANOVA Table for Latin Square Designs
For a statistical analysis, the ANOVA table for LS-Designs is used given as follows:
SOV
df
SS
MS
Fcal
F tab/P-value
Rows
$r-1 = 4$
Columns
$c-1 = 4$
Treatments
$t-1 = 4$
Error
$12$
Total
$rc-1 = 24$
Example: An experiment was conducted with three maize varieties and a check variety, the experiment was laid out under Latin Square Designs, Analyse the data given below
$C$-1
$C$-2
$C$-3
$C$-4
$Total$
$R$-1
1640(B)
1210(D)
1425(C)
1345(A)
$R$-2
1475(C)
1185(A)
1400(D)
1290(B)
$R$-3
1670(A)
710(C)
1665(B)
1180(D)
$R$-4
1565(D)
1290(B)
1655(A)
660(C)
$Total$
Solution:
A
B
C
D
1670
1640
1475
1565
1185
1290
710
1210
1655
1665
1425
1400
1345
1290
660
1180
The following formulas may be used for the computation of Latin Square Design’s ANOVA Table.
In summary, the Latin square design is an effective tool for researchers looking to control for variability and conduct efficient, straightforward analyses in their experiments.
In sampling with replacement, the units drawn are returned to the population before drawing the next unit. This means the same individual can be chosen more than once in the sampling process. The sampling with replacement may provide valuable insights while maintaining flexibility in selecting samples from a given population.
Table of Contents
Key Characteristics of Sampling with Replacement
The following are key characteristics of Sampling with Replacement:
Independence: Each selection is independent, as the same item can be selected multiple times.
Population Size: The effective population size remains the same for each draw since previously selected items are replaced.
Use Cases: This method is commonly used in algorithms, simulations, and bootstrapping techniques in statistics, where it’s important to assess variability or make inferences from a sample.
Example of Sampling with Replacement
As an example of sampling with replacement, suppose, you have a bag containing three colored balls (red, blue, and green), and you sample with a replacement, if you draw a red ball, you put it back into the bag before the next draw. As a result, in subsequent draws, you could again draw a red ball.
Drawing All Possible Samples Using Sampling with Replacement
Question: Consider a population with elements A, B, C, and D. Draw all possible samples of size 2 with replacement from this population.
Solution: In this problem, $N=4$ and $n=2$.
Possible number of samples (with replacement) = $N^n = 4^2 = 16$.
The 16 samples of size 2 are
AA
AB
AC
AD
BA
BB
BC
BD
CA
CB
CC
CD
DA
DB
DC
DD
Question: Draw all possible samples of size 3 with replacement from a population having elements 2, 4, and 6.
Solution:
Population size = $N=3$, Sample size = n = 3$
Number of possible samples are $N^n = 3^3 = 27$
There are two ways to list these samples.
First Method:
First divide possible samples (27) by the population size unit quotient 1 is returned. For example, $\frac{27}{3} = 9, \quad \frac{9}{3}, \quad \frac{9}{3}=1$.
We obtained three quotients: 9, 3, and 1. These are the number of repetitions of population units. First, write every unit 9 times, then 3 times, and lastly, write every unit 1 time.
Second Method:
First, make the samples of size 2, which are easy to draw.
2, 2
2, 4
2, 6
4, 2
4, 4
4, 6
6, 2
6, 4
6, 6
Repeat these samples three times. Since the required number of samples is 27, add every population unit at (the start or) at the end of these samples of size two.
2, 2, 2
2, 2, 4
2, 2, 6
2, 4, 2
2, 4, 4
2, 4, 6
2, 6, 2
2, 6, 4
2, 6, 6
4, 2, 2
4, 2, 4
4, 2, 6
4, 4, 2
4, 4, 4
4, 4, 6
4, 6, 2
4, 6, 4
4, 6, 6
6, 2, 2
6, 2, 4
6, 2, 6
6, 4, 2
6, 4, 4
6, 4, 6
6, 6, 2
6, 6, 4
6, 6, 6
From the table above, 2 is added in the last of the first nine samples, then 4 is added in the last of the next 9 samples and finally 6 is added in the last nine samples.
Real-Life Examples of Sampling with Replacement
The following are some real-life examples of sampling with replacement:
Lottery Draws: In some types of lotteries, numbers can be drawn multiple times before the final selection. For example, if a lottery allows for the same number to be drawn again after being selected, this is akin to sampling with replacement.
Quality Control in Manufacturing: In a factory, inspectors might draw samples of products to test for defects. After testing each item, they return it to the production line before drawing the next sample to maintain the same population size and ensure each product has a chance of being selected again.
Genetic Studies: In genetics, researchers might take DNA samples from a population to study traits or disorders. By replacing each sample with the population (considering genetic diversity), they can analyze the data while allowing for the possibility of selecting the same individual multiple times.
Surveys: When conducting surveys, researchers might randomly select participants from a population (like voters or consumers) and, after querying each individual, they can include them again in the pool for subsequent selections, especially in larger datasets where the same individuals might provide valuable insights if repeated.
Educational Testing: In standardized testing, students might take multiple attempts at a test where scores from previous attempts can be considered again in analyses to assess trends in learning or improvement.
Customer Behavior Analysis: Companies may analyze customer purchase patterns by repeatedly sampling transactions. For instance, if a customer makes multiple purchases, their transaction data might be included in each analysis to understand their buying behavior over time.
