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Test: Correlation - Commerce MCQ


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10 Questions MCQ Test Statistics for Economics - Class XI - Test: Correlation

Test: Correlation for Commerce 2024 is part of Statistics for Economics - Class XI preparation. The Test: Correlation questions and answers have been prepared according to the Commerce exam syllabus.The Test: Correlation MCQs are made for Commerce 2024 Exam. Find important definitions, questions, notes, meanings, examples, exercises, MCQs and online tests for Test: Correlation below.
Solutions of Test: Correlation questions in English are available as part of our Statistics for Economics - Class XI for Commerce & Test: Correlation solutions in Hindi for Statistics for Economics - Class XI course. Download more important topics, notes, lectures and mock test series for Commerce Exam by signing up for free. Attempt Test: Correlation | 10 questions in 10 minutes | Mock test for Commerce preparation | Free important questions MCQ to study Statistics for Economics - Class XI for Commerce Exam | Download free PDF with solutions
Test: Correlation - Question 1

Which type of correlation is observed when an increase in one variable is accompanied by a decrease in the other variable?

Detailed Solution for Test: Correlation - Question 1
Negative Correlation is observed when an increase in one variable is accompanied by a decrease in the other variable. In other words, when the two variables move in opposite directions, it indicates a negative correlation. For example, if there is a negative correlation between the sale of woollen garments and day temperature, it means that as the day temperature increases, the sale of woollen garments decreases, and vice versa.
Test: Correlation - Question 2

What does a correlation coefficient close to +1 indicate?

Detailed Solution for Test: Correlation - Question 2
A correlation coefficient close to +1 indicates a strong positive relationship between variables. This means that as one variable increases, the other variable also tends to increase. It suggests a strong linear association between the two variables, and the direction of change is in the same direction. For example, if you find a correlation coefficient of +0.9 between height and weight, it implies that as height increases, weight tends to increase as well. This is a valuable insight when studying relationships between different variables.
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Test: Correlation - Question 3

Which method provides a visual representation of the strength and direction of correlation between two variables?

Detailed Solution for Test: Correlation - Question 3
A Scatter Diagram provides a visual representation of the strength and direction of correlation between two variables. It involves plotting one variable on the X-axis and the other variable on the Y-axis, resulting in a cluster of points called a scatter plot. The proximity and overall direction of the scatter points help us analyze the relationship between the two variables visually.
Test: Correlation - Question 4
What is Spearman's Rank Correlation coefficient useful for?
Detailed Solution for Test: Correlation - Question 4
Spearman's Rank Correlation coefficient is useful for analyzing the correlation between variables that may not have a clear or objective measurement, especially when dealing with qualitative variables. It helps assess the strength and direction of the relationship between variables that can be ranked but may not have a numerical value.
Test: Correlation - Question 5
When is the Step Deviation method used for calculating Karl Pearson's Coefficient of Correlation?
Detailed Solution for Test: Correlation - Question 5
The Step Deviation method is used for calculating Karl Pearson's Coefficient of Correlation when variables have large values. This method helps reduce the computational burden by converting the values into step deviations from assumed means, making the calculations more manageable, especially when dealing with large numbers.
Test: Correlation - Question 6
What does Spearman's Rank Correlation coefficient (rs) represent when ranks are repeated in the formula?
Detailed Solution for Test: Correlation - Question 6
When ranks are repeated in Spearman's Rank Correlation formula, the terms m1, m2, and so on represent the number of repetitions of those ranks. It is essential to account for rank repetitions when calculating the correlation coefficient for qualitative variables using Spearman's method.
Test: Correlation - Question 7
What does Karl Pearson's Coefficient of Correlation range from?
Detailed Solution for Test: Correlation - Question 7
Karl Pearson's Coefficient of Correlation ranges from -1 to +1. This range represents the strength and direction of the linear relationship between two variables. A value close to +1 indicates a strong positive correlation, a value close to -1 indicates a strong negative correlation, and a value close to 0 suggests little to no linear relationship between the variables.
Test: Correlation - Question 8
What does a correlation coefficient close to zero suggest?
Detailed Solution for Test: Correlation - Question 8
A correlation coefficient close to zero suggests a weak or no relationship between the variables being studied. It indicates that there is little to no linear association between the two variables, and their changes do not correspond in a meaningful way. This is valuable information when analyzing data to determine the strength of the relationship between variables.
Test: Correlation - Question 9
What is the range of Spearman's Rank Correlation coefficient (rs)?
Detailed Solution for Test: Correlation - Question 9
Spearman's Rank Correlation coefficient (rs) also ranges from -1 to +1, similar to Karl Pearson's Coefficient of Correlation. A value close to +1 indicates a strong positive rank correlation, a value close to -1 indicates a strong negative rank correlation, and a value close to 0 suggests little to no rank correlation between the qualitative variables.
Test: Correlation - Question 10
What is the purpose of a scatter plot in correlation analysis?
Detailed Solution for Test: Correlation - Question 10
A scatter plot is used in correlation analysis to visualize and analyze the relationship between two variables. It provides a graphical representation of the data, where one variable is plotted on the X-axis, and the other is plotted on the Y-axis. The scatter points on the plot help us understand the strength and direction of the correlation between the variables, making it a valuable tool for exploring relationships in data.
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