MCQs Correlation and Regression Quiz 8

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The post is about MCQS Correlation and Regression Quiz. There are 20 multiple-choice questions bout correlation and regression analysis covering MCQs about correlation analysis, regression analysis, assumptions of correlation and regression analysis, coefficient of determination, predicted and predictor variables, etc. Let us start with the MCQS correlation and Regression Quiz.

Online Multiple Choice Questions about Correlation and Regression Analysis with Answers

1. Which of the following regression equations represents the strongest relationship between $X$ and $Y$?

 
 
 
 

2. Given that $X=1.50 + 0.50Y$, what is the predicted value for a $Y$ value of 6?

 
 
 
 

3. Two variables are said to be uncorrelated if

 
 
 
 

4. The estimated coefficient of determination is equal to all except which of the following?

 
 
 
 

5. The regression coefficients may have the wrong sign for the following reasons:

 
 
 
 

6. If one wished to test the relationship between social and juvenile delinquency with the number of siblings held constant, the most appropriate technique would be

 
 
 
 

7. The distribution of sample correlation is

 
 
 
 

8. The coefficient of multiple determination is 0.81. Thus, the multiple correlation coefficient is

 
 
 
 

9. The coefficient of partial determination differs from the coefficient of multiple determinations in that

 
 
 
 

10. Whenever predictions are made from the estimated regression line, the relation between $X$ and $Y$ is assumed to be:

 
 
 
 

11. A random sample of paired observations has been selected and the sample correlation coefficient is -1. From this result, we know that:

 
 
 
 

12. In regression equation $y=\alpha + \beta x + \varepsilon$, both $x$ and $y$ variables are

 
 
 
 

13. The output of a certain chemical-processing machine is linearly related to temperature. At $-10^\circ C$ the processor output is 200 KGs per hour at $40^\circ C$ the output is 220 KGs per hour. Calculate the linear equation for KGs per hour of output ($Y$) as a function of temperature in degrees Celsius ($X$):

 
 
 
 

14. What information is given by a value of the coefficient of determination?

 
 
 
 

15. The sum of squares of which type of deviations is minimized by the least square regression:

 
 
 
 

16. The major difference between regression analysis and correlation analysis is that in regression analysis:

 
 
 
 

17. Which of the following is NOT an assumption underlying regression analysis?

 
 
 
 

18. The regression model can be used to

 
 
 
 

19. The term homoscedasticity refers to

 
 
 
 

20. The dependent variable is also known as

 
 
 
 

Online MCQs Correlation and Regression Quiz

  • The distribution of sample correlation is
  • The major difference between regression analysis and correlation analysis is that in regression analysis:
  • The term homoscedasticity refers to
  • The sum of squares of which type of deviations is minimized by the least square regression:
  • A random sample of paired observations has been selected and the sample correlation coefficient is -1. From this result, we know that:
  • The output of a certain chemical-processing machine is linearly related to temperature. At $-10^\circ C$ the processor output is 200 KGs per hour at $40^\circ C$ the output is 220 KGs per hour. Calculate the linear equation for KGs per hour of output ($Y$) as a function of temperature in degrees Celsius ($X$):
  • What information is given by a value of the coefficient of determination?
  • Whenever predictions are made from the estimated regression line, the relation between $X$ and $Y$ is assumed to be:
  • The estimated coefficient of determination is equal to all except which of the following?
  • The coefficient of partial determination differs from the coefficient of multiple determinations in that
  • The coefficient of multiple determination is 0.81. Thus, the multiple correlation coefficient is
  • In regression equation $y=\alpha + \beta x + \varepsilon$, both $x$ and $y$ variables are
  • The dependent variable is also known as
  • The regression coefficients may have the wrong sign for the following reasons:
  • The regression model can be used to
  • If one wished to test the relationship between social and juvenile delinquency with the number of siblings held constant, the most appropriate technique would be
  • Given that $X=1.50 + 0.50Y$, what is the predicted value for a $Y$ value of 6?
  • Which of the following regression equations represents the strongest relationship between $X$ and $Y$?
  • Which of the following is NOT an assumption underlying regression analysis?
  • Two variables are said to be uncorrelated if
mcqs correlation and regression quiz

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MCQs Basic Statistics with Answers 16

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The post is about Online MCQs Basic Statistics with Answers. There are 20 multiple-choice questions about variables, data, data classification, data types, measurable and non-measurable characteristics, frequency distribution, tables, and attributes. Let us start with the MCQS Basic Statistics with Answers.

Please go to MCQs Basic Statistics with Answers 16 to view the test

Online MCQs Basic Statistics with Answers

MCQs Basic Statistics with Answers https://itfeature.com
  • A Characteristic which cannot be measurable is called
  • The number 115.9700 rounded off to the nearest tenth (one decimal place) is
  • $\sum\limits_{i=1}^n X_i=?$
  • A single value which represents the whole set of data is
  • Important and basic classification of data are
  • Data classified by geographical regions is called
  • Data classified by attributes are called
  • Data classified by the time of their occurrence is called
  • Data classified by two characteristics at a time are called
  • Data used by an agency that was originally collected by them are
  • Data in the population census reports are
  • Measurements usually provide
  • Counting or enumerations usually provide
  • Hourly temperature recorded by the Weather Bureau represents
  • The amount of milk given by a cow is a
  • The number of accidents recorded yesterday in Multan is a
  • The colour of hair is a
  • Data arranged in ascending or descending order is called
  • The process of arranging data into rows and columns is called
  • How many classes are generally used for arranging data

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Five Number Summary Statistics

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The five number summary statistics is a set of descriptive statistics that summarizes a data set under study. Five number summary statistics consists of five numerical values that divide the data set into four equal parts. The five number summary statistics are also known as quartiles five number summary.

Five Number Summary Statistics includes the following values:

  • Minimum Value: The smallest value in the data set.
  • First Quartile ($Q_1$): The value that separates the lowest 25% of the data from the remaining data sets.
  • Median ($Q_2$): The value that separates the lowest 50% from the highest 50% of the data.
  • Third Quartile ($Q_3$): The value that separates the lowest 75% of the data from the highest 25% of the data.
  • Maximum value: The largest value in the data set.

Visualization of Five Number Summary Statistics

A box plot can visually represent the five number summary statistics. The box plot displays the dataset’s range (Minimum and Maximum), the median ($Q_2$), and the quartiles ($Q_1$ and $Q_2$).

The Five number summary statistics is a useful way to quickly summarize: the central tendency, variability, and distribution of a data set.

Five Number summary Statistics https://itfeature.com

Interquartile Range

The interquartile range (IQR) is a measure of variability that is based on the five number summary of a dataset. It is the difference between the third quartile ($Q_3$) and the first quartile ($Q_1$) of a data set. The rectangle in the box plot represents the interquartile range. The box represents the middle 50% of the data (between $Q_1$ and $Q_3$), with a line inside the box marking the median ($Q_2$).

What is a Box Plot

A box plot is a graphical representation of the five number summary statistics. It is also known as a box-and-whisker plot. It is used to see the distribution of the data and to detect outliers graphically/visually.

five number summary statistics box plot

The relative positions of the quartiles and the median can provide clues about the shape of the distribution. For example, if the median is closer to $Q_1$, the distribution might be right-skewed. If the median is closer to $Q_3$, it might be left-skewed. If the median is roughly halfway between $Q_1$ and $Q_3$, the distribution might be roughly symmetric. The whiskers extend from the box to the minimum and maximum values, and sometimes outliers are plotted as individual points beyond the whiskers.

The five-number summary is a valuable tool for understanding the distribution of data and making comparisons between different datasets. It is often used in exploratory data analysis, quality control, and other statistical applications.

How to Compute the Five Number Summary Statistics:

  • First, arrange the data in ascending order.
  • Find the minimum and maximum values in the data set.
  • Find the median:
    • If the number of data points is odd, the median is the middlemost value in the sorted data.
    • If the number of data points is even, the median is the average of the two middlemost middle values of the sorted data.
  • Find $Q_1$ and $Q_3$:
    • $Q_1$ is the median of the lower half of the data (excluding the median if the number of data points is odd).
    • $Q_3$ is the median of the upper half of the data (excluding the median if the number of data points is odd).

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