Tag: linear regression

Quizzes (2) Questions (75)

Questions Related to linear regression

Multiple choice business economics and quantitative methods correlation analysis aspects of correlation scatter graphs and correlation linear regression

Match the following :
List I
1. Correlation in Bivariable Frequency table.
2. Probable error and co-efficient correlation.
3. Rank correlation.
4. Karl pearson's co-efficient of correlation.
List II
a) $\dfrac{{\Sigma}{f}{d}{x} {d}{y} - {\dfrac{{\Sigma}{f}{d}{x} {\Sigma}{f}{d}{y}}{N}}}{\sqrt{{\Sigma}{f}{d}{{x}^2} - {\dfrac{({\Sigma}{f}{d}{x})^2}{N}}}{\sqrt{{\Sigma}{f}{d}{{y}^2} - {\dfrac{({\Sigma}{f}{d}{y})^2}{N}}}}}$
b) $ r = 0.6745 \dfrac{{-r}^2}{\sqrt{N}}$
c) $ r = 1 - \dfrac{{6}{\Sigma}{{d}^2}}{n({{n}^2 - {1})}}$
d) $ r = \dfrac{{\Sigma}{x}{y}}{{N}{\sigma}{x}{\sigma}{y}}$
Codes :
1   2   3  4

  1. b $\space$ d $\space$ a $\space$ b
  2. b $\space$ a $\space$ c $\space$ d
  3. a $\space$ b $\space$ c $\space$ d
  4. d $\space$ b $\space$ c $\space$ a
Reveal answer Fill a bubble to check yourself
D Correct answer
Explanation

The correct formula for the calculation of correlation through karl pearson is option d, for bivarial frequency table is c and also for rank correlation in option c, therefore the correct option is D.

Multiple choice business economics and quantitative methods correlation analysis aspects of correlation scatter graphs and correlation linear regression

Karl Pearson's co-efficient of correlation between two variables is _____________.

  1. the product of their standard deviations

  2. the square root of the product of their regression co-efficient

  3. the co-variance between the variables

  4. none of the above
Reveal answer Fill a bubble to check yourself
B Correct answer
Explanation

Karl Pearson's correlation coefficient is defined as the geometric mean of the two regression coefficients, which is the square root of their product.

Multiple choice business economics and quantitative methods correlation analysis aspects of correlation scatter graphs and correlation linear regression

F-test is used to test the significance of the differences between ______________.

  1. co-efficient of correlation between two sample groups

  2. co-efficient of correlation among more than two sample groups

  3. average between two sample groups

  4. averages of more than two sample groups
Reveal answer Fill a bubble to check yourself
D Correct answer
Explanation

The F-test (ANOVA, or Analysis of Variance) is a statistical test used to determine whether there are any statistically significant differences between the means of three or more independent sample groups. For two groups, a t-test is typically used.

Multiple choice business economics and quantitative methods correlation analysis aspects of correlation scatter graphs and correlation linear regression

Match the following items in List - I with most suitable options in List - II:

List-I List-II
(a) Fisher 1. Inverse probability
(b) Karl Pearson 2. Normal Distribution
(c) Thomas Baye's 3. Correlation Coefficient
(d) Karl Gauss 4. Index Numbers
  1. $(a) - 4, (b) - 3, (c) - 2, (d) - 1$
  2. $(a) - 4, (b) - 3, (c) - 1, (d) - 2$
  3. $(a) - 4, (b) - 2, (c) - 3, (d) - 1$
  4. $(a) - 4, (b) - 2, (c) - 1, (d) - 3$
Reveal answer Fill a bubble to check yourself
B Correct answer
Explanation

Fisher is associated with Index Numbers (in some contexts), Karl Pearson with Correlation Coefficient, Thomas Bayes with Inverse Probability, and Karl Gauss with Normal Distribution.

Multiple choice business economics and quantitative methods correlation analysis aspects of correlation scatter graphs and correlation linear regression

__________  gives a precise numerical value of the degree of linear relationship between two variables X and Y.

  1. Scatter diagram

  2. Spearmans coefficient of correlation

  3. Karl Pearsons Coefficient of Correlation

  4. None
Reveal answer Fill a bubble to check yourself
C Correct answer
Explanation

Karl Pearson's coefficient of correlation provides a precise numerical value between -1 and 1 to indicate the strength and direction of a linear relationship.