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Probable Error & Correlation coefficient - Business Mathematics & Statistics Video Lecture | SSC CGL Tier 2 - Study Material, Online Tests, Previous Year
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FAQs on Probable Error & Correlation coefficient - Business Mathematics & Statistics Video Lecture - SSC CGL Tier 2 - Study Material, Online Tests, Previous Year
| 1. What is the probable error in business mathematics and statistics? |  |
| 2. How is the probable error calculated? | |
Ans. The probable error is calculated by taking the standard deviation of a set of data and dividing it by the square root of the sample size. This provides an estimate of the average deviation of the data from the mean. The resulting value can be used to determine the range within which the true value is likely to fall with a certain level of confidence.
| 3. What is the correlation coefficient in business mathematics and statistics? | |
Ans. The correlation coefficient in business mathematics and statistics is a measure of the strength and direction of the linear relationship between two variables. It ranges from -1 to +1, where -1 indicates a perfect negative correlation, +1 indicates a perfect positive correlation, and 0 indicates no correlation. The correlation coefficient can help determine the degree to which changes in one variable can be predicted by changes in the other variable.
| 4. How is the correlation coefficient calculated? | |
Ans. The correlation coefficient is calculated by dividing the covariance of two variables by the product of their standard deviations. The resulting value ranges from -1 to +1 and provides a measure of the strength and direction of the linear relationship between the variables. A positive value indicates a positive correlation, a negative value indicates a negative correlation, and zero indicates no correlation.
| 5. What does a correlation coefficient of 0.8 mean in business mathematics and statistics? | |
Ans. A correlation coefficient of 0.8 in business mathematics and statistics indicates a strong positive linear relationship between two variables. It suggests that changes in one variable are highly likely to be accompanied by proportional changes in the other variable. This high level of correlation indicates that the variables are closely related and can be used to predict each other's values with a relatively high degree of accuracy.
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