Correlation
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The document is a presentation on correlation coefficient that defines it, provides the formula, and outlines its types, properties, characteristics, coefficient of determination, advantages, and disadvantages. It discusses how correlation indicates a relationship between variables, is measured by r and lies between -1 and 1, and is unaffected by scale or origin changes.
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Help me with these questions please.1. Name four characteristics t.pdf
Help me with these questions please.
1. Name four characteristics that are used to describe single variable data.
2. What are the advantages of using a stemplot over a histogram? What are the disadvantages?
3. What are the advantages of using the mean as the center? What are the disadvantages?
4. What are the advantages of using the median as the center? What are the disadvantages?
5. What type of graph is used for bivariate data?
6. What is the difference between explanatory and response variables?
7. What does the correlation coefficient, r, measure?
8. What is the range of values of r?
9. What does the coefficient of determination, r^2, measure?
10. What is the range of values of r^2?
11. How is a residual plot constructed?
12. How is a residual plot interpreted?
Solution
1) i. Randomness ii. Presentable iii. Reliable iv. Genuine
2) Stemplot gives list of values as it is in a easy form of presentation.
Disadvantage is while histogram enables comparison about the data, stemplot just presents data
in a grouped form
3) Mean is easily understood and also a good central measure. So many distributions we find that
sample mean estimates population parameter but disadvantage of mean is it is affected by
extreme items or outliers thus giving wrong picture about average.
4) Median is reliable and not affected by outliers as it is affectd only by the order of the item.
The disadvantage is that for continuous data, it is not possible always to arrange in ascending and
find median.
5) Planar graph with 2 coordinates used for bivariate data.
6) Explanatory variable is the independent variable which can take any value but response
variable value depends on the value of explanatory variable
7) Correlation coefficient measures the dependence or association of two variables
8) r lies between -1 and +1
9) Coefficient of determination represent variation of y wrt x. It is square of r, and always
positive thus gives a clear picture of relation between x and y.
10) r^2 varies from 0 to 1
11) Residual plot is constructed using scatter diagram and least square line.
REsidue plot is the deviation of actual from estimated regression curve.
12) REsidual plot is the absolute difference of y and y estimate..
Multiple regression presentation
This document discusses multiple regression analysis and its use in predicting relationships between variables. Multiple regression allows prediction of a criterion variable from two or more predictor variables. Key aspects covered include the multiple correlation coefficient (R), squared correlation coefficient (R2), adjusted R2, regression coefficients, significance testing using t-tests and F-tests, and considerations for using multiple regression such as sample size and normality assumptions.
Correletion.pptx
Correlation coefficient and regression are two statistical techniques used to measure the relationship between two variables. Correlation coefficient is a measure of the strength and direction of the linear relationship between two variables, while regression is a technique used to model the relationship between a dependent variable and one or more independent variables.
Correlation Analysis for MSc in Development Finance .pdf
• Correlation is another way of assessing the relationship between variables.
– it measures the extent of correspondence between the ordering of two random variables.
• There is a large amount of resemblance between regression and correlation but for their methods of interpretation of the relationship.
– For example, a scatter diagram is of tremendous help when trying to describe the type of relationship existing between two variables.
correlationandregression1-200905162711.pdf
1. Correlation analysis measures the strength and direction of the linear relationship between two variables. It ranges from -1 to 1, where -1 is a perfect negative correlation, 0 is no correlation, and 1 is a perfect positive correlation.
2. Scatter diagrams provide a visual representation of the relationship between two variables but do not provide a precise measure of correlation. Pearson's correlation coefficient (r) calculates the numerical strength of the linear relationship.
3. Correlation is widely used in fields like agriculture, genetics, and physiology to study relationships between variables like crop yield and fertilizer use, gene linkage, and organism growth and environmental factors.
Correlation and Regression
1. Correlation analysis measures the strength and direction of the linear relationship between two variables. It ranges from -1 to 1, where -1 is a perfect negative correlation, 0 is no correlation, and 1 is a perfect positive correlation.
2. Scatter diagrams provide a visual representation of the relationship between two variables but do not provide a precise measure of correlation. Pearson's correlation coefficient (r) calculates the numerical strength of the linear relationship.
3. Correlation is widely used in fields like agriculture, genetics, and physiology to study relationships between variables like crop yield and fertilizer use, gene linkage, and organism growth and environmental factors.
Correlation and regression
The document discusses correlation and regression analysis. It defines correlation as the statistical relationship between two variables, where a change in one variable corresponds to a change in the other. The key types of correlation are positive, negative, simple, partial and multiple, and linear and non-linear. Regression analysis establishes the average relationship between an independent and dependent variable in order to predict or estimate values of the dependent variable based on the independent variable. Methods for studying correlation include scatter diagrams and Karl Pearson's coefficient of correlation, while regression analysis uses equations to model the linear relationship between variables.
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Correlation
- 3. 1.Definition 2.Correlation coefficient formula. 3.Types of correlation. 4.Properties of correlation coefficient. 5. Characteristics of correlation coefficient. 6.Coefficient of determination. 7.Advantage. 8.Disadvantage.
- 4. 1. Correlation indicates whether there is any relation between the variables . 2. It measure the relation between two variable. 3. It denoted by r.
- 5. Where n=number of observation x= independent variable y=dependent variable
- 7. 1. Correlation coefficient lies between -1 and +1. Symbolically, -1 ≤ r ≤ +1. 2. Correlation coefficient is independent of change of origin and scale of measurement. 3. Correlation coefficient has no unit.
- 9. It lies between 0 to 1. It is the proportion of the total variation in the dependent variable. It is represented by r^2.
- 10. 1. It is simple to understand and easy to calculate. 2. It is very useful in the case of data which are qualitative in nature. 3. It’s research to collect much more data than experiments.
- 11. 1. Correlation research only uncovers a relationship 2. It cannot provide a conclusive reason for why there's a relationship. 3. A correlative finding doesn't reveal which variable influences the other.