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  • 1. CORRELATION A Statistical technique that is used to analyse the strength and direction of the relationship between two quantitative variable is called Correlational analysis. Two variables are said to be in correlation if the change in one of the variable results in a change in other variable. E g :- 1) Frequency of smoking and lungs damage , 2) Sales revenue and expenses incurred on advertising.

2. Importance of correlation If variables are linearly related to each other then it helps in estimation of one from the other. Advertisement and sales Prices and Demand We use Regression Analysis to find the value of one variable from the other 3. TYPES OF CORRELATION POSITIVE AND NEGATIVE LINEAR AND NON-LINEAR SIMPLE ,PARTIAL AND MULTIPLE 4. POSITIVE CORRELATION AND NEGATIVE CORRELATION POSITIVE CORRELATION NEGATIVE CORRELATION If the variables vary in same direction, correlation is said to be POSITIVE. If one variable increases, the other also increases on the other hand, if one variable decreases, the other also decreases. If both variables vary in the opposite direction, correlation is said to be NEGATIVE. If one variable increases and the other decreases, or one decreases the other increases. 5. LINEAR CORRELATION NON- LINEAR CORRELATION LINEAR CORRELATION If the extent of change in one variable tends to have a constant ratio in the extent of change in another variable, then the correlation is said to be LINEAR. NON-LINEAR CORRELATION If the extent of change in one variable tends to have no consistent ratio in the extent of change in another variable, then the correlation is said to be NON-LINEAR. 6. SIMPLE,PARTIAL AND MULTIPLE CORRELATION When only two variables are involved, it is simple correlation When three or more than three variables are involved, we can compute either partial or multiple correlation 7. Methods of correlation graphic Scatter diagram algebraic 1. Karl pearson 2. Rank method 8. Scatter Diagram Scatter diagram is a graph or chart which helps to determine whether there is a relationship between two variables by examining the graph of the observed data. A scattered diagram can give us two types of information: Pattern that indicate that the variables are related. If the variables are related,what kind of line or estimating equation,describes this relationship. 9. KARLS PEARSONS COEFFIENT OF CORRELATION Karl Pearsons Coefficient of Correlation denoted by- r The coefficient of correlation r measure the degree of linear relationship between two variables say x & y. r = N dxdy - dx dy N dx-(dx)N dy-(dy) 10. The value of correlation coefficient r ranges from -1 to +1 If r = +1, then the correlation between the two variables is said to be perfect and positive If r = -1, then the correlation between the two variables is said to be perfect and negative If r = 0, then there exists no correlation between the variables Interpretation of Correlation Coefficient (r) 11. REGRESSION The statistical technique that express the relationship between two or more variables in the form of an equation to estimate the value of a variable, based on the given value of another variable is called regression analysis. eg :- Profit after Sales of a firm. 12. Difference between dependent variable and independent variable Independent Variable 1. The known variable is called the independent variable. 2. What we typically call X. 3. Variable that is controlled or manipulated. 4. It is plotted on horizontal axis. 5. An input variable. Dependent Variable 1. The variable we are trying to predict is the dependent variable. 2. What we typically call Y. 3. Variable that cannot be controlled or manipulated. 4. It is plotted on vertical axis. 5. An output variable. 13. Difference between Regression and Correlation Regression A statistical method used to describe the nature of relationship. In linear regression analysis one variable is considered as dependent variable and other as independent variable Correlation A statistical method used to determine whether a relationship between two or more variables exist. In correlation analysis we examine the degree of association between two variables 14. Advantages of Regression Analysis It helps in developing a regression equation by which the value of a dependent variable can be estimated given a value of an independent variable. It helps to determine standard error of estimate to measure the variability or spread of values of a dependent variable with respect to the regression line. 15. Estimation using the Regression Line The equation for a straight line where the dependent variable Y is determined by the independent variable X is: Y = a + bx Where, a = y-intercept b = slope of the line Y = value of dependent variable X = value of independent variable 16. THE METHOD OF LEAST SQUARE It is a method of having a good fit of a line which minimizes the error between the estimated points on the line and actual points that were used to draw it. In this method Y represents the individual value of the observed points measured along the Y-axis and Y(y-hat) symbolize the individual values of the estimated points. The Estimated Line is: = a + bx 17. b = XY - X2 n 2 a = -b Where, a = Y-intercept b = slope of the best-fitting estimating line. X = value of independent variable Y = value of dependent variable = mean of the values of the independent variable = mean of the values of the dependent variable x x y xn y y x 18. COEFFICIENT OF DETERMINATION The convenient way of interpreting the value of correlation coefficient is to use of square of coefficient of correlation which is called Coefficient of Determination. The Coefficient of Determination is r2. r2 = 1- (Y- )2 (Y-Y )2 19. STANDARD ERROR OF ESTIMATE Standard error of estimate measures the variability of the scatter of the observed values around the regression line. It is given by: Se= (Y- )2 n-2 If Se=0, the estimating equation is expected to be a perfect estimator of the dependent variable. 20. WHAT DOES TIME-SERIES MEAN? A time series is a sequence of data points, measured typically at successive points in time spaced at uniform time intervals. Time series is a set of measurements of a variable that are ordered through time Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data 21. DIFFERENCE WITH REGRESSION ANALYSIS Time series Analysis Regression Analysis Time series forecasting is the use of a model to predict future values based on previously observed values. Regression analysis is often employed in such a way as to test theories that the current value of one time series affects the current value of another time series. Regression analysis cannot explain seasonal and cyclical effects. It shows or suggests periodicity of a data like seasonal and cyclical effects. 22. COMPONENTS OF TIME SERIES SECULAR TREND CYCLICAL VARIATIONS SEASONAL VARIATIONS IRREGULAR VARIATIONS 23. SECULAR TREND A time-series which displays a steady tendency of either upward or downward movement in the average (or mean) value of the forecast variable (let us say y) over a long period of time is called Trend. If we talk about commodities, Secular Trend is affected by prices, productions and sales of the commodity as well as the population of the area. Examples- 1. We find that over the last few years the sales of Laptop in Ranchi has increased. so, we can say that the sales of Laptop is showing an Upward Trend. 2. Use of Landline Phone has decreased over the last few years. This shows the Declining Trend of using Landline Phone. 24. Units years Upward trend of sales of Laptops in Ranchi 2000 2001 2002 2003 2004 2005 2006 2007 2000 4000 6000 8000 10000 25. units(in000) years Declining trend of using Landline Phones in India 2000 01 02 03 05 06 07 08 09 10 11 30 60 90 120 150 180 04 26. CYCLICAL VARIATION Cyclical variations are long-term movements that represent consistently recurring rises and declines in activity. Timing is the most important factor which affect the Cyclical Variations. for example- Business Cycle, it consists of the recurrence of the up and down movements of business activity 27. depression prosperity Prosperity or boom Economicactivities time Cyclical Variation(Business cycle) 28. SEASONAL VARIATION Seasonal variations are those periodic movements in business activity which occur regularly every year. Since these variations repeat during a period of twelve months so, they can be predicted fairly accurately. Seasonal Variations are caused by climate and weather conditions, customs, festivals and habits. for example-Sales of Cold-drinks goes up in summer season than any other season 29. Units years2000 2001 2002 2003 2004 2005 2006 Sales of Cold-drinks 10000 12000 14000 16000 18000 20000 30. IRREGULAR VARIATION Irregular variations refer to such variations in business activity which do not repeat in a definite pattern. In these type of variations the pattern of the variable is unpredictable. Irregular Variations are caused by unpredictable factors like natural disasters (earthquakes, floods, wars etc.).These are unpredictable and no one has control over it. For example-Production of cars tremendously went down after earthquake came in Japan in Nov 2011. 31. 2005 2006 2007 2008 2009 2010 2011 100000 150000 200000 250000 300000 350000 units Production of cars in Japan years 32. NEED OF TIME-SERIES ANALYSIS Helpful in evaluating current accomplishments Actual performances can be compared with the expected performance and the cause of the variations analysed Facilitates comparison. Different time-series can be compared and important conclusions can be drawn from this with the help of this we can take decisions 33. Thank you