Six Sigma Black Belt Study Guides

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1 Six Sigma Black Belt Study Guides 1 Powered by POeT Solvers Limited.

2 Analyze Correlation and Regression Analysis 2 Powered by POeT Solvers Limited.

3 Variables and relationships Correlation and Regression Analysis When dealing with two or more variables, the relationships between /among them if any exists), the impact of one variable on the other (s) can be studied under correlation and regression analysis. For simplicity, let us consider two variables X and Y. Our objective is to find out whether any relation exists between them or not. If any relation exists between them say Y = F(X), then we have to find out the relation F in the best possible way. X is called an explanatory or independent variable and Y is called response or dependent variable. Scatter diagram It is a graphical representation that depicts the relationship between two variables. It provides both a visual and statistical means to test the strength of a relationship. Construction of a scatter diagram Collect the data on both variables (preferable sample size 20 or more). Plot the data points on a XY plane where variable 1 is plotted along X axis and variable 2 is plotted along Y axis. 3 Powered by POeT Solvers Limited.

4 Identify the linear relationship between them if it exists. Identify the strength of the linear relationship as strong positive, weak positive, no relationship, weak negative, and strong negative. 4 Powered by POeT Solvers Limited.

5 Correlation coefficient Correlation and Regression Analysis It is a measure of strength of the linear relationship between two variables denoted by r and the range is given by -1 r 1. If the value of r is 1 or close to 1, then the two variables have strong positive relationship. If the value of r is 1 or close to 1, then they have strong negative relationship. If the value of r is 0, then no relationship exists. E.g. Let us consider a process in ABC Services Pvt. Ltd. The team leader of the process decides to find out the impact of training on performance. The following data is collected: Hours of Training Defects Hours of Training Defects Powered by POeT Solvers Limited.

To fulfill the requirement we need to draw the scatter plot and identify the relationship between variables (training hours and number of defects).

6 To fulfill the requirement we need to draw the scatter plot and identify the relationship between variables (training hours and number of defects). Minitab steps Copy the data in the Minitab worksheet as it is given. Select Graph > Scatter plots and then select simple. Select X and Y variable as hours of training and number of defects respectively. Minitab output 6 Powered by POeT Solvers Limited.

Interpretation From the scatter plot, it is clear that the hours of training and number of defects made by the process executives (PE) have a strong negative relationship.

7 Interpretation From the scatter plot, it is clear that the hours of training and number of defects made by the process executives (PE) have a strong negative relationship. To find out the correlation coefficient we use excel function CORREL. It is given in the excel screenshot. The correlation coefficient r = Powered by POeT Solvers Limited.

8 Calculation of sample correlation coefficient (r) The sample correlation coefficient (r) is computed as where 8 Powered by POeT Solvers Limited.

9 Hypothesis Test for the Correlation Coefficient Null Hypothesis H 0 : ρ = 0 Alternative Hypothesis H 1 : ρ 0 Test statistic where T has the t-distribution with (n 2) degrees of freedom NB: The sample correlation coefficient (r) is used to estimate the population correlation coefficient (ρ). 9 Powered by POeT Solvers Limited.

10 Hypothesis Test for the Correlation Coefficient ρ Fisher s transformation of r: If W = 1 ln 1 + r then W is approximately normally distributed with r mean = 1 ln 1 + ρ & variance = ρ n - 3 Null Hypothesis H 0 : ρ = ρ 0 Alternative Hypothesis H 1 : ρ ρ 0 Test statistic: NB: This is a general test used to test the ρ against any non-zero value Powered by POeT Solvers Limited.

11 Confidence interval for the Correlation Coefficient An approximate 100(1-α)% confidence interval for ρ is given by 11 Powered by POeT Solvers Limited.

12 Simple linear regression model With the use of scatter plot and correlation coefficient, the strength of the linear relationship is detected (if any relationship exists). The next step is to determine the linear model which can be used in forecasting. A linear model is defined as a relation between two variables where changes in one variable produce a proportionate change in the other variable. Mathematically a linear model is expressed as Y = a + bx, where a and b are constants. When two variables do not have a linear relationship (that is the case in many practical situations), then their relationship can be converted into a linear model with suitable transformation. E.g. Two variables X and Y have a relation as Y = ab X Taking the log on both side produces log Y = log a + X log b. It can be written as Y = a + b X which is a linear model in X and Y. Then further analysis can be done using this linear model Powered by POeT Solvers Limited.

13 Method of least square It is a method of best fitting the linear model to the observed sample. Let us consider two variables X and Y for the study and samples (x i, y i ); i = 1, 2,.., n are collected. We want to find out a simple linear regression model Y = a + bx + ϵ, where ϵ is the error. For a set of paired data {(x i, y i ) / i = 1, 2,.., n} the least square estimates of the regression coefficient are the values a and b, for which [y i (a + bx i )] 2 is minimum. Note that [y i (a + bx i )] is the deviation of the point (x i, y i ) from the fitted line which is illustrated graphically in the next slide. Least square method is the technique of minimizing the deviations to best fit the linear model Powered by POeT Solvers Limited.

14 The line represents the fitted line and the points are (x i, y i ). The deviations are represented by the arrow mark ( ). Y X 14 Powered by POeT Solvers Limited.

15 Residuals Response Fitted Response = Residual from the fit For a set of data points (x i, y i ) if the fitted regression line is y = a + bx, then the residuals are given by (y i y i ) Goodness of Fit The total variation in the responses is given by S yy = (y i y) 2 S yy = (y i y) 2 = (y i y i ) 2 + (y i y) 2 = SS RES + SS REG SS REG summarizes the variability explained by the model. SS RES summarizes the variability between response and their fitted values (unexplained by the model). How much variation is explained by the model is a measure of goodness of fit Powered by POeT Solvers Limited.

16 Simple linear regression hypothesis testing Example: The following data refers to the number of claims (X) received by a motor insurance company in a week and the number of settlements (Y) of these claims in the following week during 10 randomly selected weeks in a year. X Y A regression model Y = a +bx + ϵ is to be fitted on the above data. Display the data in a scatter plot and comment on the selection of a linear model for regression. Test the hypothesis is b = 0 against b Powered by POeT Solvers Limited.

Select graph > Scatter plots Select simple and then click OK Minitab gives the following output The scatter diagram shows that there is a strong relationship between the

17 Select graph > Scatter plots Select simple and then click OK Minitab gives the following output The scatter diagram shows that there is a strong relationship between the number of claims and the number of settlements. The assumption of the straight line model Y = a + bx + ϵ appears to be reasonable Powered by POeT Solvers Limited.

18 Regression analysis (Fit a linear model) Select Stat > Regression > Regression Select the predictor variable as X and response variable as Y Minitab output Regression analysis: Y versus X The regression equation is Y = X Predictor Coef SE Coef T P Constant X S R-Sq 99.6% R-Sq (adj) % Analysis of Variance Source DF SS MS F P Regression Residual Error Total Powered by POeT Solvers Limited.

19 Test the hypothesis b = 0 against b 0 The following ANOVA table is needed. Analysis of Variance Source DF SS MS F P Regression Residual Error Total Table F (1, 8) at 5% level of significance is Since the calculated value of F = > (Table value), we reject the hypothesis b = Powered by POeT Solvers Limited.

20 Multiple linear regression In this case there will be more than one explanatory or independent variables. The general form of multiple linear regression model is y = b 0 + b 1 x 1 + b 2 x b k x k + ϵ The coefficients can be determined by the same method (method of least square) as used in a simple linear regression model Powered by POeT Solvers Limited.

21 Conclusion Variables and relationships Correlation co-efficient Simple linear regression model Method of least squares Residuals Simple linear regression hypothesis testing (Test for correlation coefficient, slope parameter) Sources of variation Co-efficient of determination Multiple linear regression 21 Powered by POeT Solvers Limited.

Analysis of Covariance. The following example illustrates a case where the covariate is affected by the treatments.

Analysis of Covariance. The following example illustrates a case where the covariate is affected by the treatments. Analysis of Covariance In some experiments, the experimental units (subjects) are nonhomogeneous or there is variation in the experimental conditions that are not due to the treatments. For example, a