The Report on Regression and Correlation Analysis and Hypothesis Testing - Statistics Project Example

The Report on Regression and correlation Analysis and Hypothesis testing Executive summary Regression analysis is employed to analysis the data between two continuous variables. Nevertheless, regression is ideally suited for examining functional dependencies between two factors. Functional dependency means that X establishes the level of Y. Testing statistic can be executed using t statistic. The assumption that confidence interval and hypothesis test hold is that there is existence of linearity between X and Y with independence of vicariate observation as well as existence of normality with balanced variance of residual at every point in the line of best fit. Linearity means that the link between X and Y can be explained by a straight line. The ideal straight way to appraise the linearity is using the scatter plot graph. Where the connection between X and Y is non-linear, regression must be avoided (Frederick Gravetter, 2011). On the other hand, data might be algebraically changed to straingtended out the connection where linearity is presence as part of the data. The regression model is in the form {y=a+bx} while the line is identified by the slope that explains the change in Y per unit of X while intercept has line crossing the Y-axis. The regression explains the connection between X and Y with best line of fit where Y depict the predicted value of Y, a is a constant factor which symbolize the intercept of line of best fit while bx is a variable factor which symbolize the line of the slope. Question one 1.2 The table of Pearson correlation coefficient   nfull Npart hoursw Ssize Nfull 1 Npart 0.28879875 1 Hoursw 0.53131829 0.24908371 1 Ssize 0.34967153 0.36646863 0.57590991 1 1.3 The scatter graph and interpretation of the result From the above graph analysis, it can be depicted that a positive correlation exists between sales and other variables. the correlation between part timers and number of full timers is 0.29, while hours worked and part timers is 0.53.In this regards, weak positive correlation is depicted between variables implying that as one variable increases, the other variable increases but not directly proportional since they are not strongly correlated. 1.4 The graph of correlation coefficient between npart and the sales level   sales npart Sales 1 Npart 0.288799 1 The above table depicts a positive corealtion between the sales level and number part timers of 0.29 with scatter graph portraying an inverse relationship, which is directly proportional to the level of sales. This is a weak correlation since 0.29 Read More