Total Transaction Count 140 120 100 80 60 40 20 0 0 SUMMARY OUTPUT ANOVA Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations y = -0.004x + 69.283 R = 2E-06 Regression Residual Total Intercept Customer Age 10 0.0014531 2.112E-06 -0.0113615 24.42102 df Total Transaction Count (Last 12 months) 90 20 30 Customer Age -0.0014531 40 SS 1 0.1108199 88 52481.989 596.38624 89 52482.1 50 Significance F MS F 0.1108199 0.0001858 0.9891548 60 70 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% 69.283111 13.677317 5.0655485 2.223E-06 42.102318 96.463904 42.102318 96.463904 -0.003972 0.2913859 -0.0136315 0.9891548 -0.5830402 0.5750961 -0.5830402 0.5750961 t critical 1.9872899 0.05 total transaction count = -0.003972customer age+ 69.28 Make sure that you undertake a full regression analysis, with appropriate discussion and include: 1. a scatterplot and a brief discussion 2. an estimate of the linear regression model 3. the coefficients of correlation and determination 4. a test of the hypothesis that there is no linear relationship between age and the transaction count. Ensure your scatterplot includes a line of best fit. Also, make sure you describe the relationship between the variables using R and R-square and interpret the slope, coefficients and the results of the hypothesis test. Use a significance level of 0.05. Need to submit both outputs.