Wait bait

1. Wait bait I Notice I Expect I Wonder

2. General notes • I notice via a visual inspection shows that, the initial graph shows a negative gradient curve • A linear regression line does not symmetrically represent the data as given • I expect an alternative regression model will better represent the given data curve • Both power and exponential options have been produced showing, providing a more visibly symmetrical model option. • In choosing the exponential model I notice in the bulk of data points , the model follows the line of data • I expect that this model can be further improved and this will be review in a residuals plot • The graph of x and Ln y shows a very strong linear correlation with little variation from a linear regression line • Further proof is given that the alternative power function and Ln x and ln y provides lower coefficient of determination • Using a single equation model you will note the limited accuracy of the resultant prediction • I wonder if a piecewise model will improve the effectiveness of the prospective model • The residuals plot show outliers in the initial data points a and expect that a 2 equation model will best fit

3. Continued • The model provided and exponential decay curve show that the wait bait samples are decreasing at a consistently exponential rate . • A number of limitations effect or final decisions . Samples where taken from a single bait station and thus not necessary representative . No point of reference is give a to overall populations of wait bait. Give conditions in the river including salinity, temperature , weather conditions and environmental conditions have not been included. Thus the resultant model may only be applicable to a section of the river under identical conditions . • Due the sample size we assume that the samples are taken in a normally distributed environment • Models created are only effective for interpolation. As the samples decay to zero. • Microsoft excel has inherent floating point accuracy issues when dealing with Ln equations • Provide • Raw data graph, Linear regression, describe the R^2 value of the linear equation • Alternative models and linear proof. r and R^2 evidenced correctly commented • A prediction using you model and a gradient , Give the rate of change • R^2 in terms of the regression model X% of the changes in the predicting variable are represented in the dependent variable • ADVANCED : residuals and piecewise alternatives . • Don't just prove what it is prove what it is not!!!!!