Model 1 (B) (B) Use a simple linear regression of y=Price...

Model 1 (B) (B) Use a simple linear regression of y=Price against X=AGE. State your NULL & ALTERNATE hypotheses (for each of these models) and your conclusions. Use \alpha=.05 throughout this project (for each of these models). Decide how much the value of a car is lowered if it is one year older. $Y = \beta_0 + \beta_1 x_1 + \varepsilon$ $\tilde{Y} = 35365.359 - 2307.829x_1$

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Model 1 (B) (B) Use a simple linear regression of y= Price against x=AGE. State your NULL & ALTERNATE hypotheses (for each of these models) and your conclusions. Use alpha =.05 throughout this project (for each of these models). Decide how much the value of a car is lowered if it is one year older. Y=eta _(0)+eta _(1)x_(1)+epsi tilde(Y)=35365.359-2307.829x_(1) Define all parameters - State your null and alternate hypotheses as done in text & class notes. - Use SPSS to get the required output (include it in your report) - (NOT EXCEL or other software for regression please) Prune the output- don't include unnecessary information. For graphics- any software is acceptable. - Provide Technical and Managerial conclusions for all tests of hypotheses. Be sure to clearly indicate what the data analysis showed in the managerial decision. Use a=.05 throughout For each model or question • Statement of the problem • Discussion & description of data (use graphs where appropriate) • Qualitative discussion of the models • Forecast Equation of best model • Discussion of forecasting accuracy (and results from using it) • Qualitative discussion of the strengths and weaknesses of the model Conclusions and Recommendations Model1(B) (B) Use a simple linear regression of y=Price against X=AGE. State your NULL & ALTERNATE hypotheses (for each of these models) and your conclusions. Use =.05 throughout this project (for each of these models). Decide how much the value of a car is lowered if it is one year older Y=+x+e Y=35365.3592307.829x

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