# dfadsfads - S imple Linear Regression Analysis: CH 9 Y = bo...

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Simple Linear Regression Analysis: CH 9 Y = b o + b 1 X + € Regression Output: -Enter response variable into C1 & predictor variable into C2. STAT-REGRESSION- REGRESSION… In dialogue box, specify C2 as response and C1 as PREDICTORS. Residual Plots: In the STAT-REGRESSION-REGRESSION box, input response & predictor data… In dialogue box, click GRAPHS – enter C1 in the residuals vs the variables box, click OK. Estimation and Prediction: Enter response variable into C1 & predictor into C2. STAT-REGRESSION- REGRESSION… Complete the dialogue box with C1 & C2 in response and predictor spots, click OPTIONS- enter the value of X for which an estimate of the mean of Y and/or a prediction of Y is desired in the PREDICTION INTERVALS FOR NEW OBSERVATIONS box (only 1 value at a time). Click the buttons for FITS (estimated mean value and predicted value), SDs of FITS (standard error of the estimated mean value), CONFIDENCE LIMITS, and PREDICTION LIMITS – Click OK. Correlation Coefficient: Choose STAT-BASIC STATISTICS-CORRELATION… In the dialogue box, enter C1 C2, and click OK. Selling Price Problem : Predict selling price of one house in a population of houses . Y = selling price (response variable) X = area of house in square feet (predictor variable) *if knowledge of X is useful in predicting Y, there is an association between X & Y. *there is causation if a change in X results in a change in Y. Step 1 : Graph points on a scatter plot. Stat-Regression-Regression-Response Variable is Y, Predictor is X-graphs-check normal plot-OK Step 2 : Use equations to find b 1 , b o , & €. (by hand, this info is given in Minitab output) SP(XY) =

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## This note was uploaded on 12/07/2009 for the course MGMT 302 taught by Professor Canavos during the Spring '09 term at VCU.

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dfadsfads - S imple Linear Regression Analysis: CH 9 Y = bo...

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