Pop-up coffee vendors have been popular in the city of Adelaide in 2013. A vendor is interested in knowing how temperature (in degrees Celsius) and number of different pastries and biscuits offered to customers impacts daily hot coffee sales revenue (in $00's).
A random sample of 6 days was taken, with the daily hot coffee sales revenue and the corresponding temperature and number of different pastries and biscuits offered on that day, noted.
Excel output for a multiple linear regression is given below: Coffee sales revenue 6.5105.54.53.528 Temperature 25173035409 Pastries/biscuits 71356315 SUMMARY OUTPUT Regression Statistios Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept Temperature Pastries/biscuits 0.870.750.595.956.00df2.003.005.00 Coeffients 18.68−0.500.49SS322.14106.20428.33 Standard Error 37.880.832.02MS161.0735.40 tStat 0.49−0.600.24F4.55 P-value 0.660.590.82 Significance F0.12 Lower 95%−101.88−3.15−5.94 Upper 95%139.242.156.92 Test the significance of the overall regression model, at α of 5%.
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