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 Comment on the difference between the coefficient of determination and the Adjusted coefficient of determination.
Appreciate the theoretical underpinnings of supply and demand through the lens of elasticity.
Understand the concept of price elasticity of demand and how to calculate it.
Distinguish between elastic, inelastic, and unitary elasticity of demand.
Identify factors that affect the elasticity of demand for goods and services.
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