a Use the following regression results for a model of coffee consumption as a function of the average real retail price

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a Use the following regression results for a model of coffee consumption as a function of the average real retail price

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A Use The Following Regression Results For A Model Of Coffee Consumption As A Function Of The Average Real Retail Price 1
A Use The Following Regression Results For A Model Of Coffee Consumption As A Function Of The Average Real Retail Price 1 (55.17 KiB) Viewed 76 times
A Use The Following Regression Results For A Model Of Coffee Consumption As A Function Of The Average Real Retail Price 2
A Use The Following Regression Results For A Model Of Coffee Consumption As A Function Of The Average Real Retail Price 2 (85.58 KiB) Viewed 76 times
A Use The Following Regression Results For A Model Of Coffee Consumption As A Function Of The Average Real Retail Price 3
A Use The Following Regression Results For A Model Of Coffee Consumption As A Function Of The Average Real Retail Price 3 (51.83 KiB) Viewed 76 times
a Use the following regression results for a model of coffee consumption as a function of the average real retail price of coffee to calculate the t-statistic for B1, te · Round to 2 decimal places such as 3.14. SAVE YOUR ANSWER FOR A FUTURE QUESTION Meto Data Dictionary Vide Valor Source Cup of coffee con Number of cup SunnyTINA Med per person, per tional Coffee Drink day in Sady (0) Aval metail Dollars per pound UIDED pre pet pound of real values is divided KPI cute for local and beser Summary Statistics an atd min 25% 50% 152 1012.206364 0.210251 1.962.00 20.322.5 111 010900 0,356944 0.72 0.745 0 1 1104 OLS Reres Route
11.0 1.185 1.81 OLS Regression Results Dop. Variable: Model: Method: Date: Time: No. Observations: Df Residuals: Df Model: Covariance Type: cups OLS Least Squares Sat, 03 Nov 2018 22:02:33 11 9 R-squared: Adj. R-squared: F-statistic: Prob (F-statistic): Log-Likelihood: AIC: BIC: ? 0.625 ? 0.00229 8.0481 - 12.10 -11.30 nonrobust coef std err t P>It! [0.025 0.975] Intercept price ? -0.4795 0.122 ? 22.127 ? 0.000 0.002 2.416 -0.737 2.966 -0.222 Omnibus: Prob (Omnibus): Skow Kurlosis: 2.944 0.229 0.953 2.580 Durbin-Watson: Jarque-Bera (JB): Prob (JB): Cond. No. 0.727 1.747 0.418 6.12 Warnings: (1) Standard Errors assume that the covariance matrix of the orrors is correctly
Omnibus: Prob(Omnibus): Skov: Kurtosis: 2.944 0.229 0.963 2.580 Durbin-Vatson: Jarque-Bera (JB): Prob (JB): Cond. No. 0.727 1.747 0.418 6.12 Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. ANOVA Table dr SUS 19 means price 1.0 0.292975 0.292975 Residual 9.0 70.016564 PPRC > F) 2 0.002288 NAN NaN X (no answer) Correct Answer: -4.21
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