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Based on these regression results from Lab 5.6 Excel: Forecasting Product Deman...

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Based on these regression results from Lab 5.6 Excel: Forecasting Product Demand Using Regression, which independent variables are significantly related to forecast demand?

Under regression statistics, the following values are shown: Multiple R, 0.891401552. R square, 0.794596727. Adjusted R square, 0.780591959. Standard error, 4.286745807. Observations, 48. In the ANOVA table at the center, values are shown as follows: Regression: d f, 3. S S, 3127.861777. M S, 1042.621. F, 56.73758. Significance F, 3.6376E minus 15. Residual: d f, 44. S S, 808.5523431. M S, 18.37619. Total: d f, 47. S S, 3936.41412. In the table at the bottom, the entries are shown as follows: Intercept: Coefficients, negative 123.7576339. Standard error, 22.11431598. t Stat, negative 5.59627. P value, 1.32E minus 06. Lower 95 percent, negative 168.3261093. G D P: Coefficients, 2.162221318. Standard error, 0.212246208. t Stat, 10.18733. P value, 3.76E minus 13. Lower 95 percent, 1.734467193. Weather (average Fahrenheit): Coefficients, 0.202019743. Standard error. 0.043196935. t Stat, 4.676715. P value, 2.78E minus 05. Lower 95 percent, 0.114962041. Holiday: Coefficients, 7.513785459. Standard error. 1.745077976. t Stat, 4.305702. P value, 9.17E minus 05. Lower 95 percent, 3.99681189.

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