With a new chef and a creative menu, Café Venetian has witnessed a huge surge in sales. The following data show a portion of dailysales (in $) at Café Venetian in the first 100 days after the changes.DaySales122621851001,737pictureClick here for the Excel Data Filea-1. Estimate the exponential trend model. (Negative values for regression coefficients should be indicated by a minus sign. Roundyour answers to 2 decimal places.)Predictor VariableConstantDayCoefficient0.00a-2. Use the estimated model to forecast for the 101st day. (Do not round coefficient estimates. Round final answer to 2 decimalplaces.)Forecast for the 101 day

To estimate the exponential trend model for Café Venetian's daily sales, we need to transform the…

Answered: With a new chef and a creative menu,…

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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Questions 1-30 refer to the following scenario: A company reports bi-annual (twice a year) sales data. The sales data for the last three years is shown in below Table. The intercept (a) of the regressions line (DV: Sales, IV: Obs) is equal to A 2 B 4 C 3 D 1 The slope (b) of the regressions line (DV: Sales, IV: Obs) is equal to a 4 b 2 c 3 d 1 The standard error of the regression is a 5.34 b 3.54 c 4.35 d 2.51 The standard error of the intercept is a 1.30 b 2.30 c 0.30 d 3.30 You want to test the hypothesis that the intercept is statistically significantly different from zero. To do so, you use the attached t-table. Your alpha (the risk you accept to make a Type I error) is a=0.1. How many degrees of freedom do you have? a n b n-2 c 4 d both b. and c. are correct You want to test the hypothesis that the intercept…
You estimated the following regression. What value would you predict for Y, if X = 47? (Round your final answer to zero decimal places.) Source | SS df MS Number of obs = 324 -------------+---------------------------------- F(1, 322) = 354.54 Model | 3686788 1 3686788 Prob > F = 0.0000 Residual | 3348384.74 322 10398.7104 R-squared = 0.5241 -------------+---------------------------------- Adj R-squared = 0.5226 Total | 7035172.74 323 21780.7206 Root MSE = 101.97 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 24.51522 1.301971 18.83 0.000 21.95378 27.07666 _cons | 98.70791 117.4919 0.84 0.401…
21. Consider a firm subject to quarter-to-quarter variation in its sales. Suppose that the following equation was estimated using quarterly data for the period 2011–2018 (the time variable goes from 1 to 32). The variables D1, D2, and D3 are, respectively, dummy variables for the first, second, and third quarters (e.g., D1 is equal to 1 in the first quarter and 0 otherwise). Qt =a+bt+c1D1+c2D2+c3D3 The results of the estimation are presented here: a. Calculate the intercept in each of the four quarters. What do these values imply? b. Use this estimated equation to forecast sales in the fourth quarter of 2019.
You estimated the following regression. What value would you predict for Y, if X = 81? (Round your final answer to zero decimal places.) Source | SS df MS Number of obs = 204 -------------+---------------------------------- F(1, 202) = 406.05 Model | 6131684 1 6131684 Prob > F = 0.0000 Residual | 3050340.21 202 15100.6941 R-squared = 0.6678 -------------+---------------------------------- Adj R-squared = 0.6661 Total | 9182024.21 203 45231.6463 Root MSE = 122.88 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 40.27997 1.998931 20.15 0.000 36.33853 44.22142 _cons | 192.9333 120.837 1.60 0.112…
2. Suppose you run a regression with quantity as your dependent variable and advertising as one of your independent variables. The p-value on advertising is .08. The marketing team is arguing that their advertising efforts are impacting sales, but the finance/economics department is arguing that there isn’t evidence that the advertising is impacting sales. What side would you take and why? Note that this question squarely hits the idea that stats is part science/part art...
The table shows the yield (in bushels per acre) and the total production (in millions of bushels) for corn in a country for selected years since 1950. Let x represent years since 1900. Find a logarithmic regression model (y = a + b In x) for the yield. Estimate the yield in 2029. The regression model is y=+()Inx. (Round to one decimal place as needed.) ~ Year 1950 1960 1970 1980 1990 2000 2010 X Yield 50 60 70 80 90 100 110 41 54 85 96 110 146 158
Suppose the relationship between the government's tax revenue (T) and national income (Y) is represented by the equation T = 30 +0.5Y. Plot this relationship on a scale diagram, with Y on the horizontal axis and T on the vertical axis. Interpret the equation. Use the line drawing tool to draw the equation. Make sure to plot the vertical axis as one endpoint of the line. Properly label this line. Carefully follow the instructions above, and only draw the required objects.
does the simple regression of log (price) on log (nox) produce an upward or a downward biased estimator of β1?
Consider the following estimated regression model relating annual salary to years of education and work experience. Estimated Salary=11,722.40+3182.56(Education)+1202.44(Experience)Estimated Salary=11,722.40+3182.56(Education)+1202.44(Experience) Suppose an employee with 66 years of education has been with the company for 33 years (note that education years are the number of years after 8th8th grade). According to this model, what is his estimated annual salary?
The number of pending corporate fraud cases in federal court stood at 545 at the beginning of 2008 (t = 0). The number of cases had grown to 726 at the beginning of 2012. Assume the growth has been approximately linear. Develop the equation of the line that describes this linear growth. Then use that linear equation to estimate the number of cases pending at the beginning of 2014, assuming that the linear growth continues at the same rate.
8
A company wants to develop a simple linear regression model for one of its products. Use the following 12 periods of historical data to develop the regression equation and use it to forecast the next three periods. Click the icon to view the historical data for the previous 12 periods. The simple linear regression line is F₁ =+x. (Enter your responses rounded to two decimal places and include a minus sign if necessary.) Find the forecasts for periods 13-15 based on the simple linear regression and fill in the table below (enter your responses rounded to two decimal places). X Period Forecast (F₁) fo Period (x) 1 2 3 4 5 6 7 8 9 10 11 12 at (y) 211 184 199 187 156 150 162 165 146 130 119 129 ㅁ - 13 14 15 Next

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