t1234678101112131415InterceptSalesQuarterly Sales Data for Tina's Floral ShopSales (1000s) (Yt)1769.01925.0789.01889.02098.0990.02140.02294.01009.02156.02378.01086.0YearYear 1Year 2Year 3Year 4Year 5Quarter12312312312313Coefficients1448.50153934.39781177CMA(3)1494.331534.331592.001659.001742.671808.001814.331819.671847.671873.33Forecasts -->St* ItStDeasonalized(Yt/St)1500.401513.781446.101602.181649.831814.491815.071803.961849.321828.641870.021990.44Trend Forecast1482.901517.301551.691586.091620.491654.891689.291723.681758.081792.481826.881861.28Forecast1748.371929.47846.621870.042060.69902.921991.702191.92959.222113.372323.141015.53

Answered: t 1 2 3 4 5 6 7 8 9 10 11 12 13…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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#18
ic SIS Days 1 2 Infected Individuals 678 722 a = 634.581 b = 1.073 2353 The table below shows the number of individuals infected with a disease t days after its first detected by the CDC. b = N X S For each of the following problems, enter regression equation values with at least 3 decimal places. Enter predictions to the nearest whole individual. ✓individuals III 3 4 5 756 825 882 1) Use regression to find an exponential equation that best fits the data above. The equation has form y = abt where: H 6 7 945 1017 individuals M Use the model to predict the number of individuals infected with the disease after 19 days. 8 1104 2) Use regression to find a linear equation that best fits the data above. The equation has form y = mt + b where: Use the model to predict the number of individuals infected with the disease after 19 days. 3) Use regression to find a quadratic equation that best fits the data above. The equation has form y = at² + bt + c where: S MyOpe
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Questions 6-9. Consider the data in the table. Absolute Relative Percent Time Value Change Change Increase 13.60 1 17.00 21.25 26.56 4 33.20 5 41.50 Find the model for this data. y =13.60 *(1.25)^t y = 0.25 x + 13.60 y = 13.60 *(.25)^t y= 1.25 x+13.60
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The following table gives the regression results of consumption behaviour between female and male. Consumption (C), in RM Model 1 Model 2 Constant 0.7515 0.6587 (0.024) (0.037) Y 0.8603 0.5442 (0.0301) (0.2881) Gen 0.2001 (0.0004) Y*Gen 0.3105 (0.0076) Adjusted R 0.78 0.78 No. of Observations 47 47 Note: Y=income (RM) Gen= Gender ( It is equal to 1 if person is female; 0 is for male) Figures in the parentheses are p- values a) Identify which are the qualitative, quantitative and interaction variable. Refer to Model 1: b) Write the regression equations for female and male. Draw the regression lines for female and male on the same diagram. What can you c) say? d) Interpret all the estimated slope coefficients. e) Conduct an appropriate test to determine whether variable gender is statistically significant at 5%. Should this variable be dropped from this regression model.
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Question2 The following data relate to the percentage unemployment and percentage change in wages over several years. % Unemployment (x) % Change in wages (y) 1.6 5.0 3.2 2.7 2.2 2.3 1.7 1.6 2.1 4.1 2.1 2.7 2.6 2.9 1.7 4.6 3.5 4.4 1.5 1.6 (a) Calculate the product-moment correlation coefficient between x and y. (Use Ex = 18.9, Ey = 35.2, Ex? = 37.01, Ey? = 132.22, Exy = 64.7)
i need Part E solution

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