Consider the following data. 4 6 1 2 3 5 y 100 25 11 4 Apply the whole modeling process: 1) Consider a modeling assumption and its linearized version Y = AX + B. You may assume that the model is a power function model or an exponential model. 2) Test the suitability of the model assumptions. 3) Develop a model on this basis by determining the model parameters. X 6 7 3 2
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- a.State the predictors available in this model.a) We conduct a regression of size on hhinc, owner, hhsize, hhsize2,and hhsize3. We do not include the constant. The regression output is reported in Table 3. Would you conclude that the home size increases with the household size? Interpret the sign and magnitude of the estimated coefficients of hhsize1, hhsize2, and hhsize3.After the availability of covid 19 vaccinces in the Phiippines, the data below shows the number of senior citizens who received the vacines in City of Manila. Week No. 1 5 9 Senior Citizens who are Vaccinated 100 120 210 300 250 Based on the above data, determine the model of the vaccinated senior citizens. Determine also the vaccinated senior citizens in the 15th week.
- Create the regression equations based on the research model below!Please show work in excel Stars Co. wants to advertise its products in the hope that more advertising will result in more sales. The following data have been collected in the past year showing money spent on advertising in a month and sales on the same month: a) Develop a regression equation to forecast the Sales as a function of the Advertising by drawing a scatter chart in Excel. b) Suppose that the company would like to advertise in the amount of 30 thousands. Estimate the Sales in response to this amount of advertising. Month Sales (Thousands) Advertising (Thousands) Jan 1200 30 Feb 1100 25 March 1220 32 April 990 23 May 1190 31 June 1050 24 July 770 22 Aug 1080 28 Sept 1220 33 Oct 1140 25 Nov 740 19 Dec 790 20Data were collected from a group of senior citizens, ages 65 and older, about their age and pet ownership. The results were used to calculate the model ŷ = –0.24x + 18.48, where x represents the age in years and ŷ represents the predicted number of pets owned. If a 25-year-old is predicted to have more than 12 pets, would that be a reasonable use of the model? This is reasonable because the prediction is based on someone within the original population. This is not reasonable because the prediction is based on someone outside the original population. This is reasonable because the model is based on real data. This is not reasonable because it is impossible to own more than 12 pets.
- The following table is the partial reproduction of Regression routine output in Microsoft Excel relating the total cost of production TC to the level of output Q. Use the information in the table and answer the following: a) Report the estimated equation. b) Evaluate the regression modelI do not have data analysis on my excel, I have no idea how to solve this. The table below contains the average public school classroom teacher's salaries, yy, for an 11-year period. If xx represents the number of years since 1990, use Excel to find a linear model for the data. Year 1990 1991 1992 1993 1994 1995 Salary 33855 35031 34984 34221 35488 37443 Year 1996 1997 1998 1999 2000 Salary 37188 40370 40627 41311 43954 With the salary given by y=mx+b m=? b=?Discuss the importance of a model being well documented.
- A city had a population 50,000 of people in the year 2010. The table models the population, y, after a given number of years, x. What type of model would best fit the data and why?The regression results obtained for the models: Model A: Balance Model B: Balance = = Model C: Balance = Variable Intercept are summarized in the following table. Time Prime Timex Prime 60 +61Prime + ε 60 +61 Time + 62 Prime + 63 Time × Prime + ɛ 60 +61 Prime + 6₂Time x Prime + ε, Model A 88,020 (t = 77.89) N/A -18,000 (t -11.26) N/A = 1,532,480,000 Model B 90,269 (t = 24.35) -148 (t H -0.64) -28,493 (t -5.36) 662 (t 2.03) = 0.7254 0.7198 F • 1,369,126,091 Model C 88,020 (t N/A -26,244 (t = -6.66) 514 (t = 2.27) 0.7547 0.7388 81.19) SSE R² Adjusted R² Note: The values of relevant test statistics are shown in parentheses beside the estimated coefficients. Using Model B, which of the following is the alternative hypothesis for testing the significance of Time? 1,381,128, 299 0.7526 0.7421Consider a model with an interaction term between being female and being married. The dependent variable is the log of the hourly wage: log(wage) = 0.151 - 0.038 female + 0.1 married - 0.301 female* married + 0.079 educ + 0.027 exper+0.029 tenure (0.072) (0.056) (0.055) (0.007) (0.005) (0.007) n = 536, R2 = 0.461 Numbers in parantheses are standard errors of coefficients. Given the estimation result and the observation number fill in the blanks below which aim at discussing the statistical significance of variables. The test statistic of the interaction term is The critical value at 1% significance level is Then the interaction term statistically significant at 1% significance level. (Hint: to fill the blank make a choice between "is" and "is not".)