1. Based on the data available in the attached picture, Estimate the multivariate equation econometrically using excel.2. Interpret the results (coefficient, adjusted R square, p-values)3. Write the hypothesis for each independent variable. (null hypothesis and alternative hypothesis)4. Derive the elasticities for each variable using excel.● States.●●●●●●●●Estimate demand for soft drinks using a multiple regression programAlabamaArizonaArkansasCaliforniaColoradoSC200150237135121Connecticut 118Delaware217Florida242Georgia295Idaho85Illinois114SOFT DRINK DEMAND ESTIMATIONCans/Capita/YR 6Pack $Price2.191.991.932.592.292.491.992.291.892.392.35Income Temperature F13171125192728181416246662635652505272644652

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Answered: 1. Based on the data available in the…

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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P4. The least squared method called also the least squared regression method is one of the most commonly used methods in applications . Describe the importance of this method in modeling data. Provide an illustrative example for given data and interpret your results. Using your example illustrate the importance of the results.
Choose all that apply when describing R2. (Hint: 5 options are correct) (Select all that apply.) Can be inflated by adding more variables. O Can be inflated by removing variables. Referred to as the coefficient of detemination or coefficient of multiple determination. Is used to determine the fit of a model. Represents the percent of variability in y that can be explained by the model. In simple linear regression, it is equal to the correlation coefficient 2. O Only describes the relationship between quantitative variables. O Is only used in multiple linear regression. SSres SSreg SStotal SStotal
image attached. a. State your regression model.b. Interpret the result in the table using alpha level of significance at 5%.c. What percentage of variation of the dependent variable is explained by the model?d. Is the overall model significant? Why do you say so?
3. Given are five observations collected in a regression study on two variables. Xi 13 269 20 Yi 7 18 9 18 9 26 23 a. Develop a scatter diagram for these data. b. Develop the estimated regression equation for these data. C. Use the estimated regression equation to predict the value of y when x = 6.
Using a normal curve table if a person has a score of 4.78 on a test which equals a z scire of 1.5 the percentage of cases that lie above the score is A.. 43.32% - 50% = -6.68% B.. 50% - 43.32% = 6.68% C.. 100% - 43.32% = 56.68% D.. 50% + 43.32% = 93.32%
Solve Qg, h , i. Slove Qg and h without using Excel. Qi using Rstudio.
J 1 The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response why variable is city fuel consumption in miles/gallons. The predictor X variables are weight weight in pounds, DISP engine displacement leaders NHW Y Highway fuel consumption in miles/gallons. It's only one predictor X variable is used to predict the city fuel consumption, which single variable is best? Why? The best variable is (WT/HWY/DISP) because it has the best combination of (large/small) p-value, (answer) and (large/small) adjusted R^2 (answer) (type integers are decimals. Do not round)
Which of the following is a true attribute of a regression line? A. A regression line is a tool for making predictions about future observed values for center variables B. A regression line reduces a linear relationship to its bare essential. C. A regression line allows us to analyze a relationship without being distracted by small details. D. all of the above
27. What percentage of the variability is accounted for by the regression model?A. 1% B. 2% C. 98% D. 99%28. Is there a linear relationship between the dependent variable and the independent variables?A. Cannot be knownB. MaybeC. NoD. Yes29. What will be the value of y if x = 0?A. -12.87 B. -0.08 C. 0.70 D. 0.99
1. ŷ = ___ ＋___ X (Round the constant two decimal places as needed. Round the coefficient to three decimal places as needed.) 2. Identify a characteristic of the data that is ignored by the regression line.
34
1. Health researchers exploring the link between smoking and death rates from coronary heart disease (CHD) in the human population gather data from 14 randomly selected countries from around the world. Below is computer output from a regression analysis of this relationship for those countries. The explanatory variable is annual consumption of cigarettes per person and the response variable is annual deaths from coronary heart disease per 100,000 people. Coef SE Coef T P Predictor Constant 65.44 22.82 2.87 0.014 Cigarettes 0.02268 0.01926 1.18 0.262 S - 45.2671 R-Sq = 10.4% R-Sq(adj) 2.9%

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