a.b.C.Name:3. You have been provided the following regression summery output for the demand for Good X.FdfSignificance F0.0000079852953.29202RegressionResidualTotalInterceptPrice (Px)Income (1)Price Other (Py)35659Coefficients355.8130-4.98350.8762-1.4893SS824.897209.6621,034.559Standard Error0.6324730.9394740.4879690.968633MS255.39534.792375P-value6.45E-050.0019850.0789240.027889Using the estimates above, state the demand function rounded to two decimals(Format: Qx=do+a1 Px + a ₂1+ a 3Py):Is the product called X a normal or inferior good? (motivate)Is the "Price Other (Py)" referring to a complement or substitute good (motivate) b.C.e.Is the product called X a normal or inferior good? (motivate)d.What can you say about the statistical significance of the entire regression model (the riskof this entire model having no explanatory power)?f.Is the "Price Other (Py)" referring to a complement or substitute good (motivate)What can you say about the statistical significance of the individual coefficients statisticallsignificant using the 95 percent confidence level?Using the regression summery above, compute R².

The given table shows the regression output for the demand for good X.AnovaSourceDfSSMSFSignificance…

Answered: a. b. Name: 3. You have been provided…

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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Consider the following time series: Quarter Year 1 Year 2 Year 3 1 69 66 60 2 44 36 46 3 60 62 55 4 79 82 73 (a) Choose a time series plot. (i) 100 80 60 40 20 3. 4 6 7 Period(t) 9 10 11 12 (ii) 100 80 60 40 20 1 2 3. 4 6 7 Period (t) 10 11 12
Using the following information. Coefficients Intercept -12.8094 Independent variable 2.1794 ANOVA df SS MS F Regression 1 12,323.56 12,323.56 90.0481 Residual 8. 1,094.842 136.8553 Total 9. 13,418.4 Estimate the value of Ý when X= 4.
R2
3. You estimated a regression with the following output. Source | SS df MS Number of obs = 400 -------------+---------------------------------- F(1, 398) = 716.92 Model | 22118655.1 1 22118655.1 Prob > F = 0.0000 Residual | 12279166.2 398 30852.1764 R-squared = 0.6430 -------------+---------------------------------- Adj R-squared = 0.6421 Total | 34397821.3 399 86210.0784 Root MSE = 175.65 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 5.299576 .1979268 26.78 0.000 4.910464 5.688689 _cons | 67.86719 18.3827 3.69 0.000 31.72786 104.0065…
Y
An online retailer is trying to determine how positive reviews of items affect sales. The plot below shows the relationship between the number of positive reviews and the number of items sold. The linear regression output is shown below. 1500 Regression Statistics Multiple R R Square Adjusted R Square 0.935042 1000 0.968133 0.937282 500 Standard Error 30.00616 Observations 30 110 120 130 140 Number of positive reviews R= Ex: 0.654321 What is the correlation between number of positive reviews and number of items sold? Pick v correlation Number of items sold
Using the accompanying Cost of Living Adjustments data to find the best multiple regression model to predict the salary as a function of the other variables. What would the comparable salary be for a city with the following adjustments: groceries: 4%; housing: 8%; utilities: −3%; transportation: −1%; and health care: 7%? Use a level of significance of 0.05. Using the accompanying Cost of Living Adjustments data to find the best multiple regression model to predict the salary as a function of the other variables. What would the comparable salary be for a city with the following adjustments: groceries: 4%; housing: 8%; utilities: −3%; transportation: −1%; and health care: 7%? Use a level of significance of 0.05. Determine the best multiple linear regression model to predict the salary as a function of the other rates. Select the best answer below and fill in the corresponding answer boxes to complete your choice. (Type integers or decimals rounded to…
The Toyota Camry is one of the best-selling cars in North America. The cost of a previously owned Camry depends upon many factors, including the model year, mileage, and condition. To investigate the relationship between the car's mileage and the sales price for a 2007 model year Camry, the following data show the mileage and sale price for 19 sales (PriceHub website). Click on the datafile logo to reference the data. DATA file 1. 18 Price ($1000s) 16 14 12 10 18 6 If your answer is zero, enter "0". a. Select a scatter diagram with the car mileage on the horizontal axis and the price on the vertical axis. 20 40 6,0 8,0 100 Miles (1000s) 120 223 +♡ ♡ Miles(1000s) 29 36 47 63 77 73 87 92 101 110 28 59 68 68 91 42 65 110 Price ($1000s) 16.2 16.0 13.8 11.5 12.5 12.9 11.2 13.0 11.8 10.8 8.3 12.5 11.1 15.0 12.2 13.0 15.6 12.7 8.3
2. Below is the regression output from a model for predicting the heart weight (in g) of cats from their body weight (in kg). The coefficients are estimated using a dataset of 144 domestic cat. The model output is also provided below. Estimate Std. Error t value Pr(>|t|) (Intercept) body wt -0.357 0.692 -0.515 0.607 4.034 0.250 16.119 0.000 R2 64.66% Radi 64.41% s = 1.452 (a) We see that the point estimate for the slope is positive. What are Ho and HA for evaluating whether body weight is associated with heart weight in cats? (b) State the conclusion of the hypothesis test from part (a) in context of the data. (c) Calculate a 95% confidence interval for the slope of body weight, and interpret it in context of the data. (d) Do your results from the hypothesis test and the confidence interval agree? Explain.
Refer to the data below which contains the percentage change (X) in a stock market index over the first five trading days of the year and percentage change (Y) in the index over the whole year. Complete parts a and b. (X) (Y) 1.4 0.2 14.7 - 9.4 -0.3 2.9 19.6 20.2 a. Estimate the linear regression of Y on X. A y₁ = taxi (Round to four decimal places as needed.) 2.2 - 3.8 - 1.9 27.7 - 1.3 5.7 22.6 2.4 -1.5 1.3 11.8 26 1.5 - 4.4 - 4.4 20.5 0.7 - 1.0
3. A study of beer consumption using the annual data from 1980 -2001 produced the following regression: Y = 0.41 +0.052.X₁; −0.047X2; +0.032X3; −0.018X4i (0.027) (0.011) (0.009) (0.008) (0.009) R² = 0.94 F = 66.58 DW statistic=1.32, figures in the brackets are standard errors Where: Y₁ = Annual aggregate beer consumption in year t (billion pints) X₁₁ = Real disposable income income in year t ($ billions at 2001 prices) X2i = Price of beer in year t (index number with 2001 = 100) X31 Price of wine and sprits in year t (index number with 2001 = 100) = X4₁ = Price of cigarettes in year t (index number 2001 = 100) (a) What is the effect of a change in the price of beer on beer consumption? Does it have the correct sign? Explain. (b) Comment on the signs of the other three coefficients. Are they as expected? (c) Tests the significance of the coefficients on each of the variables Xit i = 1, 2, 3, 4. What assumptions have you made in carrying out these tests? (d) What conclusion do you draw…

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