Which of the following is not a good reason to use more than one independent variables (x variables) in the regression equation:OA. We may want to control for other factors that affect our outcome variableB. We may want to explain as much of the variation in our outcome variable as possibleO C. If we have one independent var iable of primary interest, we want to see the smallest standard errors around itOD. If we have one independent variable of primary interest, we want isolate the effect it has on outcome variable from other factors

We have to choose one option that is not a good reason to use more than one independent variables (x…

Which of the following is not a good reason to use more than one independent variables (x variables) in the regression equation: OA. We may want to control for other factors that affect our outcome variable B. We may want to explain as much of the variation in our outcome variable as possible C. If we have one independent var iable of primary interest, we want to see the smallest standard errors around it D. If we have one independent variable of primary interest, we want isolate the effect it has on outcome variable from other factors

Which of the following is not a good reason to use more than one independent variables (x variables) in the regression equation: OA. We may want to control for other factors that affect our outcome variable B. We may want to explain as much of the variation in our outcome variable as possible C. If we have one independent var iable of primary interest, we want to see the smallest standard errors around it D. If we have one independent variable of primary interest, we want isolate the effect it has on outcome variable from other factors

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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Might we be able to predict life expectancies from birthrates? Below are bivariate data giving birthrate and life expectancy information for each of twelve countries. For each of the countries, both x, the number of births per one thousand people in the population, and y, the female life expectancy (in years), are given. Also shown are the scatter plot for the data and the least-squares regression line. The equation for this line is y=82.25 -0.48x. ^ Birthrate, x (number of births per 1000 people) 35.5 44.9 29.7 19.9 13.7 27.0 51.9 15.0 50.9 49.7 39.6 24.4 Send data to calculator Send data to Excel Female life expectancy, y (in years) 67.9 57.9 61.7 71.4 72.5 73.5 55.4 76.5 58.2 60.6 64.4 74.4 Based on the sample data and the regression line, complete the following. Female life expectancy (in years) 85 80+ 75+ 70- 65+ 60- 55+ 50 x X x ++ 10 15 20 25 (b) According to the regression equation, for an increase of one (birth per 1000 people) in birthrate, there is a corresponding decrease…
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You may need to use the appropriate technology to answer this question. A regression analysis involving 45 observations relating a dependent variable and two independent variables resulted in the following information. ŷ = 0.406 + 1.3385x₁ + 2x₂ The SSE for the above model is 43. When two other independent variables were added to the model, the following information was provided. ŷ = 1.9 - 3x₁ + 12x2 + 4x3 + 8x4 This model's SSE is 36. At a 0.05 level of significance, test to determine if the two added independent variables contribute significantly to the model. State the relevant null and alternative hypotheses. O Ho: One or more of the parameters is not equal to zero. H₂: B₁ = P₂ = P3 =B4 = 0 B1 O Ho: One or more of the parameters is not equal to zero. H₂: B3 =B₁ = 0 O Ho: B3 = P4 = 0 H₂: None of the parameters are equal to zero. O Ho: B3 B4= = = H₂: One or more of the parameters is not equal to zero. O Ho: P₁ = P₂ = P3= P4= H: One or more of the parameters is not equal to zero. Find…
Please see problem with the solution: after reviewing the information below, assess the appropriateness and accuracy of using a linear regression model. Discuss the meaning of the standard error of the estimate and how it affects the predicted values of Y for that analysis. Many people are also using online shopping to avoid going to stores in person. I decided to use my own data. I am going to add how many Amazon transactions I have made each month from March- August. X= the month Y= amount of purchases Simple linear regression results: Dependent Variable: saleIndependent Variable: monthsale = 0.53333333 + 1.6 monthSample size: 6R (correlation coefficient) = 0.99410024R-sq = 0.98823529Estimate of error standard deviation: 0.36514837 Parameter estimates: Parameter Estimate Std. Err. Alternative DF T-Stat P-value Intercept 0.53333333 0.50269117 ≠ 0 4 1.0609562 0.3485 Slope 1.6 0.087287156 ≠ 0 4 18.330303 <0.0001 Analysis of…
Might we be able to predict life expectancies from birthrates? Below are bivariate data giving birthrate and life expectancy information for each of twelve countries. For each of the countries, both x, the number of births per one thousand people in the population, and y, the female life expectancy (in years), are given. Also shown are the scatter plot for the data and the least-squares regression line. The equation for this line is y = 82.17 -0.47x. Birthrate, x (number of births per 1000 people) 14.3 27.4 51.1 46.8 24.9 29.9 18.2 41.6 49.4 14.1 33.9 49.3 Send data to calculator Female life expectancy, y (in years) 75.6 70.5 58.2 59.0 73.3 62.7 73.6 65.2 62.4 74.3 67.0 53.9 Send data to Excel Female life expectancy (In years) Based on the sample data and the regression line, answer the following. 85+ 80+ 75+ 70+ 65 60 55+ 50 (a) From the regression equation, what is the predicted female life expectancy (in years) when the birthrate is 29.9 births per 1000 people? Round your answer to…
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You may need to use the appropriate technology to answer this question. A regression analysis involving 45 observations relating a dependent variable and two independent variables resulted in the following information. ý = 0.406 + 1.3385X + 2X2 The SSE for the above model is 43. When two other independent variables were added to the model, the following information was provided. ŷ = 1.9 - 3x₁ + 12x₂ + 4x3 + 8x4 This model's SSE is 36. At a 0.05 level of significance, test to determine if the two added independent variables contribute significantly to the model. State the relevant null and alternative hypotheses. O Ho: One or more of the parameters is not equal to zero. H₂: B₁ = B₂= B3 = P4 = 0 OH: One or more of the parameters is not equal to zero. H₂: B3 =B₁ = 0 O Ho: B3 =B4 = 0 H₂: None of the parameters are equal to zero. H₁: B3 =B4 = 0 H₂: One or more of the parameters is not equal to zero. O Ho: B₁ = B₂= B3 =B4 = 0 H₂: One or more of the parameters is not equal to zero. ✔ Find the…
(b) What would the consequence be for a regression model if the errors were not homoscedastic?
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Might we be able to predict life expectancies from birthrates? Below are bivariate data giving birthrate and life expectancy information for each of twelve countries. For each of the countries, both x, the number of births per one thousand people in the population, and y, the female life expectancy (in years), are given. Also shown are the scatter plot for the data and the least-squares regression line. The equation for this line is y= 82.15 – 0.47x. 00 Birthrate, x Female life expectancy, y (in years) (number of births per 1000 people) 40.4 65.2 85- 50.4 59.0 80+ 18.4 71.6 75- 26.5 69.9 70 32.0 64.5 65- 51.7 52.9 60- 34.4 67.2 14.6 75.9 50.1 45.8 59.2 49.9 62.1 Birthrate 73.7 26.2 (number of births per 1000 people) 73.7 14.4 Save For Later Submit Assignment Check 2 Accessibility O 2022 McGraw Hill LLC AN Rights Reserved. Terms of Use / Privacy Center DO 80 DIl 110 17 Da SO FA F4 esc F2 & delete %24 % 8 %23 6 7 3 4 7. U T K LA G S D Female life expectancy (in years)
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