If we have a dummy variable with 15 levels, we need to add 15 variables into a linear/ logistic regression model.
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- Canada's trade balance with the rest of the world has been steadily increasing over the past several years. It is well known that the U.S. is Canada's leading trade partner. Is there a predictable linear relationship between our total trade balance and our trade balance with the U.S.? Develop a regression model to predict the trade balance with the U.S. by our trade balance with the rest of the world. Comment on the strength of the model. Develop a time-series trend line for trade balance with the rest of the world by using the time periods given. Forecast total trade balance with the rest of the world for 2020 using this equation. Trade Balance ($ millions) Year Rest of the World U.S. 2010 2,290 4,991 2011 2,486 4,709 2012 2,776 4,641 2013 2,648 5,220 2014 3,068 5,041 2015 3,688 4,750 2016 4,111 4,321 2017 4,172 4,699 2018 3,669 4,777 2019 3,493 4,809 question 1: y̅ = ?+?x question…Contains data on brain mass in different species versus glia-neuron ratio, the latter being a measurement of brain metabolism as the glia provides the metabolic needs of the neurons. The relationship between THE LOGARITHM of the brain mass (in the third column) and Glia-neuron ratio (fourth column) appears linear and it is these two variables that we wish to analyze via linear regression. We would like to know if the human brain fits the trend from the other species. Towards this end we will perform the regression on all species EXCEPT humans (Homo sapiens). Again, throw out the human data from your analysis. You will however need the human numbers for some of the questions. The analysis to be performed is as follows: 1. Calculate the regression line (slope and intercept) 2. Perform an ANOVA test of the null hypothesis for zero slope. From this analysis, obtain SStotal, SSregression and SSresidual as well as the corresponding MS statistics. 3. Perform a t-test of the null…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…
- Suppose you are interested in estimating the impact of different inputs (temperature, rainfall, fertilizer) on agricultural crop yields. Your unit of analysis is farm-level. In writing the structural regression equation, you want to control for the organic carbon soil content (soil quality, essentially). However, you cannot find data on soil quality when you go to estimate the equation. Write out your theoretical regression equation and the estimated regression equation. What happens as a result of not having data on soil quality? Specifically, show how you determine whether your estimates are upper or lower bounds of the true estimates of the impact of fertilizer. Theoretical: Y = Bo + B₁T + B₂R+B3F + B4S + ε Y = Bo + B₁T + B₂R+B3F Estimated:The maintenance manager at a trucking company wants to build a regression model to forecast the time (in years) until the first engine overhaul based on four predictor variables: (1) annual miles driven (in 1,000s of miles), (2) average load weight (in tons), (3) average driving speed (in mph), and (4) oil change interval (in 1,000s of miles). Based on driver logs and onboard computers, data have been obtained for a sample of 25 trucks. A portion of the data is shown in the accompanying table. Time Miles Load Speed Oil 7.7 42.9 22.0 44.0 16.0 0.8 98.3 20.0 47.0 34.0 6.3 61.1 22.0 62.0 15.0 E Click here for the Excel Data File b. Estimate the regression model. (Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.) Time = Miles Load Speed oil + + d. What is the predicted time before the first engine overhaul for a particular truck driven 60,000 miles per year with an average load of 25 tons, an average driving speed of 53 mph, and 21,000 miles…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…
- Imagine that you, a social psychologist, study people living with HIV. You are interested in the relationship between viral load, the amount of HIV in a sample of blood, and perceived stigma, the amount people with HIV perceive others to hold stigmatizing views about their condition. You have 100 people with HIV complete a perceived stigma scale, as well as provide a sample of blood, so their viral load can be measured. You run a regression analysis using the data you’ve collected to see whether perceived stigma can be predicted based on viral load. The output from the analysis is presented below: How much does the estimate of perceived stigma change if viral load increases by one unit?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 20Might 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)
- I need help with number 14, please.A study tests the effect of earning a Master's degree on the salaries of professionals. Suppose that the salaries of the professionals (S,) are not dependent on any other variables. Let D, be a variable which takes the value 0 if an individual has not earned a Master's degree, and a value 1 if they have earned a Master's degree. What would be the regression model that the researcher wants to test? A. S,= Po + B,D,+u, i=1, .. , n. O B. S,= Po + B, + u, i= 1, .. , n. OC. 1=6o +B1,S, + u, i= 1, .. , n. O D. 0=Bo +B, S, + u,, i= 1, .. , n. Suppose that a random sample of 160 individuals suggests that professionals without a Master's degree earn an average salary of $59,000 per annum, while those with a Master's degree earn an average salary of $80,000 per annum. The OLS estimate of the coefficient B, will be $ and that of B, will be $ Click to select your answer(s). DELLIn Step 2: Construct an estimated simple linear regression model how did you come up with the column X*X ?
