We predict the amount that a car is worth by it's age (in years). We run a linear regression and obtain the following parameter estimates. b0 = 50,000 b1 = -1,500 How much would you predict a car aged 25 years is worth? Answer:
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- You run a logistic regression model in R using the glm() function. The dependent variable is the factor variable Y and independent variables are X1 and X2 (in other words, the formula is Y~X1+X2). In the model output, the coefficient of the constant term is a0, the coefficient of X1 is a1, and the coefficient of X2 is a2. Assuming a cutoff = 0.5, which of the following defines the equation of a decision boundary? a0 + a1X1 + a2X2 = 0.5 exp(-(a0 + a1X1 + a2X2)) = 0 a0 + a1X1 + a2X2 = 0 O exp(-(a0 + a1X1 + a2X2)) = 0.5The temperature T (in °C) and length L (in mm) of a heated rod is given in the following table, if: L = a + bT where, a and b are constants, find the best value of constants for each data by using linear regression fit. L (mm) 20 30 40 50 60 70 T(°C) 600.1 600.4 600.6 600.7 600.9 601.0In bivariate regression, the regression coefficient will be equal to rXY when:
- The output below regresses Life Expectancy in a country on Log(Average Income). The equation for the regression is given by: Life Expectancy; = ßo + B₁ Log(Average Income); + ɛi What is the predicted life expectancy for a country with average income equal to $40,000? Note: Log(Averagelncome) is the natural logarithm of average income, not log base 10. reg life expectancy log_income Source SS df MS Number of obs = F(1, 184) 186 = 386.30 Model Residual 5714.78808 2722.03399 184 1 5714.78808 14.793663 Prob > F = 0.0000 R-squared = 0.6774 Total 8436.82208 185 45.6044436 Adj R-squared = Root MSE 0.6756 = 3.8463 life expec~y Coefficient Std. err. t P>|t| [95% conf. interval] log_income _ cons 4.726684 .2404885 28.68847 2.244139 19.65 0.000 12.78 0.000 4.252214 24.26092 5.201153 33.11602In python, for a sample data with 4 columns and 60 rows how do you find the parameters for the regression with the feature map (see attached) where we consider the loss function to be the square of residuals. Once this is done, how do you compute the empirical risk? I've attached some of the data below, it would be sufficient to see how you get results for the question using the above dataset. 1 14 25 620 -1 69 29 625 0 83 27 850 0 28 25 1315 1 41 25 2120 -1 153 31 1315 0 55 25 2600 0 55 31 490 1 69 25 3110 1 83 25 3535In farming, given data on crop yields over the last50 years, learn to predict next year's crop yields. Is this regression or classification?
- We are interested in estimating the total BEEF consumption per person given BEEF and POPULATION totals for n countries. One obvious way to estimate this is using a ratio estimator with y = BEEF and x = POPULATION. How can a linear regression estimator, Hansen-Hurwitz and Horwitz-Thompson estimator be used to do this?consider a population where 30% is in poverty. If you randomly sampled 10 people, how many would you expect to be in poverty?In the simple linear regression equation ŷ = bo + b₁x, how is b₁ interpreted? it is the change in that occurs with a one-unit change in y O It is the estimated value of ŷ when x = 0 O It is the change in ŷ that occurs when bo increases O it is the change in ŷ that occurs with a one-unit change in
- In linear regression, 8 tells us how meaningful the corresponding x is in predicting the output. This means, ...... O if a particular 8-value is very small compared to others, the corresponding x plays a little role in predicting the output. O if a particular 8-value is very large compared to others, the corresponding x plays a little role in predicting the output.Imagine I want to estimate the following regression in Stata TestScorei = . Bo B₁ Height; + ɛi What code would correctly estimate this regression ols height test_score ols test_score height reg test_score height reg height test_scoreYou have been hired by the European Space Agency to build a model that predicts the amount of oxygen that an astronaut consumes when performing five minutes of intense physical work. The descriptive features for the model will be the age of the astronaut and their average heart rate throughout the work. The regression model is OXYCON = w[0] + w[1] × AGE + w[2] × HEARTRATE