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- Not sure how to figure out the slope and y-interceptU.S. Population The U.S. population can be modeledby the function y = 165.6x1.345, where y is in thousands and x is the number of years after 1800.a. What was the population in 1960, according tothis model?b. Is the graph of this function concave up or concave down? What does this mean?c. Use numerical or graphical methods to findwhen the model estimates the population to be93,330,000.The table lists weights (pounds) and highway mileage amounts (mpg) for seven automobiles. Use the sample data to construct a scatterplot. Use the first variable for the x-axis. Based on the scatterplot, what do you conclude about a linear correlation? Weight (lb) Highway (mpg) Which scatterplot below shows the data? O A. 40- 20+ 2000 5000 2855 3050 3810 3985 4250 2275 3525 33 32 27 24 22 39 28 Weight (lb) Q Q O B. 40 20+ 2000 AHH 5000 Weight (lb) Q Q Is there a linear relationship between weight and highway mileage? O A. No, there appears to be a relationship, but it is not linear. O B. Yes, as the weight increases the highway mileage decreases. O C. Yes, as the weight increases the highway mileage increases. OD. No there appears to be no relationshin C O C. Q 40- M Q 20++ 2000 5000 Weight (lb) Highway ( O D. Highway (mpg) 40- 20+ 2000 5000 Weight (lb) Q
- OA linear regression model is Units 3,414-0.839xWeek. For week 45, what is the forecast for the number of units? Round your answer to the nearest whole number. OO units26) Consider the following regression line: i= -7.29 + 1.93 x YearsEducation. You are told that the t-statistic on the slope coefficient was 24.125. What is the standard error of the slope coefficient? (assume 5% level of significance) A. -0.08 B. 0.30 C. 1.64 D. 0.08(c) Diberi Jadual 3 Given Table 3 Jadual 3 Table 3 Dependent Variable: GNIG Method: Least Squares Sample: 1971 2020 Included observations: 50 Variable Coefficient Std. Error t-Statistic Prob. INV -0.029738 0.143418 -0.207349 0.8367 ME -0.403906 0.830281 -0.486469 0.6290 INF 0.171334 0.194713 0.879930 0.3837 GEXP 0.133799 0.126295 1.059421 0.2952 DUMMY -2.894680 1.990087 -1.454549 0.1529 -4.133286 6.959526 -0.593903 0.5556 R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Mean dependent var S.D. dependent var 0.191174 6.031855 0.099262 3.736466 5.481783 3.546176 Akaike info criterion 553.3159 Schwarz criterion 5.711226 -131.0446 Hannan-Quinn criter. 5.569156 1.529494 2.079971 Durbin-Watson stat Prob(F-statistic) 0.085970 di mana Dummy ialah pembolehubah dummi (D=0 untuk pra-krisis 2008; D=1 untuk pasca-krisis 2008) where Dummy, is dummy variable (D=0 for pre-crisis 2008; D=1 for post-crisis 2008) Kemelesetan besar, kemelesetan ekonomi yang…
- 1. R-squaredSuppose regression of y on an intercept and x with 50 observations yields total sum of squares 100 andexplained sum of squares 36.(a) What is ?^2?(b) What is the correlation coefficient between y and x?(c) What is the standard error of the residual?Based on the following data, estimate the slope of the equation using OLS (3 significant digits in final answer): K=ẞ1+ B2J+ε Observation. 1 2 3 U 2 4 9 K 16 15 14 Answer:The following data relate the sales figures of restaurant, to the number of customers registered that week: Week Customers Sales (SR) First 16 330 Second 12 270 Third 18 380 Fourth 14 300 a) Perform a linear regression that relates bar sales to guests (not to time). b) If the forecast is for 20 guests next week, what are the sales expected to be?
- This is how I interpret the question units of slope: distance/timeUse the graph to determine the units of the slope and the ?y‑intercept.You estimated a regression with the following output. Source | SS df MS Number of obs = 289 -------------+---------------------------------- F(1, 287) = 41986.64 Model | 664544048 1 664544048 Prob > F = 0.0000 Residual | 4542496.25 287 15827.5131 R-squared = 0.9932 -------------+---------------------------------- Adj R-squared = 0.9932 Total | 669086544 288 2323217.17 Root MSE = 125.81 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 43.81013 .2138056 204.91 0.000 43.38931 44.23096 _cons | 49.31707 16.96222 2.91 0.004 15.93094 82.70319…