Let ŷ represent the profit (or loss) for a certain company a years after 1965. Based on the data shown below, a statistician calculates a linear model ŷ = – 2.76x + 54.33. 50.05 3 46.3 4 42.25 41.4 6. 36.35 7 34.5 8. 31.85 30.5 10 26.45 11 23.1 12 20. 85 2.
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- U.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.A certain standardized test measures students' knowledge in English and math. The English and math scores for 10 randomly selected students were recorded and analyzed. The results are shown in the computer output. Predictor Coef SE Coef t-ratio Constant -124.13 78.712 0.046 Math 1.223 0.1966 6.220 0.000 S = 34.55 R-Sq = 82.8% R-Sq (Adj) = 83.5% Which of the following represents the standard deviation of the residuals? O 1.223 34.55 78.712 124.13The 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?
- Use the following STATA output to test whether the variable wgt is significant at 5% level: Source | SS df Number of obs = EC 3. Prob > F R-squared MS 392 300.76 0.0000 0.6993 Adj R-squared anba6970 4.2965 388) = Model Juu16656.4443 Residual 162,54916 5552.1481 388 18.4601782 Total Juu23818.9935 391 60.9181419 Root MSE Coef. Std. Err. P>It| [95% Conf. Interval] syl ena wat .2677968 -.012674 -.0057079 44.37096 .4130673 .0082501 .0007139 1.480685 -0.65 -1.54 -8.00 29.97 0.517 0.125 0.000 0.000 -1.079927 -.0288944 -.0071115 41.45979 .5443336 0035465 .0043043 47.28213 _cons The variable is not significant because p-value is less than 0.05. The variable is significant because p-value is less than 0.05. The variable is significant because p-value is less than 0.1. The variable is not significant because p-value is greater than 0.05Sediment buildup behind a dam can adversely affect a reservoir's storage capacity. Measurements of a reservoir in 1988 showed it had a capacity of 500,000 acre-feet of water. Measurements in 2018 showed that the reservoir's capacity has been decreasing at a rate of 2,000 acre-feet per year. Find a linear model of the number of acre-feet A of water in the reservoir n years after 1988.Paw Patrol Inc. manufactures toys for kids from 1 to 10 years old. The summary of toy sales by month in 2017 and 2018 is as follows: Dt April May 2018 18600 Period Demand 18716 June 19235 2017 September 19400 July August 19500 October 18300 19753 November 17200 September 18320 December 14998 October 17652 2018 January February March 19800 November 16200 19555 December 15563 19800 What is the Mean Absolute Deviation (MAD), when using the weighted 4-period moving average technique? Use weights of 0.6, 0.2, 0.1, and 0.1 for the most recent period, the second most recent period, the third most recent period, and the fourth most recent period, respectively. O 5445 O 1256 O 1555 O 4450 O 1140
- According to the following given information how to determine: Production Times (months) 1 9 10 11 12 13 14 15 product of 970 1,180 1,239 1,293 1,350 1,398 1,410 1,480 1,492 1,500 1,520 1,592 1,605 1,660 1,685 (Z unit) A) Break Even-point or points? B) How is to construct the relationship between entire variables through the simple drawing for all above figures and highlighting of Break Even point the drawing? C) Justify your final answer for each line of production.Describe the important characteristics of the variance of a conditional distribution of an error term in a linear regression. What are the implicationsfor OLS estimation?All questions utilize the multivariate demand function for Smooth Sailing sailboats in C6 on text page 83. Compute to three decimal places. Initial values are: PX = $9500 PY = $10000 I = $15000 A = $170000 W = 160 This function is: Qs = 89830 -40PS +20PX +15PY +2I +.001A +10W 1.(a). Use the above to calculate the arc price elasticity of demand between PS = $9000 decreasing to PS = $8000. The arc elasticity formula is: 1.(b). Judging from the computation in (a), do you expect the revenue resulting from the decrease in Ps to $8000 to increase, remain the same, or decrease relative to the revenue at Ps = $9000. (Hint: see the table on page 65 of Truett). Explain your choice. 1.(c). Calculate the point elasticity of demand for Smooth Sailing sailboats at PS = $9000 (which should make Qs = 101600). The formula is: 1.(d). Does this elasticity value indicate that Smooth Sailing demand is relatively responsive to changes in the price of these sailboats? Explain…
- Find the regression equation, letting the first variable be the predictor (x) variable. Using the listed actress/actor ages in various years, find the best predicted age of the Best Actor winner given that the age of the Best Actress winner that year is 43 years. Is the result within 5 years of the actual Best Actor winner, whose age was 45 years? Best Actress 27 30 30 61 30 32 46 28 61 22 43 56 D Best Actor 42 39 38 45 51 49 59 51 38 57 45 34 Find the equation of the regression line. y = + (Round the constant to one decimal place as needed. Round the coefficient to three decimal places as needed.) The best predicted age of the Best Actor winner given that the age of the Best Actress winner that year is 43 years is years old. (Round to the nearest whole number as needed.) Is the result within 5 years of the actual Best Actor winner, whose age was 45 years? the predicted age is the actual winner's age.How can we say that Sample Average is the Efficient Linear Estimator of E(Y)?A scatter plot shows data for the cost of a vintage car from a dealership (y in dollars) in the year a years since 1990. The least squares regression line is given by y-25,000 + 500z. Interpret the y intercept of the least squares regression line. Select the correct answer below O The predicted cost of a vintage car from a dealership in the year is 820.000 O The predicted cost of a vintage car from a dealershpin the year 1090 is 85,000. O The predicted cost of a vintage car from a dealershp in the year 1990 is sse. The yintercept should not be interpreted.