The table shows the yield (in bushels per acre) and the total production (in millions of bushels) for corn in a country for selected yearssince 1950. Let x represent years since 1900. Find a logarithmic regression model (y = a + b In x) for the yield. Estimate the yield in 2030.The regression model is y= +()Inx.(Round to one decimal place as needed.)The estimated yield in 2030 is(Round to the nearest integer.)bushels/acre.Year1950196019701980199020002010X5060708090100110Yield395978104120144148

To find the logarithmic regression model for the yield, we'll use the given data:xYield…

Answered: The table shows the yield (in bushels…

Advanced Engineering Mathematics

10th Edition

ISBN:9780470458365

Author:Erwin Kreyszig

Publisher:Erwin Kreyszig

Chapter2: Second-order Linear Odes

Section: Chapter Questions

Problem 1RQ

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(Medicare). The annual expenditures for Medicare (in billions of dollars) by the US Government for selected years since 1980 are shown in Table 4. Let x represent the years since 1980. (Use 4 decimal places in all answers.) Table 4: Medicare Expenditures Year Billion $ 1980 37 1985 72 1990 111 1995 181 2000 197 2005 330 A. Find the exponential regression model for the data.M(x) = B. What is the r2 value for this model?r2 = C. Extimate (to the nearest billion) the expenditures in 2008?The Medicare expenditures in 2008 were about $ billion.
Answer d,e and f
(d) If the life expectancy is increased by 3.5 years in a certain country, how much will the happiness index change? Round to two decimal places. (e) Use the regression line to predict the happiness index of a country with a life expectancy of 84 years. Round to two decimal places.
Data was recorded for the temperature, in degrees Celsius, of a cup of coffee over a 30-minute period. Given the regression equation, In(Temp) = 4.20 0.023(Time), what is the predicted temperature after 3 minutes? 33.45 °C 62.24 °C 65.17 °C 66.69 °C
For half-life demonstration, plot ln(half-lives) in the x-axis vs ln(no. of “tails-up” coins) in the y-axis. Perform linear regression to obtain the value of the slope, the value of the y-intercept, and the correlation coefficient r. What is the significance of these values?
Table 3 gives the market value of a luxury sedan (in dollars) xyears after its purchase. Find an exponential regression model of the form y= ab* for this data set. Estimate the purchase price of the sedan. Estimate the value of the sedan 10 years after its purchase. Round answers to the nearest dollar. TABLE 3 Value ($) 1 23,125 19,050 3 15,625 11,875 5 9,450 6 7,125 2.
Fit a linear regression model using the empirical probit as the dependent variable and the log dose level as the independent model – that is, set-up your probit model. What would be the visual Linear regression Models of c and d.
Use the data shown in the table that shows the number of bacteria present after a certain number of hours. Replace each y-value in the table with its logarithm, log y. Find the equation of the regression line for the transformed data. Then construct a scatterplot of (x, log y) and sketch the regression line with it. Number of hours, X 1 2 3 4 5 6 7 Number of bacteria, Y 284 471 819 1350 2336 3850 6456
Year Population (Billions) 2000 1.1 2010 1.9 2020 2.4 2030 2.7 2040 3.1 2050 3.8 The following table gives the actual are projected population in millions for certain city for selected years from 2000 to 2050. Use linear regression to find the linear equation that is the best fit for the data, width x equal to the number of years past 2000. Round off your coefficient to three decimal places.
The table shows the yield (in bushels per acre) and the total production (in millions of bushels) for corn in a country for selected years since 1950. Let x represent years since 1900. Find a logarithmic regression model (y = a +b ln x) for the yield. Estimate the yield in 2023. Year 1950 1960 Yield 50 37 60 1970 1980 60 84 93 70 80 1990 90 116 2000 2010 100 136 110 157 The regression model is y =+( In x. (Round to one decimal place as needed.) bushels/acre. The estimated yield in 2023 is (Round to the nearest integer.)
The income of farmers depends on various factors. To predict the income of the next year, a study was undertaken and data was gathered considering as many as possible factors that might influence the yearly income. Regression methods are used to create such a prediction function, that is, we want to predict the profit for the next year. The following were determined. X₁ = SIZE - farm size recorded × 1000 hectares X₂ AGE - how long the farm has been in operation in years X3 = RATIO - the ratio of land size to field size recorded as 0.5, 0.75, 0.8 and 0.9 X4 = METHOD - rotational and non rotational method of planting Ŷ INCOME the income per year recorded in × R 1 000 000.00 The partial dataset is as follows INCOME Y 1.3 2.4 3.2 1.5 2.1 a. C. Standardized Residual Sample Quantiles 15 1.0- 0.8- 0.6- 0.4- 0.2- 0.0- 20 SIZE X₁ 25 5 8 2 1.2 1.5 Predicted Theoretical Quantiles. 45 AGE X₂ 1.1. The analyst did some exploratory analysis and below are some of the residual plots he constructed.…

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