Concept explainers
Income distribution. Using data from the U.S. Census Bureau, an economist produced the following Lorenz curves for the distribution of U.S. income in 1962 and in 1972:
Find the Gini index of income concentration for each Lorenz curve and interpret the results.
Want to see the full answer?
Check out a sample textbook solutionChapter 6 Solutions
EP CALCULUS FOR BUSINESS..-MYLAB ACCESS
- Table 4 gives the population of a town (in thousand) from 2000 to 2008. What was the average rate of change of population (a) between 2002 and 2004, and (b) between 2002 and 2006?arrow_forwardTable 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to 2012. a. Let x represent time in years starting with x=0 for the year 1997. Let y represent the number of seals in thousands. Use logistic regression to fit a model to these data. b. Use the model to predict the seal population for the year 2020. c. To the nearest whole number, what is the limiting value of this model?arrow_forwardbThe average rate of change of the linear function f(x)=3x+5 between any two points is ________.arrow_forward
- Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?arrow_forwardThe following data give the yearly inflation rate and money supply growthrate (both measured in percentage) for 51 countries. A simple regression of Infla-tion on Growth yields the following information from RegressIt output: (a) What is the linear relationship implied by these data? (b) For a country with an 6% money supply growth, what would be theexpected inflation? (This means Growth= 6 as it is measured in percentage.) (c) A simplistic monetarist claims that a 1% increase in the money supplygrowth rate would result in a corresponding 1% increase in inflation. In otherwords, for the relationship of Inflation = β0 + β1Growth, the null hypothesis isβ1 = 1. Do the regression results support this null hypothesis?arrow_forward1.2. In order to prepare the dataset for modelling, it became clear that RATIO repeats with similar values. The analyst decided to regard them also as categorical variables. Amend the dataset fully in order to build a regression model. Use an ascending order in the coding structure. 1.3. Show the new amended data set and formulate the regression model that must be estimated. Apply all the changes done to the dataset in Question 1.2 and give the regression function. 1.4. The estimated response function is obtained using the coding structure of Question 1.2. Ŷ = 1.34 + 0.034X₁ + 0.049 X₂ -0.041 X3 + 0.029X4 +0.011 X5 +0.08X6. Explain the regression coefficients b₁, b, and b. 1.5. Find the fitted response function for the rotational METHOD of farming on a farm with RATIO of 0.8. 1.6. Refer to Question 1.5. The analyst has a suspicion that there is interaction between the size and the age of the farm if the method of farming is rotational and the field/land ratio is 0.8. Give the response…arrow_forward
- 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 x 1000 hectares X₂ = AGE - how long the farm has been in operation in years X3 = RATIO - the ratio ofland 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 x 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. Sample Quantiles 18 15 1.0- 0.8- 0.6- 04- 0.2- 0.0- SIZE X₁ 25 5 8 2 1.2 1.5 Predicted Theoretical Quantiles 45 AGE X₂ 50 20 1.1. The analyst did some exploratory analysis and below are some of the residual plots he constructed. Study the plots and…arrow_forwardThe 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 x 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 x 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 20 1.0- 0.8- 0.6- 04- 02- 0.0- SIZE X₁ 25 5 8 2 1.2 1.5 Predicted Theoretical Quantiles 45 AGE X₂ 2 20 100 1.1. The analyst did some exploratory analysis and below are some of the residual plots he…arrow_forwardIn the linear regression function, what does the parameter for the y-intercept (alpha) refer to? A. The mean value of y when x=0 B. the change in the mean of x for one unit increase in y C. The change in the mean of y for a one-unit increase in x D. The mean value of x when y=0arrow_forward
- Which of the following situations could produce data sets or plots that could have a regression line with a negative y-intercept? Select all that apply. Select all that apply: The net profit of a company as a function of the number of months since it was founded, where net income is gross income minus expenses. The number of employees of a company as a function of the number of months since it was founded. The cost of a company's flagship product as a function of the number of months since it was founded. The number of franchises of a company as a function of the number of months since it was founded.arrow_forwardQuestion 2. The amount of kerosene used by a typical household in a week (Q, in litres) is found to have a strong correlation with the price of kerosene (P, in $). For 6 observations, the following data was collected: Q P 11.4 4.00 14 3.50 16 3.00 18 2.50 20 2.00 22 1.50 (d) Calculate the price elasticity of demand for kerosene at a price of $3.75 (e) Using a further calculation, discuss how well the regression equation fits the data. (f) Discuss whether the relationship between the price and quantity could be due to the income effect, the substitution effect, or both.arrow_forwardJust as you are about to estimate a regression, massive sunspots cause magnetic interference that ruin all electrically powered machines (e.g., computers). Instead of giving up and flunking, you decided to calculate estimates from your data (on per capita income in thousands of U.S. dollars as a function of the percent of labor force in agriculture in 10 developed countries) manually. Your data are given below. Country % in Agriculture Per Capita Income A 9 6 B 10 8 C 8 8 D 7 7 E 10 7 F 4 12 G 5 9 H 5 8 I 6 9 J 7 10 Do a scatter plot between % in Agriculture and Per Capita Income using Excel. Place % in Agriculture on the X-axis. Copy and paste the scatter plot in the space below. Comment on the nature of the relationship between % in Agriculture and Per Capita income. Note: Use the numbers in the data as given. Do not convert those to percentage. Use Excel to compute Covariance and…arrow_forward
- Glencoe Algebra 1, Student Edition, 9780079039897...AlgebraISBN:9780079039897Author:CarterPublisher:McGraw HillFunctions and Change: A Modeling Approach to Coll...AlgebraISBN:9781337111348Author:Bruce Crauder, Benny Evans, Alan NoellPublisher:Cengage Learning
- Algebra & Trigonometry with Analytic GeometryAlgebraISBN:9781133382119Author:SwokowskiPublisher:CengageAlgebra and Trigonometry (MindTap Course List)AlgebraISBN:9781305071742Author:James Stewart, Lothar Redlin, Saleem WatsonPublisher:Cengage LearningCollege AlgebraAlgebraISBN:9781305115545Author:James Stewart, Lothar Redlin, Saleem WatsonPublisher:Cengage Learning