Question2The following data relate to the percentageunemployment and percentage change in wagesover several years.% Unemployment(x)% Change in wages(y)1.65.03.22.72.22.31.71.62.14.12.12.72.62.91.74.63.54.41.51.6(a) Calculate the product-moment correlationcoefficient between x and y.(Use Ex = 18.9, Ey = 35.2, Ex? = 37.01,Ey? = 132.22, Exy = 64.7)

Given : % Unemployment (x) % Change in wages (y) 1.6 5 2.2 3.2 2.3 2.7 1.7 2.1 1.6 4.1…

Answered: The following data relate to the…

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.5: Correlation And Causation

Problem 23PFA

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The U.S. Census tracks the percentage of persons 25 years or older who are college graduates. That data forseveral years is given in Table 4[14]. Determine whether the trend appears linear. If so, and assuming the trendcontinues. in what year will the percentage exceed 35%?
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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?
Over the past 50 years, there has been a strong negative correlation between average annual income and the record time to run 1 mile. In other words, average annual incomes have been rising while the record time to run 1 mile has been decreasing. (a) Do you think increasing incomes cause decreasing times to run the mile? Explain your answer. No. A strong correlation does not imply causation. No. A strong correlation implies causation. Yes. A strong correlation does not imply causation. Yes. A strong correlation implies causation. (b) What lurking variables might be causing the increase in one or both of the variables? Explain your answer.
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according to the table in the image answer the following: __ Forecast of "April" is __ Mean Absolute Deviation "MAD" is equal to * __ Mean Squared Error "MSE" is equal to * __ Absolute Percentage Error of "March" is * __ Mean Absolute Percentage Error "MAPE" is equal to *
Table 1 reports the estimates of the effects of personal characteristics on a monthly wage. The variable birth order measures order of birth, i.e. 1st child, 2nd child etc. The total variation in monthly wage is 138787905. Use this table to answer the following questions: a) In column 6, are the effects of being black, living in the south, and living in metropolitan jointly important variables in determining monthly wage? (Hint: You can use R² to test this hypothesis). b) In column 5, are the effects of being black and living in the south jointly important variables in determining monthly wage? (Hint You can use R² to test this hypothesis). c) Do explanatory (independent) variables in column 6 jointly explain monthly wages? (Hint: You can compute RSS from the unrestricted model using R2 and TSS from part e.)
True or False The correlation coefficient is a measure of thestrength of a linear relation between two variables and mustlie between - 1 and 1, inclusive.
Which of the following statements best describe the association between percent of people age 25+ with BA degree and birth rate per 1,000 population at a state level? (check all that are correct) Correlations percent of pop living in urban area 2000 percent of population age 65 and older 2005 birth rate per 1000 population 2004 percent of people age 25+ with BA degree 2005 percent of pop living in urban area 2000 Pearson Correlation N 51 percent of population age 65 and older 2005 Pearson Correlation -.227 -- Sig. (2-tailed) .109 N 51 51 birth rate per 1000 population 2004 Pearson Correlation .311 -.660** Sig. (2-tailed) .026 .000 N 51 51 51 percent of people age 25+ with BA degree 2005 Pearson Correlation .556 -.221 -.006 -- Sig. (2-tailed) .000 .120 969 N 51 51 51 51 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed). The association is present in the population (statistically significant at 0.05 level). The…

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