EBK BUSINESS STATISTICS
7th Edition
ISBN: 8220102743984
Author: STEPHAN
Publisher: PEARSON
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- Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4arrow_forwardOlympic 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_forwardDetermine the type of variation model that best fits the data in the attached image.arrow_forward
- Nassau County is located approximately 25 miles east of New York City. The accompanying data include the fair market value (in thousands of dollars), land area of the property in acres, and age, in years, for a sample of 20 single-family homes located in Glen Cove, a small city in Nassau County. Develop a multiple linear regression model to predict the fair market value based on land area of the property and age, in years. Complete parts (a) through (f). Click the icon to view the data table. a. State the multiple regression equation. Let X1; represent the land area of the property in acres and let X2 represent age, in years. Ý; = 458.4 + ( 421.7) ×1 + ( – 1.8) X21 - (Round to one decimal place as needed.) b. Interpret the meaning of the slopes, b, and b2, in this problem. Choose the correct answer below. O A. For a given age, each increase of 1 acre in land area is estimated to result in an increase in fair market value by b, dollars. For a given land area, each increase in one year…arrow_forwardWhich pair of h0/h1 is used in the two sided test for the slope of a simple linear regression?arrow_forwardA researcher interested in explaining the level of foreign reserves for the country of Barbados estimated the following multiple regression model using yearly data spanning the period 2001 to 2016: FR=a+B01L+YEXP+8FDI Where FR = yearly foreign reserves (So000's), OIL = annual oil prices, EXP = yearly total exports (S000's) and FDI = annual foreign direct investment ($000's). The sample of data was processed using MINITAB and the following is an extract of the output obtained: Predictor Coef StDev t-ratio p-value Constant 5491.38 2508.81 2.1888 0.0491 OIL 85.39 18.46 4.626 0.0006 EXP -377.08 112.19 0.0057 FDI -396.99 160.66 -2.471 s - 2.45 R-sq = 96.3% R-sq(adj) = 95.3% Analysis of Variance Source DF MS F Regression 3 1991.31 663.77 ?? Error 12 43. רר 6.45 Total 15 a) What is dependent and independent variables? b) Fully write out the regression equation c) Fill in the missing values **', **', '?'and *??"arrow_forward
- make a regression analysis about any healthcare issuearrow_forwardA researcher interested in explaining the level of foreign reserves for the country of Barbados estimated the following multiple regression model using yearly data spanning the period 2001 to 2016: FR=a+BOIL+YEXP+8FDI Where FR = yearly foreign reserves ($000’s), OIL = annual oil prices, EXP = yearly total exports (S000's) and FDI = annual foreign direct investment (S000's). The sample of data was processed using MINITAB and the following is an extract of the output obtained: Predictor Coef StDev t-ratio p-value Constant 5491.38 2508.81 2.1888 0.0491 OIL 85.39 18.46 4.626 0.0006 EXP -377.08 112.19 0.0057 FDI -396.99 160.66 -2.471 ** s = 2.45 R-sq = 96.3% R-sq (adj) = 95.3% Analysis of Variance Source DF MS F Regression 3 1991.31 663.77 ? ?? Error 12 77.4 6.45 Total 15 a) What is dependent and independent variables? b) Fully write out the regression equation c) Fill in the missing values ***, ***', ?'and ??' d) Hence test whether ß is significant. Give reasons for your answer. e) Perform…arrow_forwardFind the regression line of the points: (12.7, 18.7), (9.1, 17.2), (15.3, 14.5), (19.5, 12.9)arrow_forward
- These are the values in Irwin's data set. (1,43),(3,36),(5,22),(6,25),(8,14) Irwin determined the equation of a linear regression line, and dete (1,44),(3,35),(5,26),(6,22),(8,13) Use the point tool to graph the residual plot for the data set.arrow_forwardDescribe in detail about regression lines?arrow_forwardThe table below shows the amounts of crude oil (in thousands of barrels per day) produced by a country and the amounts of crude oil (in thousands of barrels per day) imported by a country, for the last seven years. Construc and interpret a 90% prediction interval for the amount of crude oil imported by the this country when the amount of crude oil produced by the country is 5,542 thousand barrels per day. The equation of the regression line is y = - 1.248x + 16,547.218. Oil produced, x Oil imported, y 5,759 9,307 5,724 9,146 5.642 9,611 5,482 10,011 5,199 10,185 5,092 10,116 5,052 10,088 2 Construct and interpret a 90% prediction interval for the amount of crude oil imported when the amount of crude oil produced by the country is 5,542 thousand barrels per day. Select the correct choice below and fill in the answer boxes to complete your choice. (Round to two decimal places as needed.) O A. There is a 90% chance that the predicted amount of oil imported is between and thousand barrels…arrow_forward
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