The accountant at Bintong Chen Coal Distributors, Inc., in Newark, Delaware, notes that the demand for coal seems to be tied to an index of weather severity developed by the U.S. Weather Bureau. When weather was extremely cold in the U.S. over the past 5 years (and the index was thus high), coal sales were high. The accountant proposes that one good forecast of next year's coal demand could be made by developing a regression equation and then consulting the Farmer's Almanac to see how severe next year's winter would be. The data for coal sales are shown below: Coal Sales, y (in millions of tons) 1 1 4 4 6 Weather Index, x 4 5 3 The least-squares regression equation that shows the best relationship between coal sales and weather index is (round your responses to one decimal place): where y = Coal Sales and x = Weather Index. The coefficient of correlation of the data, r, = (round your response to three decimal places). The standard error of the estimate = (round your response to two decimal places).

Provide an appropriate response. Based on findings from the Health and Nutrition Examination Survey conducted by the National Center for Health Statistics from April 1971 to June 1974, the regression equation predicting the average weight of a male aged 18-24 (y) based on his height (x) is given by y = -172.63 + 4.842x. (www.cdc.gov/nchs/datalad/ad 014acc.pdf) What is the best prediction for the weight of a male aged 18-24 who is 70 inches tall? Round your answer to the nearest pound. O 166 O 339 O 70

An amusement park owner wants to create a linear regression model to predict the number of ice cream cones sold (Y-variable) based on the day's attendance (X-variable). From his collected data he finds the mean number of ice cream cone sales to be 1,200 cones with a standard deviation of 180. The mean daily attendance is 2,000 people, with a standard deviation of 200. The correlation between ice cream cone sales and attendance is r = 0.65. What is the intercept of the linear regression model predicting ice cream cone sales from daily attendance?   200 80 130 30

The following regression equation is based on the analysis of four variables:  SM_DOLLARS is the dollar amount of a watershed conservation agency's weekly spending on social media ads.  RADIO_ADS is the number of radio advertisements aired weekly by the agency.  WS_DOLLARS is the dollar amount of the agency’s weekly spending on web search ads.  The variable WEB_VISITS is the number of weekly visitors to their educational website.  These data have been recorded every week for the past three years.   WEB_VISITS (expected) = 208 + 1.25*SM_DOLLARS + 1.5*RADIO_ADS + 1.2*WS_DOLLARS   The data meet the assumptions for regression analysis, and the regression results, including the coefficients, were found to be statistically significant. Initially, $320 was spent on social media ads, 10 radio ads were aired, and $120 spent on web search ads. How many additional weekly web visits would you predict when the agency increases its weekly spending on social media ads by $440 without changing the…