Serial correlation, also known as autocorrelation, describes the extent to which the result in one period of a time series is related to the result in the next period. A time series with high serial correlation is said to be very predictable from one period to the next. If the serial correlation is low (or near zero), the time series is considered to be much less predictable. For more information about serial correlation, see the book Ibbotson SBBI published by Morningstar. A research veterinarian at a major university has developed a new vaccine to protect horses from West Nile virus. An important question is: How predictable is the buildup of antibodies in the horse's blood after the vaccination is given? A large random sample of horses were given the vaccination. The average antibody buildup factor (as determined from blood samples) was measured each week after the vaccination for 8 weeks. Results are shown in the following time series. Original Time Series Week Buildup Factor 2 6. 2.1 4.7 6.2 7.5 8.0 9.3 10.7 12.4 To construct a serial correlation, we simply use data pairs (x, y) where x = original buildup factor data and y original data shifted ahead by 1 week. This gives us the following data set. Since we are shifting 1 week ahead, we now have 7 data pairs (not 8). Data for Serial Correlation х 2.1 4.7 6.2 7.5 8.0 9.3 10.7 4.7 6.2 7.5 8.0 9.3 10.7 12.4 For convenience, we are given the following sums. Ex = 48.5, Ey = 58.8, Ex² = 386.17, Ey² = 535.52, Exy = 452.1

An engineer wants to determine how the weight of a gas-powered car, x, affects gas mileage. v. The une ory tor ine most recent m odel year. Complete parts (a) through (d) accompanying data represent the weights of various domestic cars and their miles per gallon in BEE Click here to view the weight and gas mileage data (a) Find the least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable. y = 0.00702 * + (43.2) (Round the x coefficient to five decimal places as needed. Round the constant to one decimal place as needed.) (b) Interpret the slope and y-intercept, if appropriate. Choose the correct answer below and fill in any answer boxes in your choice.answer trom part a to find this answer O A. For every pound added to the weight of the car, gas mileage in the city will decrease by mile(s) per gallon, on average. A weightless car will get miles per gallon, on averag B. For every pound added to the weight of the car, gas…

tion of about $1 million in payroll. (E) One extra win corresponds to an addi- tion of about $17 million in payroll. 6. The residual for the least-squares regression line y = 3.422 +5.551x at the data point (9.227, y) is equal to 1.811. Which of the following is the value of y? (A) 56.452 (B) 54.641 (C) 52.830 (D) 11.038 (E) 7.416 ere to search

(b)