Read the following description of a data set.For a science project, Sue wants to see if a larger body of water has more heat energy than a smaller body of water at the same temperature. She prepared a number of buckets filled with various amounts of water at a fixed temperature and dropped an ice cube of the same size into each one.Sue then recorded the volume of water in each bucket (in milliliters), x, and the amount of time it took for each ice cube to melt (in minutes), y.The least squares regression line of this data set is:y=–0.025x+39.998How much time does this line predict it takes for the ice cube to melt in 738 milliliters of water at the fixed temperature?Round your answer to the nearest thousandth. minutesSubmit

Read the following description of a data set.Ariel is an ornithologist who monitors the wintering activity of birds in a forest clearing. She thinks that the number of different species arriving each season is related to the average winter temperature.For several winters, Ariel takes precise measurements to compute the average winter temperature (in Celsius), x. She also counts the total number of bird species, y, that appeared in the clearing.The least squares regression line of this data set is:y=0.403x–0.726If the average winter temperature is 22, what does this line predict for the number of bird species appearing?Round your answer to the nearest integer. bird species

Read the following description of a data set.Nick accidentally left food in his backyard one night and noticed a cat eating it the next morning. Nick likes helping out the neighborhood cats, so he wonders if putting out more food will result in more cats coming to his backyard.Over the next few nights, Nick leaves out different amounts of food (in grams), x, and notes the number of cats in his backyard the following morning, y.The least squares regression line of this data set is:y=0.004x–0.358Complete the following sentence:If Nick leaves out one additional gram of food at night, the least squares regression line predicts more cats will be in his backyard the next morning.

Interpret the least squares regression line of this data set.The manager of a ski resort in the Alps always worries there won't be enough snow to keep the resort open into the spring. She decided to see if there was a relationship between the temperature in January and the amount of snow in the spring.For several years, she recorded the average temperature in January (in Celsius), x. On March 1, she also measured the depth of the snow at the bottom of a particular ski slope (in centimeters), y.Average temperature (in Celsius) Snow depth (in centimeters)–3 68–1 670 471 573 415 36The least squares regression line for the data set is:y=–4.269x+56.224Use it to complete the following sentence:If the average temperature in January were one degree higher, the least squares regression line predicts there would be fewer centimeters of snow on March 1st.

Use the least squares regression line of this data set to predict a value.A food scientist wants to understand how much sugar children like in their desserts. He sets up a study where children taste cups of vanilla pudding with varying sweetness and rate each cup on a scale of 1 to 10, where 10 is excellent.For each tasting, the food scientist writes down the grams of sugar in the pudding cup, x, as well as its rating, y.Grams of sugar Rating10 318 221 924 627 9The least squares regression line of this data set is:y=0.365x–1.494What rating does this line predict for a pudding cup with 12 grams of sugar?Round your answer to the nearest integer.

Read the following description of a data set.The manager at a new bank wants to hire enough tellers to ensure no customer waits in line too long. To gather more information, she waits in line at other local banks.At each bank the manager notes the number of teller windows that are open, x, as well as the number of minutes she has to wait in line before being served, y.The least squares regression line of this data set is:y=–0.607x+4.858Complete the following sentence:The least squares regression line predicts that for each additional open window, the time spent waiting in line decreases by minutes.