1:

These data can be modelled by a logarithmic regression function of the form y=a+b-h1m Determine a logarithmic regression function that best fits these data. Round the values of a and b to the nearest tenth. F According to the logarithmic regression function, determine the y for m = 124. Round to one decimal place. y: These data can be modelled by a logarithmic regression function of the form y=a+b-h1m Determine a logarithmic regression function that best fits these data. Round the values of a and b to the nearest tenth. F According to the logarithmic regression function, determine the y for m = 124. Round to one decimal place. y: If you lift and hold a mass of 4000 grams (such as a gallon of milk), you will notice that it takes some effort. If you add 50 grams to this and lift, you may not notice any difference between 4000 and 4050 grams. However, if you add 50 grams to a 1 gram paper clip, you would certainly notice the difference! The amount of mass you would need to add to notice a difference is called the Minimum Perceivable Difference. For heavier objects, a larger difference is required to notice. Below is a table of the mass of different objects and their Minimum Perceivable Difference. Mass of object (grams) Minimum Perceivable Difference (grams) These data can be modelled by a logarithmic regression function of the form 3; = a, + b - hm: where x is the mass of the object, in grams, and y is the Minimum Perceivable Difference in mass, in grams. Determine a logarithmic regression function that could be used to model these data. Round the values of a. and b to the nearest tenth. y = + -1n(-'B) According to the logarithmic regression function, determine the Minimum Perceivable Difference for an object with a mass of 13800 grams. Round to one decimal place. The Minimum Perceivable Difference is grams