Daniel wants to predict how far he can hike based on the time he spends on the hike. He collected some data on
the time in hours) and distance (in kilometers) of some of his previous hikes. A line was fit to the data to model
the relationship

Daniel can read his data and refer to line as best line of fit and estimate an average per set of hours.

Step-by-step explanation:

A line of fit draws a solid conclusion to the average for the hours spent during the amount of indicated hours. We draw a line of fit central fit and aim similar centrality as that similar results of the mean (without working out the mean we can draw a line perpendicular to the number of mean, but in line of fit we go central to all the descending or cascading results to include all results but just using one line), with one further consideration and that is balance if anything sticks out from the norm ie) weather conditions including data, we suggest if there is nothing to weigh the line of fit to a balancing outcome that shows the opposite of kilometres walked (eg. extreme higher mileage within the hour/s) then it may just alter the line a fraction of how many treks he did, but not in data less than 30 entries. Have attached an example where they classify in economics something outside the norm is called a misfit. Daniel can read his data and refer to line as best line of fit and estimate an average per set of hours. Here on the attachment you can read any misfit info and use the line coordination perpendicular to guide the indifference, the attachment shows it is not really included in the best line of fit as other dominating balances have occurred and therefore we have a misfit, all whilst using best line of fit to balance everything fairly.

mental cunning

explanation:

he is often referred to as a man of twists and turns

a

step-by-step explanation: