PLEASE HELP ME, I BEG OF YOU TO HAVE MERCY ON MY POOR SOUL
1. Calculate the slope of the trend line. (Choose two points on the line and find vertical change over horizontal change.)

2. Using the slope and y-intercept, write the equation of the trend line (y = mx + b).

3. Choose a "calories from fat" value that is not in your collected data set and that is at least 10 fat calories away from any collected value. Use the equation calculated in step to predict the number of fat grams in an item having that number of fat calories. Be sure to show your work.

4. Search for an item in a fast food menu having the same number of fat calories as the one you chose above. (If you cannot find the exact value, get as close as you can.) Compare the calculated value from step VI to this actual value. Explain why (or why not) you would have expected your prediction (calculated value) to be close to the actual value.

The regression line is y = 10x + 300, and the value of calories is 90 and when x = 14 the value of y = 440 from the regression line, but the actual value is 330.

What is the line of best fit?

A mathematical notion called the line of the best fit connects points spread throughout a graph.

It's a kind of linear regression that uses scatter data to figure out the best way to define the dot's relationship.

From the data given, we can write the slope and y-intercept form:

y = mx + b

here, m = 10 and b = 300

y = 10x + 300

The number of fat grams in an item has that number of fat calories.

As we have the regression line:

y = 10x + 300

Put x = 1 in the regression line:

y = 10(1) + 300

y = 310 calories

For x = 14

y = 10(14) + 300 = 440

But the actual value from the graph is 330.

Thus, the regression line is y = 10x + 300, and the value of calories is 90 and when x = 14 the value of y = 440 from the regression line, but the actual value is 330.

Learn more about the line of best fit here:

brainly.com/question/14279419

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