Question

Question 1
The table represents data collected on the time spent studying (in minutes) and the resulting test grade.

Time Spent Studying (min) 52 37 31 9 26 40 22 10 45 34 19 60
Grade on Test 95 84 72 58 77 86 72 43 90 81 62 98
Part 1: Create a scatter plot with the predicted line of best fit drawn on it. Determine the type of correlation (if any), and predict the model that will be used.
Part 2: Find the line of best fit for the data either by hand or using technology. Explain your method. Find the predicted score for each time listed in the table.

Answer 1

Part 3: Find the residuals, and decide if your model is a good fit. Explain your method. (If your model is not a good fit, complete Part 2 again with a different set of points or choose a different model.)

Answer 2

please show all work

Answer 3

please help

Answer 4

I feel like I have already answered this question to someone else.. but I can't find my post. Well regardless, do you use a program that can find the line of best fit ?

Answer 5

no

Answer 6

Hm.. it will be much easier if you use a program to do this. You could actually use Wolfram Alpha... If you organize your data into pairs like this:
{time, grade}

Then you can type the common below into wolfram alpha to get the line of best fit. Just as an example using the 2st two points from your data, you'd type:

regression line{{52,95}, {37,84}}

website:
http://www.wolframalpha.com/

Answer 7

It will give a line of best fit equation. To find the predicted values, you'll have to plug in the x-value (the time) into the equation... the values for the time in your table, and then the y-value that comes out of it will be the predicted value

Answer 8

can u heelp me because my inteernet is being gay and i cant go on any other websites

Answer 9

Well "gay" is a silly word to use here.

Anyway, you say that you do not have a program you can use to do this? Can your calculator do it? Because it seems like you won;t be able to download one either if your Internet is not working. Otherwise, you have to manually crunch out the formula for the regression line by hand which is quite tedious

Answer 10

the regression line is \(y=\beta_0 + \beta_1 x\) where:
\[\beta_0 = \bar{y}-\beta_1\bar{x}\]
\[ \beta_1=\frac{\sum_{i=1}^n(x_i-\bar{x})(y_i-\bar{y})}{\sum_{i=1}^n(x_i-\bar{x})^2}\]