# Linear Regression On The Calculator Common Core Algebra 1 Homework Answers !!HOT!!

## Linear Regression On The Calculator Common Core Algebra 1 Homework Answers

Linear regression is a statistical method that allows you to find the equation of the line that best fits a set of data with two variables, such as height and weight, or time and distance. Linear regression can help you understand how the two variables are related and make predictions based on the data.

## Linear Regression On The Calculator Common Core Algebra 1 Homework Answers

If you are taking Common Core Algebra 1, you may have to solve linear regression problems in your homework. But how do you do that using your calculator? And how do you check your answers using your calculator? In this article, we will show you how to use your calculator to perform linear regression and find the equation of the line of best fit, the correlation coefficient, and the coefficient of determination. We will also give you some tips and tricks to avoid common mistakes and improve your accuracy.

## How to Perform Linear Regression on Your Calculator

The first step to perform linear regression on your calculator is to enter the data into two lists. You can use any two lists, such as L1 and L2, but make sure they have the same number of elements. To enter data into a list, press STAT and then select EDIT. Then use the arrow keys to move to the list you want to use and enter the data one by one, pressing ENTER after each entry.

For example, suppose you have the following data on the number of hours studied and the test scores of 10 students:

HoursScore

265

475

685

370

580

790

160

895

9100

10105

You can enter the hours into L1 and the scores into L2 as follows:

The next step is to create a scatter plot of the data. A scatter plot is a graph that shows how the two variables are related by plotting each pair of data as a point. To create a scatter plot on your calculator, press 2ND and then Y=. This will take you to the STAT PLOT menu. Then select Plot1 and turn it ON. Choose the scatter plot icon (the first one) and select L1 for Xlist and L2 for Ylist. You can also choose a mark for your points, such as a dot or a square.

For example, to create a scatter plot of the hours and scores data, you can set up Plot1 as follows:

To view the scatter plot, press ZOOM and then select 9:ZoomStat. This will adjust the window settings to fit your data. You should see something like this:

The scatter plot shows that there is a positive linear relationship between hours studied and test scores. The more hours a student studies, the higher their score tends to be.

The final step is to find the line of best fit for the data. The line of best fit is the line that minimizes the sum of squared errors between

the actual data points and

the predicted values on

the line.

To find

the line of best fit on your calculator,

press STAT

and then select CALC.

Then choose 4:LinReg(ax+b)

and enter L1,L2,Y1 after it.

This will perform linear regression on L1

and L2

and store

the equation of

the line in Y1.

For example,

to find

the line of best fit for

the hours and scores data,

you can enter LinReg(L1,L2,Y1) as follows:

The calculator will display

the equation of

the line in slope-intercept form (y=ax+b),

where a is

the slope

and b is

the y-intercept.

It will also display some statistics about

the line,

such as r

and r.

r is called

the coefficient of determination

and it measures how well

the line fits

the data.

It ranges from 0 to 1,

with 1 being

a perfect fit.

r is called

the correlation coefficient

and it measures how strong

and in what direction

the relationship between

the two variables is.

It ranges from -1 to 1,

with -1 being

a perfect negative linear relationship,

0 being no linear

relationship,

and 1 being

a perfect positive linear relationship.

In this case,

the equation of

the line is y=5x+55,

r=0.9976,

and r=0.9988.

This means that

the line fits

the data very well (r is close to 1)

and that there is

a very strong positive linear relationship between hours studied and test

scores (r is close to 1).

To view

the line of best fit on your scatter plot,

press GRAPH.

You should see something like this:

## How to Check Your Homework Answers Using Your Calculator

If you have solved linear regression problems on your homework by hand or using another method,

you can use your calculator to check your answers.

Here are some ways you can do that:

To check if you have found

the correct equation of

the line of best fit,

compare it with

the one displayed by your calculator after performing linear regression.

Make sure they have

the same slope and y-intercept (or equivalent forms).

To check if you have found

the correct value of r or r,

compare it with

the one displayed by your calculator after performing linear regression.

Make sure they are equal or very close (rounding errors may occur).

To check if you have found

the correct predicted value for a given x-value or y-value,

plug it into

the equation of

the line of best fit stored in Y1 and press ENTER.

Make sure it matches with

your answer.

To check if you have found

## How to Use Linear Regression to Solve Common Core Algebra 1 Homework Problems

Now that you know how to perform linear regression on your calculator, you can use it to solve some common types of homework problems in Common Core Algebra 1. Here are some examples:

Finding the equation of the line of best fit for a given set of data. For example, suppose you have the following data on the number of hours worked and the amount of money earned by 8 employees:

HoursMoney

432

648

864

1080

1296

14112

16128

18144

To find the equation of the line of best fit for this data, you can enter the hours into L1 and the money into L2, create a scatter plot, and perform linear regression as explained in the previous section. The calculator will display the equation of the line as y=8x+0, which means that the slope is 8 and the y-intercept is 0. This means that for every hour worked, the employee earns 8 dollars, and that if they work zero hours, they earn zero dollars.

Finding the correlation coefficient and the coefficient of determination for a given set of data. For example, suppose you have the following data on the number of books read and the test scores of 9 students:

BooksScores

170

275

380

485

590

695

7100

6c859133af