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
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