linear regression equation calculator on ti 30

#Linear regression equation calculator on ti 30 how to#
#Linear regression equation calculator on ti 30 manual#

I have a TI-58C with excellent tactile feedback, although I believe that this is a model that was produced a bit later. TI had much higher quality calculators of superior construction at the time. The nice thing about the TI-30 was that it was cheap. Message #2 Posted by rsenzer on, 1:20 a.m., I just found it interesting the ways that companies can make a bad feature sound good. I showed this to my friend (who perfers his TI's) and he was amazed. This is obviously a stab at the rechargeable batteries of the HP's of the time. You can take weeks of computing power with you wherever you go by simply carrying a few spare batteries.'

'Disposable battery power source provides independence from AC power supplies. This is quite obviously a stab at RPN (as by 76, HP had a good many calculators out). 'Algebraic mode of entry allows you to enter mathematical sequences in the same order that they are algebraically stated' I started reading, and one of its features reads:

#Linear regression equation calculator on ti 30 manual#

It most definitely does not have the click (even pressing a button for an instant will cause it to register 2 or 3 presses) I took the manual out of its incredibley ugly case in order to find the date it was made. I then realized, well maybe the TI-30 that I took from my grandmother's house has that same click, so I took it out, and tried punching a few buttons. I was actually discussing HP calculators with a friend just right now, and I was telling him about the 'click' which I have never felt. No, this post is not glorifying the 1976 TI-30. Message #1 Posted by Ben Salinas on, 11:49 p.m. In this example, 96.02% of the variation in happiness can be explained by hours and hours 2.TI-30 Manual Humor from 1976 The Museum of HP Calculators We can also see that the r-squared for the regression model is r 2 = 0.9602. This is the proportion of the variance in the response variable that can be explained by the explanatory variables. Happiness = -0.1012(60) 2 + 6.7444(60) – 18.2536 = 22.09Ĭonversely, an individual that works 30 hours perk week is predicted to have a happiness level of 92.99: We can use this equation to find the predicted happiness of an individual, given the number of hours they work per week.įor example, an individual that works 60 hours per week is predicted to have a happiness level of 22.09: The following output will automatically appear:įrom the results, we can see that the estimated regression equation is as follows: Scroll down to Calculate and press Enter. Press Stat and then scroll over to CALC. Then scroll down to 5: QuadReg and press Enter.įor Xlist and Ylist, make sure L1 and L2 are selected since these are the columns we used to input our data. Next, we will perform quadratic regression. This upside down “U” shape in the scatterplot indicates that there is a quadratic relationship between hours worked and happiness, which means we should use quadratic regression to quantify this relationship. We can see that happiness tends to increase as number of hours worked increases from zero up to a certain point, but then begins to drop lower as the number of hours worked increases further. This will automatically produce the following scatterplot: Next, press zoom and then press 9:ZoomStat. Make sure the plot is on and that L1 and L2 are selected for Xlist and Ylist, respectively: Next, press 2nd and then press y= to access the statplot menu.

Enter the following values for the explanatory variable (hours worked) in column L1 and the values for the response variable (happiness) in column L2: Use the following steps to perform a quadratic regression on a TI-84 calculator.īefore we can use quadratic regression, we need to make sure that the relationship between the explanatory variable (hours) and response variable (happiness) is actually quadratic.įirst, we will input the data values for both the explanatory and the response variable. Press Stat and then press EDIT . We have the following data on the number of hours worked per week and the reported happiness level (on a scale of 0-100) for 11 different people:

Suppose we are interested in understanding the relationship between number of hours worked and happiness. Example: Quadratic Regression on a TI-84 Calculator

#Linear regression equation calculator on ti 30 how to#

This tutorial explains how to perform quadratic regression on a TI-84 Calculator. However, when two variables have a quadratic relationship, we can instead use quadratic regression to quantify their relationship. When two variables have a linear relationship, we can often use simple linear regression to quantify their relationship.