![]() Fortunately, the TI-84 calculator makes it much easier to quickly calculate the line of best fit and interpret the results. In this example, 96.02% of the variation in happiness can be explained by hours and hours 2.Calculator, Line Of Best Fit, Linear Regression, Scatter Plots, Statistics, Ti-84 Introductionįinding the line of best fit for data sets can be a difficult task if you’re not comfortable with statistics or mathematics. 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 ![]() 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.
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