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Catégorie :Category: nCreator TI-Nspire
Auteur Author: Tina23232323
Type : Classeur 3.0.1
Page(s) : 1
Taille Size: 3.17 Ko KB
Mis en ligne Uploaded: 21/10/2024 - 04:30:30
Uploadeur Uploader: Tina23232323 (Profil)
Téléchargements Downloads: 1
Visibilité Visibility: Archive publique
Shortlink : http://ti-pla.net/a4264776
Type : Classeur 3.0.1
Page(s) : 1
Taille Size: 3.17 Ko KB
Mis en ligne Uploaded: 21/10/2024 - 04:30:30
Uploadeur Uploader: Tina23232323 (Profil)
Téléchargements Downloads: 1
Visibilité Visibility: Archive publique
Shortlink : http://ti-pla.net/a4264776
Description
Fichier Nspire généré sur TI-Planet.org.
Compatible OS 3.0 et ultérieurs.
<<
Residuals : The left-over vertical variation in the response variable (y) from the LSRL Residual Plot : scatterplot of the residuals plotted against the explanatory variable and is used to determine if a linear model is appropriate for the data. Residual = y - ^y AP - Actual value - predicted value Y = actual value of the response variable ^y = predicted value of the response variable Example 1: Use the regression line created in the last lesson (example 1) to calculate the residual for a student who spent 3 hours on social media and scored a 91 on the test. Show your work. Interpret in context. The student who scored a 91 while spending 3 hours on social media scored 9.62594 points better than the expected value. ^y = y = 94.0763 4.23408x = 94.0763-4.23408(3) =81.37406 (91-81.37406=9.62594 Example 2: Use the regression line created in the last lesson (example 6) to calculate the residual for a person who got 8 hours of sleet and is 25 years old. Show your work. Interpret in context. y^ = 9.95218 0.0807x = 9.95218 - 0.0807(25) = 7.93468 8-7.93468 = 0.06532 The person who got 8 hours of sleep at age 25 sleeps 0.06532 hours more than the expected value. Residual Facts If we add up all of the prediction errors (residuals) the positive and negative residuals cancel out. That is why we use squared residuals to make the regression line. The mean of the residuals is ALWAYS 0. A residual plot turns the regression line horizontal. It magnifies the deviations for the line allowing us to see any unusual observations or patterns. We want to see no left-over pattern in the residual plot. A random scattering of residuals tells us the linear model is a good fit for our data. We will discuss the following residual plots. 1Enter data in lists. 2Stat > Tests > LinRegTTest 3Stat > Edit aHighlight L3 at top. b2nd, [List] ... Select RESID, hit enter. 4StatPlot, Use L1 & L3 cZoom > 9:Stats Made with nCreator - tiplanet.org
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Compatible OS 3.0 et ultérieurs.
<<
Residuals : The left-over vertical variation in the response variable (y) from the LSRL Residual Plot : scatterplot of the residuals plotted against the explanatory variable and is used to determine if a linear model is appropriate for the data. Residual = y - ^y AP - Actual value - predicted value Y = actual value of the response variable ^y = predicted value of the response variable Example 1: Use the regression line created in the last lesson (example 1) to calculate the residual for a student who spent 3 hours on social media and scored a 91 on the test. Show your work. Interpret in context. The student who scored a 91 while spending 3 hours on social media scored 9.62594 points better than the expected value. ^y = y = 94.0763 4.23408x = 94.0763-4.23408(3) =81.37406 (91-81.37406=9.62594 Example 2: Use the regression line created in the last lesson (example 6) to calculate the residual for a person who got 8 hours of sleet and is 25 years old. Show your work. Interpret in context. y^ = 9.95218 0.0807x = 9.95218 - 0.0807(25) = 7.93468 8-7.93468 = 0.06532 The person who got 8 hours of sleep at age 25 sleeps 0.06532 hours more than the expected value. Residual Facts If we add up all of the prediction errors (residuals) the positive and negative residuals cancel out. That is why we use squared residuals to make the regression line. The mean of the residuals is ALWAYS 0. A residual plot turns the regression line horizontal. It magnifies the deviations for the line allowing us to see any unusual observations or patterns. We want to see no left-over pattern in the residual plot. A random scattering of residuals tells us the linear model is a good fit for our data. We will discuss the following residual plots. 1Enter data in lists. 2Stat > Tests > LinRegTTest 3Stat > Edit aHighlight L3 at top. b2nd, [List] ... Select RESID, hit enter. 4StatPlot, Use L1 & L3 cZoom > 9:Stats Made with nCreator - tiplanet.org
>>
