Lab 3.docx | bartleby

Carina Rocha A17544340 Math 11 Section B04 April 10, 2023 The regression equation is BMR = 75.97 + 0.2822 Mass Model Summary S R-sq R-sq(adj) 300.522 84.87% 84.85% Analysis of Variance Source DF SS MS F P Regression 1 303570137 303570137 3361.29 0.000 Error 599 54097824 90314 Total 600 357667961 1. The equation for the regression line is 75.97 + 0.2822 (mass of animal) = basal metabolic rate. My scatterplot shown above displays the basal metabolic rate compared to body mass. 2. The regression line for the Cape Porcupine is 75.97 + 0.2822 (11300) = 3264.83. In this case, the regression equation predicts the correlation between the explanatory variable mass in grams and the responsive variable of BMR in millimeters per hour. Since the

mass is 11300 grams the predicted BMR is about 3000. The San Diego Pocket Mouse has a regression line equation of 75.97 + 0.2822 (19.6) = 81.501. The predicted BMR for the Pokect Mouse is less than 1000. My predictions matched up with the observations from the linear regression. 3. The residual plot is not appropriate for predicting the basal metabolic rate from Mass because its distribution of residuals is cone-shaped. As shown in the residual plot above the variance for residuals increases as the mass increases therefore the plot effectively depicts the data for small masses but not for large masses since the residuals increase from the horizontal line.

The regression equation is LNBMR = 1.474 + 0.6736 LNmass Model Summary S R-sq R-sq(adj) 0.369134 93.04 % 93.03% 4. The histograms with the original variables have a heavy right skewness, resulting in outliers. While the transformed variables have a bimodal distribution. Therefore the transformed variables have a reduced skew compared to the original variables. 5. The regression equation is ln(BMR) = 1.474 + 0.6736 ln(mass) 6. The b value in my regression equation is 0.6736 which is closer to ⅔ supporting my results. 7. In the transformed plots, the residual plot is random and perfectly depicts the data. The randomness means the linear regression has found the best-suited regression line for the data. In the original scatter plot the data only works best for smaller mass but not larger mass as it is harder to estimate since there is no correlation between larger variations. Unlike the transformed scatter plot, the values have a clear correlation near the regression line. As the explanatory variable increases (LN mass)increases so does the responsive variable (LN of BMR), this data is best suited for linear regression since there is a strong correlation between mass and BMR. 8. My predicted equation is BMR = c*(Mass)^b, for the San Diego Pocket Mouse which is BMR =4.36(19.6)^(0.6736). My product was 32.355. For the Cape Porcupine, BMR = 4.36(11300)^(0.6736). My product for the porcupine was 2342.28. Yes, these predictions

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help