QUESTION 20 1 POINTA medical experiment on tumor growth gives the following data table.PreviousX45728892115y33579899100ٹی21/2The least squares regression line was found. Using technology, it was determined that the total sum of squares (SST) was3789.2 and the sum of squares of regression (SSR) was 3192.4. Calculate R², rounded to three decimal places.

The objective of this question is to calculate the coefficient of determination (R²) using the given…

Answered: QUESTION 20 1 POINT A medical…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Is It Getting Harder to Win a Hot Dog Eating Contest?Every Fourth of July, Nathan’s Famous in New York City holds a hot dog eating contest. The table below shows the winning number of hot dogs and buns eaten every year from 2002 to 2015, and the data are also available in HotDogs. The figure below shows the scatterplot with the regression line. Year Hot Dogs 2015 62 2014 61 2013 69 2012 68 2011 62 2010 54 2009 68 2008 59 2007 66 2006 54 2005 49 2004 54 2003 45 2002 50 Winning number of hot dogs in the hot dog eating contest Winning number of hot dogs and buns Click here for the dataset associated with this question. (a) Is the trend in the data mostly positive or negative? Positive Negative (b) Using the figure provided, is the residual larger in 2007 or 2008?Choose the answer from the menu in accordance to item (b) of the question statement 20072008 Is the residual positive or…
A regression study was done for 20 cities with latitude and average May temperature as the explanatory variable and response variable respectively. The latitude is ranged from 26 to 47 degrees and the average May temperature is measured in degrees Fahrenheit. Given that the regression equation is ?̂ = 49.4 − 0.313?. (i) Find the proportion of variation that explained by the average May temperature if the total sum of squares and the error sum of squares are 4436.6 and 1185.8 respectively. (ii) By using suitable coefficient(s), comment on the strength of the relationship between the latitude and the average May temperature.
In baseball, two statistics, the ERA (Earned Run Average) and the WHIP (Walks and Hits per Inning Pitched), are used to measure the quality of pitchers. For both measures, smaller values indicate higher quality. The following computer output gives the results from predicting ERA by using WHIP in a least-squares regression for the 2017 baseball season. Variable DF Estimate SE T Intercept 1 -5.0 0.26 - 19.3 WHIP 1 6.8 0.14 47.4 Which of the following statements is the best interpretation of the value 6.8 shown in the output? ERA is predicted to increase by 6.8 units for each 1 unit increase of WHIP. WHIP is predicted to increase by 6.8 units for each 1 unit increase of ERA. For a pitcher with 0 units of WHIP, the ERA is predicted to be approximately 6.8 units. For a pitcher with 0 units of ERA, the WHIP is predicted to be approximately 6.8 units. Approximately 6.8% of the variability in ERA is due to its linear relationship with WHIP.
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A study was conducted to determine the relationship between starting salaries (RM thousands) for recent statistics graduates and their grade point averages in the major course. A linear regression model was fitted to the data and the estimates regression function was obtained. Part of the computer output for the above analysis is given below: ANOVA Model Sum of df Mean F Sig. Squares Square Regression 147.28 .000 Error 734.9 40.828 Total 6748.2 Coefficients Unstandardized Coefficients Model Sig. Std. B Error Constant GPA -8.42 3.007 3.395 0.2477 -2.48 12.14 0.011 0.000 (a) Complete the ANOVA table (blue boxes). (b) Write down the estimated regression function. Interpret the estimated parameters. (c) Test whether there is a linear association between salaries and grade point average. Use a = 0.05. (d) Determine the coefficient of determination for the model and interpret its meaning.
From the parameter estimation output, which of the following is FALSE? Coefficients Standard Error t Stat p-value 0.783 0.0159 Intercept Diameter (mm) of granules of sand -2.476 -3.161 17.159 2.034 8.438 0.000065 For a one-unit increase in the independent variable, the predicted-y is decrease by – 2.476. The independent variable is significant. For a one-unit increase in the independent variable, the predicted-y is increase by 17.159. The predictor variable is significant.
Suppose you are to estimate a simple regression for the following population model: Y=B₁ + B₁X + µl From a population of over thousands of observations, a small number of samples were randomly selected. The following is some of the information from the randomly selected sample.
A researcher records data on 7 adult pairs' heights (in inches) to compare the physical characteristics of brothers and sisters. Brother Sister 71 69 68 64 6 65 67 63 70 65 71 62 66 62 Mean 68.4285 64.2857 SD 2.2253 2.4299 r=0.4050 What would the least-squares regression equation be for predicting the brother's height from the sister's? A. brother's height = 0.037+44.58 * sister's height B. brother's height = 44.58 + 0.371* sister's height C. brother's height = 20.71 + 0.029 * sister's height D. brother's height = 3.28- 40.68 * sister's height If the sister's height is the same as the mean (64.2857 inches), what would the brother's predicted height be A. 68.4285 (the same as the mean as well) B. 62.1234 C. 70.8990 D. None of the above. Which of the following would be correct? A. The pair of means, (68.4285, 64.2857), lies on the linear regression line. B. The effectiveness of the linear regression model is about 16%, C. The effectiveness of the linear regression model is 10096. D. Both…
You’ve run a regression of the effect of years of schooling on wages for a sample of 102,498 individuals. Your regression results are: wage = 2.051 + 0.29 × Years of Schooling (0.0802) (0.0168) Interpret the coefficients from this regression. (The coefficients are shown in the regression equation itself.) The numbers in parentheses under the equation are the standard errors of the estimates for the respective coefficients. Determine whether each coefficient is statistically significant at each of the conventional significance levels. The R2 for this regression is 0.284. Interpret the meaning of this value.
The least-square regression line for the given data is y = 0.449x - 30.27. Determine the residual of a data point for which x = 90 and y=10, rounding to three decimal places. Temperature, x Number of absences, y OA. -0.14 OB. 20.14 C. 115.78 OD. 10.14 72 3 85 7 91 10 90 10 88 8 98 15 75 100 4 15 80- 5
Use the following information to answer questions 6 and 7. In a study, nine tires of a particular brand were driven on a track under identical conditions. Each tire was driven a particular controlled distance (measured in thousands of miles) and the tread depth was measured after the drive. Tread depth is measured in "mils." Here, 1 mil is 0.001 inch. The least-squares regression line was computed and added to a scatterplot of these data. On the plot, one data point is marked with an "X." The equation of the least-squares regression line is: Tread depth = 360.64 - 11.39 Miles The data value marked with "X" in the provided scatterplot has Tread Depth (Mils) 60 80 150 0 5 O a negative value for the residual. O a positive value for the residual. O a zero value for the residual. O a zero value for the correlation. 10 15 Miles (x 1000) 20 25 30
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