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- If your graphing calculator is capable of computing a least-squares sinusoidal regression model, use it to find a second model for the data. Graph this new equation along with your first model. How do they compare?Suppose the regression in Equation is estimated using LoSTR andLoEL in place of HiSTR and HiEL, where LoSTR = 1 - HiSTR is anindicator for a low-class-size district and LoEL = 1 - HiEL is an indicatorfor a district with a low percentage of English learners. What are thevalues of the estimated regression coefficients?When is the standard error of the estimate (in linear regression) large? When the X values are very close to the Y values. When the residuals are large on average. When the average distance between actual Y scores and predicted Y scores is small. When the coefficient of determination is small.
- It is required to use the data given in the table to estimate the parameters of the simple linear regression equation by any of the estimation methods:A scientific foundation wanted to evaluate the relation between y= salary of researcher (in thousands of dollars), x1= number of years of experience, x2= an index of publication quality, x3=sex (M=1, F=0) and x4= an index of success in obtaining grant support. A sample of 35 randomly selected researchers was used to fit the multiple regression model. Parts of the computer output appear below. Based from the table, what is the constant term of the multiple linear regression?It is required to use the data given in the table to estimate the parameters of the multiple linear regression equation by any of the estimation methods:
- Suppose there is 1 dependent variable (dissolved oxygen, Y) and 3 independent variables (water temp X1, depth X2, and hardness of water X3). Below is the result of the multiple linear regression.Which of the three independent variable(s) is (are) significant predictor(s) of dissolved oxygen? water temperature only since its p-value is less than 0.05 level of significance intercept only since it has the greatest t statistic depth and hardness since their p-values are greater than 0.05 level of significance water temperature and intercept since their p-values are less than 0.05 level of significanceA trucking company considered a multiple regression model for relating the dependent variable of total daily travel time for one of its drivers (hours) to the predictors distance traveled (miles) and the number of deliveries of made. After taking a random sample, a multiple regression was performed and the output is given below. Interpret the slope of the deliveries variable. When deliveries increases by 0.805 units, time increases by 1 hour, holding all other variables constant. 2) We do not have enough information to say. 3) When deliveries increases by 1 unit, time decreases by 0.805 hours, holding all other variables constant. 4) When deliveries decreases by 1 unit, time increases by 0.805 hours, holding all other variables constant. 5) When deliveries increases by 1 unit, time increases by 0.805 hours, holding all other variables constant.Suppose there is 1 dependent variable (dissolved oxygen, Y) and 3 independent variables (water temp X1, depth X2, and hardness of water X3). Below is the result of the multiple linear regression.Which of the following is NOT true in the multiple linear regression outputs? In the F-test ANOVA result, if Ho is rejected, this means that the regression model overall predicts the dependent variable significantly well. If a predictor is having a significant impact on our ability to predict the outcome then the regression coefficient b should be significantly different from 1.0. The F-test ANOVA assesses all of the regression coefficients jointly whereas the t-test for each coefficient examines them individually. It is possible that a model is significant, but not enough to conclude that any individual variable is significant.
- If I add the additional condition which is the labor is female using the following: #People who is femalefemale = x*0.46 Will it become dependent variable and how will I do linear regression model by adding this condition?I have some doubts regarding linear regression. if any 2 variables in X1, X2 AND Y have a positive correlation, then in the linear regression Y = b0 + b1X1 +b2X2 +e, will the sign of b1 and b2 both be positive? will the residuals that we get from linear regression will always be uncorrelated given X?Suppose that a regional express delivery service company wants to estimate the cost of shipping a package (Y) as a function of cargo type, where cargo type includes the following possibilities: fragile, semi-fragile, and durable. Costs for 15 randomly chosen packages of approximately the same weight and same distance shipped, but of different cargo types, are provided in the file P14_16.xlsx. a. Estimate a regression equation using the given sample data, and interpret the estimated regression coefficients. b. According to the estimated regression equation, which cargo type is the most costly to ship? Which cargo type is the least costly to ship? c. How well does the estimated equation fit the given sample data? How might the fit be improved? d. Given the estimated regression equation, predict the cost of shipping a package with semi-fragile cargo.
