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- Residuals I Tell what each of the residual plots below indicates about the appropriateness of the linear model that was fit to the data.Understanding the Concepts and Skills Explain why the predictor variable is useless as a predictor of the response variable if the slope of the population regression line is 0.Fill in the blanks
- Statistical studies in which researchers control variables of interest are control observational studies non-experimental studies observational studies experimental studiesBreakfast cereals We saw in Chapter 7 that the calorie con-tent of a breakfast cereal is linearly associated with its sugar content. Is that the whole story? Here’s the output of aregression model that regresses Calories for each serving onits Protein(g), Fat(g), Fiber(g), Carbohydrate(g), andSugars(g) content.Dependent variable is CaloriesR-squared = 84.5, R-squared (adjusted) = 83.4,s = 7.947 with 77 - 6 = 71 degrees of freedomSource Sum ofSquares dfMeanSquare F-RatioRegression 24367.5 5 4873.50 77.2Residual 4484.45 71 63.1613Variable Coefficient SE(Coeff) t-Ratio P-ValueIntercept 20.2454 5.984 3.38 0.0012Protein 5.69540 1.072 5.32 60.0001Fat 8.35958 1.033 8.09 60.0001Fiber -1.02018 0.4835 -2.11 0.0384Carbo 2.93570 0.2601 11.3 60.0001Sugars 3.31849 0.2501 13.3 60.0001Assuming that the conditions for multiple regressionare met,a) What is the regression equation?b) Do you think this model would do a reasonably goodjob at predicting calories? Explain.c) To check the conditions, what…Annual Revenue million of dollars Franchise value of dollars 244 486 154 237 206 439 171 165 202 330 248 636 241 478 257 656 189 330 247 589 235 428 259 635 164 174 178 190 243 630 Dialog content ends PrintDone The value of a sports franchise is directly related to the amount of revenue that a franchise can generate. The accompanying data table gives the value and the annual revenue for 15 major sport teams. Suppose you want to develop a simple linear regression model to predict franchise value based on annual revenue generated. b. Use the least-squares method to determine the regression coefficients b0 and b1. b0 = enter your response here b1 = enter your response here (Round to two decimal places as needed.)
- Size and Rent Data Size (square feet) 850 1,440 1,085 1,232 718 1,495 1,126 726 700 966 1,100 1,295 1,985 1,359 1,175 1,235 1,245 1,249 1,160 896 1,371 1,030 745 990 1,200 Rent ($) 1,950 2,600 2,225 2,475 1,925 2,725 2,625 1,935 1,900 2,125 2,400 2,650 3,300 2,775 2,375 2,425 2,075 2,700 2,175 2,175 2,600 2,625 2,200 1,775 2,775Vital staticsPulmonary Disease A study was performed looking at the effect of mean ozone exposure on change in pulmonary function. Fifty hikers were recruited into the study; 25 study participants hiked on days with low-ozone exposure, and 25 hiked on days with high-ozone exposure. The change in pulmonary function after a 4-hour hike was recorded for each participant. The results are given in the table below. (Let population 1 be high-ozone exposure and population 2 be low-ozone exposure. Use high-low.) Comparison of change in FEV on high-ozone vs. low-ozone days Ozone level Mean change in FEV* High H₁: Low USE SALT 0.102 0.049 sd 0.254 25 0.105 25 (a) What test can be used to determine whether the mean change in FEV differs between the high-ozone and low-ozone days? (You may need to use the Distribution Calculators page in SALT to check conditions.) O paired t test O F test for the equality of two variances O two-sample t test for independent samples with equal variances O two-sample t test for…
- true if the statement is absolutely true, otherwise answer false if the statement is either true or falseMonthly savings in PKR DR 125000 10 85000 15 105000 28 98000 35 54000 44 800000 56 55000 68 7000 74 24000 28 0 35 Suppose that we have data on “Monthly savings in PKR” and “dependency Ratio (DR)”(percentage share of non-earners to earners, in a family). Run a regression that can help us predict the monthly savings, given the data of DR. Predict monthly savings, if DR=10, and if DR=90; interpret the results in terms of prediction accuracy
