1P Consider a multiple regression model predicting Calories 6.53+ 30.84 BMI + 90.14 Gender + 30.94 Age, where BMI is body mass index (- -), gender (0 for males and 1 for females). height weight Assume all variables are statistically significant at a 5% level. When interpreting the model, is true to say that, females intake, on average, 90.14 more calories than males, holding everything else constant. True O False
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- What does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?QUESTION 4 Based on the regression equation, we can measure the pearson correlation coefficient predict the value of the dependent variable given a value of the independent variable measure the association between two variables predict the value of the independent variable given a value of the dependent variable.QUESTION 7 Estimate the population regression model. TestScore-B+B STR+English+(STR x English) + where English is the percentage of English Learners in school district i. Using the estimation results we wish to test the hypothesis below. H: The effect on test score of class size does NOT depend on the percentage of English learners. Choose the correct statement on the estimation results and/or hypothesis testing a. Since the OLS estimate is not significant at the 5% level, we should reject H Ob. The sample correlation coefficient between English and the interaction term (STRX English) is greater than 0.98. Thus, we should drop English or (STRX English) to avoid perfect multicollinearity. Oc. The p-value for testing His between 0.01 and 0.05. d. We cannot reject H at the 5% significance level. e. We cannot test Hbecause we do not include in the model the dummy variable for school districts with a high proportion of English learners 0
- Consider a hypothetical regression predicting if someone will be married or not by the age of 40, MARRIED? (1 means this person is married by the age of 40 and 0 means this person is not married by the age of 40). The regression is as follows (all variables are statistically significant): MARRIED? = 0.2 + 0.03*EDUCATION - 0.01*BMI Where EDUCATION is the number of years of education someone's had and BMI is their body mass index. Suppose someone had 20 years of education and a BMI of 25. What is the predicted value of MARRIAGE? 0.35, which makes sense even though MARRIED? can only be a zero or one 0, because the calculated value is 0.35 so we round down. 0.55, which makes sense even though MARRIED? can only be a zero or one. Calculating a predicted value should not be done here because the dependent variable is a dummy variable. 1, because the calculated value is 0.55 so we round up.Consider a hypothetical regression predicting if someone will be married or not by the age of 40, MARRIED? (1 means this person is married by the age of 40 and 0 means this person is not married by the age of 40). The regression is as follows (all variables are statistically significant): MARRIED? = 0.2 + 0.03*EDUCATION - 0.01*BMI Where EDUCATION is the number of years of education someone's had and BMI is their body mass index. Suppose someone had 20 years of education and a BMI of 25. What is the predicted value of MARRIAGE? a 0, because the calculated value is 0.35 so we round down. b 0.55, which makes sense even though MARRIED? can only be a zero or one. c 0.35, which makes sense even though MARRIED? can only be a zero or one d Calculating a predicted value should not be done here because the dependent variable is a dummy variable. e 1, because the calculated value is 0.55 so we round up.Consider a hypothetical regression predicting if someone will be married or not by the age of 40, MARRIED? (1 means this person is married by the age of 40 and 0 means this person is not married by the age of 40). The regression is as follows (all variables are statistically significant): MARRIED? = 0.2 + 0.03*EDUCATION - 0.01*BMI Where EDUCATION is the number of years of education someone's had and BMI is their body mass index. Suppose someone had 20 years of education and a BMI of 25. Complete this sentence: For every additional year of education someone has:... a ...their chance of getting married by 40 increases by 0.03 percentage points. b ...their chance of getting married by 40 increases by 3 percentage points. c ...their chance of getting married by 40 increases by 0.03. d ...their chance of getting married by 40 increases by 3%. e This regression means nothing because the dependent variable is a dummy variable.
- Consider a hypothetical regression predicting if someone will be married or not by the age of 40, MARRIED? (1 means this person is married by the age of 40 and 0 means this person is not married by the age of 40). The regression is as follows (all variables are statistically significant): MARRIED? = 0.2 + 0.03*EDUCATION - 0.01*BMI Where EDUCATION is the number of years of education someone's had and BMI is their body mass index. Suppose someone had 20 years of education and a BMI of 25.The estimated regression equation for a model involving two independent variables and 10 observations follows.The plot presents some results for a simple linear regression analysis between two variables. (IMG 1) Is it true to say that the linear correlation coefficient is 0.6839? this is true or false?
- Section 10.2 Question #7 Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 85mm Hg. Use a significance level of 0.05. Right Arm 103 102 96 79 79 Left Arm 174 167 147 143 145 View the critical values of the Pearson correlation coefficient r Data table Dialog content starts Critical Values of the Pearson Correlation Coefficient r n α=0.05 α=0.01 NOTE: To test H0: ρ=0 against H1: ρ≠0,reject H0 if the absolute value of r is greater than the critical value in the table. 4 0.950 0.990 5 0.878 0.959 6 0.811 0.917 7 0.754 0.875 8 0.707 0.834 9 0.666 0.798 10 0.632 0.765 11 0.602 0.735 12 0.576…Question #6 Listed below are altitudes (thousands of feet) and outside air temperatures (°F) recorded during a flight. Find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. For the prediction interval, use a 95% confidence level with the altitude of 6327 ft (or 6.327 thousand feet). Altitude 4 8 14 24 27 31 32 Temperature 55 37 20 −5 −27 −41 −57 a. Find the explained variation. ______________ (Round to two decimal places as needed.) b. Find the unexplained variation. _______________ (Round to five decimal places as needed.) c. Find the indicated prediction interval. _____________°F < y < ____________ °F (Round to four decimal places as needed.)If the coefficient of determination of a simple regression equation is 0.81, the correlation coefficient is
