Multiple regression is a statistical method that includes ____ predictor variable(s) in the equation of the regression line. two two or more one zero
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- P4. The least squared method called also the least squared regression method is one of the most commonly used methods in applications . Describe the importance of this method in modeling data. Provide an illustrative example for given data and interpret your results. Using your example illustrate the importance of the results.The correlation coefficient between midterm and final scores in a large statistics class is r=0.6. A scatterplot of the two variables is football shaped. A particular student has a midterm score that is 0.5 SDs below the average midterm score of all students. Using regression, we would predict that the student's final score is _________ SDs _________ the average final score of all students and therefore at the _________ percentile of final scores of all students.X 33 46 72 105 114 Y 209 228 170 127 109 1) What is slope of the regression line predicting Y from X,rounded to 2 decimal places? 2) What is the intercept of the regression line predicting Y from X, rounded to 2 decimal places? 3) What is the correlation between X and Y, rounded to 2 decimal places?
- 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.Jdz score z score for each for each value of value of Zzły х х -0.278 0.536 -0.149 -0.089 0.696 -0.062 -1.253 -1 -1.652 2.070 -0.714 -0.928 0.663 2.461 1.473 1.671 0.536 0.371 0.199 -0.089 4 -0.278 0.025 Σ: ỹ = 3.857 S, = 3.078 = 5.207 T= 4.286 s, = 3.200
- Part and b. Thank you!Fill in the blanks. a. Multicollinearity is considered to be severe if the VIF for one or more predictor variables is ______. or greater. b. If the coefficient of multiple determination for the regression of the predictor variable x11 on all the other predictor variables in a regression equation is 0.6, then the VIF for x1 is ______. c. The effect of multicollinearity in a polynomial regression analysis can be reduced by ______. the predictor variable.21. Which of the following statements is true regarding the sources of variation present in an analysis of regression? SSy is partitioned into variation explained by the regression model and residual variation. If most of the variability in Y is associated with residual variation, then X predicts Y. There are three sources of variation in an analysis of regression: regression variance, residual variance, and error variance. Regression variation measures variability in X, whereas residual variation measures variability in Y.
- 4) How to conduct multiple regression in Excel (Provide the steps)? Driving Experience (years) Monthly Auto Insurance Premium 5 $64 2 87 12 50 9 71 15 44 6 56 25 42 16 60 Does the insurance premium depend on the driving experience or does the driving experience depend on the insurance premium? Do you expect a positive or a negative relationship between these two variables?511. For temperature (x) and number of ice cream cones sold per hour (y). (65, 8), (70, 10), (75, 11), (80,13), (85, 12), (90, 16). Interpret the coefficient of determination. Optional Answers: 1. 88.2% of the variability in the number of cones sold is explained by the least-squares regression model. 2. 93.9% of the variability in the number of cones sold is explained by the least-squares regression model. 3. 88.2% of the variability in the temperature is explained by the least-squares regression model. 4. 93.9% of the variability in the temperature is explained by the least-squares regression model.
