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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- 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?On-base percentage plus slugging (OPS) is a statistic used in baseball to measure a team's batting success. The number of runs scored and OPS for 30 baseball teams was used to conduct a linear regression analysis. The scatterplot and computer output for the regression analysis is shown. 900- 850- 800- Number of 750- Runs Scored 700- 650- 600- 0.650 0.675 0.700 0.725 0.750 0.775 0.800 OPS Term Coef SE Coef Constant -838.40 77.99 OPS 2144.3 107.1 T-Value -10.75 20.01 P-Value <0.0001 < 0.0001 S = 19.516 R-Sq = 93.47% R-Sq(adj) 93.23% Which of the following is the most appropriate interpretation of the statistic 93.47% in the regression output? (A) There is a strong, positive, linear relationship between number of runs scored and OPS. (B) The typical deviation between observed and predicted number of runs scored is 0.9347. (C) For each one-unit increase in OPS, the regression model predicts an increase of 93.47 runs scored. (D) 93.47% of the observed number of runs scored are close to the…Jd
- z 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, = 3.200Part 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.What's a good topic for a regression topic? Make sure you have two quantitative variables (for example # of siblings and heart rate). Ask for another variable like academic year, major, hobbies, favorite genre of movie. - State the independent and dependent variables and what is the population.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.4)