What are the things we need to do in order to define and come up with solution parameters for statistically validated X's?
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What are the things we need to do in order to define and come up with solution parameters for statistically validated X's?
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- How do I determine what the decsion variables are in a problem?Can someone simply explain linear regression? Can linear regression be automatically calculated in SPSS?Discuss how the coefficient of determination and the coefficient of correlation are related and how they are used in regression analysis. Be sure to provide examples to illustrate your understanding of these concepts.
- Problem 1: The following data was taken from experiment. The data can be modeled by the following equation. ya = b10va(x²+0.5) 0.10 0.30 0.45 0.55 0.70 y 0.52 0.60 0.68 0.75 1.00 a) Find the values of a and b using least square regression. b) Then use the resulting model to predict y at x 0.035Chart and Regression analysis : What does the intercept predict? X: C16 (number of cars on the sales lot) versus Y: C17 (cars sold per day) Equation: y=2.9x + 14.5 Slope:2.9 Intercept:14.5 Does the intercept mean the intercept is 14.5 means that the cars sold per day( Y) predicted number of cars on sale lot(X) to be 14.5, but this intercept has no meaning. So, I will not use to predict cars sold per day?What is linear regression? Can linear regression be automatically calculated in SPSS?
- The following multiple regression printout can be used to predict a person's height (in inches) given his or her shoe size and gender, where gender = 1 for males and 0 for females. Regression Analysis: Height Versus Shoe Size, Gender Coefficients Term Coef Constant 55.28 SE Coef 1.04 T-Value P-Value Shoe Size 0.105 Gender 0.268 0.12 0.489 53.1 0.875 0.000 0.000 0.548 0.000 (a) The dependent variable in this regression is which of the following? height gender shoe size constant (b) What is the regression coefficient of shoe size? (c) What is the regression coefficient of gender?You work as a sales operations analyst in a company that makes 3D printers. Your manager has asked you to determine if a salesperson's sales volume (in terms of the number of 3D printers they sell in a year) depends on the number of client calls they make. After analyzing past data and creating a linear regression model, you've found the following relationship: No. of printers sold = 18.47 + 1.13 times the number of client calls. 1. Based on this, how many client calls will a salesperson need to make to sell 245 printers next year? a. 200 (rounds to) b. 215 (rounds to) c. 230 (rounds to) d. 240 (rounds to)Write Comments on the Use of Linear Regression Analysis?