Describe the use of analysis of variance (ANOVA) in analyzing experimental results. Further, describe the use of a null hypothesis and why we use this concept to validate experiments.
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- The owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables. The data are listed in the file P13_17.xlsx. a. Estimate a simple regression equation involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression. b. Add another explanatory variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?The Baker Company wants to develop a budget to predict how overhead costs vary with activity levels. Management is trying to decide whether direct labor hours (DLH) or units produced is the better measure of activity for the firm. Monthly data for the preceding 24 months appear in the file P13_40.xlsx. Use regression analysis to determine which measure, DLH or Units (or both), should be used for the budget. How would the regression equation be used to obtain the budget for the firms overhead costs?what is the standardized regression? what do the standardized regression weights or coefficients tell us about the ability of the predictors to predict the dependent variable?
- Provide an example of the null hypothesis and a corresponding example of a alternative Hypothesis. Use example related to a business or a school.what do the standardized regression weights or coefficients tell you about the ability of the predictors to predict the dependent variable?Explain why longitudinal studies and multiple regression analyses can help address the issues of temporal precedence and internal validity concerns that limit the conclusions we can make using bivariate correlations.
- A sample of twenty automobiles was taken, and the miles per gallon (MPG), horsepower, and total weight were recorded. Develop a linear regression model to predict MPG using horsepower as the only indepen- dent variable. Develop another model with weight as the independent variable. Which of these two models is better? Explain. MPG 44 44 40 37 37 34 35 32 30 28 26 26 25 22 20 21 18 18 16 16 4 HORSEPOWER 67 50 62 69 66 63 90 99 63 91 94 88 124 97 114 102 114 142 153 139 WEIGHT 1,844 1,998 1,752 1,980 1,797 2,199 2,404 2,611 3,236 2,606 2,580 2,507 2,922 2,434 3,248 2,812 3,382 3,197 4,380 4,036Answer the following Research Questions Differentiate quantitative and qualitative indicators. Discuss the difference between a control group and an experimental group. How cofounding variables really matter in research? Is it possible that a variable can be both independent and dependent?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?