Interpret the assocfation between agea nd math scores in the univariate regression model, and is the association between them significant? How much variance in math score is explained by age in the univariate regression model?
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Interpret the assocfation between agea nd math scores in the univariate regression model, and is the association between them significant?
How much variance in math score is explained by age in the univariate regression model?
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- Distinguish between the following: Heteroskedasticity and autocorrelation specified regression model vs estimated regression equation data type vs level of measurement ANOVA and Multiple Regression Outliers vs InfluencersBrand Kidzania wanted to study the impact of its brand personality on the customer brand engagement. The five elements of brand personality are Sincerity, Excitement, Competence, Sophistication, Ruggedness. The multiple regression analysis of the data collected is as follows: personality are Sincerity Model Summary Model R R square Adjusted R square std. error of the estimate 1 .928^a .863 .835 3.42991 Anova Model Sum of squares df mean square F Sig 1 Regression 134.904 5 26.981 2.293 .038^b Residual 2023.455 172 11.764 Total 2158.360 177 coefficient Unstandardized standardized coefficients coefficient Model B std. error Beta t Sig 1 ( constant) 13.318 1.059 12.577 .000 Sincerity -.226 .118 -2.54 -1.921 .046 Excitement .319 .141 .348 2.267 .025 competence -0.52 .121 -0.59 -415 .679 sophistication .059 .165 .050 357 .722 ruggedness .127 .187 .074 . 687 .499 a. is this model significant ? what is the null and alternate hypothesis. b. what is the sample size?Brand Kidzania wanted to study the impact of its brand personality on the customer brand engagement. The five elements of brand personality are Sincerity, Excitement, Competence, Sophistication, Ruggedness. The multiple regression analysis of the data collected is as follows: personality are Sincerity Model Summary Model R R square Adjusted R square std. error of the estimate 1 .928^a .863 .835 3.42991 Anova Model Sum of squares df mean square F Sig 1 Regression 134.904 5 26.981 2.293 .038^b Residual 2023.455 172 11.764 Total 2158.360 177 coefficient Unstandardized standardized coefficients coefficient Model B std. error Beta t Sig 1 ( constant) 13.318 1.059 12.577 .000 Sincerity -.226 .118 -2.54 -1.921 .046 Excitement .319 .141 .348 2.267 .025 competence -0.52 .121 -0.59 -415 .679 sophistication .059 .165 .050 357 .722 ruggedness .127 .187 .074 . 687 .499 a. is this model significant ? State the null and alternate hypothesis. b. which are the independent (IV) and dependent…
- Brand Kidzania wanted to study the impact of its brand personality on the customer brand engagement. The five elements of brand personality are Sincerity, Excitement, Competence, Sophistication, Ruggedness. The multiple regression analysis of the data collected is as follows: personality are Sincerity Model Summary Model R R square Adjusted R square std. error of the estimate 1 .928^a .863 .835 3.42991 Anova Model Sum of squares df mean square F Sig 1 Regression 134.904 5 26.981 2.293 .038^b Residual 2023.455 172 11.764 Total 2158.360 177 coefficient Unstandardized standardized coefficients coefficient Model B std. error Beta t Sig 1 ( constant) 13.318 1.059 12.577 .000 Sincerity -.226 .118 -2.54 -1.921 .046 Excitement .319 .141 .348 2.267 .025 competence -0.52 .121 -0.59 -415 .679 sophistication .059 .165 .050 357 .722 ruggedness .127 .187 .074 . 687 .499 a. is this model significant ? what is the null and alternate hypothesis. b. which are the independent (IV) and dependent…Brand Kidzania wanted to study the impact of its brand personality on the customer brand engagement. The five elements of brand personality are Sincerity, Excitement, Competence, Sophistication, Ruggedness. The multiple regression analysis of the data collected is as follows: personality are Sincerity Model Summary Model R R square Adjusted R square std. error of the estimate 1 .928^a .863 .835 3.42991 Anova Model Sum of squares df mean square F Sig 1 Regression 134.904 5 26.981 2.293 .038^b Residual 2023.455 172 11.764 Total 2158.360 177 coefficient Unstandardized standardized coefficients coefficient Model B std. error Beta t Sig 1 ( constant) 13.318 1.059 12.577 .000 Sincerity -.226 .118 -2.54 -1.921 .046 Excitement .319 .141 .348 2.267 .025 competence -0.52 .121 -0.59 -415 .679 sophistication .059 .165 .050 357 .722 ruggedness .127 .187 .074 . 687 .499 d. what is the percentage of variation explained by independent variable in dependent variable? e. Write the regression…Explain the linear multiple regression model, the independent variables and the dependent variable, assumptions of the model, as well as the objectives Given the data, what approach is taken to construct the model? Explain the effect of multicollinearity in multiple regression, and how multicollinearity is detected? Show that the estimates of the coefficients are unbiased estimates of actual values. Explain the hypotheses on coefficients of the regression and how the results of testing these hypotheses are interpreted about significance of these coefficients? Include both unidirectional and bidirectional situations How do you interpret the effect of significant coefficients? How are the distribution of the observed residuals of the constructed model tested for normality? What is the coefficient of determination, and what is its significance? Why is the adjusted coefficient of determination used as an alternative assessment How can the regression model be used for…
- Show that an interaction term of a dummy variable and a regressor changes the slope of a regression line..Issue of multicollinearity impacted the ‘validity and trustworthiness’ of a regression model. Demonstrate how this issue can be a problem by using appropriate hypothetical example.(INCLUDE TABLE AND FIGURES)The model developed from sample data that has the form of Yhat = bo +bjX is known as the multiple regression model with two predictor variables. (True or False) O True O False
- can ridge regression be applied if sample size is smaller than the number of predictors?Using the California Department of Education's API 2000 dataset with sample size be 45. This data file contains a measure of school academic performance as well as other attributes of the elementary schools, such as, class size, enrollment, poverty, etc. Let's dive right in and perform a regression analysis using the variables api00, acs_k3, meals and full. Fit the regression model and get the following SAS output. The following are the outputs from SAS. Source Model Error Corrected Total Variable Intercept acs_k3 meals full Label DF 3 a b Intercept avg class size k-3 pct free meals. pct full credential Sum of Squares DF Analysis of Variance 1 1 1 1 с 1271 e Mean Square 875 d Parameter Estimates Parameter Estimate 875.05923 -2.50651 -3.31802 0.12031 Answer the question using the SAS output. F Value f Standard Error 31.03465 1.59469 0.23408 0.12072 Pr > F g t Value 32.08 -1.92 -24.04 1.20 Pr > |t| <.0001 0.0553 <.0001 0.2321This assignment involves putting together a scenario where you are asking a question that requires analyzing data - doing a hypothesis test or determining what variables are statistically significant using your favorite statistical analytical tool learned in this course. The tools you have learned include z and t-test one population, two population, and ANOVA (regression analysis and prediction using linear regression or multivariable regression). Pretend you are the CEO and the analyst doing the analysis. Write the scenario using APA style formatted paper: Title page, Introduction, Body (with category sectional headings), Conclusion, and Reference.