What assumption is not required for a bivariate regression to be a valid description of the relationship between X and Y?
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- A researcher's results are shown below using Femlab(labor force participation rate among females) to try to predict Cancer (death rate per 100,000 population due to cancer) in the 50 US states. What is the R2 for this regression? Source of variation df ss ms f Regression 1 5377.836 5377.836 5.228879 residual 48 49367.389 1028.487 total 49 54745.225True or False and explain if false: 1. The slope estimate from a regression model is an example of descriptive statistic. 2. if a p-value is 0.9, then we may appropriately conclude that no relationship between X and Y exists 3. Assuming that X variable is controlled, one way to decrease standard errors in a sample linear regression analysis is to assign value of X variable that are further apart.Give proper explanation
- What is the least-squares regression line with the point (9,13) included in the data set? Data Set x y 3 6 4 5 5 7 7 6 8 9 8 8 10 8 11 9 11 7 12 10 13 12 13 10 14 11 This is a reading assessment question. ..... y hat = ______x + ______ Type integers or decimals rounded to 4 decimal places as neededData for 50 U.S. “states" was used to examine the relationship between violent crime rate (violent crimes per 100,000 persons per year) and the independent variables of urbanization (percentage of the population living in urban areas) and poverty rate. 50 observations were examined. The Excel sheet output for the analysis of this data is shown in the Figure (with some information intentionally left blank). SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square 0.69 Standard Error 8.54 Observations 50 ANOVA df SS MS Significance F Regression 2060 54.9 0.000 Residual 47 18.77 Total 2942 Coefficients Standard Error t Stat P-value B0 B1 B2 31.9 148.2 -2.17 0.035 -4.6 1.654 2.83 0.007 39.3 13.52 2.91 0.006LINEAR CORRELATION PROJECT / Topic: Does per capita income affect birth rates I need help with the trendline, the hypothesis, and conclusion, any confounding variables there might be and the r- value, and compare the r-value to the appropriate cutoff value, with correct interpretation. (remember for a sample size of 20, the absolute value of r should be greater than 0.45) The regression equation is: y=21.129−0.108xwhere y: average birth rate and x: per capita incomey and x is negatively related i.e with increase in x , y decreses and vice versa ((* for a sample size of 20, the absolute value of r should be greater than 0.45)) Country Per Capita Income Average Birth Rate Afghanistan $ 530 37.9 Austria $ 51,460 9.5 Cambodia $ 1,530 23 Canada $ 46,370 10.3 Denmark $ 63,950 10.5 Ecuador $ 6,090 17.9 Ethiopia $…
- Rewrite the regression model to include coefficients from your regression analysis output and then answer the following question What would be the company's loss if the significant variable(s) change per unit? SUMMARY OUTPUT Regression Statistics Multiple R 0.93082 R Square 0.866425 Adjusted R Square 0.85833 Standard Error 4108.993 Observations 36 ANOVA df SS MS F Significance F Regression 2 3.61E+09 1.81E+09 107.0261 3.75E-15 Residual 33 5.57E+08 16883824 Total 35 4.17E+09 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 3996.678 6603.651 0.605223 0.549171 -9438.55 17431.91 -9438.55 17431.91 X Variable 1 43.5364 3.589484 12.12887 1.05E-13 36.23354 50.83926 36.23354 50.83926 X Variable 2…Which of the following are true? (Select all that apply.) 1 An assumption of a linear regression is a consistent dispersion of the observations from the regression line. 2 An assumption of a linear regression is that the residuals form a normal distribution. 3 Omitted variable bias is when you don’t include the dependent variable.Which of the following statements is the CORRECT expression in APA format of the SPSS simple linear regression results provided below? Model Summary Adjusted R Square R R Square 817 .668 627 a. Predictors: (Constant), Experience years (year) ANOVA Regression Residual Sum of Squares 92.844 46.118 138.962 df Std. Error of the Estimate 2.401 1 Total a. Dependent Variable: Income (10 million VND/month) b. Predictors: (Constant), Experience years (year) Mean Square 92.844 5.765 Coefficients Unstandardized Coefficients B Std. Error (Constant) -.651 Experience years (year) 7856642.174 37841636.85 a. Dependent Variable: Income (10 million VND/month) .162 Standardized Coefficients Beta -.817 F 16.105 -4.013 .208 Sig. 004 Sig. 004 .841 A regression analysis was conducted with Income as criterion variable and Experience in Years as the predictor. Experience in Years was NOT a significant predictor of Income, 3= -0.82, t(9)= -4.013, p>0.05, and accounted for 66.80% of the variance in Income…