3. The regression model below estimated the impact of tax on income using 41 observations.a. Is the slope parameters of variable of tax significant at 5 percent level? Explain.b. What the R-square in this regression indicates.consttaxModel 1: High-Precision OLS, using observations 1-41Dependent variable: incomeCoefficient1.781917.00074Mean dependent varS.D. dependent varSum squared residS.E. of regressionR-squaredAdjusted R-squaredF(1,49)P-value(F)Std. Error0.8803240.0575306T-ratioPvalue2.024121.70.0484<0.00017.0558.301.144.829.979.961.481.79**

Given that, the p-value for slope parameter (tax) is <0.0001

Answered: 3. The regression model below estimated…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Using the accompanying Cost of Living Adjustments data to find the best multiple regression model to predict the salary as a function of the other variables. What would the comparable salary be for a city with the following adjustments: groceries: 4%; housing: 8%; utilities: −3%; transportation: −1%; and health care: 7%? Use a level of significance of 0.05. Using the accompanying Cost of Living Adjustments data to find the best multiple regression model to predict the salary as a function of the other variables. What would the comparable salary be for a city with the following adjustments: groceries: 4%; housing: 8%; utilities: −3%; transportation: −1%; and health care: 7%? Use a level of significance of 0.05. Determine the best multiple linear regression model to predict the salary as a function of the other rates. Select the best answer below and fill in the corresponding answer boxes to complete your choice. (Type integers or decimals rounded to…
Could you please help interpret this SPSS output, i already conducted the multiple regression analysis i just need 1 and 2...THIS IS THE COMPLETE QUESTION Conduct a multiple regression analysis, using ‘monthly sales value’ as the Dependent Variable and entering all other variables simultaneously as the predictors. Determine the regression equation. Which are the best predictors and which predictors contribute very little to the estimate of mean monthly sales value? Interpret the regression results in terms of (1) the variance explained, (2) the overall significance of the regression equation, and (3) the significance, sign and value of each regression coefficient.
Please help me with question d, e, f, g, h, and I. Thank you.
please give me answer in relatable
An economist is interested to see how consumption for an economy (in $ billions) is influenced by gross domestic product ($ billions) and aggregate price (consumer price index). The Minitab output of this regression is reproduced below. Coefficients Term Constant GDP Price Model Summary S 0.299 Error Total Coef Analysis of Variance Source Regression -0.0861 0.7654 -0.0006 R-sq 0.98% DF • 2 7 9 SE Coef 0.5644 0.0574 0.0028 R-sq(adj) 0.98% Adj SS 33.4163 0.6277 34.0440 T-Value O a. 7 O b. 9 O c. 10 O d. Cannot be determined from the information given. -0.1520 13.3400 -0.1290 Adj MS 16.7082 0.0897 P-Value 0.8837 0.0001 0.8330 F-Value P-Value 186.325 0.049 Referring to the table above, how many observations were there in this study?
None
The attached images show linear regression analysis to evaluate the ability of independent variables full and part-time FTEs, number of Medicare certified beds and urban vs. rural setting to predict dependent variable, occupancy rate. How do you interpret these results, what are the basic assumptions for regression analysis?
Zagat restaurant guides publish ratings of restaurants for many large cities around the world. The restaurants are rated on a 0 to 30 point scale based on quality of food, decor, service, and cost. Suppose that someone wants to predict the cost of dinner at a restaurant in a city based on the Zagat food quality ratings. If 10 restaurants in a city are sampled and the regression output is given below, what can we conclude about the slope of food quality? Since we are not given the dataset, we do not have enough information to determine if the slope differs from 0. 2) The slope is 2.521 and therefore differs from 0. 3) The slope significantly differs from 0. 4) Not enough evidence was found to conclude the slope differs significantly from 0. 5) The slope is equal to 0.
Define Residuals or errors in Alternative Regression Models?
The service regresses the number of complaints lodged against an employee last year on the hourly wage of the employee for the year. The analyst ran a simple linear regression shown below. Table 7: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .854a .730 .695 6.6235 a. Predictors: (Constant), Hourly Wage Table 8: ANOVA ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1918.458 1 1918.458 129.783 .000a Residual 709.567 48 14.782 Total 2628.025 49 a. Predictors: (Constant), Hourly Wage b. Dependent Variable: Number of Complaints Table 9: Coefficients Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 20.2 4.357 4.636 .000 Hourly Wage -1.20 .088 -.946 -13.636 .000 a. Dependent Variable: Number of…
Why this type of plot is used in analysing regression models
Develop a scatterplot and explore the correlation between customer age and net sales by each type of customer (regular/promotion). Use the horizontal axis for the customer age to graph. Find the linear regression line that models the data by each type of customer. Round the rate of changes (slopes) to two decimal places and interpret them in terms of the relation between the change in age and the change in net sales. What can you conclude? Hint: Rate of Change = Vertical Change / Horizontal Change = Change in y / Change in x

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