(b) Do you see evidence that some predictors were unhelpful? (You may select more than one answer. Single click the box with the question mark to produce a check mark for a correct answer and double click the box with the question mark to empty the box for a wrong answer.)- [?] Length- [?] Width- [?] Weight(c) Do you suspect that multi-collinearity is a problem?- O Yes- O No ### Regression Analysis for Predicting City Fuel EfficiencyAn analyst used regression analysis on data from 43 vehicles to predict **CityMPG** (miles per gallon in city driving). The predictor variables were:- **Length** (length of car in inches),- **Width** (width of car in inches),- **Weight** (weight of car in pounds).#### Model Summary- **R²**: 0.682- **Adjusted R²**: 0.658- **R (Correlation Coefficient)**: 0.826- **Standard Error**: 2.558- **Dependent Variable**: CityMPG#### ANOVA Table| Source | SS | df | MS | F | p-value ||--------------|----------|----|----------|------|--------------|| Regression | 547.3722 | 3 | 182.4574 | 27.90| 8.35E-10 || Residual | 255.0929 | 39 | 6.5408 | | || Total | 802.4651 | 42 | | | |#### Regression Output| Variables | Coefficients | Std. Error | t Stat | p-Value| Confidence Interval (95%) | VIF ||------------|--------------|------------|--------|--------|---------------------------|-------|| Intercept | 39.4492 | 8.1678 | 4.830 | 0.0000 | (22.9283, 55.9701) | || Length (in)| -0.0016 | 0.0454 | -0.035 | 0.9725 | (-0.0934, 0.0902) | 2.669 || Width (in) | -0.0463 | 0.1373 | -0.337 | 0.7379 | (-0.3239, 0.2314) | 2.552 || Weight (lbs)| -0.0043 | 0.0008 | -5.166 | 0.0000 | (-0.0060, -0.0026) | 2.836 |#### Conclusion(a

Introduction: The response variable in this regression analysis is CityMPG or miles per gallon while…

Answered: Using test data on 43 vehicles, an…

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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A sales manager for an advertising agency believes there is a relationship between the number of contacts that a salesperson makes and the amount of sales dollars earned. A regression analysis shows the following results. Coefficients Standard Error t-Stat p-value Intercept -12.201 6.560 -1.860 0.100 Number of contacts 2.195 0.176 12.505 0.000 ANOVA df SS MS F Significance F Regression 1.00 |13,555.42 |13,555.42 156.38 0.00 Residual 8.00 693.48 86.68 Total 9.00 14,248.90 Assume that X = 33.4 and E(X – X) 2814.4. Rounding to one decimal place, the 95% confidence interval for 30 calls is
You may need to use the appropriate technology to answer this question. Following is a portion of the computer output for a regression analysis relating y = maintenance expense (dollars per month) to x = usage (hours per week) of a particular brand of computer terminal. Analysis of Variance SOURCE DF Adj SS Adj MS Regression 1 1575.76 1575.76 Error 8 349.14 43.64 Total 9 1924.90 Predictor Coef SE Coef Constant 6.1092 0.9361 X 0.8951 0.1490 Regression Equation Y = 6.1092 + 0.8951 X #1) Write the estimated regression equation. ŷ = #2) Find the value of the test statistic. (Round your answer to two decimal places.)Find the p-value. (Round your answer to three decimal places.) #3)Use the estimated regression equation to predict monthly maintenance expense (in dollars per month) for any terminal that is used 15 hoursper week. (Round your answer to the nearest cent.) $ _____per month
A Bivariate Regression was conducted to evaluate the predictive relationship between total years of schooling and annual income. The results of the regression model were F(1,88) = 4.1, p < .05. What can be concluded about these results? Group of answer choices total years of schooling is a significant predictor of annual income. total years of schooling is not a significant predictor of annual income.
A student used multiple regression analysis to study how family spending (y) is influenced by income (x) family size (x2), and addition to savings(x3). The variables y, x1, and x3. The variables y, x1, and x3 are measured in thousands of dollars . The following results were obtained. ANOVA df SS Regression 3 45.9634 Residual 11 2.6218 Total Coefficient Standard Error Intercept 0.0136 X1 0.7992 0.074 X2 0.2280 0.190 X3 -0.5796 0.920 Write out the estimated regression equation for the relationship between the variables. Compute coefficient of determination. What can you say about the strength of this relationship? Carry out a test to determine whether y is significantly related to the independent variables. Use a 5% level of significant. Carry out a test to see if X3 and y are significantly related. Use a 5% level of significance
Q) Plot the relationship between total operating costs and machine hours. Draw the regression line.
Use the Stata output below. The data comes from students in an undergraduate economics course. The regression of interest is: final =B₁ + B₁ atndrte + ß₂hwrte + Â¸priGPA+ Â¹ ACT .reg final atndrte hwrte priGPA ACT Source Model 3094.70776 11929.9465 Residual Total final SS atndrte hwrte priGPA ACT _cons 15024.6543 df 773.676939 4 669 17.832506 MS 673 22.324895 Coef. Std. Err. .0138725 .0183476 .010863 1.906347 .3750236 .3990516 .0535332 9.225908 1.46863 Number of obs F(4, 669) Prob > F R-squared Adj R-squared Root MSE t P>|t| ||||||||||||| 674 [95% Conf. Interval] Approximate the p-value from the null hypothesis that all of the slope parameters are equal to zero.
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 54745.225
Does Job stress influence overall life satisfaction? A sample of 7,814 people were surveyed, and this is the result of the regression analysis: Descriptive Statistics Mean Std. Deviation N Life satisfaction 7.97 1.194 7817 Job stress 3.16 1.667 7817 Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .228a .052 .052 1.162 .052 427.062 1 7815 .000 a. Predictors: (Constant), Job stress b. Dependent Variable: Life satisfaction ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 577.130 1 577.130 427.062 .000b Residual 10561.161 7815 1.351 Total 11138.291 7816 a. Dependent Variable: Life satisfaction b. Predictors: (Constant), Job stress Coefficientsa Model Unstandardized…
The following regression model was fitted to sample data with 12 observations: = 30 +4.50x. What is the residual for an observation (x = 2, y = 40)? 0.50 -1 1 -0.50
The following regression output can be used to predict the weight of a Shetland pony based on the animal's age: Regression Statistics Multiple R 0.97224608 R Square 0.94526245 Adjusted R Square 0.9270166 Standard Error 14.0700917 Observations Intercept Age Coefficients Standard Error 55.7 tStat 12.08558531 P-value 4.61142 0.01918 7.19771 0.00553 5.9 0.818914857 True or false? The pony's weight is related to its age at the .01 level of significance. A True B) False
2
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