We will predict hourly income based on the number of years of experience. Use analpha level of 5% and the output below to answer the questions:SUMMARY OUTPUTRegression StatisticsMultiple RR SquareStandard ErrorObservationsANOVARegressionResidualTotal0.9562.57911df110SSInterceptexperienceWhat can we conclude?59.864688.545Coefficients Standard Error16.2231.7201.4100.177There is a linear relationship between the two variables.There is NO linear relationship between the two variables.

Given information:n= 11 observation k= 1 independent variable

Answered: We will predict hourly income based on…

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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You run a Regression analysis using the Data Analysis Toolpak in Excel. The table below shows part of the output. Given these values, what is the equation of the regression line (trend line)?
Give proper explanation
Please fill in the blanks and select the correct answers over the image provided. Thank you
APPLIED STATISTICS (MULTIPLE REGRESSION) UPVOTE WILL BE GIVEN. PLEASE ANSWER THE PROBLEM BY USING SPSS OR EXCEL ONLY (NOT HANDWRITTEN). NO LONG EXPLANATION NEEDED. TAKE A SCREENSHOTS AND ANSWER THE QUESTION IN SENTENCES. a. Identify the dependent/s and independent variable then estimate the multiple regression equation. b. What is the r-squared value and what is its implication? c. What variable/s are considered significant? What is the p-value?
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…
Interpret the estimated R2 Does the residual plot suggest that the fitted regression line is inappropriate for these data? Explain why or why not. Suppose that a new subject is added to the study data and that subject is 12.5 years old with an ATST of 580 minutes. If the regression model were to be refit with this additional data point, would the new slope be greater than or less than -14.041? Justify your response.
Bluereef real estate agent wants to form a relationship between the prices of houses, how many bedrooms, House size in sq ft and Lot Size in sq ft. The data pertaining to 100 houses were processed using MINITAB and the following is an extract of the output obtained:The regression equation is ????? = ? + ???????? + ?????? ???? + ???? ????Predictor Coef SE Coef T PConstant 37718 14177 2.66 **Bedrooms 2306 6994 0.33 0.742House Size 74.3 52.98 * 0.164Lot Size -4.36 17.02 -0.26 0.798S= 25023 R-Sq=56.0% R-Sq(adj)=54.6%Source DF SS MS F PRegression 3 76501718347 25500572782 *** ****Residual Error 96 60109046053 626135896Total 99a) Write out the regression equation. b) Fill in the missing values *, **, *** and ****. c) Use the p-value approach to determine if ? is significant at the 5% significance leveld) Is ? significantly different from -0.5? e) Perform the F test at the 1% level, making sure to state the null and alternative hypotheses. f) Give an interpretation to the term “R-sq” and…
Please do only the last three
An automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:. Summary measures Multiple R 0.9689 R-Square 0.9387 Adj R-Square 0.9212 StErr of Estimate 72.218 Regression coefficients Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.
Please assist second part? Thank you!
Based on the data shown below, calculate the regression line. y 1 4.27 3.64 3 3.31 5.08 5.85 6. 2.42 y = [three decimal accuracy] [three decimal accuracy] 45
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…

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We will predict hourly income based on the number of years of experience. Use an alpha level of 5% and the output below to answer the questions: SUMMARY OUTPUT Regression Statistics Multiple R R Square Standard Error Observations ANOVA Regression Residual 0.956 2.579 11 df 1 SS 59.864