Analyse the following regression model CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0%Intercept-70.21566883.5899819-0.8400010.40144881-234.5852494.1539002-234.5852494.1539002LandArea5.4726E-062.86E-050.191345660.84835946-5.077E-056.1712E-05-5.077E-056.1712E-05Rooms-12.6772310.5132648-1.20583190.22865076-33.3502917.9958309-33.3502917.9958309EquivArea3.787363820.2464094815.37020341.3343E-413.302829444.27189823.302829444.2718982Condition16.081201210.29540041.561979190.11914546-4.16345636.3258584-4.16345636.3258584Years2.138127010.326737846.543860921.9948E-101.495636642.780617381.495636642.78061738

Answered: Image /qna-images/answer/d388ce6e-4ed1-472c-8064-8b5d1ac3d405.jpg

Answered: Analyse the following regression model…

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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the following multiple regression model using yearly data spanning the period 2001 to 2016: ??=?+????+????+???? Where FR = yearly foreign reserves ($000’s), OIL = annual oil prices, EXP = yearly total exports ($000’s) and FDI = annual foreign direct investment ($000’s). The sample of data was processed using MINITAB and the following is an extract of the output obtained: Hence test whether ? is significant. Give reasons for your answer. Perform the F Test making sure to state the null and alternative hypothesis. Given an interpretation of the term “R-sq” and comment on its value.
The data are the ages of criminals and their victims. The regression output is shown in a separate tab from the data. Do the data support that a prediction of victim age can be obtained given the data provided in the file? Cite the elements of the output you used to draw your conclusion.
2
Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 1.7 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05. Overhead Width (cm) Weight (kg) Click the icon to view the critical values of the Pearson correlation coefficient r. F n example Get more help. The regression equation is y=-159.5+ 43.2 x (Round to one decimal place as needed.) 3 # The best predicted weight for an overhead width of 1.7 cm is kg. (Round to one decimal place as needed.) I 80 E 26 D 7.8 161 C $ 4 Q F4 8.3 212 R F Stv 9.9 276 07 20 V F5 T 9.1 215 G 6 B 9.7 262 746 MacBook Air FO 7.3 166 Y H & 7 N F7 U X +00 8 J DII FB - M ( 9 K O < I ) O |qqqqqqqqi L F10 Clear all P . V : ; F11 { + [ command option Check answer = ? 11 I F12 1
What is the slope of the regression line (slope and intercept) Please put the answer in bold.
19
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.
What does the slope and the intercept mean in this setting?
A researcher is interested in examining the relationship between spousal abuse and child abuse. Specifically, they are interested in determining whether there is a predictive relationship between spousal abuse and child abuse in 5 county social services offices. Calculate the linear regression line for the following data. Note you have already calculated the first step to this analysis (Pearson's Correlation)
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…
Let's study the relationship between brand, camera resolution, and internal storage capacity on the price of smartphones. Use α = .05 to perform a regression analysis of the Smartphones01CS dataset, and then answer the following questions. When you copy and paste output from MegaStat to answer a question, remember to choose to "Keep Formatting" to paste the text. a. Did you find any evidence of multicollinearity and variance inflation among the predictors. Explain your answer using a VIF analysis. b. Copy and paste the normal probability plot for your analysis. Is there any evidence that the errors are not normally distributed? Explain. c. Copy and paste the Residuals vs. Predicted Y-values. Does the pattern support the null hypothesis of constant variance for the errors? Explain. d. Study the residuals analysis. Which observations, if any, have unusual residuals? e. Study the residuals analysis. Calculate the leverage statistic. Which observations, if any, are high leverage…
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…

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	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	-70.215668	83.5899819	-0.840001	0.40144881	-234.58524	94.1539002	-234.58524	94.1539002
LandArea	5.4726E-06	2.86E-05	0.19134566	0.84835946	-5.077E-05	6.1712E-05	-5.077E-05	6.1712E-05
Rooms	-12.67723	10.5132648	-1.2058319	0.22865076	-33.350291	7.9958309	-33.350291	7.9958309
EquivArea	3.78736382	0.24640948	15.3702034	1.3343E-41	3.30282944	4.2718982	3.30282944	4.2718982
Condition	16.0812012	10.2954004	1.56197919	0.11914546	-4.163456	36.3258584	-4.163456	36.3258584
Years	2.13812701	0.32673784	6.54386092	1.9948E-10	1.49563664	2.78061738	1.49563664	2.78061738