You will not need to operate data and software, the results have been given in the attachment, please answer the following questions according to the results. Thanks!

In a pretest, respondents were asked by Company XYZ to express their preference for an outdoor lifestyle using a 7-point scale: 1 = not at all preferred, to 7 = greatly preferred (V1). They were also asked to indicate the importance of the following variables on a 7-point scale: 1 = not at all important, to 7 = very important.

V2 = enjoying nature

V3 = relating to the weather

V4 = living in harmony with the environment

V5 = exercising regularly

V6 = meeting other people

The sex of the respondent (V7) was coded as 1 for females and 2 for males. The location for residence (V8) was coded as: 1 = midtown/downtown, 2 = suburbs, and 3 = countryside.

XYZ is interested in examining the role of variables V2 to V6 in predicting the respondents' preference for outdoor lifestyle (V1). In this regard a multiple regression analysis was performed, and the results are provided in attachment . Based on the output in attachment 1 respond to the following questions:

(a) Write out the equation for the regression model that was constructed to predict preference for outdoor lifestyle for XYZ.

(b) Comment on the overall model fit and indicates which variables are significant predictors of preference for outdoor lifestyle (at the 5% significance level)? Support your answer with the appropriate evidence

(c) Rank the independent variables in terms of their relative importance in predicting preference for outdoor lifestyle. Support your answer with the appropriate evidence

(d) Comment on the ANOVA table. Using the regression results, what advice would you offer to XYZ to improve the preference for outdoor lifestyle high service quality perceptions in the Asian market

Attachment 1 Regression Variables Entered/Removed Variables Variables Model Entered Removed Method Meeting People, Enter Harmony with Environment, Exercising Regularly, Relating to Weather, Enjoying Nature a. All requested variables entered. Model Summary Adjusted R Std. Error of the Model R R Square Square Estimate Durbin-Watson 909 826 .790 .897 1.943 a. Predictors: (Constant), Meeting People, Harmony with Environment, Exercising Regularly, Relating to Weather, Enjoying Nature b. Dependent Variable: Preference for Outdoors ANOVA Model Sum of Squares df Mean Square Sig. Regression 91.651 5 18.330 22.776 .000a Residual 19.315 24 805 Total 110.967 29 a. Predictors: (Constant), Meeting People, Harmony with Environment, Exercising Regularly Relating to Weather, Enjoying Nature b. Dependent Variable: Preference for Outdoors Coefficients a Unstandardized Coefficients Model B Std. Error Constant) 563 654 Enjoying Nature .031 118 Relating to Weather .566 117 Harmony with Environment -.288 .128 Exercising Regularly .594 .117 Meeting People .191 .109 5Coefficientsa Standardized Coefficients Model Beta Sig (Constant 861 .398 Enjoying Nature .029 .258 .799 Relating to Weather .502 4.850 000 Harmony with Environment .240 -2.250 034 Exercising Regularly .508 5.08 .000 Meeting People 182 1.743 094 a. Dependent Variable: Preference for Outdoors Residuals Statistics Minimum Maximum Mean Std. Deviation N Predicted Value 1.27 6.96 1.03 1.778 30 Residual -1.305 2.032 000 .816 30 Std. Predicted Value -1.557 1.645 000 1.000 30 Std. Residual -1.454 2.265 000 .910 30 a. Dependent Variable: Preference for Outdoors 6