Inspired by a Canadian study that suggests that commuters cycling to work are the happier, a researcher from Melboume investigated the effects of taking the bike to work. One aspect they are interested is a possible relationship between happiness and time spent commuting on a bike. For a sample of 25 participants who cycle to work, the weekly time spend on the bike commuting (WBC) to work is correlated with the results from a happiness questionnaire (HQ). After entering the data into R! a correlation analysis and a linear model was performed with the following results: >contest (HQ,WBC) Pearson's product-moment correlation data: HQ and WBC t = 1.9708, df = 23, p-value = 0. 0609 alternative hypothesis: true correlation is not equal to e 95 percent confidence interval: -0. 01769297 0. 67399984 sample estimates: cor 9.3890952 Call: Im ( formula = HQ ~ WBC) Residuals: Min 10 Median 30 Max -28.730 -10.235 -1.859 9.270 30.518 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 30. 7993 6. 8692 4.498 0.080166 *#* WBC 1. 8119 9.9194 1.971 0.060909 Signif. codes: 0 * 0.091 "* 0.91 "# 0.05 ".' 0.1 ' ' 1 Residual standard error: 14.12 on 23 degrees of freedom Multiple R-squared: 0.1445, Adjusted R-squared: 0.1073 F-statistic: 3.884 on 1 and 23 DF, p-value: 0.0609 Question a. Based on this analysis, what can be said about the relation between HQ and WBC? Question b. The 95% confidence interval of the correlation analysis suggests: Question c. What is the estimated regression equation?: Question d. Based on the estimated regression equation, what is the expected value of HQ for a person with a WBC of 9?: Question e. Interpret the relationship between HQ and WBC, based on this analysis: Question f. Which of the following is true: Cycling makes you happier. C. WBC is significantly correlated with HQ. WBC and HQ are not significantly correlated. The model's ability to predict HQ from WBC is poor, accounting for 21.455 percent of the data