The last part of Sloan's report concerns the connection between cycling and the level of stress. Toinvestigate this link, Sloan first collected information about how many hours a week on average asample of Kiloton bicycle owners go cycling. She then invited them to a lab where their level of stresswas measured with a combination of tests that included heartrate monitoring and oxygenconsumption measurement. With this measurement, the stress level can be as high as 10 and as lowas 0. Sloan collected 80 responses. To understand her data better, she plotted the average hours ofcycling per week against the level of stress on a scatter plot shown Figure 1 below.Sloan decides to fit a simple linear regression model to the data to investigate the relationshipbetween stress levels and average time spent cycling. The fitted regression line has beensuperimposed on the scatter plot in Figure 8.1. Table 8.1 shows the selected outputs from theregression analysis.Figure 8.1: Stress levels against hours of cycling with fitted regression line superimposed on the plot.Scatterplot of levels of stress and hours cycling a week108.76431102030405060Average hours of cycling (week)Level of stress9, Table 8.1: Selected Excel output from a simple linear regression of levels of stress and average hoursof cycling a week; n=80ANOVASignificancedfMSFRegression1.330.0887 330.0887 439.59268.61E-34Residual7858.56994 0.750897Total79 388.6586StandardCoefficientsErrort StatP-valueInterceptHours of cycling8.174779154 0.201398 40.59015<0.0001(week)-0.151563181 0.007229 -20.9665<0.0001By looking at the output in Table 8.1, write down the equation of the fitted regression model, andexplain the meaning of the regression coefficients in context. Comment on the statistical significanceof the regression coefficients.

In this case, the independent variable is hours of cycling (x) and the dependent variable is levels…

Table 8.1: Selected Excel output from a simple linear regression of levels of stress and average hours of cycling a week; n=80 ANOVA Significance F df S MS Regression 1 330.0887 330.0887 439.5926 8.61E-34 Residual 78 58.56994 0.750897 Total 79 388.6586 Standard Coefficients Error t Stat P-value Intercept Hours of cycling 8.174779154 0.201398 40.59015 <0.0001 (week) -0.151563181 0.007229 -20.9665 <0.0001 By looking at the output in Table 8.1, write down the equation of the fitted regression model, and explain the meaning of the regression coefficients in context. Comment on the statistical significance of the regression coefficients.