In this task, you can use all functions of R to help you. In a recently conducted study, it was tested whether there is a connection between job satisfaction and income and seniority in nine randomly selected employees. The value attached to job satisfaction is each employee's own assessment, where 1 describes low satisfaction and 10 high satisfaction. Material:  annual income (per thousand dollars) years spent in job satisfaction 47 8 5.6 42 4 6.3 54 12 6.8 48 9 6.7 56 16 7.0 59 14 7.7 53 10 7.0 62 15 8.0 66 22 7.8   The result was the material below a) Fit a two-explainer linear regression model to the data with R's function lm(). What happens to job satisfaction as the number of years increases? For explanatory variables, can the null hypothesis that the corresponding regression coefficient is equal to zero be rejected? b) It is assumed that job satisfaction is only related to years in that job. Estimate the regression parameters using R software. What happens to job satisfaction as the number of years increases? Can we reject the null hypothesis that the regression coefficient is equal to zero? c) What difference do you notice in the answers to (a) and (b)? What could be the reason for this difference? Tip: What is assumed about the explainers?

Determine whether the study depicts an observational study or an experiment.   A scientist was studying the effects of a new fertilizer on crop yield. She randomly assigned half of the plots on a farm to group one and the remaining plots to group two. On the plots in group​ one, the new fertilizer was used for a year. On the plots in group​ two, the old fertilizer was used. At the end of the year the average crop yield for the plots in group one was compared with the average crop yield for the plots in group two.

What percentage of data would you predict would be between 25 and 50 and what percentage would you predict would be more than 50 miles? Use the Week 4 spreadsheet again to find the percentage of the data set we expect to have values between 25 and 50 as well as for more than 50. Now determine the percentage of data points in the dataset that fall within each of these ranges, using same strategy as above for counting data points in the data set. How do each of these compare with your prediction and why is there a difference? Predicted percentage between 25 and 50: ________________________ Actual percentage: Predicted percentage more than 50 miles: Actual percentage: ___________________________________________ Comparison ____________________________________________________ Why? __________________________________________________________ Drive: 44 20 88 6 71 42 76 63 61 63 84 28 55 33 88 80 86 83 5 85 25 25 54 54 81 73 29 76 78…

Rail ridership during the summer months in the North-East is believed to beclosely related to the number of tourists visiting the region. The following tableshows the data on the number of tourists and North-East ridership for the past16 years. (Answer in Excel showing formulas for answers)Year      Tourists( in millions)     Rail Ridership (in millions) 2000              9                                         2.32001              4                                         1.9 2002              7                                         1.7 2003              5                                         1.8 2004              12                                       2.8 2005              13                                       2.9 2006              14                                       2.42007              20                                       4.8 2008              7                                        1.3 2009              5                                         1.12010…