Average Age and Length of Service The data show the average ages and lengths of service in months. Age 25 25.5 26.5 26.6 26.9 Time 66.2 71.2 72.6 88.1 82.7     The correlation coefficient for the data is  =r0.817  and  =α0.05 . Should regression analysis be done?     The regression analysis should not be done.     The regression analysis should be done.     Find the equation of the regression line. Round the coefficients to at least three decimal places.   =y′+abx     =a     =b     If a service person is  26.0  years old, predict the time that the person will serve. Round the answer to at least one decimal place.   Theservicepersonwillservemonths.

You generate a scatter plot using Excel. You then have Excel plot the trend line and report the equation and the ² value. The regression equation is reported as y = 49.18x + 40.52 and the r² = 0.5041. What is the correlation coefficient for this data set? r =

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using Excel

Use the value of the linear correlation coefficient to calculate the coefficient of determination. What does this tell you about the explained variation of the data about the regression​ line? About the unexplained​ variation? r= -0.438   please dont copy paste the content from other sites

Forest Fires and Acres Burned Numbers (in thousands) of forest fires over the year and the number (in hundred thousands) of acres burned for  7  recent years are shown.   Number of fires  x 58 47 84 62 57 72 69 Number of acres burned  y 54 43 80 58 53 68 65 The correlation coefficient for the data is  =r1  and  =α0.05 . Should regression analysis be done?   The regression analysis should not be done.     The regression analysis should be done.     Find the equation of the regression line.   =y′+abx     =a     =b     Find  y′  when  =x50 .     =y′

Pls someone help I really need these answers fast Using the graphing calculator, type in the table and complete a linear regression. The value of the correlation coefficient is found next to the letter (__) in the table. The value of the correlation coefficient is (__) [round to the hundredths place]. When the correlation coefficient is interpreted, it means that the correlation is (__) and (__). This means the points will be (__) [close or further away] to the line.

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 2.5 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) 7.6 7.4 9.8 8.8 9.3 7.3   Weight​ (kg) 142 163 256 188 231 156       The regression equation is y= + x. ​(Round to one decimal place as​ needed.)   The best predicted weight for an overhead width of 2.5 cm is kg? ​(Round to one decimal place as​ needed.)   Can the prediction be​ correct? What is wrong with predicting the weight in this​ case?     A. The prediction cannot be correct because a negative weight does not make sense. The width in this case is beyond the scope of the available sample data.   B. The prediction cannot be correct because a negative weight does not make sense and because there is…

Forest Fires and Acres Burned Numbers (in thousands) of forest fires over the year and the number (in hundred thousands) of acres burned for  6  recent years are shown. Number of fires  x 69 58 62 84 47 72 Number of acres burned  y 62 42 19 15 26 42 Send data to Excel   The correlation coefficient for the data is  =r−0.054  and  =α0.01 . Should regression analysis be done?   The regression analysis should not be done.     The regression analysis should be done.     Find the equation of the regression line.   =y′+abx     =a     =b     Find  y′  when  =x60 .     =y′

Data Set: {(-3, 4), (-2, 3), (–1,3), (0, 7), (1, 5), (2, 6), (3, 1)} 1. The regression line is: y = when the 2. Based on the regression line, we would expect the value of response variable to be explanatory variable is 0. 3. For each increase of 1 in of the explanatory variable, we can expect a(n) in the response variable. 4. If x = -3.5, the y = This is an example of 5. The correlation coefficient is r = (Round to the nearest hundredth.) Check of