Wedding Cost Attendance 62700 300 50000 350 47000 150 43000 200 35000 250 32500 150 28000 250 28000 300 26000 250 26000 200 25000 150 25000 200 22000 200 22000 200 20000 200 20000 200 19000 100 18000 150 18000 200 18000 150 17000 100 12000 100 12000 150 6000 50 3000 50   Interpret the​ Y-intercept of the regression equation. Choose the correct answer below.     A. It is not appropriate to interpret the​ Y-intercept because it is outside the range of observed wedding costs.   B. The​ Y-intercept indicates that a wedding with a cost of​ $0 has a mean predicted attendance of b 0b0 people.   C. It is not appropriate to interpret the​ Y-intercept because it is outside the range of observed attendances.   D. The​ Y-intercept indicates that a wedding with an attendance of 0 people has a mean predicted cost of ​$b 0b0. Part 4 Identify and interpret the meaning of the coefficient of determination in this problem. Select the correct choice below and fill in the answer box to complete your choice.   ​(Round to three decimal places as​ needed.)   A. The coefficient of determination is Upper R squaredR2equals=enter your response here. This value is the probability that the correlation between the variables is statistically significant.   B. The coefficient of determination is Upper R squaredR2equals=enter your response here. This value is the proportion of variation in wedding cost that is explained by the variation in attendance.   C. The coefficient of determination is Upper R squaredR2equals=enter your response here. This value is the proportion of variation in attendance that is explained by the variation in wedding cost.   D. The coefficient of determination is Upper R squaredR2equals=enter your response here. This value is the probability that the slope of the regression line is statistically significant.

A cafe company wants to determine how the money they spend on Google ads impacts their monthly revenue. Over 6 consecutive months, they vary the amount they spend on their Ads (in $) and record the associated revenue (in $) for each month. The data is shown below: l Revenue 50 427 75 472 100 467 125 529 150 518 175 543 A) Develop a regression equation for predicting monthly revenue based on the amount spent with Ads. What is the y-intercept?  B) What is the sample correlation between these two variables? C) What is the slope of your regression equation? Give your answer to two decimal places. D) Using a 0.05 level of significance, does this regression equation appear to have any value for predicting revenue based on Ads?

Graph the scatterplot and find the equation of the regression line GDP.       CO2 emissions (in millions metric tons) 1.7.          552.6 1.2.          462.3 2.5.          475.4 2.8.          374.3 3.6.          748.5 2.2.          400.9 0.8.          253.0 1.5.           318.6 2.4.           496.8 5.9.           1180.6

The value of a sports franchise is directly related to the amount of revenue that a franchise can generate. The following data represents the value in 2014 (in $millions) and the annual revenue (in $millions) for the 30 Major League Baseball franchises. Suppose you want to develop a simple linear regression model to predict franchise value based on annual revenue generated.          Team  Revenue  Value  Baltimore  245  1000  Boston  370  2100  Chicago White Sox  227  975  Cleveland  207  825  Detroit  254  1125  Houston  175  800  Kansas City  231  700  Los Angeles Angels  304  1300  Minnesota  223  895  New York Yankees  508  3200  Oakland  202  725  Seattle  250  1100  Tampa Bay  188  625  Texas  266  1220  Toronto  226  870  Arizona  211  840  Atlanta  267  1150  Chicago Cubs  302  1800  Cincinnati  227  885  Colorado…

need help with following