9. Cell Phones The following table shows the num- ber, in millions, of cell phone subscribers in the United States at the end of the given year. Year Subscribers (millions) 2010 296.3 2011 316.0 2012 326.5 2013 335.6 2014 355.4 a. Plot the data points. b. Use exponential regression to construct an expo- nential model for the subscriber data. c. Add the graph of the exponential model to the plot in part a. d. What was the yearly percentage growth rate from the end of 2010 through the end of 2014 for cell phone subscribership? e. In 2014, an executive had a plan that could make money for the company, provided that there would be at least 380 million cell phone subscribers by the end of 2016. Solely on the basis of an exponen- tial model for the data in the table, would it be rea- sonable for the executive to implement the plan?

Use the border crossings data below and a = = 0.3 and ẞ = 0.2. Truck Crossings Month February 113,691 March 139,204 April 121,498 May 127,511 June 131,500 July 105,245 August 126,606 Copy Data Step 2 of 2: Calculate the adjusted exponential smoothing forecast for September 2011. Assume the forecast component for February 2011 to be the actual truck crossing value minus the initial trend. Let the initial trend be 10,000 trucks. Round your answer to two decimal places.

The personnel director of a large hospital is interested in determining the relationship (if any) between an employee’s age and the number of sick days the employee takes per year. The director randomly selects ten employees and records their age and the number of sick days which they took in the previous year.  Employee 1 2 3 4 5 6 7 8 9 10 Age 30 50 40 55 30 28 60 25 30 45 Sick Days 7 4 3 2 9 10 0 8 5 2 Copy Data The estimated regression line and the standard error are given.  Sick Days=14.310162−0.2369(Age)   se=1.682207se=1.682207  Find the 95% confidence interval for the average number of sick days an employee will take per year, given the employee is 26. Round your answer to two decimal places.

Multiple Choice Question If you were given the data and scatterplot below, which model would be the best fit for this data? (Gross Federal Government Debt Graph attached) A. Linear Model B. Exponential Model C. You cannt tell from the information given

A. run a simple regression- dependent variable is Weeks, independent variable is Age.    B. run a multiple regression with dependent variable weeks and independent variable-age, married, head, manager and sales. C. Create the regular and standardized residual plots for both.  Please show the tables when entering values of the regression for both the outputs and the scatter plots.

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Desmos | Graphing Calc Year Time (seconds) 1912 82.2 1924 72.4 1932 66.8 1952 66.8 1960 61.2 1968 60.0 1976 55.65 1984 55.92 1992 54.64 2000 53.8 2008 53.1 a. Decide which variable should be the independent variable and which should be the dependent variable. b. Draw a scatter plot of the data. C. Does it appear from inspection that there is a relationship between the variables? Why or why not? d. Find the equation in the form of: y= a + bx. e. Find the correlation coefficient. f. Find the estimated gold medal time for 1932. Find the estimated time for 1984. g. Why are the answers from part f different from the chart values? h. Does it appear that a line is the best way to fit the data? Why or why not? i. Use the least-squares line to estimate the gold medal time for the next Summer Olympics (Assuming they would be in 2020). Do you think that your answer is reasonable? Why or why not? Note that the fastest speed time in recorded history as of 2020 was 51.22 seconds for the 100 meter…

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Using the attached information, answer the following:   1. Fill in the blank: For these data, mileages that are greater than the mean of the mileages tend to be paired with used selling prices that are _____ the mean of the used selling prices. Choose one greater than less than 2. According to the regression equation, for an increase of one thousand miles in Cadet mileage, there is a corresponding decrease of how many thousand dollars in the used selling price?   3. What was the observed used selling price (in thousands of dollars) when the mileage was 37.7 thousand miles?   4. From the regression equation, what is the predicted used selling price (in thousands of dollars) when the mileage is 37.7 thousand miles? (Round your answer to at least one decimal place.)

the table shows the percent of households with internet access for selected years from 2009 and projected through 2015. Year Percent of households 2009 67 2010 70 2011 72.5 2012 75 2013 76.5 2014 77.2 2015 78 Use the model to predict the percent of households with internet access in 2022.