Correlation and Regression: 7) Is there a relationship between how much cheese we eat and how likely we are to die by becoming entangled in our bedsheets? We have the information on these variables over the past ten years. Let x represent the average amount of cheese consumed per capita annually, and let y represent the numbers of deaths in the United States attributed to persons becoming entangled in their bedsheet. X 29.8 30.1 30.5 30.6 31.3 31.7 32.6 33.1 32.732.8 Average Cheese Consumption Per Capita (pounds) Number of Deaths from Becoming Entangled in Bedsheets Y 327 456 509 497 596 573 661 741 809 717

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.

K An institution reports many demographic statistics about countries of the world. Data were collected about the Fertility rate (births per woman) and the female Life Expectancy at birth (in years) for 200 countries of the world. The regression output and a scatterplot of the data are shown below. Complete parts a through d below. Response variable is: Life expectancy 219 total cases of which 19 are missing R squared 71.9% $= 4.303 Variable Intercept Fertility Coefficient 83.3753 -4.54984 OA. Yes, an outler occurs at approximately x (Round to one decimal place as needed.) ICCO Life Expectancy OB. Yes, an outlier occurs at approximately x= (Round to one decimal place as needed.) OC. No, there is no outlier. c) Interpret the value of R². Choose the correct answer below. 90- 80- 70- 60- 50+ 40- 2 Fersity This outier has an unusually high life expectancy given its fertility rate. b) is there an outlier? If so, identify it. Which data value is unusual? Select the correct choice below and,…

Linear Regression Application, Interpolation and Extrapolation Use the data and story to answer the following questions The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. X 11.4 8.2 y 13.9 6.6 3.6 2.4 2.3 2.3 0.4 11 9.3 6.8 6.3 5.9 6.3 4.3 = thousands of automatic weapons y = murders per 100,000 residents Use your calculator to determine the equation of the regression line. (Round to 2 decimal places) Determine the regression equation in y = ax + b form and write it below. Answer = A) How many murders per 100,000 residents can be expected in a state with 4 thousand automatic weapons? Answer = Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 11 thousand automatic weapons? Round to 3 decimal places.

Linear Regression Application, Interpolation and Extrapolation Use the data and story to answer the following questions The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. x 11.3 8.5 6.8 3.5 2.4 2.4 2.4 0.3 y 13.8 11.6 9.7 6.9 6.1 6.1 6 4.1 x = thousands of automatic weapons y = murders per 100,000 residents Use your calculator to determine the equation of the regression line. (Round to 2 decimal places) Determine the regression equation in y = ax + b form and write it below. A) How many murders per 100,000 residents can be expected in a state with 8.3 thousand automatic weapons? Answer = Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 5.3 thousand automatic weapons? Answer= Round to 3 decimal places.

. Determine the regression equation using values you create for x and y for at least 10 pairs of data.  Show the regression equation, correlation coefficient, and coefficient of determination.  Then switch the x and y values for each data point.  Based on that, again show the regression equation, correlation coefficient, and coefficient of determination.  Discuss the similarities and differences between the results.

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A study was done to look at the relationship between number of vacation days employees take each year and the number of sick days they take each year. The results of the survey are shown below.   Vacation Days 5 15 13 3 9 0 3 7 7 14 4 Sick Days 5 5 1 5 4 9 9 6 5 3 9 Use the model to predict the number of sick days taken for an employee who took 3 vacation days this year.Sick Days = ? (Please round your answer to the nearest whole number.)   Interpret the slope of the regression line in the context of the question:   As x goes up, y goes down. The slope has no practical meaning since a negative number cannot occur with vacation days and sick days. For every additional vacation day taken, employees tend to take on average 0.40 fewer sick days. Interpret the y-intercept in the context of the question: The y-intercept has no practical meaning for this study. If an employee takes no vacation days, then that employee will take 8 sick days. The average number of sick days is…

Suppose the correlation between height and weight for adults is +0.80. What proportion of the variability in weight can be explained by the relationship with height? A. 20% B. 36% C. 64% D. 80%

The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. X Y 11.4 8.6 7.2 3.6 13.5 11.3 10.1 x = thousands of automatic weapons y = murders per 100,000 residents 2.4 2.8 2.3 0.9 7 6.1 6.6 5.9 4.7 This data can be modeled by the equation y = 0.83x +4.06. Use this equation to answer the following; Special Note: I suggest you verify this equation by performing linear regression on your calculator. Answer = A) How many murders per 100,000 residents can be expected in a state with 2.7 thousand automatic weapons? Answer = Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 11 thousand automatic weapons? Round to 3 decimal places.