tudent1 Student2 Student3 Student4 Student5 Student6 Student7 Student8 Student9 Height (inch.) 63   67 68 68 69 70 71 74 75 Weight (lbs.) 140 160 140 149 165 125 235 260 190 b. Construct the equation of the regression line. c. Predict the weight of a student who is 68 inches tall.

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Fill in all boxes for part a through c please. Thank you!

Construct a scatter plot of the data in the table AND create a linear model. 2 10 12 10 14 14 16 18 18 24 a EGO (99- 2.

Using least square method, find the regression line model. Interprete your result. Calculate the coefficient of correlation and explain. Calculate the coefficient of determination and explain your answer.

Height, x 772 628 518 508 496 483 stories, y 51 48 44 43 37 35 Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line.​ (The pair of variables have a significant​ correlation.) Then use the regression equation to predict the value of y for each of the given​ x-values, if meaningful. The table below shows the heights​ (in feet) and the number of stories of six notable buildings in a city. (a) x = 498 feet  (b) x = 640 feet  (c) x = 345 feet  (d) 735 feet Find the regression equation.Now construct a scatter plot of the data and draw the regression line. ​(a) Now use the regression equation to predict the value of y for each of the given​ x-values, if meaningful. Because the correlation between x and y is​ significant, the equation of the regression line can be used to predict​ y-values. However, prediction values are meaningful only for​ x-values in the range of the data.  Begin with x = 498. Since x = 498…

A. Write the estimated model. Compute and interpret the adequacy of the regression model.  Estimated Model: ______________________________________________________ Model Adequacy: ____________ Interpretation: B. Is the quadratic regression term is significant at α=5%.  Ho (symbols and words): ______________________________________________________________ p-value: _______________ Conclusion: ___________________________

Need help answering A, B and C. B) Perform the linear regression calculation and provide the linear regression equation that describes the relationship between Y (number of air conditioning units sold) and X (outside temperature in degrees Fahrenheit. Calculate: Coefficient of determination (r2) and the coefficient of correlation (r).  SST, SSE and SSR for this linear regression.  Estimate for the variance (σ2) and the standard deviation for the linear regression model you have developed. C) Using the linear regression equation that you developed in topic (b), calculate the estimated sales for a day that will reach 72 degrees F and for a day that will reach 94 F and for both temperature levels calculate the error “e” when comparing the estimated value against the actual data provided. At which of the two temperatures, is your model more accurate? Justify.

Production Volume (units) Total Cost ($) 100 1730 200 2792 300 4129 400 5385 500 6497 600 7764 700 8835 800 10180 900 11412                 Which of the following is your estimated regression equation?   Total Cost = 468 + 12.1(Production Volume) + e Production Volume = 468 + 12.1(Total Cost) + e Total Cost = 468(Production Volume) + 12.1 + e Production Volume = 468(Total Cost) + 12.1 + e   Will you reject the null hypothesis that the slope is zero at a significance level of 1%?   Yes No Not enough information   What is the R-square from your regression analysis? (Round your answer to four decimal places.) Which of the following is the correct interpretation of the R-square?   Production volume explains 99.96% of the variation in total cost. Total cost explains 99.96% of the variation in production volume. The dependent variable explains 99.96% of the variation in the independent variable. 96% of the data points are…

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