Calculation of Regression Coefficients Beer (X) Cigarettes (Y) 8 16 6 13 2 4 4 9
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Calculation of Regression Coefficients
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Beer (X) |
Cigarettes (Y) |
|
8 |
16 |
|
6 |
13 |
|
2 |
4 |
|
4 |
9 |
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- Demand for Smartphones The following table shows worldwide sales of a type of phone and their average selling prices in 2012, 2013, and 2017. Year 2012 2013 2017 Selling Price p ($100) 4 3 2 Sales q (billions) 0.7 1 2 Find the regression line (round coefficients to one decimal place). q(p) = Use the regression line to estimate the demand (in millions of units sold) when the selling price was $350. million3. Multiple Regression Analysis You have been asked to estimate the numberof trip productions that will result from anew development (single zone) being considered. While you asked forforecasted number of trips, your interns were busy checking Facebook, surfing the web, and texting their friends and collected the following data which is actually the forecasted number of households in your trafficanalysis zone based upon Auto Ownership and Family Size. Auto Ownership 1 Family Size 2 1 15 75 25 2 36 98 127 45 120 198 In a last-ditch effort to have something to show your boss in the morning, you scoured the internet for some type of regression equation you could use to estimate the numberof trips produced based upon the data your interns collected for you. You identified the following three possible regression equations to use: EQUATION 1: 0, = 0.08 + 0.895(SIZE) + 0.549(AO) EQUATION 2: 0, = 0.10 - 2.53(SIZE) + 1.64(AO.) EQUATION 3: 0, = 175.12 + 0.005(SIZE.) + 0.549(AO) Where: 0, = total…The tables show the amount of revenue (y) in billions for different companies x years after 2005. Match the table with the correct description of its regression model and correlation. Negative Linear Regression 1. a0 1 2 3 4 5 6 78 9 10 Strong Negative Correlation y 12 12 13 14 15 16 18 19 21 22 25 Positive Linear Regression * 0 1 2 3 4 5 6 7 89 10 y 20 18 17 15 13 14 16 18 21 25 28 2. Strong Positive Correlation Quadratic Regression * 0123 4 5 6 7 8 9 10 3. Strong Positive Correlation y 5679 13 15 18 24 25 28 30 Increasing Exponential Regression 4. x0 1 2 3 4 5 6 789 10 Strong Positive Correlation y 20 20 19 18 16 15 12 8765 Decreasing Exponential Regression z0 1 2 3 4 5 6 7 8 9 10 5. Strong Negative Correlation y 20 19 20 18 16 15 13 11 10 11 10
- P4. The least squared method called also the least squared regression method is one of the most commonly used methods in applications . Describe the importance of this method in modeling data. Provide an illustrative example for given data and interpret your results. Using your example illustrate the importance of the results.how to solve?16. Residuals and significance testing with a regression equation Experienced observers use aerial survey methods to estimate the number of snow geese in their summer range area west of Hudson Bay, Canada. A small aircraft flies over the range, and when a flock of geese is spotted, the observer estimates the number of geese in the flock. To investigate the reliability of the estimates, an airplane carrying two goose observers flies over 45 flocks. Each observer makes an independent estimate of the number of geese in each flock. A photograph is taken of each flock and a count made of the number of geese in the photograph. The sample data for the 45 flocks appear in the DataView tool. [Data source: These data were obtained from Lunneborg, C. E. (1994). Modeling experimental and observational data. Pacific Grove, CA: Duxbury Press.] Data SetGeese Sample Variables = 3 Observations = 45 Geese Flock ↓ Photo ↓ A Estimate ↓ B Estimate ↓ 1 62 40 50 2 26 30 20 3 88…
- What is the regression lineThe following table shows worldwide sales and projected sales of a certain type of phone and their average selling prices in 2020, 2022, and 2024. Year 2020 2022 2024 Selling Price ($100) p 6 5 4.5 Sales (billions) q 1 1.2 1.3 Find the regression line. 9(p) = Use the regression line to estimate the demand (in millions of units sold) when the selling price was $250. X million 1.6 Need Help? Read It Master ItDemand for Smartphones The following table shows worldwide sales of a type of phone and their average selling prices in 2012, 2013, and 2017. Year 2012 2013 2017 Selling Price p ($100) 4 Sales q (billions) 0.5 3 1 2 2 Find the regression line (round coefficients to one decimal place). q(p) = 1.0166 X Use the regression line to estimate the demand (in millions of units sold) when the selling price was $320. 1016.6 million
