Run a regression with S3 as the dependent variable and PRICE, AD, SHELF POS and STORE SIZE as independent variables. (1) Interpret the output.
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Q: Brandon works as a statistician for the Toronto Blue Jays, and wants to analyze the relationship…
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- The chart to the right shows a country's annual egg production. Model the data in the chart with a linear function, using the points (0,51.7) and (4,60.2). Let x = 0 represent 1995, x = 1 represent 1996, and so on, and let y represent the egg production (in billions). Predict egg production in 2001. How does the result compare to the actual data given in the table, 69.5? Year Egg production (in billions) 51.7 52.5 54.3 57 60.2 63.8 69.5 1995 1996 1997 1998 1999 2000 2001 The linear model for the data is (Type an equation using x as the variable. Type your answer in slope-intercept form. Use integers or decimals for any numbers in the equation. Round to the nearest thousandth as needed.)Brandon works as a statistician for the Toronto Blue Jays, and wants to analyze the relationship between a player's age and how many strikeouts they accumulate in a season. He takes a sample of 8 Blue Jays players with age between 25 and 34 and finds there is a linear relationship between their ages and the number of strikeouts they had in the 2015 season. Here are the numerical summaries for age and the number of strikeouts: r = 0.67, age = 28.4, Sage = 3.96, strikeout= 102.9, S strikeout = 7.7 (a) What is the value of b₁, i.e. the fitted slope? (Round your answer to 3 decimal places) Answer: (b) What the value of bo, i.e. the fitted intercept? (Round your answer to 3 decimal places.) Answer: (c) What is the percent of variation of the number of strikeouts that is explained by age using a linear regression? (Round your answer to 2 decimal places.) Answer: % (d) Can we use this linear regression to predict the number of strikeouts for a player age 38? Answer: O No, because the…The slope of a regression line tells you how much or little a change in your dependent variable impacts your independent variable. O TrueO False
- Develop a scatterplot and explore the correlation between customer age and net sales by each type of customer (regular/promotion). Use the horizontal axis for the customer age to graph. Find the linear regression line that models the data by each type of customer. Round the rate of changes (slopes) to two decimal places and interpret them in terms of the relation between the change in age and the change in net sales. What can you conclude? Hint: Rate of Change = Vertical Change / Horizontal Change = Change in y / Change in xThe managing director of a consulting group has the accompanying monthly data on total overhead costs and professional labor hours to bill to clients. Complete parts a through c Click the icon to view the monthly data. a. Develop a simple linear regression model between billable hours and overhead costs. Overhead Costs = 247733.3 +(43.2000) x Billable Hours (Round the constant to one decimal place as needed. Round the coefficient to four decimal places as needed. Do not include the $ symbol in your answers.) b. Interpret the coefficients of your regression model. Specifically, what does the fixed component of the model mean to the consulting firm? Interpret the fixed term, bo. if appropriate. Choose the correct answer below. OA. The value of by is the predicted overhead costs for 0 billable hours. OB. For each increase of 1 unit in overhead costs, the predicted billable hours are estimated to increase by bo OC. It is not appropriate to interpret by. because its value is the predicted…Write out the full linear model including all dummy variables below. Don’t worry about estimating regression coefficients just yet. Feel free to abbreviate variable names so long as they are clearly distinguishable.
- Q1) A real estate consultant is considering developing a series of price models for residential houses in different areas of the province of Ontario, in Canada. The dataset, provided by the Windsor and Essex County Board, covers residential home sales in Windsor. To develop the model, the consultant performs a linear regression to estimate the price (in Canadian dollars) as a linear function of the size of the apartment (in square feet) and obtains the following Excel result. SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.53580413 0.28708607 0.28577556 41101.9362 546 ANOVA df MS Significance F 1 3.70082E+11 3.7008E+11 219.065461 6.74643E-42 Regression Residual Total 544 9.19017E+11 1689369158 545 1.2891E+12 Coefficients Standard Error 62171.5874 4537.036582 13.7031268 6.2969E-37 53259.33066 71083.8441 t Stat P-value Lower 95% Upper 95% Intercept LOTSIZE 12.0187669 0.81203165 14.8008601 6.7464E-42 10.42366523 13.6138685 a) Write…An ice cream truck owner collects data on the number of sales made each day and the average temperature that day. He computes a regression line for predicting the number of sales based on how far the daily temperature is from freezing (0 degrees Celsius) and finds sales = 3.22 - 1.8 (degrees over 0 Celsius). Identify the "y-intercept". A. -1.8 B. 1.8 C. 3.22 D. 0Range of ankle motion is a contributing factor to falls among the elderly. Suppose a team of researchers is studying how compression hosiery, typical shoes, and medical shoes affect range of ankle motion. In particular, note the variables Barefoot and Footwear2. Barefoot represents a subject's range of ankle motion (in degrees) while barefoot, and Footwear2 represents their range of ankle motion (in degrees) while wearing medical shoes. Use this data and your preferred software to calculate the equation of the least-squares linear regression line to predict a subject's range of ankle motion while wearing medical shoes, ?̂ , based on their range of ankle motion while barefoot, ? . Round your coefficients to two decimal places of precision. ?̂ = A physical therapist determines that her patient Jan has a range of ankle motion of 7.26°7.26° while barefoot. Predict Jan's range of ankle motion while wearing medical shoes, ?̂ . Round your answer to two decimal places. ?̂ = Suppose Jan's…
- In simple linear regression, the coefficient of determination measures: a.The amount of variability of the y variable that is explained by the x variable b.The amount of variability of the x variable that is explained by the y variable c.The amount of variability between the treatments d.The amount of variability within the treatmentsIn linear regression analysis, the coefficient for the x-variable when the y-variable is regressed on the x-variable can be thought of as: Note: more than one answer may be correct Group of answer choices How much the value of the predicted y-variable will change when the x-variable changes by one unit. In the simple (two variable) linear regression model, the coefficient can be thought as the slope coefficient that measures the responsiveness of y to changes in x. The estimated coefficient will change if the sample containing x and y changes. The sample coefficient is an estimate of the population coefficient Before interpreting the coefficient for the x-variable, we should test whether the coefficient is statistically significant.A multiple regression analysis has more than onea. Fixed costsb. Dependent variablec. independent variabled. both fixed costs and dependent variable