a. Plot the data and a regression line on the same graph. b. Determine the correlation coefficient and interpret it.
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Please see the screen shot and help me solve the problem. Please give me the steps that you used to find the solution so that I can learn to solve:
For the given dataset, I would plot the data, add the trend or regression line, I would also determine the correlation coefficient, The detailed calculations are presented below,
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- The Baker Company wants to develop a budget to predict how overhead costs vary with activity levels. Management is trying to decide whether direct labor hours (DLH) or units produced is the better measure of activity for the firm. Monthly data for the preceding 24 months appear in the file P13_40.xlsx. Use regression analysis to determine which measure, DLH or Units (or both), should be used for the budget. How would the regression equation be used to obtain the budget for the firms overhead costs?Do the sales prices of houses in a given community vary systematically with their sizes (as measured in square feet)? Answer this question by estimating a simple regression equation where the sales price of the house is the dependent variable, and the size of the house is the explanatory variable. Use the sample data given in P13_06.xlsx. Interpret your estimated equation, the associated R-square value, and the associated standard error of estimate.Under what conditions might a firm use multiple forecasting methods?
- A small computer chip manufacturer wants to forecast monthly ozperating costs as a function of the number of units produced during a month. The company has collected the 16 months of data in the file P13_34.xlsx. a. Determine an equation that can be used to predict monthly production costs from units produced. Are there any outliers? b. How could the regression line obtained in part a be used to determine whether the company was efficient or inefficient during any particular month?The file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars) through credit unions. a. Use these data to forecast consumer revolving credit through credit unions for the next 12 months. Do it in two ways. First, fit an exponential trend to the series. Second, use Holts method with optimized smoothing constants. b. Which of these two methods appears to provide the best forecasts? Answer by comparing their MAPE values.1.) Use the following dummy variables to develop an estimated regression equation to account for seasonal effects only in the data. Qtr1 = 1 if Quarter 1, 0otherwise; Qtr2 = 1 if Quarter 2, 0 otherwise, Qtr3 = 1 if Quarter 3, 0otherwise. Based only on the seasonal effects in the data, compute estimates of quarterly sales for year 6.a. Report the estimate of sales for Year 6 Quarter 1. (Enter a whole value.)b. Report the estimate of sales for Year 6 Quarter 4. (Enter a whole value.)2.) Let Period t = 1 refer to the observation in quarter 1 of year 1; Period t = 2refer to the observation in quarter 2 or year 1; … and Period t = 20 refer to the observation in quarter 4 of year 5. Using the dummy variables defined in part (b) and Period (t), develop an estimated regression equation to account for seasonal effects and any linear trend in the time series. Based upon the seasonal effects in the data and linear trend, compute the estimates of quarterly sales for year 6.a. Report the estimate…
- Regression analysis. The owner of a small hardware store has noted a sales pattern for window locks that seems to parallel the number of break-ins reported each week in the newspaper. The data are: Sales: 46 18 20 22 27 34 14 37 30Break-ins: 9 3 3 5 4 7 2 6 4a. Plot the data to determine which type of equation, linear or nonlinear, is appropriate.b. Obtain a regression equation for the data.c. Estimate average sales when the number of break-ins is five.A study to determine the correlation between bankdeposits and consumer price indices in Birmingham, Alabama,revealed the following (which was based on n = 5 years of da ta):• LX = 15• Lx2 = 55• Lxy = 70• Ly = 20• L/ = 130a) What is the equation of the least-squares regression line?b) Find the coefficient of correlation. What does it imply to you?c) What is the standard error of the estimate?7. The following multiple-regression model was developed to predict job performance as measured by a company job performance evaluation index based on a preemployment test score and college grade point average (GPA): y=35+20x1+50x2, where y=job performance evaluation index, x1=preemployment test score, and x2=college GPA. Part 2 a) For an applicant who had a 3.0 GPA and scored 80 on the preemployment test, the forecast for the job performance index= __________ (enter your response as a whole number).
- The following data were collected during a study of consumer buying patterns:Observation x y Observation x y 1 19 72 8 20 77 2 21 81 9 17 73 3 43 79 10 13 77 4 28 81 11 20 85 5 56 98 12 28 93 6 49 96 13 34 94 7 32 82 14a. Obtain a linear regression line for the data.b. What percentage of the variation is explained by the regression line?c. Use the equation determined in part b to predict the expected value of y for x = 49.Calculate a regression line for the data and indicate the forecast of sales for a store with advertising spending of R240,000? and R300,000? Store Advertising Sales 1 14 6 2 11 3 3 15 5 4 16 5 5 24 15 6 28 18 7 22 17 8 21 12 9 26 15 10 43 20 11 34 14 12 9 5Chart and Regression analysis : What does the intercept predict? X: C16 (number of cars on the sales lot) versus Y: C17 (cars sold per day) Equation: y=2.9x + 14.5 Slope:2.9 Intercept:14.5 Does the intercept mean the intercept is 14.5 means that the cars sold per day( Y) predicted number of cars on sale lot(X) to be 14.5, but this intercept has no meaning. So, I will not use to predict cars sold per day?
