Concept explainers
Suppose
a. Interpret the regression coefficient associated with variable
b. Interpret the regression coefficient associated with variable
c. Suppose that the
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BASIC BUSINESS STATISTICS-STUD.SOLN.MAN
- Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4arrow_forwardOlympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?arrow_forwardA trucking company considered a multiple regression model for relating the dependent variable y = total daily travel time for one of its drivers (hours) to the predictors x₁ = distance traveled (miles) and x₂ = the number of deliveries made. Suppose that the model equation is Y = -0.800+ 0.060x₁ +0.900x₂ + e (a) What is the mean value of travel time when distance traveled is 50 miles and four deliveries are made? hr (b) How would you interpret ₁ = 0.060, the coefficient of the predictor x₁? O When the number of deliveries is constant, the average change in travel time associated with a ten-mile (i.e. one unit) increase in distance traveled is 0.060 hours. O The total daily travel time increases by 0.060 hours when the distance traveled increases by 1. O When the number of deliveries is held fixed, the average change in travel time associated with a one-mile (i.e. one unit) increase in distance traveled is 0.060 hours. O The average change in travel time associated with a one-mile (i.e.…arrow_forward
- If the linear correlation coefficient between the explanatory variable (x) and response variable (y) is r = 0.73, the slope of the regression line is negative O not enought information to answer O positivearrow_forwardSuppose that a regional express delivery service company wants to estimate the cost of shipping a package (Y) as a function of cargo type, where cargo type includes the following possibilities: fragile, semi-fragile, and durable. Costs for 15 randomly chosen packages of approximately the same weight and same distance shipped, but of different cargo types, are provided in the file P14_16.xlsx. a. Estimate a regression equation using the given sample data, and interpret the estimated regression coefficients. b. According to the estimated regression equation, which cargo type is the most costly to ship? Which cargo type is the least costly to ship? c. How well does the estimated equation fit the given sample data? How might the fit be improved? d. Given the estimated regression equation, predict the cost of shipping a package with semi-fragile cargo.arrow_forwardA company specializing in home decoration items would like to build a regression model consisting of 5 factors to predict sales. Data for the past 24 months on sales and 5 factors were collected for one particular home decoration item and the SPSS package was used to get the output. The relevant outputs are given below in Tables 1 and 2. The variables for which the data has been collected are as follows Dependent variable Y = monthly sales in lakhs (for one particular home decoration item) Independent variables 1)advertising cost in lakhs 2)competition index 3)number of existing customers 4) number of dealer outlets 5) number of personnel delivering to the customer The company decided to follow Stepwise Multiple Linear Regression. Read the output data given below and answer the questions given Table 1 Multiple R 0.965 R square 0.931 Adjusted R square 0.895 Standard Error 4.235 From the ANOVA table, the extracted P value is 0.001 Table 2…arrow_forward
- Identify two different conditions under which the regression line should not be used to make predictions.arrow_forwardA county real estate appraiser wants to develop a statistical model to predict the appraised value of houses in a section of the county called East Meadow. One of the many variables thought to be an important predictor of appraised value is the number of rooms in the house. Using data collected for a sample ofn 91 houses in East Meadow, the appraiser fit the data with the following simple, linear regression model: y = 91.80 + 19.72, where x = number of rooms and y = appraised value of the house (in thousands of dollars). Additionally, the appraiser determined the coefficient of correlation to be r = .93 and the coefficient of determination to be r %3D = .86. Give a practical interpretation of the coefficient of correlation. B IUS Ix E三 三 三 E E E Insert Formula IIarrow_forwardA house developer company wants to investigate the influencing factors when the customers buy a house. The developer went to a local real estate agency and obtained the data consisting of the following variables: Y = selling price of house (RM'000) X, = age of house (years) X, = lot size (100 square feet) X3 number of bedrooms if intermediate lot X = 0if corner lot Refer to Appendix 2 for the computer output. (a) Write down the fitted regression function. (b) If the customer plan to buy a 5 year old intermediate house that has a lot size of 1400 square feet and 4 bedrooms, what price would you expect? (c) Does an increase in lot size by 100 square feet change the sales price by RM5000? Test by using the appropriate hypothesis, Use g =0,05.arrow_forward
- A market study found that the sales for a firm were related to advertising expenditure, as follows: Advertising Expenditure (Kshs ‘000’) Sales (Kshs ‘000’) 0 13 1 16 2 14 3 22 4 17 5 21 6 26 Required Draw a scatter diagram with the line of best fit to show the relationship. Determine the regression line equation for estimating the sales for a given level of advertising expenditure What is the estimated sale in thousand, if no advertising expenditure is incurred?arrow_forward4b) The data shows a systolic and a diastolic blood pressure of certain patients. Find the linear regression equation, using the first variable x (systolic) as the independent variable. Find the best predicted diastolic blood pressure for a patient with a systolic blood pressure (y) reading of 140. What is the correlation coefficient, r? Using a significance level of a = 0.05, is there a significant linear relationship between systolic and diastolic blood pressure? Blood Pressure: Systolic Diastolic 112 125 115 136 143 116 123 124 elimii 70 89 65 90 97 64 SUTT nisinoo aqdM 21.SS bns aqdM 78 ahoqnis erit te zbesqz steb ils to 69 bns ago 20.EI to adimil srit terit sonabilnos 2 nistnoo aqdM 21.SS bnc agdM sgsavs arit ferli mislo a hoqnis orti roqque lovedni sonsbilnos 3028 wolsd insmsisiz tomo artezorio SeqdM 2.55 al 2.SS to sulavadi znistmoodi ezusaed mish ads toqque ton zaob 2.55 to sulsy sdt anistroo ti sausosd mislo ert hoqquz 200b to sulav orit nisinoo ton zoob 11 saussed misbb adi…arrow_forwardGive the data: Y. 12,14, 15, 17,18, 15,16 X1. 8,8,6,5,3, 4,7 X2. 0,1,1,2, 3,3,4 Obtain the least square estimates of the parameters in the multiple regression model and test the over all significance using Anova.arrow_forward
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