Concept explainers
Lobster fishing study. Refer to the Bulletin of Marine Science (April 201 0) study of teams of fishermen fishing for the red spiny lobster in Baja California Sur, Mexico, Exercise 11.50 (p. 645). Recall that simple linear regression was used to model y = total catch of lobsters (in kilograms) during the season as a
XLS TAT output for Exercise 11 .70
a. Locate and interpret the coefficient of determination, r2 , on the printout.
b. Locate and interpret the coefficient of
c. In Exercise 11.50, you conducted a test to determine that total catch (y) is negatively linearly related to search frequency (x). Which of the two statistics, r or r2, can be used to partially support this inference? Explain.
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Statistics for Business and Economics (13th Edition)
- Olympic 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_forwardWhat does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?arrow_forwardTable 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to 2012. a. Let x represent time in years starting with x=0 for the year 1997. Let y represent the number of seals in thousands. Use logistic regression to fit a model to these data. b. Use the model to predict the seal population for the year 2020. c. To the nearest whole number, what is the limiting value of this model?arrow_forward
- Apple CAPM Data file posted on has data about the monthly return on Apple Inc. from 1981 to 2005 (300 months) as well as the monthly return on the whole stock market (measured by a value-weight stock market index) and the monthly return on 30-day Treasury Bills. Use this file to answer the following questions: Show all your work, Use Excel. A) Run a regression of the excess return on Apple Inc. on the excess return on the whole marketand write down your estimated equation. B) What is Beta for Apple Inc.? Is it significant C) What is Alpha for Apple Inc.? Is it significantarrow_forwardNonearrow_forwardA 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_forward
- A student is preparing to take a standardized exam. She was told that she needs to get plenty of sleep the night before the exam. She is interested in the relationship between the number of hours of sleep a student gets before the exam and the score earned on the exam. She collects information from 10 other students who have already taken the exam as shown in the table. Based on the residual plot, is the linear model appropriate? No, there is no clear pattern in the residual plot. Yes, there is no clear pattern in the residual plot. No, the student who got the most sleep had a negative residual. Yes, there are more negative residuals (6) than positive residuals (4).arrow_forwardFriesen and Shine (2019) wanted to determine whether male Australian cane toads have different testes sizes in different parts of the species' range (edge of the range vs. core of the range). As part of the study, they needed to quantify how big a toad's testes are relative to the toad's body size. They decided to perform a linear regression of total testes mass (in mg) against body mass (in g) and use the residual for each toad as a measure of the toad's relative testes size. The Coefficient Estimates table for their least-squares regression procedure is shown below. Term Coefficient Standard Error t-value Pr > |t| (Intercept) 19.192 43.082 0.44547 0.65643 body mass 3.0063 0.36387 8.262 1.5733e-1 (a) Suppose the researchers do a t-test for the slope of the linear model. Write the null hypothesis for the test and show that, under the null hypothesis, the observed value of the t-statistic is indeed 8.262. (That is, do the appropriate computations to prove the value is 8.262) (b) The…arrow_forwardA researcher interested in explaining the level of foreign reserves for the country of Barbados estimated the following multiple regression model using yearly data spanning the period 2001 to 2016: FR=a+BOIL+YEXP+8FDI Where FR = yearly foreign reserves ($000’s), OIL = annual oil prices, EXP = yearly total exports (S000's) and FDI = annual foreign direct investment (S000's). The sample of data was processed using MINITAB and the following is an extract of the output obtained: Predictor Coef StDev t-ratio p-value Constant 5491.38 2508.81 2.1888 0.0491 OIL 85.39 18.46 4.626 0.0006 EXP -377.08 112.19 0.0057 FDI -396.99 160.66 -2.471 ** s = 2.45 R-sq = 96.3% R-sq (adj) = 95.3% Analysis of Variance Source DF MS F Regression 3 1991.31 663.77 ? ?? Error 12 77.4 6.45 Total 15 a) What is dependent and independent variables? b) Fully write out the regression equation c) Fill in the missing values ***, ***', ?'and ??' d) Hence test whether ß is significant. Give reasons for your answer. e) Perform…arrow_forward
- A major brokerage company has an office in Miami, Florida. The manager of the office is evaluated based on the number of new clients generated each quarter. Data were collected that show the number of new customers added during each quarter between 2015 and 2018. A multiple regression model was developed with the number of new customers as the dependent and the following four independent variables: Period (1, …, 16): A variable that measures the trend; Q1 = 1 for first quarter, Q1 = 0 otherwise; Q2 = 1 for second quarter, Q2 = 0 otherwise; Q3 = 1 for third quarter, Q3 = 0 otherwise. Questions: 1. Explain each of the four slopes (Period, Q1, Q2, Q3). 2. How many new customers would you expect in the second quarter of the following year (2019)?arrow_forwardIn an experiment, the independent variable is the percentage of hydrocarbons and the dependent variable is the purity of oxygen produced in a chemical distillation process that are present in the main condenser of the distillation unit. The simple linear regression and correlation analysis is performed in a sample of 9 observations. Results are as shown below: SSxx : 113.7356 SSyy = 0.5156 уу a = -4.2869 b = 0.0648 %3Darrow_forwardB b. What does the scatter diagram developed in part (a) indicate about the relationship between the two variables? The scatter diagram indicates a positive linear relationship between a = average number of passing yar and y = the percentage of games won by the team. c. Develop the estimated regression equation that could be used to predict the percentage of games won given the avera passing yards per attempt. Enter negative value as negative number. WinPct =| |)(Yds/Att) (to 4 decimals) d. Provide an interpretation for the slope of the estimated regression equation (to 1 decimal). The slope of the estimated regression line is approximately So, for every increase : of one yar number of passes per attempt, the percentage of games won by the team increases by %. e. For the 2011 season, the average number of passing yards per attempt for the Kansas City Chiefs was was 5.5. Use th regression equation developed in part (c) to predict the percentage of games won by the Kansas City Chiefs.…arrow_forward
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