EBK BASIC BUSINESS STATISTICS
14th Edition
ISBN: 9780134685168
Author: STEPHAN
Publisher: YUZU
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Chapter 16, Problem 33PS
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Solve question 11 for part D only.
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The line of best fit for a data set is y= 1.1x+3.4. Find the residual for each of the coordinate pairs (x,y).
2. Solve for the Pearson's r of the following data regarding height and weight of 10 students and interpret the results.
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Chapter 16 Solutions
EBK BASIC BUSINESS STATISTICS
Ch. 16 - If you are using exponential smoothing for...Ch. 16 - Consider a nine-year moving average used to smooth...Ch. 16 - You are using exponential smoothing on an annual...Ch. 16 - Prob. 4PSCh. 16 - Prob. 5PSCh. 16 - How have stocks performed in the past? The...Ch. 16 - Prob. 7PSCh. 16 - Prob. 8PSCh. 16 - Prob. 9PSCh. 16 - Prob. 10PS
Ch. 16 - The linear trend forecasting equation for an...Ch. 16 - There has been much publicity about bounces paid...Ch. 16 - Prob. 13PSCh. 16 - Prob. 14PSCh. 16 - Prob. 15PSCh. 16 - The data shown in the following table and stored...Ch. 16 - Prob. 17PSCh. 16 - Prob. 18PSCh. 16 - Prob. 19PSCh. 16 - Prob. 20PSCh. 16 - Prob. 21PSCh. 16 - Prob. 22PSCh. 16 - You are given an annual time series with 40...Ch. 16 - Prob. 24PSCh. 16 - Prob. 25PSCh. 16 - Prob. 26PSCh. 16 - Prob. 27PSCh. 16 - Prob. 28PSCh. 16 - Prob. 29PSCh. 16 - Using the average baseball salary from 200 through...Ch. 16 - Using the yearly amount of solar power generated...Ch. 16 - The following residuals are from a linear trend...Ch. 16 - Prob. 33PSCh. 16 - Prob. 34PSCh. 16 - Prob. 35PSCh. 16 - Prob. 36PSCh. 16 - Prob. 37PSCh. 16 - Prob. 38PSCh. 16 - Prob. 39PSCh. 16 - Prob. 40PSCh. 16 - In forecasting daily time-series data, how many...Ch. 16 - In forecasting a quarterly time series over the...Ch. 16 - Prob. 43PSCh. 16 - Prob. 44PSCh. 16 - Are gasoline prices higher during the height of...Ch. 16 - Prob. 46PSCh. 16 - Prob. 47PSCh. 16 - The file Silver-Q contains the price in London for...Ch. 16 - Prob. 49PSCh. 16 - What is a time series?Ch. 16 - What are the different components of a time-series...Ch. 16 - What is the difference between moving average and...Ch. 16 - Prob. 53PSCh. 16 - How does the least-squares linear trend...Ch. 16 - How does autoregressive modelling differ from the...Ch. 16 - What are the different approaches to choosing an...Ch. 16 - What is the major difference between using SYX and...Ch. 16 - How does forecasting for monthly or quarterly data...Ch. 16 - Prob. 60PSCh. 16 - The monthly commercial and residential prices for...Ch. 16 - The data stored in McDonalds represent the gross...Ch. 16 - Teachers’ Retirement System of the City of New...Ch. 16 - Prob. 64PS
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- Repeat Example 5 when microphone A receives the sound 4 seconds before microphone B.arrow_forwardFind 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_forward
- What does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?arrow_forward. Suppose we wish to assess whether there is a relationship between exercise on campus and students' living arrangements. The data are shown below. No Regular Exercise Sporadic Exercise Regular Exercise Dormitory 32 30 28 On-Campus Apartment 74 64 42 Off-Campus Apartment 110 25 15 At Home 39 6 5 Based on the data, use the chi square test to determine the relationship between exercise and student's living arrangement? Do you think where a person lives affect their exercise status? Let = 0.05.arrow_forwardSolve question 11 for parts A, B and C.arrow_forward
- T5.14. Based on previous records, 17% of the vehicles passing through a tollbooth have out-of-state plates. A bored tollbooth worker decides to pass the time by count- ing how many vehicles pass through until he sees two with out-of-state plates.27 (a) Describe the design of a simulation to estimate the average number of vehicles it takes to find two with out- of-state plates. Explain clearly how you will use the partial table of random digits below to carry out your simulation. (b) Perform three repetitions of the simulation you de- scribed in part (a). Copy the random digits below onto your paper. Then mark on or directly above the table to show your results. 41050 920 3 1 06449 05059 59884 31880 53115 844 6 9 94868 57967 05811 84514 84177 067 57 1761 75011 13006 63395 55041 15 8 66 06589 3 15582 51 506 81435arrow_forward4. If H0 is true, then the proportion of left-handed girls must be the same for the proportion of left-handed boys. Determine Chi square statistics. (write the result .XXXX) 5. If H0 is true, then the proportion of left-handed girls must be the same for the proportion of left-handed boys. Determine Chi square statistics for alpha of .05 (write the result in X.XXX) 6. Conclude and discuss the resultsarrow_forward3.2. Consider a fictitious dataset of n Total Error Calculate the (unadjusted) coefficient of determination, R². C. Consider a fictitious dataset of n = 100 observations with sy = 80. We run a regression with three explanatory variables to get s = 50. We also get %3D 100 20 20 20 20 90 30 40 (X'X)" 20 30 80 50 20 40 50 70 a. Determine the standard error of b3, se(bz). b. Determine the estimated covariance between b2 and bz. c. Determine the estimated correlation between b2 and b3. d. Determine the estimated variance of 4b2 + 3b3.arrow_forward
- The following are the cystatin C levels (mg/L) for the patients described in Exercise 15 (A-17).Cystatin C is a cationic basic protein that was investigated for its relationship to GFR levels. Inaddition, creatinine levels are also given. (Note: Some subjects were measured more than once.)Cystatin C (mg/L) Creatinine (mmol/L)1.78 4.69 0.35 0.142.16 3.78 0.30 0.111.82 2.24 0.20 0.091.86 4.93 0.17 0.121.75 2.71 0.15 0.071.83 1.76 0.13 0.122.49 2.62 0.14 0.111.69 2.61 0.12 0.071.85 3.65 0.24 0.101.76 2.36 0.16 0.131.25 3.25 0.17 0.091.50 2.01 0.11 0.122.06 2.51 0.12 0.062.34Source: Data provided courtesy of D. M. Z. Krieser, M.D.(a) For each variable, compute the mean, median, variance, standard deviation, and coefficient ofvariation.(b) For each variable, construct a stem-and-leaf display and a box-and-whisker plot.(c) Which set of measurements is more variable, cystatin C or creatinine? On what do you base youranswer?arrow_forwardSuppose the following estimated equation. Standard errors are in parentheses. Y = 3638.25 + 0.148X, + 11.13X2 + 3.20X3 n = 400 R2 = 0.465 |(112.28) (0.017) (4.88) (1.45) You are to test if the variable of X3 shows a significant positive impact on the model. Answer the followings. a. State the hypotheses. b. What is the p-value? C. Should the variable of X3 be included in the model? Explain.arrow_forward1U.2.22 Question Help The data show the bug chirps per minute at different temperatures. Find the regression equation, letting the first variable be the independent (x) variable. Find the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute. Use a significance level of 0.05. What is wrong with this predicted value? Chirps in 1 min Temperature ("F) 948 1227 934 985 946 925 78.1 93.8 81.2 79.2 74.5 76.6 What is the regression equation? (Round the x-coefficient to four decimal places as needed. Round the constant to two decimal places as needed.)arrow_forward
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