Delta Southwest Delta Southwest Delta Southwest Delta Southwest Delta 113 49 142 66 16 138 80 120 49 36 56 7 131 45 58 33 29 119 113 31 39 125 74 111 57 67 25 82 110 26 141 90 94 26 54 49 113 89 13 31 16 9 143 115 51 a. Formulate the hypotheses that can be used to test for a difference between the population mean minutes late for delayed flights by these two airlines. μ₁ = population mean minutes late for delayed Delta flights μ2 = population mean minutes late for delayed Southwest flights Ho 1-2 Ha 1-2 * 0 0 b. What is the sample mean number of minutes late for delayed flights for each of these two airlines? Sample mean for Delta = Sample mean for Southwest = (rounded to 2 decimals) (rounded to 2 decimals) c. Using a 0.05 level of significance, what is the p-value and what is your conclusion? df (rounded down to nearest whole number) p-value = (rounded to 4 decimals) We cannot reject Ho.

Delta Southwest Delta Southwest Delta Southwest Delta Southwest Delta 113 49 142 66 16 138 80 120 49 36 56 7 131 45 58 33 29 119 113 31 39 125 74 111 57 67 25 82 110 26 141 90 94 26 54 49 113 89 13 31 16 9 143 115 51 a. Formulate the hypotheses that can be used to test for a difference between the population mean minutes late for delayed flights by these two airlines. μ₁ = population mean minutes late for delayed Delta flights μ2 = population mean minutes late for delayed Southwest flights Ho 1-2 Ha 1-2 * 0 0 b. What is the sample mean number of minutes late for delayed flights for each of these two airlines? Sample mean for Delta = Sample mean for Southwest = (rounded to 2 decimals) (rounded to 2 decimals) c. Using a 0.05 level of significance, what is the p-value and what is your conclusion? df (rounded down to nearest whole number) p-value = (rounded to 4 decimals) We cannot reject Ho.

Essentials of Business Analytics (MindTap Course List)

2nd Edition

ISBN:9781305627734

Author:Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, David R. Anderson

Publisher:Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, David R. Anderson

Chapter7: Linear Regression

Section: Chapter Questions

Problem 4P: A sociologist was hired by a large city hospital to investigate the relationship between the number...

See similar textbooks

Similar questions

On-time arrivals, lost baggage, and customer complaints are three measures that are typically used to measure the quality of service being offered by airlines. Suppose that the following values represent the on-time arrival percentage, amount of lost baggage, and customer complaints for 10 U.S. airlines. a. Based on the data above, if you randomly choose a Delta Air Lines flight, what is the probability that this individual flight will have an on-time arrival? b. If you randomly choose 1 of the 10 airlines for a follow-up study on airline quality ratings, what is the probability that you will choose an airline with less than two mishandled baggage reports per 1,000 passengers? c. If you randomly choose 1 of the 10 airlines for a follow-up study on airline quality ratings, what is the probability that you will choose an airline with more than one customer complaint per 1,000 passengers? d. What is the probability that a randomly selected AirTran Airways flight will not arrive on time?
Suppose the following data represent the number of persons unemployed for a given number of months in Killeen, Texas. The values in the first column show the number of months unemployed and the values in the second column show the corresponding number of unemployed persons. Let x be a random variable indicating the number of months a randomly selected person is unemployed. Use the data to develop an empirical discrete probability distribution for x. Show that your probability distribution satisfies the conditions for a valid discrete probability distribution. What is the probability that a person is unemployed for two months or less? Unemployed for more than two months? What is the probability that a person is unemployed for more than six months?
Consider the following time series data: Construct a time series plot. What type of pattern exists in the data? Develop a three-week moving average for this time series. Compute MSE and a forecast for week 8. Use α = 0.2 to compute the exponential smoothing values for the time series. Compute MSE and a forecast for week 8. Compare the three-week moving average forecast with the exponential smoothing forecast using α = 0.2. Which appears to provide the better forecast based on MSE? Use trial and error to find a value of the exponential smoothing coefficient α that results in a smaller MSE than what you calculated for α = 0.2.
Consider the following time series data: Construct a time series plot. What type of pattern exists in the data? Develop a three-week moving average for this time series. Compute MSE and a forecast for week 7. Use α = 0.2 to compute the exponential smoothing values for the time series. Compute MSE and a forecast for week 7. Compare the three-week moving average forecast with the exponential smoothing forecast using α = 0.2. Which appears to provide the better forecast based on MSE? Explain. Use trial and error to find a value of the exponential smoothing coefficient α that results in a smaller MSE than what you calculated for α = 0.2.
Use the following sample data to calculate the following summary statistics X Y 5 20 7 50 4 10 5 20 5 30 5 20 3 10 8 50 2 10 2 5 A.Calculate the mean for X and Y b. Calculate the standard deviation for X and Y c. Calculate the correlation coefficient (r) using formula
A random sample of 200 from a population of 1000 accounts receivable. Total misstatement for the sample is $3000, total recorded amount for the sample is $60,000, total population recorded amount is $330,000. What is the estimated total population misstatement under difference estimation??
The standard deviation of.alt=”f$ar{X},f$ “> is usually called the A.standard error of the sample B.randomized standard error C.standard error of the mean D.standard error of the population
A highway department is studying the relationship between traffic flow and speed during rush hour on Highway 193. The data in the file TrafficFlow were collected on Highway 193 during 100 recent rush hours. a. Develop a scatter chart for these data. What does the scatter chart indicate about the relationship between vehicle speed and traffic flow? b. Develop an estimated simple linear regression equation for the data. How much variation in the sample values of traffic flow is explained by this regression model? Use a 0.05 level of significance to test the relationship between vehicle speed and traffic flow. What is the interpretation of this relationship? c. Develop an estimated quadratic regression equation for the data. How much variation in the sample values of traffic flow is explained by this regression model? Test the relationship between each of the independent variables and the dependent variable at a 0.05 level of significance. How would you interpret this model? Is this model superior to the model you developed in part (b)? d. As an alternative to fitting a second-order model, fit a model using a piecewise linear regression with a single knot. What value of vehicle speed appears to be a good point for the placement of the knot? Does the estimated piecewise linear regression provide a better fit than the estimated quadratic regression developed in part (c)? Explain. e. Separate the data into two sets such that one data set contains the observations of vehicle speed less than the value of the knot from part (d) and the other data set contains the observations of vehicle speed greater than or equal to the value of the knot from part (d). Then fit a simple linear regression equation to each data set. How does this pair of regression equations compare to the single piecewise linear regression with the single knot from part (d)? In particular, compare predicted values of traffic flow for values of the speed slightly above and slightly below the knot value from part (d). f. What other independent variables could you include in your regression model to explain more variation in traffic flow?
A sociologist was hired by a large city hospital to investigate the relationship between the number of unauthorized days that employees are absent per year and the distance (miles) between home and work for the employees. A sample of 10 employees was chosen, and the following data were collected. a. Develop a scatter chart for these data. Does a linear relationship appear reasonable? Explain. b. Use the data to develop an estimated regression equation that could be used to predict the number of days absent given the distance to work. What is the estimated regression model? c. What is the 99% confidence interval for the regression parameter 1? Based on this interval, what conclusion can you make about the hypotheses that the regression parameter 1 is equal to zero? d. What is the 99% confidence interval for the regression parameter 0? Based on this interval, what conclusion can you make about the hypotheses that the regression parameter 0 is equal to zero? e. How much of the variation in the sample values of number of days absent does the model you estimated in part (b) explain?
Consider the following sample values: 7, 11, 13, 20, 22, 30 What is the sample mean? Express your answer with two decimal places
For each term in the first column below, identify its definition (or partial definition). Each definition may be used once or not at all. # 1 2 3 4 5 6 7 8 9 Definition (or Partial Definition) A defined rate of departure from prescribed controls. Also referred to as occurrence rate or exception rate. A sampling plan for locating at least 1 deviation, providing that the deviation occurs in the population with a specified frequency. Also referred to as precision, a measure of the difference between a sample estimate (projection) and the tolerable rate of deviation or tolerable misstatement at a specified sampling risk. An estimate of the most likely amount of monetary misstatement in a population. The complement of the risk of incorrect acceptance. The maximum population rate of deviations from a prescribed control that the auditors will accept without modifying the planned assessment of control risk. The possibility that the assessed level of control risk based on the sample is less…
Consider a sample with data values of 10, 20, 12, 17, and 16. a. Compute the mean and median. b. Consider a sample with data values 10, 20, 12, 17, 16, and 12. How would you expect the mean and median for these sample data to compare to the mean and median for part a (higher, lower, or the same)? Compute the mean and median for the sample data 10, 20, 12, 17, 16, and 12.