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business
business statistics a first course
Introduction To Business Statistics 7th Edition Ronald M. Weiers - Solutions
Explain the difference between random and assignable process variation.
Discuss the Kaizen concept of ongoing improvement.
Differentiate between defect prevention and defect detection strategies for the management of quality.
Understand the nature and importance of the process orientation that is central to total quality management.
Discuss the concept of total quality management.
Apply incremental analysis to inventory-level decisions.
Express and analyze the decision situation in terms of opportunity loss and expected opportunity loss.
Calculate and interpret the expected value of perfect information.
Determine the expected payoff for a decision alternative.
Differentiate between non-Bayesian and Bayesian decision criteria.
Express a decision situation in terms of decision alternatives, states of nature, and payoffs.
Use index numbers to compare business or economic measurements from one period to the next.
Use the Durbin-Watson test to determine whether regression residuals are autocorrelated.
Use the mean absolute deviation (MAD) and mean squared error (MSE)criteria to compare how well fitted equations or curves fit a time series.
Use the trend extrapolation and the exponential smoothing forecast methods to estimate a future value.
Determine seasonal indexes and use them to compensate for the seasonal effects in a time series.
Smooth a time series with the centered moving average and exponential smoothing techniques.
Fit a linear or a polynomial trend equation to a time series.
Describe the trend, cyclical, seasonal, and irregular components of the classical time series model.
Determine which of several competing models might be most suitable for the data and the situation
Apply stepwise regression in selecting which variables to use in a model.
Identify and compensate for multicollinearity in the data.
Use logarithmic transformations in constructing exponential and multiplicative models.
Apply qualitative variables representing two or more categories.
Build polynomial regression models to describe curvilinear relationships.
Use residual analysis in examining the appropriateness of the multiple regression model and the extent to which underlying assumptions are met.
Explain both the meaning and the applicability of a dummy variable.
Construct confidence intervals and carry out hypothesis tests involving the partial regression coefficients.
Interpret the value of the coefficient of multiple determination and carry out a hypothesis test for its significance.
Obtain and interpret the multiple regression equation; then make point and interval estimates regarding the dependent variable.
Explain how the scatter diagram and least-squares concepts apply to multiple regression.
Use residual analysis in examining the appropriateness of the linear regression model and the extent to which underlying assumptions are met.
Test the significance of the correlation coefficient.
Construct confidence intervals and carry out hypothesis tests involving the slope of the regression line.
Describe the meaning of the coefficient of determination.
Determine and interpret the value of the coefficient of correlation.
Determine the least-squares regression equation, and make point and interval estimates for the dependent variable.
Explain the individual terms in the simple linear regression model, and describe the assumptions that the model requires.
Apply each of the nonparametric methods in the chapter to tests of hypotheses for which they are appropriate.
Determine when a nonparametric hypothesis test should be used instead of its parametric counterpart.
Explain the advantages and disadvantages of nonparametric versus parametric testing.
Differentiate between nonparametric and parametric hypothesis tests.
Appreciate that computer assistance is especially important in analysis of variance tests and be able to interpret computer outputs for these tests.
Use the one-way, randomized block, and two-way analysis of variance methods in testing appropriate hypotheses relative to experimental data.
Arrange data into a format that facilitates their analysis by the appropriate analysis of variance procedure.
Differentiate between the one-way, randomized block, and two-way analysis of variance techniques and their respective purposes.
Understand the relationship between analysis of variance and the design of experiments.
Describe the general approach by which analysis of variance is applied and the type of applications for which it is used.
Determine the confidence interval for, and carry out hypothesis tests for a population variance.
Apply the chi-square distribution in comparing the proportions of two or more independent samples.
Apply the chi-square distribution in testing whether two nominalscale(category) variables could be independent.
Apply the chi-square distribution in testing whether a sample could have come from a population having a specified probability distribution.
List and understand the general procedures involved in chi-square testing.
Explain the nature of the chi-square distribution.
Determine and explain the operating characteristic curve for a hypothesis test and a given decision rule.
Determine and explain the power curve for a hypothesis test and a given decision rule.
Explain how confidence intervals are related to hypothesis testing.
Determine and explain the p-value for a hypothesis test.
Carry out a hypothesis test for a population mean or a population proportion, interpret the results of the test, and determine the appropriate business decision that should be made.
Describe what is meant by Type I and Type II errors, and explain how their probabilities can be reduced in hypothesis testing.
Transform a verbal statement into appropriate null and alternative hypotheses, including the determination of whether a two-tail test or a one-tail test is appropriate.
Describe the meaning of a null and an alternative hypothesis.
Determine whether two independent samples could have come from populations having the same standard deviation.
Test the difference between proportions for two independent samples.
Test the difference between sample means when the samples are not independent.
Select and use the appropriate hypothesis test in comparing the means of two independent samples.
Use Excel and Minitab to construct confidence intervals.
Determine how large a simple random sample must be in order to estimate a population mean or proportion at specified levels of accuracy and confidence.
Decide whether the standard normal distribution or the t distribution should be used in constructing a given confidence interval.
Use the t distribution in constructing a confidence interval for a population mean.
Use the standard normal distribution in constructing a confidence interval for a population mean or proportion.
Explain the difference between a point estimate and an interval estimate for a population parameter.
Determine the effect on the sampling distribution when the samples are relatively large compared to the population from which they are drawn.
Explain the central limit theorem and its relevance to the shape of the sampling distribution of a mean or proportion.
Understand and determine the sampling distribution of proportions for samples from a given population.
Understand and determine the sampling distribution of means for samples from a given population.
Understand that the sample mean or the sample proportion can be considered a random variable.
6.4Construct contingency tables.
6.4Construct a dotplot and a scatter diagram.
6.4Visually represent data by using graphs and charts.
6.4• Construct a stem-and-leaf diagram to represent data.
6.4Construct relative and cumulative frequency distributions.
6.4Construct a frequency distribution and a histogram.
6.4 Repeat Applet Exercise 6.1 for k values of 6 through 12, in each case identifying the actual binomial probability that there will be no more than k females in the sample.For each value of k, also note the normal approximation result and its closeness to the actual binomial probability.
6.3 With n 5 100 and 5 0.6, what is the actual binomial probability that there are no more than k 5 60 females in the sample of 100? How does this compare with the corresponding probability using the normal approximation?What effect has the larger sample size had on the closeness of the
6.2 With n 5 5 and 5 0.6, what is the actual binomial probability that there are no more than k 5 3 females in the sample of 5? How does this compare with the corresponding probability using the normal approximation?What effect has the smaller sample size had on the closeness of the approximation?
6.1 With n 5 15 and 5 0.6, as in the example in Section 7.4, what is the probability that there are no more than k 5 9 females in the sample of 15 walkers? How does this compare with the corresponding probability using the normal approximation to the binomial distribution?
5.4 Drag the left and right edges of the shaded area so that the left boundary corresponds to z 5 0 and the right boundary corresponds to z 5 11.0. What probability is now associated with the shaded area?
5.3 By dragging the left and right edges of the shaded area, determine the probability that a randomly selected plane will have flown between 130 and 160 hours during the year. What values of z correspond to 130 hours and 160 hours, respectively?
5.2 With the mean and standard deviation set at 120 and 30, respectively, drag the edges of the shaded area so that its left boundary is at 120 and its right boundary is at 180.What is the probability that a randomly selected plane will have flown between 120 and 180 hours during the year?What
5.1 Using the text boxes, ensure that the mean is 120 and the standard deviation is 30, as in the distribution of flying hours in Figure 7.5. (Don’t forget to hit the Enter or Return key while the cursor is still in the text box.) Next, ensure that the left and right boundaries of the shaded area
8. Comparing the BOUNCE scores when BALL 5 1 through 200 to those when BALL 5 201 through 240, does it appear that the process may have changed in some way toward the end of the work shift? In what way? Does it appear that the machine might be in need of repair or adjustment? If so, in what way
7. Examining the graph and the horizontal lines drawn in part (6), does it appear that about 90% of the balls have BOUNCE scores between the two horizontal lines you’ve drawn, as the normal distribution would suggest when 5 30.00 and 5 2.00?
6. On the graph obtained in part (5), draw two horizontal lines—one at each of the BOUNCE scores determined in part (3).
5. Using the computer, generate a line graph in which BOUNCE is on the vertical axis and BALL is on the horizontal axis.
4. What is the probability that, for three consecutive balls, all three will happen to score below the lower of the two values determined in part (3)?Two hundred forty golf balls have been subjected to the bounce test during the most recent 8-hour shift. From the 1st through the 240th, their scores
3. Given the distribution of bounce test scores described above, what score value should be exceeded only 5%of the time? For what score value should only 5% of the balls do more poorly?
2. Repeat part (1), but use the normal approximation to the binomial distribution. Do the respective probabilities appear to be very similar?
1. A driving range has just purchased 100 golf balls. Use the computer and the binomial distribution in determining the individual and cumulative probabilities for x 5 the number of balls that scored at least 31.00 inches on the “bounce” test.
4.3 Using the two sliders, position the red curve so that its mean is as small as possible and its shape is as narrow as possible. Compare the numerical values of the mean and standard deviation to those of the blue curve (mean 5 0, standard deviation 5 1).
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