J Chapter 3- Learning objectives Describe data using measures of central tendency and dispersion for a set of individual data values, and for a set of grouped data Convert data to standardized values Use the computer to visually represent data

l Chapter 2-Learning objectives Convert raw data into a data array Construct a frequency distribution a relative frequency distribution a cumulative relative frequency distribution Construct a stem-and-leaf diagram Visually represent data by using graphs and charts

Sampling distribution of the mean When the population is normally distributed Shape: Regardless of sample size, the distribution of sample means will be normally distributed Center: The mean of the distribution of sample means is the mean of the population. Sample size does not affect the center of the distribution

Chapter 10-Learning Objectives Describe the logic of and transform verbal statements into null and alternative hypotheses Describe what is meant by Type I and Type Il errors Conduct a hypothesis test for a single population mean or proportion

Describe the relationship between analysis of variance, the design of experiments, and the types of applications to which the experiments are applied. Differentiate one-way randomized block, and two-way analysis of variance techniques Arrange data into a format that facilitates their analysis by the appropriate analysis of variance

Chapter 14-Learning objectives Differentiate between nonparametric and parametric hypothesis tests Determine when a nonparametric test should be used instead of its parametric counterpart Appropriately apply each of the nonparametric methods introduced o 2002 The Wadsworth Group

Chapter 16 Learning Objectives Obtain and interpret the multiple regression equation Make estimates using the regression model: Point value of the dependent variable,y Intervals: Confidence interval for the conditional mean of y Prediction interval for an individual y observation

Chapter 18-Learning Objectives Describe the trend, cyclical, seasonal, and irregular components of the time series model Fit a linear or quadratic trend equation to a time series Smooth a time series with the centered moving average and exponential smoothing techniques

Chapter 20-Learning Objectives Understand the philosophy of total quality management(TQM) Be able to distinguish between defect prevention and defect detection strategies for the management of qualit Be able to distinguish random variation from assignable variation Understand the fundamentals of statistical process control charts

Chapter 19- Learning Objectives Express a decision situation in terms of decision alternatives, states of nature, and payoffs. Differentiate between non-Bavesian and Bavesian decision criteria. Determine the expected payoff for a decision alternative. Calculate and interpret the expected value of perfect information