WGU C723 Quantitative Business Analysis Final

Hypothesis - is an assumption about a population parameter such as a mean or a proportion null hypothesis (H0) - -represents the status quo -states a belief that the population parameter is ≤, =, or ≥ a specific value -believed to be true unless there is overwhelming evidence to the contrary alternative hypothesis (H1) - -represents the opposite of the null hypothesis -believed to be true if the null hypothesis is found to be false -always states that the population parameter is >, ≠, or < a specific value two-tail hypothesis test - is used whenever the alternative hypothesis is expressed as ≠ one-tail hypothesis test - is used when the alternative hypothesis is stated as < or > Type I error - -occurs when the null hypothesis is rejected when it is true -when it occurs the producer is looking for a problem in its process that does not exist Type II error - -occurs when we fail to reject the null hypothesis when it is not true -when it occurs the customer is getting a product from a process that is not performing properly Correlation analysis - -is used to measure both the strength and direction of a linear relationship between two variables -A relationship is linear if the scatter plot of the independent and dependent variables has a straight-line pattern correlation coefficient, r - -indicates both the strength and direction of the linear relationship between the independent and dependent variables population correlation coefficient (ρ) - refers to the correlation between all values of two variables of interest in a population confidence interval for the mean - is an interval estimate around a sample mean that provides us with a range within which the true population mean is expected to lie confidence level - is defined as the probability that the interval estimate will include the population parameter of interest Student's t-distribution - is used in place of the normal probability distribution when the sample standard deviation, s, is used in place of the population standard deviation, σ probability sample - is a sample in which each member of the population has a known, nonzero, chance of being selected for the sample simple random sample - is a sample in which every member of the population has an equal chance of being chosen Sampling error - is defined as the difference between the sample statistic and the population parameter Central Limit Theorem - states that the sample means of large-sized samples will be normally distributed regardless of the shape of their population distributions normal probability distribution - is useful when the data tend to fall into the center of the distribution and when very high and very low values are fairly rare exponential distribution - is used to describe data where lower values tend to dominate and higher values don't occur very often. uniform distribution - describes data where all the values have the same chance of occurring Discrete data - -Values are whole numbers (integers) -Usually counted, not measured Continuous data - -Can potentially take on any value, depending only on the ability to measure accurately -Often measured, fractional values are possible Variance - a measure of the spread of the individual values around the mean of a data set expected monetary value (EMV) - is the mean of a discrete probability distribution when the discrete random variable is expressed in terms of dollars Probability - -a numerical value ranging from 0 to 1 -indicates the chance, or likelihood, of a specific event occurring Experiment - The process of measuring or observing an activity for the purpose of collecting data Sample space - All the possible outcomes, or results, of an experiment joint probability - probability of the intersection of two events mutually exclusive - Two events cannot occur at the same time during the experiment Conditional probability - the probability of Event A occurring, given the condition that Event B has occurred Permutations - are the number of different ways in which objects can be arranged in order Central tendency - is a single value used to describe the center point of a data set z-score - -identifies the number of standard deviations a particular value is from the mean of its distribution -has no units Chebyshev's Theorem - -states that for any number z greater than 1, the percent of the values that fall within z standard deviations above and below the mean will be at least -applies regardless of distribution five-number summary - consists of these five values: -The minimum value -The first quartile -The second quartile -The third quartile -The maximum value frequency distribution - shows the number of data observations that fall into specific intervals Relative frequency distributions - display the proportion of observations of each class relative to the total number of observations Statistics - the mathematical science that deals with the collection, analysis, and presentation of data, which can then be used as a basis for inference and induction Primary data - data that you have collected for your own use Secondary Data - data collected by someone else Descriptive statistics - Collecting, summarizing, and displaying data Standard Deviation - -square root of variance -common measure of consistency in business applications, such as quality control -measures the amount of variance around the mean Law of large numbers - states that when an experiment is conducted a large number of times, the empirical probabilities of the process will converge to the classical probabilities