WGU C723 Quantitative Analysis for Business- Final Exam Already Passed

WGU C723 Quantitative Analysis for Business- Final Exam Already Passed 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