Business Statistics [Maymester – Midterm]

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Statistics (Definition)
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…the science of collecting, organizing, analyzing, interpreting, and presenting data. Some experts prefer to call statistics data science, a trilogy of tasks involving data modeling, analysis, and decision making.
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Statistic (Definition)
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…a single measure, reported as a number, used to summarize a sample data set.
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Descriptive Statistics (Definition)
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…a single measure, reported as a number, used to summarize a sample data set.
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Inferential Statistics (Definition)
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…generalizing from a sample to a population, estimating unknown population parameters, drawing conclusions, and making decisions
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Pitfalls of Statistics (List)
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Pitfall 1: Conclusions from Small Samples Pitfall 2: Conclusions from Nonrandom Samples Pitfall 3: Conclusions from Rare Events Pitfall 4: Poor Survey Methods Pitfall 5: Assuming a Causal Link Pitfall 6: Generalization to Individuals Pitfall 7: Unconscious Bias Pitfall 8: Significance versus Importance
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Statistics vs. Probability
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Statistics summarizes history, while probability quantifies future uncertainty.
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Observation (Definition)
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…a single member of a collection of items that we want to study, such as a person, firm, or region.
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Variable (Definition)
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…a characteristic of the subject or individual, such as an employee’s income or an invoice amount
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Data Set (Definition)
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…consists of all the values of all of the variables for all of the observations we have chosen to observe.
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Univariate Data Set (Definition, Example and Typical Tasks)
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One variable; Ex: Income; Typical Tasks: Histogram, descriptive statistics, frequency tallies.
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Bivariate Data Set (Definition, Example, and Typical Tasks)
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Two variables; Ex: Income and age; Typical Tasks: Scatter plots, correlations, regression modeling…
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Multivariate Data Set (Definition, Example, and Typical Tasks)
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More than two variables; Ex: Income, age and gender; Typical Tasks: multiple regression, data mining, econometric modeling…
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Categorical Data (Verbal Label, Coding and Binary Values)
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(also called qualitative data); have values that are described by words rather than numbers. Verbal Labeling: cars are called small, mid-sized, sudan, etc… Coding: numbers can represent words; i.e. 1=cash, 2=check, 3=credit card. Binary Values: variables only have two values (i.e. employed and unemployed).
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Numerical Data (Discreet and Continuous Data)
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(also called quantitative data – statistics or tables) counting, measuring something, or some kind of mathematical operation – provides insight into characteristics of a data set using mathematics. Discreet Data: takes on a numerical value – you can count it on your fingers (no negatives – only integers). Continuous Data: number might represent a percentage of customers out of an entire group surveyed – can take on fractional values.
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Times Series Data vs. Cross Sectional Data
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Time Series Data: each observation in the sample represents a different equally spaced point in time (i.e. years, months, days…) – we are interested in trends and patterns over time. Cross Sectional Data: each observation represents a different individual unity at the same point in time – we are interested in variation among observations. We can combine the two data types to get pooled cross sectional and time series data.
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Levels of Measurement (List)
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Nominal Ordinal Interval Ratio
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Nominal Data (Definition)
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(Latin from “name”) identifying categories only; i.e. eye color (blue, brown, green, etc…)
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Ordinal Data (Definition)
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Rank has meaning; no clear meaning to distance; i.e. full sized, compact, subcompact.
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Interval Data (Definition)
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Distance has meaning; i.e. temperature.
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Ratio Data (Definition)
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Meaningful zero exists; i.e. accounts payable – 20$ is twice as much as 10$ (ratio of 2:1) – 0 point means the absence of something.
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Likert Scales (Describe)
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Example: “College-bound high school students should be required to study a foreign language – Check one box.” Box Options: “Strongly Agree,” “Somewhat Agree,” “Neither Agree Nor Disagree,” “Somewhat Disagree,” “Strongly Disagree”…
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Parameter (Definition)
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a measurement or characteristic of the population (i.e. a mean or proportion). Usually unknown since we can rarely observe the entire population; i.e. a census of a certain target population is impossible – so these parameters would be estimated using a sample.
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Target Population (Definition)
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…the population that we’re interested in.
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Sampling Frame (Definition)
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… the group from which we take the sample; i.e. phone books, directories, email addresses from a certain online newsletter, etc…
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Sampling Methods (List)
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1. Simple Random Sample 2. Systematic Sample 3. Stratified Sampling 4. Cluster Sampling 5. Judgement Sample 6. Convenience Sample 7 Focus Groups
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Simple Random Sample (Describe)
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… use random numbers to select items from a list.
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Systematic Sample (Describe)
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… select every n-th item from a list or sequence (e.g., restaurant customers); every fifth car gets randomly pulled over.
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Stratified Sampling (Describe)
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… select randomly within defined strata (e.g. by age, occupation, gender)…
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Cluster Sampling (Describe)
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… like stratified sampling except strata are geographical areas (e.g. zip codes)… trying to find locations in their particular market.
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Judgement Sample (Describe)
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… use expert knowledge to choose “typical” items (e.g. which employees to interview for yearly reviews).
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Convenience Sample (Describe)
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… use a sample that happens to be given (e.g. a coworker that just happens to be at lunch with you).
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Focus Groups (Describe)
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… in-depth dialogue with a panel of representative or specific individuals (i.e. iPod users).
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Basic Steps to Survey Research (List)
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Step 1: State the goals of the research. Step 2: Develop the budget (time, money, staff). Step 3: Create a research design (target population, frame, sample size). Step 4: Choose a survey type and method of administration. Step 5: Design a data collection instrument (questionnaire). Step 6: Pretest the survey instrument and revise as needed. Step 7: Administer the survey (follow up if needed). Step 8: Code the data and analyze it.
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Visual Data Representation
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(charts and graphs) provides insight into characteristics of a data set without using mathematics.
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Stem and Leaf Plot
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…a tool of exploratory data analysis (EDA) that seeks to reveal essential data features in an intuitive way. A stem-and-leaf plot is basically a frequency tally, except that we use digits instead of tally marks. For two-digit or three-digit integer data, the stem is the tens digit of the data, and the leaf is the ones digit.
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Dot Plots
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A dot plot is the simplest graphical display of n individual values of numerical data – pretty much like a stem-and-leaf plot, but instead of digits, it uses dots. A stacked dot plot compares two dot plots (stacked on top of one another).
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Left Skewed Histogram
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… negatively skewed, with a long left “tail.”
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Right Skewed Histogram
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… positively skewed, with long right “tail.”
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Symmetric Histogram
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… both “tails” are the same length.
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Pareto Charts
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… special type of bar chart used in quality management to display the frequency of defects or errors of different types; categories are displaced in descending order of frequency.
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Calculate the Mean
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… add up all of the numbers, and then divide by how many numbers there were. =AVERAGE(Data)
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Calculate the Median
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… the 50th percentile, or midpoint, of the sorted sample data. =MEDIAN(Data)
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Calculate the Mode
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… the most frequently occurring data value; i.e. 2 2 5 5 5 6 7 8 8… 5 is the mode. =MODE.SNGL(Data)
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Calculate the Midrange
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the point halfway between the lowest and highest values of x. [x(1) + x(2)] / 2 = (MIN(Data)+MAX(Data))/2
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Trimmed Mean
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… gets rid of outliers, used for economic data. Remove the highest and lowest k percent of the observation. =TRIMMEAN(Data, 0.1)
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Sample Standard Deviation (S)
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=STDEV.S(Data); A low standard deviation indicates that the data points tend to be very close to the mean; high standard deviation indicates that the data points are spread out over a large range of values.
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Mean Absolute Deviation (MAD)
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… reveals the average distance from the center. Appealing because of its simple interpretation. =AVEDEV(Data)
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Range
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Max(Data) – Min(Data).
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Empirical Rule
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The Empirical Rule states that for data from a normal distribution, we expect the interval ? ± k? to contain a known percentage of data.
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Method of Medians
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… find the median of all of the data – then, find the two medians of the upper and lower sections of the original median.
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Level of Confidence (Definition)
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… a measure of how confident we are in a given marin of error; i.e. 90% level of confidence that an estimate based on a sample will differ by no more than 1.6 standard errors from the “true” population value because of sampling error.
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Random Experiment (Definition)
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… an observational process whose results cannot be known in advance.
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Sample Space (Definition)
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… the set of all outcomes (S) of a random experiment.
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Discrete Sample Space (Definition)
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… a sample space with a countable number of outcomes; i.e. grades; A to F; the probabilities of all simple events must sum to 1.
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Continuous Sample Space (Definition)
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… the sample space cannot be listed but can be described by a rule; i.e. the sample space for the length of a randomly chosen cell phone call would be S={all X such that X>0}, because you don’t know how long cell phone calls can be.
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Event (Definition)
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… any subset of outcomes in the sample space.
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Simple Event / Elementary Event (Definition)
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(“Elementary, my dear Watson!” – which Sherlock Holmes never ACTUALLY said, by the way); a single outcome.
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Probability (Definition)
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… the probability of an event is a number that measures the likelihood that the event will occur; the probability of event A must lie within the interval from 0-1.
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Empirical Approach (Definition)
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…use the empirical or relative frequency approach to assign probabilities by counting the frequency of observed outcomes defined on the experimental sample space – based on HISTORICAL DATA; i.e. default rates on student loans: P(a student defaults)= f/n = (number of defaults / number of loans)
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The Law of Large Numbers (Definition)
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… says that as the number of trials increases, any empirical probability approaches its theoretical limit; i.e. flip a coin 50 times; we would theoretically expect the proportion of heads to be near .50.
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Classical Approach [A priori] (Definition)
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A priori: the process of assigning probabilities before the event is observed or the experiment is conducted; based on logic not experience; Think “priori = PRIOR.” Instead of performing the experiment, we can use deduction to determine the probability of an event.
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Subjective Approach (Definition)
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… reflects someone’s informed judgement about the likelihood of an event; used when there is no repeatable random experiment; i.e. What is the probability that the price of Ford’s stock will rise within the next 30 days?
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Complement of an Event (Definition)
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… of an event A is denoted by A’ and consists of everything in the sample space S except event A.
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Union of Two Events (Definition)
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… consists of all outcomes in the sample space S that are contained either in event A or in event B, or in both.
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Intersection of Two Events (Definition)
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… the event consisting of all outcomes in the sample space S that are contained in both events A and B.
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Mutually Exclusive Events (Definition)
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… two events are mutually exclusive if their intersection is the null set which contains no elements.
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Special Law of Addition (Definition)
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… in the case of mutually exclusive events, the addition law reduces to: P(A) + P(B).
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Collectively Exhaustive Events (Definition)
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… if their union is the entire sample space S; there can be more than two collectively exhaustive events, as long as they take up the entirety of sample space S.
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Conditional Probability (Definition)
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… the probability of an event A given that event B has occurred; i.e.: P(A in Physics) = 0.2 P(A in Calculus) = 0.2, so… P(A in Physics | A in Calculus) = 0.8 P(A in Physics | C in Calculus) = 0.15
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Independent Events (Definition)
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Event A is independent of event B if the conditional probability P(A | B) is the same as the marginal probability P(A).
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Contingency Table (Definition)
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… also called a cross-tabulation table; used often when gathering empirical data. NOTE: Learn how to create / read / analyze contingency tables – MUY IMPORTANTE.
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Random Variable (Definition)
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… a function or rule that assigns a numerical value to each outcome in the sample space of a random experiment. Uppercase letters (X, Y, etc…) represent random variables. Lowercase letters (x, y, etc…) represent values of random variables.
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Discrete Random Variable (Definition)
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… a variable that has a countable number of distinct values.
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Discrete Probability Distribution
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… assigns a probability to each value of a discrete random variable X.
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Calculating the Variance
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… (x-mu)^2 So, X minus the average of the xP(x) value.
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Probability Distribution Function (PDF)
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… a mathematical function that shows the probability of each X-value.
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Cumulative Distribution Function (CDF)
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… a mathematical function that shows the cumulative sum of probabilities, adding from the smallest to the largest X-value, gradually approaching unity; i.e… x | CDF 1 | .2 2 | .2 + .3 3 | .2 + .3 + .4
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Bernoulli Experiments
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… a random experiment with only 2 outcomes; one outcome is labeled “success” (x=1) and the other a “failure” (x=0). Success is defined as the less likely outcome.
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Binomial Distribution (Definition)
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… arises when a Bernoulli experiment is repeated “n” times; each trial is independent.
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Binomial Distribution
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=BINOM.DIST(x, n, ?, 0 or 1).

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