being tested, the null hypothesis is the statement that the relationship does not exist. The null

WGU C207 Terms

• Histograms: Measures how continuous data is distributed over various ranges. A graph that

displays continues data.

• null hypothesis, or H0: The statement that there is no relationship. For whatever relationship is

being tested, the null hypothesis is the statement that the relationship does not exist. The null hypothesis is always the statement that is being tested.

• alternative hypothesis, or HA,: It is the opposite statement to the null hypoth- esis. It states

that there is a relationship for whatever relationship is being tested.

• Critical value: The tipping point between where we reject the null hypothesis and where

we fail to reject the null hypothesis.

• Linear Programing: A mathematical technique used to find a maximum or minimum of

linear equations containing several variables.

• Crossover Analysis: Allows a decision maker to identify the crossover point, which

represents the point at which we are indifferent between the plans.

• A chi-squared test

(also written as "§2" or "chi-square"): A chi-squared test is commonly used in statistics to draw inferences about a population, by testing sample data. Employed for categorical data

• ANOVA: Analysis of Variance is a technique used to determine if there is a significant

difference among three or more means.

• Regression Analysis definition: Statistical method to measure the average amount of

change in a dependent variable associated with a unit change in one or more independent variables; considered an associate model as it incorporates the factors (variables) that might influence the quantity being forecasted

• Time Series Analysis: Forecasting technique that employs a series of past data points to

make a forecast

• Cluster Analysis: The process of arranging terms or values based on different variable into

"natural" groups. A forecasting technique that employs a series of past data points to make a forecast

• Decision Analysis: Forecasting technique that employs a series of past data points to make

a forecast

• R2 or R-squared: Provides a measure of "goodness of fit."; ranges in value from 0 to 1. A

value close to 1 indicates that the estimation error is small and our data closely aligns to the regression line.

• Standard error (SE) of estimate, denoted se,: The average deviation of the data points from

the predictive regression line or curve.

• Time series analysis: Technique where time is used as an independent variable to assess any

influence it may have on an output. 1 / 2

• Logistic Regression: A type of regression analysis that predicts the result of a binary,

categorical dependent variable (yes/not, treated/untreated, republican/de- mocrat). Dependent variable is either on the interval or ratio scale (age, income, rating, etc.).

• Cyclicality: Repetition of up (peaks) and down movements (troughs) that follow or

counteract a business cycle that can last several years.

• Autocorrelation: A relationship between two variables that is inherently non-lin- ear
• Simple Linear Regression: A form of regression analysis with only one inde- pendent

variable. Aka, Least Squares Regression.

• Multiple Linear Regression: A statistical method used to model the relationship between one

dependent (or response) variable and two or more independent (or explanatory) variables by fitting a linear equation to observed data (can number of heroin deaths be predicted from percent of adults who use cocaine, hallucinogens, marijuana).

• Regression Analysis: A statistical analysis tool that quantifies the relationship between a

dependent variable and one or more independent variables (is on-time progress for course work related to GPA). Is there a trend over time. Possibility of predicting the value of a specific target variable given the value of one ore more predictor variables (predict number of months to graduate based on OA score for this course).

• Heteroscedasticity: A regression in which the variances in y for the values of x are not

equal

• Random Variation: The variability of a process which might be caused by irreg- ular

fluctuations due to chance that cannot be anticipated, detected, or eliminated

• Time Series Analysis: Regression analysis that uses time as the independent variable
• Multicollinearity: A multiple regression equation is flawed because two vari- ables

thought to be independent are actually correlated to be independent

• Irregularity: One-time deviations from expectations caused by unforeseen cir- cumstances

such as war, natural disasters, poor weather, labor strikes, single-oc- currence company- specific surprises or macroeconomic shocks

• Homoscedasticity: A regression in which the variances in y for the values of x are equal or

close to equal

• Random Errors: Error in measurement caused by unpredictable statistical fluc- tuations
• Information Bias: A prejudice in the data that results when either the respondent or the

interviewer has an agenda and is not presenting impartial questions or responding with truly honest responses, respectively

• Ratio Data: Similar to interval data in that the data is ordered within a range and with

each data point being an equal interval apart, also has a natural zero point which indicates none of the given quality

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