WGU C207 Terms
1. Histograms: Measures how continuous data is distributed over various ranges.A graph that
displays continues data.
2. 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.
3. 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.
4. Critical value: The tipping point between where we reject the null hypothesisand where
we fail to reject the null hypothesis.
5. Linear Programing: A mathematical technique used to find a maximum orminimum of
linear equations containing several variables.
6. Crossover Analysis: Allows a decision maker to identify the crossover point,which
represents the point at which we are indifferent between the plans.
7. 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. Employedfor categorical data
8. ANOVA: Analysis of Variance is a technique used to determine if there is asignificant
difference among three or more means.
9. Regression Analysis definition: Statistical method to measure the average amount of
change in a dependent variable associated with a unit change in one ormore independent
variables; considered an associate model as it incorporates thefactors (variables) that might
influence the quantity being forecasted
10. Time Series Analysis: Forecasting technique that employs a series of past datapoints to
make a forecast
11. 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
12. Decision Analysis: Forecasting technique that employs a series of past datapoints to make
a forecast
13. R2 or R-squared: Provides a measure of "goodness of fit."; ranges in value from0 to 1. A
value close to 1 indicates that the estimation error is small and our data closely aligns to the
regression line.
14. Standard error (SE) of estimate, denoted se,: The average deviation of thedata points from
the predictive regression line or curve.
15. Time series analysis: Technique where time is used as an independent variableto assess any
influence it may have on an output.
, 16. 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.).
17. Cyclicality: Repetition of up (peaks) and down movements (troughs) that followor
counteract a business cycle that can last several years.
18. Autocorrelation: A relationship between two variables that is inherently non-lin-ear
19. Simple Linear Regression: A form of regression analysis with only one inde-pendent
variable. Aka, Least Squares Regression.
20. Multiple Linear Regression: A statistical method used to model the relationshipbetween one
dependent (or response) variable and two or more independent (or explanatory) variables by
fitting a linear equation to observed data (can number ofheroin deaths be predicted from
percent of adults who use cocaine, hallucinogens,marijuana).
21. Regression Analysis: A statistical analysis tool that quantifies the relationshipbetween 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).
22. Heteroscedasticity: A regression in which the variances in y for the values of xare not
equal
23. 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
24. Time Series Analysis: Regression analysis that uses time as the independentvariable
25. Multicollinearity: A multiple regression equation is flawed because two vari-ables
thought to be independent are actually correlated to be independent
26. 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
27. Homoscedasticity: A regression in which the variances in y for the values of x are equal or
close to equal
28. Random Errors: Error in measurement caused by unpredictable statistical fluc-tuations
29. Information Bias: A prejudice in the data that results when either the respondentor the
interviewer has an agenda and is not presenting impartial questions or responding with truly
honest responses, respectively
30. Ratio Data: Similar to interval data in that the data is ordered within a range andwith
each data point being an equal interval apart, also has a natural zero point which indicates
none of the given quality
1. Histograms: Measures how continuous data is distributed over various ranges.A graph that
displays continues data.
2. 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.
3. 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.
4. Critical value: The tipping point between where we reject the null hypothesisand where
we fail to reject the null hypothesis.
5. Linear Programing: A mathematical technique used to find a maximum orminimum of
linear equations containing several variables.
6. Crossover Analysis: Allows a decision maker to identify the crossover point,which
represents the point at which we are indifferent between the plans.
7. 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. Employedfor categorical data
8. ANOVA: Analysis of Variance is a technique used to determine if there is asignificant
difference among three or more means.
9. Regression Analysis definition: Statistical method to measure the average amount of
change in a dependent variable associated with a unit change in one ormore independent
variables; considered an associate model as it incorporates thefactors (variables) that might
influence the quantity being forecasted
10. Time Series Analysis: Forecasting technique that employs a series of past datapoints to
make a forecast
11. 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
12. Decision Analysis: Forecasting technique that employs a series of past datapoints to make
a forecast
13. R2 or R-squared: Provides a measure of "goodness of fit."; ranges in value from0 to 1. A
value close to 1 indicates that the estimation error is small and our data closely aligns to the
regression line.
14. Standard error (SE) of estimate, denoted se,: The average deviation of thedata points from
the predictive regression line or curve.
15. Time series analysis: Technique where time is used as an independent variableto assess any
influence it may have on an output.
, 16. 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.).
17. Cyclicality: Repetition of up (peaks) and down movements (troughs) that followor
counteract a business cycle that can last several years.
18. Autocorrelation: A relationship between two variables that is inherently non-lin-ear
19. Simple Linear Regression: A form of regression analysis with only one inde-pendent
variable. Aka, Least Squares Regression.
20. Multiple Linear Regression: A statistical method used to model the relationshipbetween one
dependent (or response) variable and two or more independent (or explanatory) variables by
fitting a linear equation to observed data (can number ofheroin deaths be predicted from
percent of adults who use cocaine, hallucinogens,marijuana).
21. Regression Analysis: A statistical analysis tool that quantifies the relationshipbetween 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).
22. Heteroscedasticity: A regression in which the variances in y for the values of xare not
equal
23. 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
24. Time Series Analysis: Regression analysis that uses time as the independentvariable
25. Multicollinearity: A multiple regression equation is flawed because two vari-ables
thought to be independent are actually correlated to be independent
26. 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
27. Homoscedasticity: A regression in which the variances in y for the values of x are equal or
close to equal
28. Random Errors: Error in measurement caused by unpredictable statistical fluc-tuations
29. Information Bias: A prejudice in the data that results when either the respondentor the
interviewer has an agenda and is not presenting impartial questions or responding with truly
honest responses, respectively
30. Ratio Data: Similar to interval data in that the data is ordered within a range andwith
each data point being an equal interval apart, also has a natural zero point which indicates
none of the given quality
