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Quiz von Maggie Samson, aktualisiert more than 1 year ago
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Erstellt von Maggie Samson
vor etwa 5 Jahre
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Frage 1
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Which of the following explains why spatial statistics are useful? (select all that apply)
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Assists in the process of determining whether or not sample data is inaccurate and incomplete
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Assists in the process of summarizing large data sets in order to make sense of them
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Assists in the process of making a decision to decide whether an observed difference in a relationship between two sets of sample data is significant
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Assists in the process of making inferences to communicate characteristics of a population based on data collected from a sample
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Grouped frequency tables provide an overview of the data set
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- True
- False
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Graphical methods are subjective
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- True
- False
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Numerical summaries mask the detail and sometimes are skewed by outliers
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- True
- False
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Which of the following is used to determine the value around which data are concentrated
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Bivariate regression
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Bivariate correlation
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Bivariate correlation-regression
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Measures of Dispersion
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Measures of Central Tendency
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What are the measures of central tendency? (select all that apply)
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Mean
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Median
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Mode
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Standard Deviation
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What are the measures of central tendency dispersion? (select all that apply)
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Range
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Variance
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Standard Deviation
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Median
Frage 8
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In relation to the standard normal distribution which of the following is true?
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68% of values fall within +/- 3.00 standard deviations from the mean
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68% of values fall within +/- 1.00 standard deviations from the mean
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86% of values fall within +/- 2.00 standard deviations from the mean
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99% of values fall within +/- 2.00 standard deviations from the mean
Frage 9
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How can Spatial Autocorrelation be illustrated?
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What happens at one location depends on what is occurring to other variables at nearby locations
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What happens at one location depends on what is occurring to same variable at nearby locations
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How can Spatial Correlation be illustrated?
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What happens at one location depends on what is occurring to other variables at nearby locations
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What happens at one location depends on what is occurring to same variable at nearby locations
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[blank_start]Negative spatial autocorrelation[blank_end] occurs when features that are close together are dissimilar in attributes.
[blank_start]Positive spatial autocorrelation[blank_end] occurs when features that are close together also have similar attributes.
[blank_start]Zero autocorrelation[blank_end] occurs when attributes are independent of location.
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Negative spatial autocorrelation
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Positive spatial autocorrelation
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Zero autocorrelation
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Positive spatial autocorrelation
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Zero autocorrelation
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Negative spatial autocorrelation
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Zero autocorrelation
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Positive spatial autocorrelation
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Negative spatial autocorrelation
Frage 12
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Which of the following are common measures of spatial autocorrelation? (select all that apply)
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Moran's I
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Regression Analysis
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Geary's C
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Getis-Ord General G
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[blank_start]Moran's I Index[blank_end] - Measured how much close objects are in comparison with other close objects
[blank_start]Geary's C Index[blank_end] - Compares the variance between small regions or neighbourhoods to the overall variance for the entire data set
[blank_start]Getis-Ord General G Index[blank_end] - Measures the concentration of high or low values for a given study area
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Moran's I Index
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Geary's C Index
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Getis-Ord General G Index
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When features that are close together are dissimilar in attributes this is termed?
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Negative spatial autocorrelation
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Skewed spatial autocorrelation
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Positive spatial autocorrelation
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Zero spatial autocorrelation
Frage 15
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Which of the following are descriptive statistics that summarize the character of a population?
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G (Getis) Statistics
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Moran’s I
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Geary’s C
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Measures of Central Tendency
Frage 16
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Which of the following are descriptive statistics that summarize the character of a population?
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Moran’s I
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Geary’s C
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Measures of Dispersion
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G (Getis) Statistics
Frage 17
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Which of the following are inferential statistics that make an inference in the form of a null hypothesis about a population? (Select all that apply)
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Moran's I
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Measures of Dispersion
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Geary's C
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Measures of Central Tendency
Frage 18
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Which of the following is used most frequently for summarizing relationship between two numeric attributes?
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Bivariate correlation
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Bivariate correlation-regression
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Measures of Central Tendency
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Measures of Dispersion
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Bivariate regression
Frage 19
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Which of the following is used to summarize the nature and strength of relationships in data?
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Bivariate correlation-regression
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Bivariate correlation
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Measures of Dispersion
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Measures of Central Tendency
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Bivariate regression
Frage 20
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Which of the following is a parametric measure of the relationship between two sets of interval data values?
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Chi-Square Test
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Spearman Rank for ranked data
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Geary’s C Index
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Pearson’s Correlation Coefficient
Frage 21
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Which of the following can be used to test the difference between the observed distribution and one that may have occurred due to chance or probability?
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Chi-Square Test
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Pearson’s Correlation Coefficient
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Geary’s C Index
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Spearman Rank for ranked data
Frage 22
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Which of the following statements is NOT true of the Chi-Square test?
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It is a non-parametric test
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You reject the null hypothesis is your test statistics is greater than the critical value
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It is a parametric test
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It compares observed to expected frequencies
Frage 23
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Which of the following can be used to measure the degree to which near and distant things are related?
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Spearman Rank for ranked data
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Pearson’s Correlation Coefficient
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Chi-Square Test
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Geary’s C Index
Frage 24
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Which of the following can be used to measure the relationship between two sets of ordinal (ranked) values?
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Chi-Square Test
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Pearson’s Product Moment Correlation Coefficient
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Geary’s C Index
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Spearman Rank for ranked data
Frage 25
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Which of the following statistical tests was used to assist in the process of integrating hydrological factors and demarcating groundwater prospect zones in the Gangolli basin of Karnataka State, India?
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Chi-Square Test
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Pearson’s Correlation Coefficient
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Regression
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Spearman Rank for ranked data
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High correlation always indicates a causal relationship
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- True
- False
Frage 27
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When looking at scatter-plot, high correlations indicate a causal relationship
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- True
- False
Frage 28
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When calculating correlation coefficients, a +1 value indicates:
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a positive relationship where increasing values of one attribute are associated with increasing values of another attribute
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a positive relationship where increasing values of one attribute are associated with decreasing values of another attribute
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a negative relationship where increasing values of one attribute are associated with increasing values of another attribute
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None of the choices
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Which of the following statistical tests was used to assist in the process of evaluating social stressors and air pollution across New York City communities?
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Pearson’s Correlation Coefficient
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Spearman Rank for ranked data
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Regression
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Chi-Square Test
Frage 30
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Select from the following choices the true statements about Linear Regression Analysis
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One common method is called ordinary least squares (OLS)
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Common approach for building simple models to analyze geographic processes
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Used to predict the value of the dependent variable or to determine whether an independent variable in fact influences the dependent variable, and to what extent
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As with chi-square analysis a pair of values for each feature can be plotted
Frage 31
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Select from the following choices the true statements about Linear Regression Analysis
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With all pairs plotted it is possible to see a graphic representation of the relationship
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As with correlation analysis a pair of values for each feature can be plotted
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The idea is to find the best fit of a line between the data points on the chart –that line represents the relationship.
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It is a non-parametric test
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OLS and GWR are both linear methods
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- True
- False
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The regression line is represented using a line of best-fit, where Y is predicted by X
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- True
- False
Frage 34
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The dependent variable represents what you are trying to model, predict, or explain—it is dependent on other factors
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- True
- False
Frage 35
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Select from the following choices the true statements about Ordinary least-squares (OLS) regression
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Tests for independence
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It is a generalized linear modelling technique
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A common statistical method used to generate predictions or to model a dependent variable in terms of its relationships to a set of explanatory variables
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Shows the relationship between two variables –the independent variable, x, used to predict, and dependent variable y, which is what we seek to predict
Frage 36
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Select from the following choices the true statements about Ordinary least-squares (OLS) regression
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It is a non-parametric test
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Minimizes the squared distance from the points to the line, measured parallel to the y-axis.
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Can be applied to single or multiple explanatory variables
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A form of bivariate regression
Frage 37
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OLS models the relationship between a independent variable (Y) and an explanatory variable (X)
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- True
- False
Frage 38
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Which type of statistical analysis was utilized in the analysis of the influence of social and economic factors on CO2 emissions as a result of energy consumption in the 101 counties of Inner Mongolia’s industrial sector
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Spearman Rank for ranked data
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Geographically Weighted Regression
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Pearson’s Correlation Coefficient
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Chi-Square Analysis
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Which type of software was utilized in the Global and Local Moran's I test for spatial autocorrelation in the analysis of the influence of social and economic factors on CO2 emissions as a result of energy consumption in the 101 counties of Inner Mongolia’s industrial sector
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SPSS
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GeoDA
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QGIS
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ArcGIS
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In the GWR analysis of the influence of social and economic factors on CO2 emissions as a result of energy consumption in the 101 counties of Inner Mongolia’s industrial sector the others discovered a relationship between CO2 emissions and five explanatory variables which produced an overall model fit of 99%
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- True
- False
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Which of the following explanatory variables did the authors find to be statistically significant in the CO2 emissions GWR statistical analysis? (select all that apply)
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Income
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Urbanization Rate
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GDP
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Industrial Structure
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Population
Frage 42
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Residuals represent the error between predicted value of Y and explanatory variable
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- True
- False
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A statistical result of regression analysis that shows what percentage of the variation in the dependent variable is being explained by the independent variables
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Aikake’s Information Criterion (AIC)
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Coefficient
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Adjusted R-Squared
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Jarque-Bera statistic
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A statistical result of regression analysis that can be used to compare other models that are using the same dependent variable. The lower this number is, the better.
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Coefficient
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Aikake’s Information Criterion (AIC)
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Jarque-Bera statistic
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Adjusted R-Squared
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A value associated with each independent variable in a regression equation, representing the strength and type of relationship the independent variable has to the dependent
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Variable Inflation Factor (VIF)
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Coefficient
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Jarque-Bera statistic
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Adjusted R-Squared
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A test that indicates whether the residuals (the observed/known dependent variable values minus the predicted/estimated values) are normally distributed with a mean of zero.
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Coefficient
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Jarque-Bera statistic
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Koenker statistic
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Variable Inflation Factor (VIF)
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A test to determine whether the explanatory variables in the model have a consistent relationship to the dependent variable both in geographic space and in data space.
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Koenker statistic
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Variable Inflation Factor (VIF)
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Jarque-Bera statistic
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Coefficient
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A measure of variable redundancy and can help you decide which variables can be removed from your model without jeopardizing the model.
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Coefficient
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Residual
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Adjusted R-Squared
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Variable Inflation Factor (VIF)
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Differences between actual observed values and predicted values
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Variable Inflation Factor (VIF)
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Residual
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Coefficient
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Adjusted R-Squared
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In Linear Regression when a variable has strong explanatory power in a region but is insignificant or even switches signs in another region it is referred to as:
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Spatially autocorrelated residuals
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Inconsistent variance
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Nonstationarity
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Multicollinearity
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In Linear Regression when there is spatial clustering of the under-/over predictions coming out of the model, it introduces an over counting type of bias and renders the model unreliable that is referred to as:
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Inconsistent variance
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Spatially autocorrelated residuals
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Nonstationarity
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Multicollinearity
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In Linear Regression when the model predicts well for small values of the dependent variable but becomes unreliable for large values this is referred to as:
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Inconsistent variance
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Spatially autocorrelated residuals
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Nonstationarity
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Multicollinearity
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In Linear Regression when one or a combination of explanatory variables is redundant this is referred to as:
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Spatially autocorrelated residuals
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Inconsistent variance
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Nonstationarity
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Multicollinearity
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In Linear Regression when relationships between your dependent and explanatory variables are inconsistent across your study area, computed standard errors will be artificially inflated. This is referred to as:
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Multicollinearity
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Inconsistent variance
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Nonstationarity
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Spatially autocorrelated residuals
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When key explanatory variables are missing from a regression model, coefficients and their associated p-values cannot be trusted.
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- True
- False
Frage 56
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Residuals should exhibit a normal distribution
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- True
- False
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Influential outliers can pull modeled regression relationships away from their true best fit, biasing regression coefficients
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- True
- False
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