Question 1
Question
Which of the following explains why spatial statistics are useful? (select all that apply)
Answer

Assists in the process of determining whether or not sample data is inaccurate and incomplete

Assists in the process of summarizing large data sets in order to make sense of them

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

Assists in the process of making inferences to communicate characteristics of a population based on data collected from a sample
Question 2
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Grouped frequency tables provide an overview of the data set
Question 3
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Graphical methods are subjective
Question 4
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Numerical summaries mask the detail and sometimes are skewed by outliers
Question 5
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Which of the following is used to determine the value around which data are concentrated
Question 6
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What are the measures of central tendency? (select all that apply)
Answer

Mean

Median

Mode

Standard Deviation
Question 7
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What are the measures of central tendency dispersion? (select all that apply)
Answer

Range

Variance

Standard Deviation

Median
Question 8
Question
In relation to the standard normal distribution which of the following is true?
Answer

68% of values fall within +/ 3.00 standard deviations from the mean

68% of values fall within +/ 1.00 standard deviations from the mean

86% of values fall within +/ 2.00 standard deviations from the mean

99% of values fall within +/ 2.00 standard deviations from the mean
Question 9
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How can Spatial Autocorrelation be illustrated?
Question 10
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How can Spatial Correlation be illustrated?
Question 11
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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.
Answer

Negative spatial autocorrelation

Positive spatial autocorrelation

Zero autocorrelation

Positive spatial autocorrelation

Zero autocorrelation

Negative spatial autocorrelation

Zero autocorrelation

Positive spatial autocorrelation

Negative spatial autocorrelation
Question 12
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Which of the following are common measures of spatial autocorrelation? (select all that apply)
Answer

Moran's I

Regression Analysis

Geary's C

GetisOrd General G
Question 13
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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]GetisOrd General G Index[blank_end]  Measures the concentration of high or low values for a given study area
Question 14
Question
When features that are close together are dissimilar in attributes this is termed?
Answer

Negative spatial autocorrelation

Skewed spatial autocorrelation

Positive spatial autocorrelation

Zero spatial autocorrelation
Question 15
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Which of the following are descriptive statistics that summarize the character of a population?
Question 16
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Which of the following are descriptive statistics that summarize the character of a population?
Answer

Moran’s I

Geary’s C

Measures of Dispersion

G (Getis) Statistics
Question 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)
Question 18
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Which of the following is used most frequently for summarizing relationship between two numeric attributes?
Question 19
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Which of the following is used to summarize the nature and strength of relationships in data?
Question 20
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Which of the following is a parametric measure of the relationship between two sets of interval data values?
Question 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?
Question 22
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Which of the following statements is NOT true of the ChiSquare test?
Answer

It is a nonparametric test

You reject the null hypothesis is your test statistics is greater than the critical value

It is a parametric test

It compares observed to expected frequencies
Question 23
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Which of the following can be used to measure the degree to which near and distant things are related?
Question 24
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Which of the following can be used to measure the relationship between two sets of ordinal (ranked) values?
Question 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?
Question 26
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High correlation always indicates a causal relationship
Question 27
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When looking at scatterplot, high correlations indicate a causal relationship
Question 28
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When calculating correlation coefficients, a +1 value indicates:
Answer

a positive relationship where increasing values of one attribute are associated with increasing values of another attribute

a positive relationship where increasing values of one attribute are associated with decreasing values of another attribute

a negative relationship where increasing values of one attribute are associated with increasing values of another attribute

None of the choices
Question 29
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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?
Question 30
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Select from the following choices the true statements about Linear Regression Analysis
Answer

One common method is called ordinary least squares (OLS)

Common approach for building simple models to analyze geographic processes

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

As with chisquare analysis a pair of values for each feature can be plotted
Question 31
Question
Select from the following choices the true statements about Linear Regression Analysis
Answer

With all pairs plotted it is possible to see a graphic representation of the relationship

As with correlation analysis a pair of values for each feature can be plotted

The idea is to find the best fit of a line between the data points on the chart –that line represents the relationship.

It is a nonparametric test
Question 32
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OLS and GWR are both linear methods
Question 33
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The regression line is represented using a line of bestfit, where Y is predicted by X
Question 34
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The dependent variable represents what you are trying to model, predict, or explain—it is dependent on other factors
Question 35
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Select from the following choices the true statements about Ordinary leastsquares (OLS) regression
Answer

Tests for independence

It is a generalized linear modelling technique

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

Shows the relationship between two variables –the independent variable, x, used to predict, and dependent variable y, which is what we seek to predict
Question 36
Question
Select from the following choices the true statements about Ordinary leastsquares (OLS) regression
Answer

It is a nonparametric test

Minimizes the squared distance from the points to the line, measured parallel to the yaxis.

Can be applied to single or multiple explanatory variables

A form of bivariate regression
Question 37
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OLS models the relationship between a independent variable (Y) and an explanatory variable (X)
Question 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
Answer

Spearman Rank for ranked data

Geographically Weighted Regression

Pearson’s Correlation Coefficient

ChiSquare Analysis
Question 39
Question
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
Question 40
Question
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%
Question 41
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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)
Answer

Income

Urbanization Rate

GDP

Industrial Structure

Population
Question 42
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Residuals represent the error between predicted value of Y and explanatory variable
Question 43
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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
Question 44
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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.
Question 45
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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
Question 46
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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.
Question 47
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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.
Question 48
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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.
Question 49
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Differences between actual observed values and predicted values
Question 50
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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:
Question 51
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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:
Question 52
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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:
Question 53
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In Linear Regression when one or a combination of explanatory variables is redundant this is referred to as:
Question 54
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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:
Question 55
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When key explanatory variables are missing from a regression model, coefficients and their associated pvalues cannot be trusted.
Question 56
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Residuals should exhibit a normal distribution
Question 57
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Influential outliers can pull modeled regression relationships away from their true best fit, biasing regression coefficients