# GEOM 109 - Lecture Focus 3

Quiz by Maggie Samson, updated more than 1 year ago Created by Maggie Samson over 1 year ago 7 0

### Description

Geography Quiz on GEOM 109 - Lecture Focus 3, created by Maggie Samson on 04/12/2019. ## Resource summary

### Question 1

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

Question
Grouped frequency tables provide an overview of the data set
• True
• False

### Question 3

Question
Graphical methods are subjective
• True
• False

### Question 4

Question
Numerical summaries mask the detail and sometimes are skewed by outliers
• True
• False

### Question 5

Question
Which of the following is used to determine the value around which data are concentrated
• Bivariate regression
• Bivariate correlation
• Bivariate correlation-regression
• Measures of Dispersion
• Measures of Central Tendency

### Question 6

Question
What are the measures of central tendency? (select all that apply)
• Mean
• Median
• Mode
• Standard Deviation

### Question 7

Question
What are the measures of central tendency dispersion? (select all that apply)
• Range
• Variance
• Standard Deviation
• Median

### Question 8

Question
In relation to the standard normal distribution which of the following is true?
• 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

Question
How can Spatial Autocorrelation be illustrated?
• What happens at one location depends on what is occurring to other variables at nearby locations
• What happens at one location depends on what is occurring to same variable at nearby locations

### Question 10

Question
How can Spatial Correlation be illustrated?
• What happens at one location depends on what is occurring to other variables at nearby locations
• What happens at one location depends on what is occurring to same variable at nearby locations

### Question 11

Question
[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.
• 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

Question
Which of the following are common measures of spatial autocorrelation? (select all that apply)
• Moran's I
• Regression Analysis
• Geary's C
• Getis-Ord General G

### Question 13

Question
[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
• Moran's I Index
• Geary's C Index
• Getis-Ord General G Index

### Question 14

Question
When features that are close together are dissimilar in attributes this is termed?
• Negative spatial autocorrelation
• Skewed spatial autocorrelation
• Positive spatial autocorrelation
• Zero spatial autocorrelation

### Question 15

Question
Which of the following are descriptive statistics that summarize the character of a population?
• G (Getis) Statistics
• Moran’s I
• Geary’s C
• Measures of Central Tendency

### Question 16

Question
Which of the following are descriptive statistics that summarize the character of a population?
• Moran’s I
• Geary’s C
• Measures of Dispersion
• G (Getis) Statistics

### Question 17

Question
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)
• Moran's I
• Measures of Dispersion
• Geary's C
• Measures of Central Tendency

### Question 18

Question
Which of the following is used most frequently for summarizing relationship between two numeric attributes?
• Bivariate correlation
• Bivariate correlation-regression
• Measures of Central Tendency
• Measures of Dispersion
• Bivariate regression

### Question 19

Question
Which of the following is used to summarize the nature and strength of relationships in data?
• Bivariate correlation-regression
• Bivariate correlation
• Measures of Dispersion
• Measures of Central Tendency
• Bivariate regression

### Question 20

Question
Which of the following is a parametric measure of the relationship between two sets of interval data values?
• Chi-Square Test
• Spearman Rank for ranked data
• Geary’s C Index
• Pearson’s Correlation Coefficient

### Question 21

Question
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?
• Chi-Square Test
• Pearson’s Correlation Coefficient
• Geary’s C Index
• Spearman Rank for ranked data

### Question 22

Question
Which of the following statements is NOT true of the Chi-Square test?
• It is a non-parametric 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

Question
Which of the following can be used to measure the degree to which near and distant things are related?
• Spearman Rank for ranked data
• Pearson’s Correlation Coefficient
• Chi-Square Test
• Geary’s C Index

### Question 24

Question
Which of the following can be used to measure the relationship between two sets of ordinal (ranked) values?
• Chi-Square Test
• Pearson’s Product Moment Correlation Coefficient
• Geary’s C Index
• Spearman Rank for ranked data

### Question 25

Question
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?
• Chi-Square Test
• Pearson’s Correlation Coefficient
• Regression
• Spearman Rank for ranked data

### Question 26

Question
High correlation always indicates a causal relationship
• True
• False

### Question 27

Question
When looking at scatter-plot, high correlations indicate a causal relationship
• True
• False

### Question 28

Question
When calculating correlation coefficients, a +1 value indicates:
• 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

Question
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?
• Pearson’s Correlation Coefficient
• Spearman Rank for ranked data
• Regression
• Chi-Square Test

### Question 30

Question
Select from the following choices the true statements about Linear Regression Analysis
• 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 chi-square 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
• 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 non-parametric test

### Question 32

Question
OLS and GWR are both linear methods
• True
• False

### Question 33

Question
The regression line is represented using a line of best-fit, where Y is predicted by X
• True
• False

### Question 34

Question
The dependent variable represents what you are trying to model, predict, or explain—it is dependent on other factors
• True
• False

### Question 35

Question
Select from the following choices the true statements about Ordinary least-squares (OLS) regression
• 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 least-squares (OLS) regression
• It is a non-parametric test
• Minimizes the squared distance from the points to the line, measured parallel to the y-axis.
• Can be applied to single or multiple explanatory variables
• A form of bivariate regression

### Question 37

Question
OLS models the relationship between a independent variable (Y) and an explanatory variable (X)
• True
• False

### Question 38

Question
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
• Spearman Rank for ranked data
• Geographically Weighted Regression
• Pearson’s Correlation Coefficient
• Chi-Square 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
• SPSS
• GeoDA
• QGIS
• ArcGIS

### 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%
• True
• False

### Question 41

Question
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)
• Income
• Urbanization Rate
• GDP
• Industrial Structure
• Population

### Question 42

Question
Residuals represent the error between predicted value of Y and explanatory variable
• True
• False

### Question 43

Question
A statistical result of regression analysis that shows what percentage of the variation in the dependent variable is being explained by the independent variables
• Aikake’s Information Criterion (AIC)
• Coefficient
• Jarque-Bera statistic

### Question 44

Question
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.
• Coefficient
• Aikake’s Information Criterion (AIC)
• Jarque-Bera statistic

### Question 45

Question
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
• Variable Inflation Factor (VIF)
• Coefficient
• Jarque-Bera statistic

### Question 46

Question
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.
• Coefficient
• Jarque-Bera statistic
• Koenker statistic
• Variable Inflation Factor (VIF)

### Question 47

Question
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.
• Koenker statistic
• Variable Inflation Factor (VIF)
• Jarque-Bera statistic
• Coefficient

### Question 48

Question
A measure of variable redundancy and can help you decide which variables can be removed from your model without jeopardizing the model.
• Coefficient
• Residual
• Variable Inflation Factor (VIF)

### Question 49

Question
Differences between actual observed values and predicted values
• Variable Inflation Factor (VIF)
• Residual
• Coefficient

### Question 50

Question
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:
• Spatially autocorrelated residuals
• Inconsistent variance
• Nonstationarity
• Multicollinearity

### Question 51

Question
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:
• Inconsistent variance
• Spatially autocorrelated residuals
• Nonstationarity
• Multicollinearity

### Question 52

Question
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:
• Inconsistent variance
• Spatially autocorrelated residuals
• Nonstationarity
• Multicollinearity

### Question 53

Question
In Linear Regression when one or a combination of explanatory variables is redundant this is referred to as:
• Spatially autocorrelated residuals
• Inconsistent variance
• Nonstationarity
• Multicollinearity

### Question 54

Question
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:
• Multicollinearity
• Inconsistent variance
• Nonstationarity
• Spatially autocorrelated residuals

### Question 55

Question
When key explanatory variables are missing from a regression model, coefficients and their associated p-values cannot be trusted.
• True
• False

### Question 56

Question
Residuals should exhibit a normal distribution
• True
• False

### Question 57

Question
Influential outliers can pull modeled regression relationships away from their true best fit, biasing regression coefficients
• True
• False

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