ased on the latitude of formation of the hurmicane. The response variable is the binary variable Type.new (type of cane) and the predictor variable is FirstLat (First Latitude). Using R, we build a model by applying the glm () ion. For the logistic regression model, we specify family = "binomial". The data is available at //userpage.fu-berlin.de/soga/200/2010 data sets huricanes.xlsx. R code is = up filename ilename <- paste0 (getwd (),'/','my-temporary-downloadfile.xlsx') ownload file file <- download.file (url = 'https://userpage.fu- lin.de/soga/200/2010_data_sets/hurricanes.xlsx', destfile = my.filename, mode="wb") ead file into memory cary (readxl) cicanes <- read_excel ('my-temporary-downloadfile.xlsx') model <- glm (Type.new - FirstLat, data = hurricanes, family = 'binomial') mary (log.model) tput Call: glm(formula = Type.new - FirstLat, family = "binomial", data = hurricanes) Deviance Residuals: Min -2.1841 -0.4954 -0.1664 10 Median 30 0.4718 Маx 3.2397 Coefficients: (Intercept) -9.08263 FirstLat Estimate Std. Error z value Pr (>|z1) 0.96148 -9.446 <2e-16 *** 0.03947 9.447 <2e-16 *** 0.37283 --- Signif. codes:0 ***** 0.001 '*** 0.01 '** 0.05 '.' 0.1 ''1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 463.11 on 336 degrees of freedom
ased on the latitude of formation of the hurmicane. The response variable is the binary variable Type.new (type of cane) and the predictor variable is FirstLat (First Latitude). Using R, we build a model by applying the glm () ion. For the logistic regression model, we specify family = "binomial". The data is available at //userpage.fu-berlin.de/soga/200/2010 data sets huricanes.xlsx. R code is = up filename ilename <- paste0 (getwd (),'/','my-temporary-downloadfile.xlsx') ownload file file <- download.file (url = 'https://userpage.fu- lin.de/soga/200/2010_data_sets/hurricanes.xlsx', destfile = my.filename, mode="wb") ead file into memory cary (readxl) cicanes <- read_excel ('my-temporary-downloadfile.xlsx') model <- glm (Type.new - FirstLat, data = hurricanes, family = 'binomial') mary (log.model) tput Call: glm(formula = Type.new - FirstLat, family = "binomial", data = hurricanes) Deviance Residuals: Min -2.1841 -0.4954 -0.1664 10 Median 30 0.4718 Маx 3.2397 Coefficients: (Intercept) -9.08263 FirstLat Estimate Std. Error z value Pr (>|z1) 0.96148 -9.446 <2e-16 *** 0.03947 9.447 <2e-16 *** 0.37283 --- Signif. codes:0 ***** 0.001 '*** 0.01 '** 0.05 '.' 0.1 ''1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 463.11 on 336 degrees of freedom
Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)
6th Edition
ISBN:9781337111348
Author:Bruce Crauder, Benny Evans, Alan Noell
Publisher:Bruce Crauder, Benny Evans, Alan Noell
Chapter5: A Survey Of Other Common Functions
Section5.3: Modeling Data With Power Functions
Problem 3TU
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Question
Transcribed Image Text:Suppose that we want to build a model that predicts the group membership of a hurricane, either tropical (0) or non-tropical
(1) based on the latitude of formation of the huricane. The response variable is the binary variable Type.new (type of
hurricane) and the predictor variable is FirstLat (First Latitude). Using R, we build a model by applying the glm ()
function. For the logistic regression model, we specify family = "binomial". The data is available at
https://userpage.fu-berlin.de/soga/200/2010 data sets hurricanes.xlsx.
The R code is
#set up filename
my.filename <- paste0 (getwd (0,/','my-temporary-downloadfile.xlsx')
+ download file
my.file <- download.file (url = 'https://userpage.fu-
berlin.de/soga/200/2010_data_sets/hurricanes.xlsx',
destfile = my.filename,
mode="wb")
+ read file into memory
library (readxl)
hurricanes <- read_excel ('my-temporary-downloadfile.xlsx')
log.model <- glm (Type.new - FirstLat, data = hurricanes, family = 'binomial')
summary (log.model)
Routput
## Call:
# glm (formula = Type.new - FirstLat, family = "binomial", data = hurricanes)
## Deviance Residuals:
10 Median
4 -2.1841 -0.4954 -0.1664
30
0.4718
Min
Мах
3.2397
*# Coefficients:
+ (Intercept) -9.08263
## FirstLat
Estimate Std. Error z value Pr (>1z|)
<2e-16 ***
0.96148 -9.446
0.37283
0.03947
9.447
<2e-16 ***
---
*# Signif. codes: 0 '*** 0.001 **' 0.01 '*' 0.05'.' 0.1 '' 1
# (Dispersion parameter for binomial family taken to be 1)
Null deviance: 463.11 on 336 degrees of freedom
*# Residual deviance: 232.03 on 335 degrees of freedom
*# AIC: 236.03
*# Number of Fisher Scoring iterations: 6
What is the interpretation of the p-value of the predictor variable Eirstiat
a. There is no relationship between the occurrence of a non-tropical hurricane and the formation latitude.
b. There is no relationship between the occurrence of a tropical hurricane and the formation latitude.
c. There is a relationship between the occurrence of a non-tropical huricane and the formation latitude.
d. There is a relationship between the occurrence of a tropical hurricane and the formation latitude.
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