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
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
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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