In: Statistics and Probability
Open Hurricanes data.
Test if there is a significant difference in the death by Hurricanes and Min Pressure measured. Answer the questions for Assessment. (Pick the closest answer)
7. What is the P-value?
8. What is the Statistical interpretation?
9. What is the conclusion?
Year Name MinPressure_before
Gender_MF Category alldeaths
1950 Easy 958 1
3 2
1950 King 955 0
3 4
1952 Able 985 0
1 3
1953 Barbara 987 1
1 1
1953 Florence 985 1
1 0
1954 Carol 960 1
3 60
1954 Edna 954 1
3 20
1954 Hazel 938 1
4 20
1955 Connie 962 1
3 0
1955 Diane 987 1
1 200
1955 Ione 960 0
3 7
1956 Flossy 975 1
2 15
1958 Helene 946 1
3 1
1959 Debra 984 1
1 0
1959 Gracie 950 1
3 22
1960 Donna 930 1
4 50
1960 Ethel 981 1
1 0
1961 Carla 931 1
4 46
1963 Cindy 996 1
1 3
1964 Cleo 968 1
2 3
1964 Dora 966 1
2 5
1964 Hilda 950 1
3 37
1964 Isbell 974 1
2 3
1965 Betsy 948 1
3 75
1966 Alma 982 1
2 6
1966 Inez 983 1
1 3
1967 Beulah 950 1
3 15
1968 Gladys 977 1
2 3
1969 Camille 909 1
5 256
1970 Celia 945 1
3 22
1971 Edith 978 1
2 0
1971 Fern 979 1
1 2
1971 Ginger 995 1
1 0
1972 Agnes 980 1
1 117
1974 Carmen 952 1
3 1
1975 Eloise 955 1
3 21
1976 Belle 980 1
1 5
1977 Babe 995 1
1 0
1979 Bob 986 0
1 1
1979 David 970 0
2 15
1979 Frederic 946 0
3 5
1980 Allen 945 0
3 2
1983 Alicia 962 1
3 21
1984 Diana 949 1
2 3
1985 Bob 1002 0
1 0
1985 Danny 987 0
1 1
1985 Elena 959 1
3 4
1985 Gloria 942 1
3 8
1985 Juan 971 0
1 12
1985 Kate 967 1
2 5
1986 Bonnie 990 1
1 3
1986 Charley 990 0
1 5
1987 Floyd 993 0
1 0
1988 Florence 984 1
1 1
1989 Chantal 986 1
1 13
1989 Hugo 934 0
4 21
1989 Jerry 983 0
1 3
1991 Bob 962 0
2 15
1992 Andrew 922 0
5 62
1993 Emily 960 1
3 3
1995 Erin 973 1
2 6
1995 Opal 942 1
3 9
1996 Bertha 974 1
2 8
1996 Fran 954 1
3 26
1997 Danny 984 0
1 10
1998 Bonnie 964 1
2 3
1998 Earl 987 0
1 3
1998 Georges 964 0
2 1
1999 Bret 951 0
3 0
1999 Floyd 956 0
2 56
1999 Irene 987 1
1 8
2002 Lili 963 1
1 2
2003 Claudette 979
1 1 3
2003 Isabel 957 1
2 51
2004 Alex 972 0
1 1
2004 Charley 941 0
4 10
2004 Frances 960 1
2 7
2004 Gaston 985 0
1 8
2004 Ivan 946 0
3 25
2004 Jeanne 950 1
3 5
2005 Cindy 991 1
1 1
2005 Dennis 946 0
3 15
2005 Ophelia 982 1
1 1
2005 Rita 937 1
3 62
2005 Wilma 950 1
3 5
2005 Katrina 902 1
3 1833
2007 Humberto 985 0
1 1
2008 Dolly 963 1
1 1
2008 Gustav 951 0
2 52
2008 Ike 935 0
2 84
2011 Irene 952 1
1 41
2012 Isaac 965 0
1 5
2012 Sandy 945 1
2 159
Solution-7:
Ho mu1=mu2
Ha;mu1 not =mu2
alpha=0.05
use t.test function in R to get t and p value
Rcode:
df=read.table(header = TRUE, text ="
Year Name MinPressure_before Gender_MF Category alldeaths
1950 Easy 958 1 3 2
1950 King 955 0 3 4
1952 Able 985 0 1 3
1953 Barbara 987 1 1 1
1953 Florence 985 1 1 0
1954 Carol 960 1 3 60
1954 Edna 954 1 3 20
1954 Hazel 938 1 4 20
1955 Connie 962 1 3 0
1955 Diane 987 1 1 200
1955 Ione 960 0 3 7
1956 Flossy 975 1 2 15
1958 Helene 946 1 3 1
1959 Debra 984 1 1 0
1959 Gracie 950 1 3 22
1960 Donna 930 1 4 50
1960 Ethel 981 1 1 0
1961 Carla 931 1 4 46
1963 Cindy 996 1 1 3
1964 Cleo 968 1 2 3
1964 Dora 966 1 2 5
1964 Hilda 950 1 3 37
1964 Isbell 974 1 2 3
1965 Betsy 948 1 3 75
1966 Alma 982 1 2 6
1966 Inez 983 1 1 3
1967 Beulah 950 1 3 15
1968 Gladys 977 1 2 3
1969 Camille 909 1 5 256
1970 Celia 945 1 3 22
1971 Edith 978 1 2 0
1971 Fern 979 1 1 2
1971 Ginger 995 1 1 0
1972 Agnes 980 1 1 117
1974 Carmen 952 1 3 1
1975 Eloise 955 1 3 21
1976 Belle 980 1 1 5
1977 Babe 995 1 1 0
1979 Bob 986 0 1 1
1979 David 970 0 2 15
1979 Frederic 946 0 3 5
1980 Allen 945 0 3 2
1983 Alicia 962 1 3 21
1984 Diana 949 1 2 3
1985 Bob 1002 0 1 0
1985 Danny 987 0 1 1
1985 Elena 959 1 3 4
1985 Gloria 942 1 3 8
1985 Juan 971 0 1 12
1985 Kate 967 1 2 5
1986 Bonnie 990 1 1 3
1986 Charley 990 0 1 5
1987 Floyd 993 0 1 0
1988 Florence 984 1 1 1
1989 Chantal 986 1 1 13
1989 Hugo 934 0 4 21
1989 Jerry 983 0 1 3
1991 Bob 962 0 2 15
1992 Andrew 922 0 5 62
1993 Emily 960 1 3 3
1995 Erin 973 1 2 6
1995 Opal 942 1 3 9
1996 Bertha 974 1 2 8
1996 Fran 954 1 3 26
1997 Danny 984 0 1 10
1998 Bonnie 964 1 2 3
1998 Earl 987 0 1 3
1998 Georges 964 0 2 1
1999 Bret 951 0 3 0
1999 Floyd 956 0 2 56
1999 Irene 987 1 1 8
2002 Lili 963 1 1 2
2003 Claudette 979 1 1 3
2003 Isabel 957 1 2 51
2004 Alex 972 0 1 1
2004 Charley 941 0 4 10
2004 Frances 960 1 2 7
2004 Gaston 985 0 1 8
2004 Ivan 946 0 3 25
2004 Jeanne 950 1 3 5
2005 Cindy 991 1 1 1
2005 Dennis 946 0 3 15
2005 Ophelia 982 1 1 1
2005 Rita 937 1 3 62
2005 Wilma 950 1 3 5
2005 Katrina 902 1 3 1833
2007 Humberto 985 0 1 1
2008 Dolly 963 1 1 1
2008 Gustav 951 0 2 52
2008 Ike 935 0 2 84
2011 Irene 952 1 1 41
2012 Isaac 965 0 1 5
2012 Sandy 945 1 2 159
"
)
df
t.test(df$MinPressure_before ,df$alldeaths)
Output:
Welch Two Sample t-test
data: df$MinPressure_before and df$alldeaths
t = 46.089, df = 94.058, p-value < 0.00000000000000022
alternative hypothesis: true difference in means is not equal to
0
95 percent confidence interval:
884.2762 963.8958
sample estimates:
mean of x mean of y
964.22581 40.13978
we got p-value < 0.00000000000000022
p=0.000
p<0.05
Reject Ho
d. None of these
Solution-8:
c. The P-value is much smaller than 5% thus we are certain that the average of hurricane deaths is significantly different from average min pressure.
Solution-9:
c. Statistics confirms that hurricanes’ pressure does relate to the death count.